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La recherche empirique : méthodes et pratiques

En sciences sociales et humaines, le recours à la recherche empirique et la recherche de terrain, que celles-ci soient de nature qualitative ou quantitative, va de soi. En science juridique, ces méthodes de recherche demeurent peu utilisées et leurs pratiques, peu documentée. Pourtant, pour rendre compte de l’articulation entre le droit et les activités sociales, la diversification des méthodes de recherche est un allié précieux, voire indispensable. Or, les juristes souhaitant explorer de telles pratiques de recherche sont susceptibles de faire face à plusieurs interrogations pratiques : Comment parvenir à élaborer une problématique de recherche dont le point de départ n’est pas nécessairement la norme juridique et son application par les autorités chargées de sa mise en œuvre ? Comment recourir à certaines sources formelles du droit, comme la jurisprudence, autrement que dans une perspective exégétique ? Pourquoi et dans quelles circonstances convient-il de recourir à une méthodologie qualitative ou quantitative ? À quelles fins et avec quels partenaires fait-on de le recherche empirique ? L’objectif de cet ouvrage est de rendre des défis, limites et potentialités de ces pratiques dans le champ juridique.

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Empirical Studies in Translation: Methodological and Epistemological Questions [1] Études empiriques en traduction : questions de méthodologie et d´épistémologie

Wilhelm Neunzig

…more information

Wilhelm Neunzig Facultat de Traducció i d’Interpretació, Edificio K, Universitat Autònoma de Barcelona, 08193 Bellaterra (Barcelona), ESPAÑA [email protected]

Online publication: Jan. 7, 2013

An article of the journal TTR

Volume 24, Number 2, 2e semestre 2011 , p. 15–39 Cartographie des métodologies en traduction

When a qualitative leap forward is taken in any scientific discipline, the change is usually accompanied by an increased interest in research. This occurred in Translation Studies in the 1950s and in the 1990s. This paper outlines some of the most important epistemological and methodological questions faced by researchers who want to apply the so called “scientific method” to empirical research in translation. We will discuss the main steps in the research process: designing an experiment, selecting subjects or the object of study, defining experimental and control groups, controlling independent variables, choosing instruments that will measure what we want to measure and which will give us reliable data to analyse. The whole procedure should be intelligible and transparent, the objectives relevant and the results clear.

Translation Studies,
methodology,
scientific method,
empirical studies,

Dans tout domaine scientifique, lorsqu’un saut qualitatif se produit, il est généralement accompagné d´une véritable passion pour la recherche. C’est ce qui est advenu en traduction, tout d’abord dans les années 1950 et ensuite dans les années 1990. L’objectif de ce travail est d’examiner certaines des principales questions de méthodologie et d´épistémologie auxquelles se trouve confrontée la recherche empirique en traduction lorsqu’il s’agit d’appliquer ce qu’il est convenu d’appeler la « méthode scientifique ». Nous étudierons les points essentiels devant être pris en considération au cours du processus de recherche : conception de l’étude, sélection des individus et des sujets de la recherche, définition des groupes expérimentaux et de contrôle, contrôle des variables indépendantes et choix des instruments servant à mesurer les paramètres pertinents afin d’obtenir des données fiables. Une telle procédure devrait être caractérisée par l’intelligibilité et la transparence du processus scientifique, ainsi que par la pertinence des objectifs et la clarté des résultats.

Mots-clés :

traductologie,
méthodologie,
méthode scientifique,
études empiriques,

Article body

Introduction.

When a scientific field undergoes a qualitative leap such as that which occurred in translation, firstly in the mid-1950s when translator training became a university discipline with the creation of translation faculties in Montreal, Leipzig and Paris, and later in the 1990s when other countries, for instance Spain, started undergraduate and doctorate programmes in translation and interpretation, this change is usually accompanied by a marked enthusiasm for research. There was a move in Translation Studies to appear more “scientific,” to obtain the “truly scientific status” postulated by Gideon Toury. From the middle of the 1980s onwards, Translation Studies began adopting the formalism and even the symbolism of the natural and social sciences [2] and appropriating the methodology and tools of these approaches. The argument was that however good theoretical principles were at explaining observable phenomena in a specific field, these constructs only acquired scientific and epistemic value if they could be operationalised ; in other words, if they could be compared via a systematic observation or thorough examination in an experimental study. As Toury insists, empiricism “constitutes the subject matter of a proper discipline of Translation Studies [...] it involves [...] (observable and reconstructable) facts of real life rather than merely speculative entities from preconceived hypotheses and theoretical models” (1995, p. 1). Without wishing to belittle this tendency (which without a doubt has represented a great step forward in our research) we are beginning to notice a certain “empiricism for empiricism’s sake” within the field. A huge number of studies and experiments are being carried out into very isolated issues or issues of very little scientific relevance. As Chesterman put it: “trivial problem, no problem, irrelevant discussion” (1998). [3] Either experimental designs are being engaged in that have been badly set out (Chesterman himself criticised circular argument, illicit generalisations based on atypical cases, the confusion between correlation and causality, false induction, and so forth), or else, in many cases, there is a failure to define the general theoretical backgrounds against which results should be understood. We support the reflection made by Amparo Hurtado referring to research in our field: “We consider it urgent that the relevance of data in research across Translation Studies is established; that methods and tools should be chosen in accordance with the object under study and the study’s planned aims; that replication is encouraged, as well as contact between researchers” (2001, p. 199).

The aim of this article is to note, in a necessarily concise way, some of the main methodological issues that emerge when applying empirical research methods to translation. We look at the issues raised when closely examining the postulates that have defined the natural sciences and which have been integrated into the social sciences; in other words, the problems that arise when applying the “scientific method.” Additionally, there are some reflections on the implications this has on our research. We discuss the main steps to take into account in the research process when designing a study and we propose a research procedure shaped by the intelligibility and transparency of the scientific process, as well as by the relevance of its aims, its evidence and its results. [4] We believe it must also be one centred on the integrity of all research founded on rationalism and pragmatism.

1. The “Scientific Method”: A Phases Model

The “scientific method,” which is only one approach among many possible “scientific” approaches, is usually described through a phases model outlined in the accompanying illustration.

1.1. The Theoretical-Conceptual Level

On a conceptual level (when defining the problem and formulating testable theoretic hypotheses empirically and validating these hypotheses through the results obtained) despite inherent difficulties, in Translation Studies research there are still guidelines and procedures to follow in common with all scientific fields that have opted for an empirical approach to solving problems. When analysing empirical data and contrasting this with a starting hypothesis, Translation Studies can adapt and apply the well-defined tools used by the natural, social or human sciences. Research problems associated with each field arise, in particular, at a methodological level: when choosing an approach, designing and planning the research and when gathering data (especially designing instruments to measure and compile data) that can be used to validate our hypothesis; always respecting the accurate criteria associated with different approaches as defined by logical positivism.

Figure 1 : Research design, adapted from Portell, M. (2001)

-> See the list of figures

1.2. The Methodological Level: Applying Accurate Criteria

Let us recall here, very briefly, the main criterion of “meticulous observation” demanded by the scientific method to illustrate the problems of applying this methodology to our scientific field.

Objectivity : The design of an experiment has to guarantee that the approach and tools used are independent of the researcher who will use them. In other words, that in the hypothetical case that the study were to be carried out by other researchers, then equivalent, or very similar, results would be obtained. The problem posed here is that the research (or lecturer in the case of didactic research) can manipulate (consciously or unconsciously) the stimulus and the results. [5] A solution to this could consist of standardising the instructions, interventions and the tools used for measurement in such a way that other researchers would get the same results, or, at the very least, ensure clarity, transparency, intelligibility, comprehensibility and logic in a methodological approach, so that other researchers would understand the procedures that led to the results in question.

Internal validity : This criterion refers to internal consistency in the approach. It demands that the design of the experiment guarantees control of all factors that could distort the results. In other words, all confusing variables (in our case these would be linguistic knowledge and general awareness of culture, previous experience, pedagogical input, time, and so on) and the accuracy of the instruments measuring them. A solution might consist of selecting the subjects and validating the instruments, as will be expanded on below.

Repeatability : The design must guarantee that the results obtained in a particular experiment can be repeated in parallel experiments with other subjects, which implies complete transparency when selecting the sample.

Reliability : Measures should be taken to ensure that results are reliable indicators for the objectives that you wish to attain. In other words, it is essential to ensure that you are indeed measuring what you set out to measure. This is a crucial requirement in an empirical approach to research. Problems can emerge not only when designing the instruments for measuring what we wish to measure, but also when operationalising constructs (e.g., “translator competence,” “privileged translation”) as well as when defining the environment (population, corpus of texts) from which the sample will be taken. A solution lies in justifying the relevance of the selected corpus or subjects.

Extrapolability : The experiment has to be designed in such a way that the results obtained can be extrapolated into other situations, or, at least, can serve as a basis for formulating a working hypothesis for later research. In the case of translation didactics for example, the only experiments that make any sense are those whose results are valid for many translation situations and which have a general relevance in the field of teaching, and by extension in translation theory.

Quantifiability : This criterion refers to the idea that the data obtained must be quantifiable (in other words, expressible in numbers). Many researchers erroneously believe that results which are not the fruit of a Chi-square test, a t-test, a Pearson-r test, or of an analysis of variances have no explanatory strength whatsoever. However, in our field, categorical or qualitative analyses can be equally appropriate if the precepts of Cartesian evidence are always borne in mind when interpreting the results.

Ecological validity or environmental validity : The experiment should reflect a real situation; it should represent the least artificial circumstances possible. It is obvious that this is the most serious problem for all laboratory experiments, since laboratories, by definition, are artificial. It is difficult to design a situation in which the subjects, for example, translators, are not influenced by the environment itself or by the mere fact that they know they are involved in an experiment. It is here where the tools of modern Translation Studies demonstrate their greatest weakness (TAPs, interviews, surveys, physiological measurements). One possible solution involves applying the old research trick of concealing the real aim of the experiments when presenting them to subjects or in their instructions.

In addition to the pre-requisites already mentioned, the design of the experiment must respect other criteria (what we could call “experimental pragmatics”), amongst which we would like to particularly note practicability or scientific economy , criteria described by Giegler (1988, pp. 785-786) demanding that experiments be designed in the simplest way possible to avoid overloading the subjects; as well as ensuring that they are manageable as a whole and that the analysis of the results does not imply excessive effort by the researchers.

1.3. The Aim of Translation Studies Research

When a science such as Translation Studies, whose research objectives do not involve a description and control of the world (as in natural sciences research), human behaviour (social sciences) nor an analysis of interpretation of real human intervention (historical, juridical or philological sciences), but rather represent a search for an ideal state (the potential results of a human intervention) then this will tend to lead to the development of that science’s own form of theoretical abstraction and a search for a new research path. The resulting research procedure should not only be focused on the accuracy postulated by the “scientific method,” but give prevalence to the practicality, transparency and relevance of the scientific process. It would appear obvious that in a field as complex as ours the same rules governing methodological accuracy (not to be confused with experimental integrity) cannot apply as they do in fields such as thermodynamics or biomedicine, for example.

2. Transparency in Translation Research Procedures

This chapter has subdivisions based on the key steps that define the empirical research process, as described, for example by Bunge (1972) or Neunzig and Tanqueiro (2007). The aim is to ensure the transparency of our conduct, in other words, to ensure the intelligibility of the procedure for scientists not connected with the project, or, as Umberto Eco (1977) postulated in his work about how to create a thesis, this should include all the elements necessary for the public to be able to follow it.

2.1. Justifying the Relevance of a Study

Karl Popper (1963) affirmed that the starting point for all scientific work is the problem and not the gathering of data. The eminent German pedagogues Tausch and Tausch (1991) insisted that the importance of a study derives from the relevance of the problem it deals with. So, the first step in any research should be enunciating well-formulated and credibly fertile questions and justifying interest in the topic, in short, stressing its relevance.

All research serves (or at least should serve) to enrich human knowledge in general; we may speak of this as intellectual relevance (or research of general interest). Research of general interest would include investigative efforts aimed at improving life or improving human coexistence. These are of great value for society ( socially relevant ) and would include certain research in the fields of medicine, epidemiology, sociology, psychology, and so forth, and in particular in the area of research into maintaining peace.

All scientific fields also need research (of scientific relevance ) dedicated, essentially, to opening up new paths, evaluating new ideas and providing results (interesting facts from the point of view of a particular discipline). All of this is usually known as basic research. The problem here is twofold: on the one hand, every researcher (who receives finance for carrying out the project or who is interested in broadening, in a spectacular way, their curriculum of publications) will say that their work is of “the utmost relevance for this field” and that they are providing “previously unsuspected truths;” of course, nobody is interested in “trivial truths.” However, on the other hand, it can never be said that research which today appears “esoteric” will not turn out to be of great relevance one day (imagine, for a moment, research aimed at isolating the “translator gene”).

Economic relevance would appear to be the dominion of the technological sciences (concentrated in R&D), but in our area there are also projects that can be defined essentially for their economic relevance, such as automatic translation, lexicography or computer-aided translation tools. In close relationship with the economic importance of research are those studies aimed at making the “life” of professionals in the field easier, studies which we might say are of professional relevance.

Finally, there are scientific areas where research is also marked by pedagogical relevance. These are studies aimed at seeking the best way of transmitting knowledge acquired by one generation to the next. In our field, these studies acquire great importance, since the more complex the task is (not more difficult), the more knowledge, competences, abilities and strategies you need to update in order to achieve your objective: (one which, in the end, defines our profession) the vital task of finding (researching) the best way of transmitting knowledge from one generation to another.

2.2. Introducing the Referential Framework

Starting with a bibliographical analysis, the referential framework is established and the antecedents of the study are described. This framework helps us systemise the question posed and aids us when drawing up a model, as well as helps us to decide on our research focus. Umberto Eco (1977), in his advice on writing a doctoral thesis, recommends that sources should be practical, in other words, easily accessible for the PhD student and that they should be manageable, in other words, within the doctoral student’s cultural reach.

2.3. Formulating Well-Defined Hypotheses

A well-defined hypothesis is formulated as a statement describing a fact in the scientific field susceptible to being compared and contrasted in order to obtain data that confirms or rejects the hypothesis. Hypotheses structure the relationships between variables that can be observed using deductive or inductive methods.

2.3.1. Criteria

In empirical studies, hypotheses must respect the criteria set out by Karl Popper or Carl Gustav Hempel, amongst others.

Compatibility : This refers to the fact that hypotheses should be compatible with scientific knowledge, with previous objective knowledge.

Verifiability : This refers to the fact that they should be able to be verified, or rejected, in empirical studies that are not mere speculations, such as in the following example: “The translator gets under the skin of the author, guessing his chain of thought when that is not sufficiently explicit.”

Intelligibility : This means that other scientists can intellectually assimilate the reasoning used.

Verisimilitude : This means that they must be logical, no matter how verifiable or intelligible a hypothesis might be, such as: “The planets orbit around the sun with movements in step with Johann Strauss’s Blue Danube .” It makes no sense wasting money and time proving that.

Relevance : This means there should be an obvious point to the exercise; it should have some scientific or professional interest. We have no idea what hypothesis motivated doctors Alan Hirsch and Charles Wolf, of Chicago, [6] to study the growth of President Clinton’s nose when he was telling lies during the Lewinsky case. They reached the conclusion that his nose grew, even though the slight enlargement was not obvious at first sight. In our opinion, such a study has no scientific relevance whatsoever.

Figure 2 : Hypotheses in empirical studies

2.3.2. Three Levels of Hypotheses

Following the phase model, we draw up our hypothesis on three levels:

Theoretical hypotheses : These are presumptions or suppositions derived directly from an established theoretical model. They are formulated in a general way and cannot be directly verified using systematic observations (empirically), an example is the theoretical hypothesis of the Pacte group: “Translation competence is expert knowledge […] made up of a system of sub-competences that are interrelated” (2003, p. 48). There is no sense negating a theoretical hypothesis, the research would itself make no sense. Test, this statement cannot be negated: “TC is expert knowledge that is NOT made up of interrelated sub-competences.”

We decide on our research focus in relation to our theoretical hypothesis. We do not wish to discuss the scientific credentials of every approach here, all roads are equally “scientific,” but it is obvious that there are problems, even “paradigmatic” problems, when choosing between one focus or another. We either decide to do a non-empirical study, [7] in other words we go for a theoretical approach, or else we choose an empirical study within which we formulate our hypothesis.

Working hypotheses : These are deductions based on a theoretical hypothesis that are open to being validated by observation. They can be proven through multiple studies or experiments with the most varied instruments and designs, for example: “There exists a relationship of cause and effect between the degree of translation expertise and the identification of the problems of translation.” It should be possible to reject the working hypothesis trough an empirical study. Therefore, it should be possible to formulate the negation of this hypothesis: “There does NOT exist any relationship of cause and effect [...].” One danger is in formulating tautological hypotheses, such as: “There exists a cause and effect relationship between the degree of TC and the accuracy of the translation.”

Operational hypotheses : This refers to concrete studies aimed at confirming or rejecting the above, and, indirectly, the theoretical hypothesis and the model. They predict the result of the behaviour of the variables in a particular study and they are derived from the experimental approach.

2.4. Deciding the Research Focus and the Strategy for Gathering Data

Depending on the problem posed, if a researcher decides on an empirical focus, he or she will then opt for an empirical-observational investigation, or else will design an experiment. This decision will be dictated by the aims and objectives of the research. If we are planning, for example, a study within the field of literary translation, we can take an empirical-observational approach. However, in order to know how students will react when they receive a certain type of feedback from their lecturer, it would be logical to set up an exploratory experiment. Furthermore, in an investigation as wide ranging as that of the Pacte group (who are interested in clarifying what translation competence is and how it is acquired) it would be logical for this experimental study to follow the hypothetical-deductive method. Consequently, the strategy adopted for gathering data will also depend on the problem posed and the object under study.

Important researchers in our field have opted for case studies precisely because they maintain that the act of translation is so complex that research into a wide range of samples leads to researchers getting lost amidst too much information. In the end, it is not a case of knowing how “translators” translate, but rather learning how the great geniuses of our field do it. Paul Kuβmaul (1993) defends this approach in his studies into creativity and Helena Tanqueiro (2004) in her studies on self-translation within literary translation theory.

Within the field of Translation Studies, exploratory studies are more common. These attempt to verify if a conviction, or an idea, that a scholar has extracted from his or her own professional experience is verifiable in reality and observable on a more general level and whether the tendencies observed can be extrapolated to other similar cases; in other words, if there is any foundation for them. Often these are open approaches of the “What will happen if...” type that Daniel Gile (1998) called open experiments . He further argued that concentrating only “on what was being looked for” leads to a lot of data being overlooked (possibly the most interesting pieces).

There are branches of Translation Studies that, of necessity, must be based on directed observation, on gathering data without any type of intervention or manipulation ( field research ). These include literary translation, where research is based on existing and unalterable data that cannot be manipulated experimentally. The problem with this type of study, based essentially on analysing translations, is a tendency to “falsify” results, with researchers choosing only those sources and those examples that support their hypothesis. We believe that transparent research that opts for an observational approach must ensure two things (as well as, naturally, taking into account what has already been mentioned with regard to the rationality of theoretical approaches). On the one hand, it should ensure the operationality of theoretical principles through definitions that allow solid empirical verification, while on the other hand, it must justify the selection of subjects, or works, or sources of analysis (why those particular works or subjects were chosen) as well as the criteria governing the gathering of data (for example, the types of examples that will be taken into account or discarded).

In recent years, there has been an increase in the designing of laboratory experiments , in which experimental conditions are controlled, offering the possibility of eliminating confusing variables and manipulating those variables in which we are interested, while, in addition, offering more accurate measurement. The main problem here is the lack of environmental validity, i.e., the very artificiality of the situation in which data is obtained. Nevertheless, carrying out measurements in a natural environment (field work) where subjects perform in a natural context is rare in our research (except in didactic situations) because it requires a great deal of effort by the researcher.

2.5. Defining the Variables of a Study and its Indicators

Variables in experimental research, or in an observational study, can be defined as everything which, from a quantitative or qualitative point of view, we are going to measure, control or study; in short, everything that is in close relationship with our operational hypotheses, anything that influences a study. These include independent variables, those which can be selected or manipulated, and dependent variables, which reflect the result of an action by the independent variables and confusing variables (which should be controlled, as much as possible, so as not to distort any results obtained).

Independent variables are understood to have the capacity to influence, have a bearing on, or affect the phenomenon that we are observing. For example: “the degree of translation expertise has a bearing on the translation process and the final product.” Pacte (2008) had to define the variable “expertise” and decided to work with a variable dichotomy: “expertise(+)”: generalist translators, being those having six or more years of professional translation experience, with translation being their main activity (at least 70% of their income), and “expertise(-)”: foreign language teachers in the Spanish EOIs (Official Language Schools) having six or more years of experience, but without having any professional experience in translation.

Figure 3: Variables in experimental research

Dependent variables can be defined as the observable consequences of the manipulation, or selection, of an independent variable by the researcher. Finding one or more dependent variables that are valid for measuring what we really wish to measure, is of major importance in the design of a study. In order to make a variable measurable, it is crucial to start from the theoretical definition already drawn up and to define the proportions into which the variable can be broken down. These “proportions” which correspond to the theoretical concepts we are interested in empirical correlations are the indicators of the variables we are trying to measure.

Confusing variables are external influences that can distort the results obtained in the study (the influence of a confusing variable is often attributed erroneously to an independent variable). They should be eliminated or controlled when designing the study. Control of confusing variables is a huge problem in literary Translation Studies. This feature of Translation Studies research can be seen through one of the characteristic dilemmas of that research, determining which translations form part of a corpus to be analysed (the entry of unqualified people into the profession has meant that translations have been published by “translators” who do not have even basic linguistic competence). Interest in the study of self-translations revolves around the fact that we are able to eliminate these undesired variables. Self-translators do not misinterpret themselves; they have sound bilingual and bicultural competence. In experimental studies, control can be carried out in different ways: through the elimination of an undesirable variable (e.g., if the study deals with inverse translation, then students with German as their mother tongue are excluded from it), or by maintaining their influence constant (forming groups that are truly “parallel”). In tracking studies (e.g., in didactic research) it is very difficult to control external factors (length of stay in a foreign country—infatuations included—work experience placements or work, and so on).

2.6. Defining the “Universe” of the Study and Extracting a Sample

This implies determining who (or what) we wish to observe. The universe (the “population” or the “collective”) is a set of reference elements—whether subjects (e.g., professional translators) or objects, (e.g., self-translated works)—that are subject to observations. It is defined by a distinctive common characteristic, which is what is studied. However, we find that in our field, there are no external criteria for defining and delimiting the references of this universe, e.g., “expert translators.” The solution to this problem is a pragmatic decision, i.e., the Pacte decision mentioned earlier.

Defining the universe (and with it the drawing up of a sample) is vital when it comes to interpretation (always subjective) and extrapolation (only valid within a defined universe) of data gathered. As it is not possible to observe the entire population (the “universe”) that we are interested in analysing, a representative sample of the universe we wish to analyse is taken. The most common way of obtaining such a sample is by random selection, which, for obvious reasons, is not very common in our field. It is also not very common to find sampling through quotas (selecting in accordance with certain percentages of the population, for example, male or female translators, translators of different ages). Here we are in full agreement with Daniel Gile when he warned us of the difficulty of selecting random samples and proposed “convenience” sampling: “An acceptable approximation can sometimes be found in the form of critically controlled convenience sampling, in which subjects are selected because they are easy to access but are screened on the basis of the researcher’s knowledge of the field and (ideally) of empirical data derived from observation and experimentation” (1998, p. 77).

In order to create “parallel” samples (experimental groups and control groups), the selected subjects are divided, normally at random. However, choosing at random can play havoc when we are dealing with a small sample (which is what Translation Studies normally does). For this reason, we can consider pre-tests which allow us to draw up “matching samples.” In other words, the sample is divided into “pairs” (or into “trios,” or into “blocks”) all with similar behaviour. Then one member of each “pair” is assigned to one group, and the other to a second group (and so on, in turn). There are statistical tests that allow us to determine if two groups are really parallel.

2.7. Determining the Tools for Gathering Data

In the unlikely event that we were to believe that translation competence is essentially hereditary, our investigation would concentrate on isolating the “translation gene” and we would then bring into play all of the tools of genetic research. If, on the other hand, we suspected that this competence depended on the character of the professional, we would turn to one of the standard personality tests available to us in the field of psychology. However, as we are convinced that TC is expert knowledge resulting from the interrelationship between different sub-competences and we do not have at our disposition standardised instruments, we try to adopt our ends to the tools of other sciences, or, when this is not possible, create our own tools and validate them empirically.

To ensure the objectivity, reliability, extrapolability and the environmental validity of the empirical approach and, especially, the relevance of the results obtained, it is of vital importance to have effective instruments for gathering data and measuring it accurately. One of the main problems is the lack of previous experience along with the above-mentioned lack of standardised tools. That is why we usually limit ourselves, essentially, to using tools that could be called “classic” tools (translations, questionnaires and interviews) and, more recently, TAPs. In recent years, Translation Studies research (especially research in interpreting) has utilised physiological and psychological indicators (for example, memory tests and autonomic nervous system responses) to clarify the translation process. Even more recently, computer and communications technology has been used (monitoring and recording programs such as Translog or Proxy, eye-tracking and others).

The choice of texts to be translated is complicated by the fact we need comparable texts for studies involving repeated measurement (e.g., longitudinal studies) or for studies that include more than one language. A possible solution is to use a single text which is then divided into separate sections. This method was used in Neunzig and Tanqueiro (2005) and ensured that the “texts” (i.e., the various sections of a single text) were very similar: they were written by the same author, concerned the same subject matter, employed the same style and register. Another method for assuring comparable texts is to use tools from psycho-sociology, such as Charles Osgood’s Semantic Differential adapted for our field (see Neunzig, 2004). Pacte solved the problem of finding parallel texts in different languages in the following way: texts were sought in German, French and English on the same subject (computer viruses) with similar difficulties (based on “rich points”) and submitted to validation through an experiment. One of the questions the subjects were asked was as follows: What degree of difficulty would you estimate for this text? In response, translators had to mark an X at a certain point along a line that went from “this translation is very easy” to “this translation is very difficult.” An “index of difficulty” was calculated with the results shown in the table.

Index of difficulty

The most obvious result of this validation of the comparability of the parallel texts was the homogeneity of the translators’ estimation of the difficulty of the three texts proposed for direct translation. In other words, the target language did not influence the subjects’ perception of the difficulty of the original text. This provides an indication (evidence) that both texts and subjects were well selected (see Pacte, 2008).

The use of questionnaires demonstrates the difficulty of formulating questions in such a way that everyone understands them in the same way (that they will truly measure what we have set out to measure). This implies a great additional effort by researchers, since most of our questionnaires have to be drawn up and validated especially for the specific project undertaken, i.e., the work carried out by Neunzig and Kuznik (2007) for Pacte.

Then there is the think aloud method (while translating), better known by its abbreviation TAP. This has proven efficient in researching what happens within the mind of the translator, within the so-called Black Box. However, the method’s detractors criticise the artificiality of the situation (translators rarely work facing a video camera and explaining what they are thinking). These critics note the difficulty some people have in verbalising what they are thinking and they maintain that TAPs give no access to automatic processes. In our opinion, of more value is the reflection made by the Copenhagen group (see Hansen et al. , 1998) which concludes that one of the main problems of using TAPs is the difficulty of carrying out two similar activities simultaneously, such as translating and verbalising thoughts.

Immediate retrospection (retrospective TAPs) is intended to clarify what the subject has been thinking during the translation process. It has the advantage of not interfering with the translation process, and yet the process is still fresh in the translator’s mind. However, it does present serious problems of objectivity and validity, since what is being measured may be something you have not set out to measure, such as, the subject’s memory, or his or her capacity to adapt to the researcher’s expectations.

In order to avoid these difficulties, some researchers opt to formalise the conversations ( TAP dialogue ) with two or three subjects carrying out a joint translation. In this way, translators make more proposals, present more arguments, reports, criticisms, seek support, and so forth. However, even one of the strongest defenders of this tool, Paul Kußmaul (1993), notes that it may be just registering interesting data about the psychodynamic process rather than the translation process.

2.8. Gathering Data

A good piece of advice for data collection is to limit the data to what is relevant for our study, especially when dealing with academic work such as written papers, essays or doctoral theses. Those who say “I’ll take a note of this data about so-and-so; it could come in handy” contravene the principle of practicality and scientific economy as described by Giegler (1988). As mentioned above, he insisted that experiments be designed in the simplest way possible to avoid overloading subjects, as well as to ensure manageability and that the analysis of the results does not imply excessive effort on the part of the researchers.

2.9. Carrying Out a Statistical Analysis of the Data

Statistics puts at our service procedures and techniques that allow us to describe and analyse any data obtained. We use descriptive statistical methods in the hope that they will coincide with the basic characteristics of the “population” (the “ universe ” from which the sample has been taken) and inferential statistical methods, which are those based on calculations of probabilities and which attempt to extend or extrapolate out to the entire population the information obtained from a representative sample.

2.10. Interpreting the Results and Communicating Them to the Scientific Community

The last step in our scientific work is to compare the results with our hypothesis in order to corroborate our idea or reject it. The latter would oblige us to modify our hypothesis or theory, or our model. This modification would in turn be validated in an empirical process, bringing full circle the “wheel of science.” The following references are to publications that I have been involved in where we have tried to apply the principles of transparency in translation research procedures presented in this article.

To summarise the contents of this article, we offer the following illustration, a synthesis of the steps required to achieve transparency in translation research procedures.

Figure 4: Transparency in translation research procedures

Pour cet article, l’auteur a reçu le Prix Vinay et Darbelnet, décerné par l’ACT (N.D.L.R.).

We must not forget that in the Nomenclator of UNESCO, our science is currently wedded to the social sciences.

Handout presented by Andrew Chesterman at a workshop on methodology at the EST Congress in Granada, 23-26 September 1998.

Let us remind ourselves of the first rule of Descartes’ method, the precept of evidence: “I would not accept anything as true which I did not clearly know to be true. That is to say, I would carefully avoid being over hasty or prejudiced, and I would understand nothing by my judgments beyond what presented itself so clearly and distinctly to my mind that I had no occasion to doubt it” (1633).

Max Weber (1922), the father of sociology, postulated that research should be wertfrei , that is, free of value judgements; a value judgement cannot be objective!

See El País (1999).

The deductive-axiomatic approach leads to the danger of becoming mere “speculationism,” in other words, of axiomatically starting from a speculation that is understood to be “true.” As Tausch and Tausch criticised: “Quoting established authorities a thousand times, whether they are called Pestalozzi, Freud or Skinner, does not convert speculation into science” (1971, p. 35).

Bibliographie

Bunge, Mario (1972). La investigación científica. Su estructura y su filosofía. [ Scientific Research. Its Structure and Philosophy. ] Barcelona, Ariel. Google Scholar
CHESTERMAN, Andrew (1998). “Handout.” Workshop on methodology , EST Congress, Granada, 23-26 September. Google Scholar
DESCARTES, René (1633). Discourse on Method . Available at: http://records.viu.ca~johnstoidescartesdescvartes1.htm [consulted 10 July 2010]. Google Scholar
ECO, Umberto (1997). Como se hace una tesis: técnicas y procedimientos de investigación, estudio y escritura . [ How to Write a Thesis. ] Barcelona, Gedisa. Google Scholar
EL PAÍS (1999). “Dos médicos confirman que la nariz crece al decir mentiras.” [“Two Doctors Confirm That Noses Really Do Grow When We Lie.”] Available at: http://elpais.comdiario19990523sociedad927410408_850215.html [consulted 6 May 2012]. Google Scholar
GIEGLER, Helmut (1994). “Test und Testtheorie.” [“Test and Test Theory.”] In Roland Asanger and Gerd Wenninger, eds. Wörterbuch der Psychologie . [ Dictionary of Psychology. ] Weinheim, Psychologie Verlags Union, pp. 782-789. Google Scholar
Gile , Daniel (1998). “Observational Studies and Experimental Studies in the Investigation of Conference Interpreting.” Target , 10, 1, pp. 69-93. 10.1075/target.10.1.04gil Google Scholar
Hansen , Gyde (1998). “The Translation Process: From Source Text to Target Text.” In Gyde Hansen et al. , eds. Copenhagen Working Papers in LSP , 1-1998, pp. 59-71. Google Scholar
Hurtado Albir , Amparo (2001). Traducción y Traductología . [ Translation and Translation Studies .] Madrid, Cátedra. Google Scholar
KUßMAUL, Paul (1993). “Empirische Grundlagen einer Übersetzungsdidaktik: Kreativität im Übersetzungsprozeß.” [“Empirical Bases for Translation Didactics: Creativity in the Translation Process.”] In Justa Holz-Mänttäri and Christiane Nord, eds. Traducere navem: Festschrift für Katharina Reiss zum 70. Geburtstag. [ Traducere navem: A Book in Homage to Katharina Reiss on Her 70th Birthday. ] Tampere, Tampereen Yliopisto, pp. 335-350. Google Scholar
Neunzig , Wilhelm (2004). “Das Semantische Differential zur Erfassung der Textrezeption.” [“The Semantic Differential in Measuring the Reception of a Text.”] In Eberhardt Fleischman, Peter A. Schmitt and Gerd Wotjak, eds. Translationskompetenz [ Translation Competence. ] Tübingen, Stauffenburg, pp. 159-173. Google Scholar
Neunzig , Wilhelm and Anna Kuznik (2007). “Probleme der übersetzungswissenschaftlichen Forschung: zur Entwicklung eines Einstellungsfragebogens.” [“Problems in Translation-Based Research: Developing a Questionnaire on Translation Knowledge.”] In Peter A. Scvhmitt and Heike E. Jüngst, eds. Translationsqualität. [ Translation Quality. ] Frankfurt, Peter Lang Verlag, pp. 445-456. Google Scholar
NEUNZIG, Wilhelm and Helena Tanqueiro (2005). “Teacher Feedback in Online Education for Trainee Translators.” Meta , 50, 4. CD-ROM. Google Scholar
NEUNZIG, Wilhelm and Helena Tanqueiro (2007). Estudios empíricos en traducción: enfoques y métodos. [ Empirical Studies in Translation: Approaches and Methods. ] Girona, Documenta Universitaria. Google Scholar
NEUNZIG, Wilhelm and Helena Tanqueiro (2011). “Zur Anwendung der naturwissenschaftlichen Methode in der Translationsforschung.” [“The Application of the ‘Scientific Method’ in Translation Research.”] In Peter A, Schmitt, Susann Herold and Annette Weilandt, eds. Translationsforschung. [ Translation Research. ] Frankfurt, Peter Lang Europäischer Verlag der Wissenschaften, pp. 599-614. Google Scholar
PACTE (2003). “Building a Translation Competence Model.” In Alves, Fabio, ed. Triangulating Translation: Perspectives in Process Oriented Research. Amsterdam, John Benjamins, pp. 43-66. Google Scholar
PACTE (2008). “First Results of a Translation Competence Experiment: ‘Knowledge of Translation’ and ‘Efficacy of the Translation Process.’” In John Kearns, ed. Translator and Interpreter Training. Issues, Methods and Debates . London, Continuum, pp. 104-126. Google Scholar
POPPER, Karl (1963). Vermutungen und Widerlegungen . [ Conjectures and Refutations. ] Mohr Siebeck Verlag, Tübingen. Google Scholar
PORTELL, Mariona (2001). “Handout.” Seminari on Methodology , Universitat Autònoma de Barcelona, 23-25 April. Google Scholar
Tanqueiro , Helena (2004). “A Pesquisa em Tradução Litéraria—Proposta Metodológica.” [“Research in Literary Translation—A Methodological Approach.”] Polissema , 4, pp. 29-40. Google Scholar
Tausch , Reinhard and Anne-Marie Tausch (1991). Erziehungspsychologie. Begegnung von Person zu Person [ Educational Psychology. A Face to Face Encounter. ] Göttingen, Hogrefe. Google Scholar
Toury , Gideon (1995). Descriptive Translation Studies and Beyond . Amsterdam, John Benjamins Publishing. Google Scholar
WEBER, Max (1922). “Der Sinn der ‘Wertfreiheit’ der soziologischen und ökonomischen Wissenschaften.” [“The Significance of Impartiality in the Sociological and Economic Sciences.”] In Max Weber, ed. Gesammelte Aufsätze zur Wissenschaftslehre [ Essays in the Philosophy of Science. ] Mohr Siebeck Verlag, Tübingen, pp. 489-540. Google Scholar

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Empirical Research

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The term “empirical” entails gathered data based on experience, observations, or experimentation. In empirical research, knowledge is developed from factual experience as opposed to theoretical assumption and usually involved the use of data sources like datasets or fieldwork, but can also be based on observations within a laboratory setting. Testing hypothesis or answering definite questions is a primary feature of empirical research. Empirical research, in other words, involves the process of employing working hypothesis that are tested through experimentation or observation. Hence, empirical research is a method of uncovering empirical evidence.

Through the process of gathering valid empirical data, scientists from a variety of fields, ranging from the social to the natural sciences, have to carefully design their methods. This helps to ensure quality and accuracy of data collection and treatment. However, any error in empirical data collection process could inevitably render such...

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Bibliography

Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. Textbooks Collection . Book 3.

Google Scholar

Comte, A., & Bridges, J. H. (Tr.) (1865). A general view of positivism . Trubner and Co. (reissued by Cambridge University Press, 2009).

Dilworth, C. B. (1982). Empirical research in the literature class. English Journal, 71 (3), 95–97.

Article Google Scholar

Heisenberg, W. (1971). Positivism, metaphysics and religion. In R. N. Nanshen (Ed.), Werner Heisenberg – Physics and beyond – Encounters and conversations , World Perspectives. 42. Translator: Arnold J. Pomerans. New York: Harper and Row.

Hossain, F. M. A. (2014). A critical analysis of empiricism. Open Journal of Philosophy, 2014 (4), 225–230.

Kant, I. (1783). Prolegomena to any future metaphysic (trans: Bennett, J.). Early Modern Texts. www.earlymoderntexts.com

Koch, S. (1992). Psychology’s Bridgman vs. Bridgman’s Bridgman: An essay in reconstruction. Theory and Psychology, 2 (3), 261–290.

Matin, A. (1968). An outline of philosophy . Dhaka: Mullick Brothers.

Mcleod, S. (2008). Psychology as science. http://www.simplypsychology.org/science-psychology.html

Popper, K. (1963). Conjectures and refutations: The growth of scientific knowledge . London: Routledge.

Simmel, G. (1908). The problem areas of sociology in Kurt H. Wolf: The sociology of Georg Simmel . London: The Free Press.

Weber, M. (1991). The nature of social action. In W. G. Runciman (Ed.), Weber: Selections in translation . Cambridge: Cambridge University Press.

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Published: 25 July 2024

The measurement, level, and influence of resource allocation efficiency in universities: empirical evidence from 13 “double first class” universities in China

Biao Chen 1 ,
Yan Chen 2 ,
Yajing Sun 1 ,
Yu Tong 1 &
Ling Liu 1

Humanities and Social Sciences Communications volume 11 , Article number: 955 ( 2024 ) Cite this article

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Metrics details

Social policy

China’s higher education system is shifting from quantitative expansion to connotative development to advance its quality. Since 2015, Chinese governments have been implementing a strategic policy for higher education called “double first-class”, which aims to promote a number of Chinese top universities to construct world-class universities or to establish world-class disciplines. “Double first-class” universities have received a large amount of educational resources through this policy. Taking advantage of resources efficiently is an important element in promoting the development of higher quality higher education. However, research on resource allocation in China’s “double first-class” universities is incomplete. Current research has not clarified the level of resource allocation efficiency or the factors affecting China’s “double first-class” universities. With the help of the superefficient data envelopment analysis (DEA)-Malmquist–Tobit model, this study actively explores the current status of the resource allocation efficiency of China’s “double first-class” universities to fill this gap in the field. Specifically, the development level and change trend of the resource allocation efficiency of 13 “double first-class” universities in China from 2015 to 2019 were measured with the help of the superefficient DEA-Malmquist model. The internal and external factors affecting the resource allocation efficiency of “double first-class” universities are also analysed with the help of the Tobit model. The overall level of resource allocation efficiency of “double first-class” universities is high, but the internal variability is large. From the perspective of efficiency decomposition, it is found that both technical efficiency change (EFch) and technical progress efficiency (TEch) play important roles in improving the total factor productivity (TFP) of resource allocation. Compared with TEch, EFch plays a more significant pulling role. This study confirms that the factors affecting resource allocation efficiency are complex. Among them, the regional economic environment, faculty title structure, and degree of international exchange have significant roles in promoting the resource allocation efficiency of “double first-class” universities, but local financial support and the time of policy implementation have certain negative effects.

Adopting the Hirschman–Herfindahl Index to estimate the financial sustainability of Vietnamese public universities

Not-for-profit or for-profit? Research on the high-quality development path of private universities in China based on system dynamics

Do staff capacity and performance-based budgeting improve organisational performance? Empirical evidence from Chinese public universities

Introduction.

As comprehensive reform in higher education has been developing in a thorough manner, the scale of modern universities has been constantly expanding. It is key to have an accurate measure of the resource allocation efficiency of higher education institutions. To ensure the sustainable development of higher education, an increasing number of countries are paying attention to the efficiency of university resource allocation (Zhang et al., 2022 ). China has introduced a series of higher education development strategies, including the “211 Project” and the “985 Project”. The development of higher education in China has been strongly influenced by these strategies, and clear requirements have been put forward for the allocation of university resources (Yang and Welch, 2012 ). With the development of practice, the original strategies were noted to have limitations such as rigid development and inefficiency (Zong and Zhang, 2017 ). In 2015, to promote the high-quality development of higher education, China’s Ministry of Education (MOE) released the Overall Programme for Coordinating and Promoting the Construction of World-Class Universities and First-Class Disciplines (Ministry of Education of China, 2015 ). The policy is referred to as “double first class”. In 2017, China’s Ministry of Education, Ministry of Finance, and National Development and Reform Commission jointly issued the “Implementation Measures for Coordinating the Promotion of the Construction of World-Class Universities and First-Class Disciplines” and the “Circular on the Announcement of the List of Universities and Disciplines to be Constructed by World-Class Universities and First-Class Disciplines” (Ministry of Education of China, 2017 ). These documents clearly define the implementation steps and the list of universities in which the “double first-class” policy would be applied. Universities included in the construction list are called “double first-class” universities (Peters and Besley, 2018 ).

The principle of “gathering high-quality resources to cultivate first-class talents” has helped these “double first-class” universities absorb a large amount of human, material, financial and other educational resources (Wang et al., 2018 ; Li and Xue, 2023 ). As top universities in China, the development of “double first-class” universities will have an important leading influence on Chinese higher education (Chang et al., 2020 ; Lu and Wen, 2023 ). However, Chinese scholars’ research on resource allocation in “double first-class” universities is still very limited. Wang ( 2019 ), Chen et al. ( 2023 ) and others have attempted to explore different aspects of this topic but have not yet reached a research consensus. The level of resource allocation efficiency of “double first-class” universities and the factors that may affect the efficiency of these top universities remain unclear. The lack of such research may constrain the sustainable development of these universities.

In terms of research methods, most of the current studies rely on a single model, such as SFA or DEA, for efficiency calculations. For example, McClure et al. ( 2023 ) used the SFA model to analyse the resource allocation of public research universities in the United States from 2005 to 2015. Malesevic Perovic and Kosor ( 2020 ) used the DEA model to calculate the efficiency of public expenditures on public universities in Europe. Wu et al. ( 2020 ) used a three-stage DEA model to calculate the utilisation of university resources in China in 2016. Bai et al. ( 2020 ) measured the conversion rate of investment in higher education in China from 2014–2017 based on the DEA model. With the continuous deepening of related research, these single models have been found to have certain limitations (Caballero et al., 2004 ; Silva et al., 2023 ). The SFA model, which is a typical representative of parametric analysis, requires high-quality data and is applicable mainly to single-output scenarios (Chen and Shu, 2021 ). The DEA model can be adapted to complex scenarios involving multiple inputs and multiple outputs, but it is not capable of secondary ranking of decision-making units (DMUs) on the same production frontiers. DEA models are also unable to realise the dynamic need for continuous comparisons across time (Huang et al., 2019 ; Cordero et al., 2021 ). These insurmountable shortcomings make it impossible for single models such as SFA or DEA to continue to meet the higher demands for complex and accurate computations in modern higher education.

To fill this research gap, this study conducted a study on the measurement of resource allocation efficiency and an analysis of the factors affecting it for 13 “double first-class” universities in China. To meet the needs of the study, we became fully informed of the latest research results in the field of efficiency measurement. Considering the actual development of China’s “double first-class” universities, this study ultimately selected the efficiency measurement methods used by Agasisti and Wolszczak-Derlacz ( 2016 ), Orsini et al. ( 2021 ) and other scholars in the fields of healthcare, agriculture and other areas. This study flexibly combined the superefficiency DEA Malmquist–Tobit model with the study of higher education resource allocation in China. The research methodology adopted in this study is an extension of the traditional single model. New methodology systematically measures the level of resource allocation efficiency of China’s “double first-class” universities and analyses the internal and external factors affecting efficiency improvement in a more comprehensive way. The superefficiency DEA model is used to characterise the resource allocation efficiency of China’s “ double first-class” universities at the static level. The Malmquist model is used to analyse the trend of the resource allocation efficiency of Chinese “double first-class” universities from a dynamic perspective. The Tobit model is subsequently used to analyse the internal and external factors affecting the level of resource allocation efficiency of China’s “double first-class” universities.

Compared with other studies, we have a much greater focus on the actual development of Chinese higher education. For example, in the research design section, an indicator system highlighting the characteristics of “double first-class” universities is constructed according to the actual situation in China. In the research methodology section, the high-relevance research method is introduced into the research field of resource allocation for “double first-class” universities. In the results analysis section, the results are analysed in light of the actual development of “double first-class” universities. We hope that the results of this study can help improve the resource allocation efficiency of “double first-class” universities and other similar higher education institutions in China. Figure 1 shows the main research framework.

The conceptual model consists of two main components: measurement of efficiency levels and analysis of influencing factors. The model briefly describes the main ideas and core methodology of this study.

The other parts of this study can be summarised as follows. The second part reviews the literature closely related to the research topic from the perspective of internal and external influence and proposes specific research hypotheses. The third section introduces the superefficiency DEA-Malmquist–Tobit model, variables and data. The fourth section describes the level of resource allocation efficiency and the influencing factors. And the fifth section summarises the conclusions, limitations and presents suggestions for future research.

Literature review

Ginder et al. ( 2021 ) clarified the functional position of organisations in terms of internal and external factors based on attribution theory. Higher education institutions are multitasking entities that are both school- and society-oriented. Wolszczak-Derlacz and Parteka ( 2011 ) used the two-stage DEA model to analyse the efficiency of public universities in European countries and found that efficiency values were influenced by both external and internal factors. Guo et al. ( 2019 ), and Wang and Fu ( 2023 ) analysed the efficiency of Chinese universities and found that efficiency was affected by both internal and external factors. Li et al. ( 2018 ) and Shen et al. ( 2019 ) categorised the factors affecting the efficiency of the allocation of university resources as education funding, human capital and regional economic. Although scholars have begun to explore the factors influencing university resource allocation efficiency from internal and external perspectives, existing studies have not reached a consensus on the effects and paths of influence. Therefore, this study explores the factors influencing the allocation efficiency of university resources from the perspectives of the external environment and internal structure.

External environmental factors

The efficiency of a university’s resource allocation is jointly influenced by multiple sets of external variables, which are the result of an external environment that the university cannot control and cannot change in the short term. However, these factors may affect university resource allocation efficiency performance (Warning, 2004 ; Bonaccorsi et al., 2006 ). Some of the more representative external environmental factors are the level of economic development (Kempkes and Pohl, 2010 ), financial support (Jeon and Kim, 2018 ), and policy support (Fussy, 2018 ).

Economic environment

Influenced by resource allocation theory, scholars have found that regional economic development has spillovers. Berbegal-Mirabent et al. ( 2013 ) assessed the efficiency of resource allocation in Spanish universities; the economic activity of the region in which the university is located was found to be a key factor influencing the efficiency level of the university. The regional economic level was the element that effectively increased total education funding (Guo et al., 2013 ; Marquez-Ramos and Mourelle, 2019 ). Bao et al. ( 2016 ) and Rambeli et al. ( 2021 ) stated that regional economics can reduce the difference in funding caused by differences in university hierarchy and rank. However, some scholars stated that there is no obvious synchronous relationship between strengthening regional economic power and improving the efficiency of higher education resource allocation (Rolim et al., 2021 ). Furthermore, when the resource allocation structure is irrational, as the regional economy continues to develop, it may exacerbate the problems of redundant inputs or insufficient outputs (Drucker, 2016 ; Jahic and Pilav-Velic, 2021 ).

Government financial support

Based on public goods theory, scholars have noted that universities are important nonprofit organisations. Financial support from the government is an important material basis for ensuring the development of universities (Carrington et al., 2007 ). If the government pays attention to the development of universities and actively expands financial inputs, it will effectively alleviate the problem of resource constraints on universities (Angelopoulos et al., 2011 ). Based on comprehensive incentive theory, some scholars also noted that obtaining financial support from the government is one of the most important ways to affirm the reputation of universities. This can further motivate university teachers and students, thus contributing to the desired output (Lovakov et al., 2021 ). However, Fukui ( 2021 ), based on the new institutional economics theory, suggested that financial support behaviour may crowd out external funding channels such as market investment and social donations. In addition, government finance mostly adopts differentiated funding behaviour. This may lead to opportunism, gameism or the Matthew effect, which may ultimately affect universities’ incentives to improve their own resource allocation efficiency (Fadda et al., 2022 ).

Policy implementation

Influenced by the management system, education policy plays an important guiding role in the allocation of higher education resources in China (Zong and Zhang, 2017 ). The government promotes higher education by shifting resources towards universities in weak areas and special types of universities through policy instruments (Guo, 2023 ). Hendin ( 2022 ) argued that policy implementation can provide strong support for university resource acquisition. However, Nisar ( 2015 ) suggested that university reflections on policies follow a nonlinear and nondeterministic pattern. The actual impact of policy implementation on universities cannot be easily predicted. The Chinese government has implemented the “985” and “211” projects to promote higher education. However, as policies continue to be implemented, some researchers have found that these policies have actually broken through initial research assumptions and have created problems such as identity consolidation and ineffective competition (Xu, 2020 ).

Internal characterisation factors

In addition to external environmental factors, several internal characterisation factors drive the development of higher education. These factors can help explain university resource allocation efficiency performance (Kremen et al., 2022 ). The direction of efficiency improvement can also be further clarified by exploring the internal characteristics of university resource allocation efficiency (Li, 2019 ). The faculty title structure (Mayerle et al., 2022 ; Chen and Yang, 2021 ) and the degree of external communication (Crossouard, 2010 ; Berbegal-Mirabent et al., 2013 ), which are important indicators that describe the operation and development of higher education institutions, have been widely used by scholars (Zha et al., 2022 ).

Teacher title structure

Mayerle et al. ( 2022 ) used the title ratio as an important indicator of the overall quality of teachers. Chen and Yang ( 2021 ), based on human capital theory, further proposed that teachers with senior titles can play an important role in talent cultivation and scientific research by virtue of their high research level and rich research resources. This helps to promote the efficiency of university resource allocation. However, Salas-Velasco ( 2020 ), based on two-factor incentive theory, argued that the proportion of teachers with high-ranking titles does not reflect the level of development of universities and is not an important indicator of the comprehensive output of universities. He argued that teachers with high-ranking titles may lack the endogenous motivation to continuously improve their overall output.

External communication

Crossouard ( 2010 ) and Berbegal-Mirabent et al. ( 2013 ) indicated that the impact of research findings is positively correlated with the degree of external communication of the host institution. On the basis of knowledge transfer theory, Malesevic Perovic and Kosor ( 2020 ) found that international academic exchange activities are important for universities to integrate multiple resources and enhance their international influence. Dalla Corte and Mendes ( 2018 ), and Weerasinghe and Deden ( 2020 ) further stated that cross-geographical exchanges and cooperation in the context of globalisation can facilitate knowledge flow. This approach will help to enhance the quality and impact of the comprehensive output of universities. However, Chen and Yang ( 2021 ), based on differences in academic environments, suggested that there are large differences in higher education management systems in different countries. Therefore, there is no necessary relationship between universities’ external communication behaviour and resource allocation efficiency. In addition, extra features, such as text translation and language embellishment, may result in unnecessary waste of resources (Behr, 2017 ).

Previous studies have shown that university external environmental factors and internal characteristic factors play important roles in influencing university resource allocation. However, scholars have not formed a consistent understanding of the specific effects and paths of influence (see Fig. 2 ). Therefore, this study proposes the following research hypotheses based on the overall development of the “double first-class” universities in China. This study will closely focus on the five research hypotheses (see Fig. 3 ).

The chart consists of two main parts: external environmental factors and internal characteristic factors. The chart focuses on the main points of previous studies.

This chart mainly shows the five main research hypotheses of this study from the perspective of the external environmental factors and the internal characteristic factors.

H1 . There is a positive correlation between the level of economic development of the region and the resource allocation efficiency of China’s “double first-class” universities.

H2 . There is a positive correlation between financial support in the region and the resource allocation efficiency of China’s “double first-class” universities.

H3 . There is a positive correlation between the time of policy implementation and the resource allocation efficiency of China’s “double first-class” universities.

H4 . There is a positive correlation between the proportion of highly titled teachers and the resource allocation efficiency of China’s “double first-class” universities.

H5 . There is a positive correlation between the degree of external communication at universities and the resource allocation efficiency of “double first-class” universities.

Methodology and data description

Methodology: superefficient dea-malmquist–tobit modelling.

The DEA model based on the envelope idea was first proposed by Charnes et al. (Azadeh and Kokabi, 2016 ). As a typical nonparametric analytical method, the DEA model can avoid subjective interference to the greatest extent. Due to the simplicity of the method, it has been widely accepted by scholars (Du et al., 2010 ). This approach is mainly used to solve the problem of relative efficiency between DMUs in complex systems with multiple inputs and multiple outputs (Lee and Zhu, 2012 ). As efficiency research continues, researchers have found that DEA models cannot be used to rank DMUs in a fine-grained way. This creates difficulties for subsequent inferential analyses (Lovell, 2003 ). The superefficiency DEA model further compares the relative efficiency values of DMUs based on the traditional DEA model (Banker et al., 1984 ; Xue and Harker, 2002 ). It is designed to meet the computational requirements of real situations for differentiated characteristics. Assuming that there are n decision units, the following formula can be used for the jth decision unit ( j = 1,2…, n ) for superefficiency calculations (Chen, 2005 ; Cook et al., 2009 ).

where x and y represent the input and output variables, respectively. ${{s}}^{-}$ and $\,{{s}}^{+}$ are slack variables representing input redundancy and output deficit, respectively. ${\theta }$ is the relative efficiency value. When ${\theta }\,$ < 1, the decision unit DEA is invalid. ${\theta }$ ≥ 1 and ${{s}}^{+}$ ≠ 0 or ${{s}}^{-}$ ≠ 0 indicates that the decision unit DEA is weakly efficient. When ${\theta }$ ≥ 1, ${{s}}^{+}\,$ = 0 and ${{s}}^{-}$ = 0, the decision unit DEA is valid. Statistically, DEA inefficiency means that the decision unit has not reached the optimal production allocation. A weakly valid DEA means that the input‒output efficiency of the decision unit cannot be further improved, but there is a certain input redundancy or output deficit. DEA validity means that the input‒output mix of the decision unit is in the optimal state and that there is no output deficit or input redundancy.

Although the superefficiency DEA model can help the traditional DEA model solve problems, it has its own limitation of not being able to be compared continuously across periods (Jiang et al., 2020 ). Malmquist constructed a nonparametric efficiency model based on the DEA model that can measure the change in efficiency across periods (Malmquist, 1953 ). The Malmquist model uses a distance function to calculate the change in relative efficiency from period t to period t + 1. The model can help researchers accurately capture the dynamic and continuous trend of efficiency changes (Ganji and Rassafi, 2019 ). The Malmquist model is formulated as follows (Dai et al., 2016 ).

where x denotes the input variable and y denotes the output variable. $0$ denotes a decision-making unit. ${{M}}_{0}$ denotes the change in relative efficiency from period t to period t + 1. A value of ${{M}}_{0}$ > 1 indicates that the relative efficiency has increased from the base period level and is overall in an upward phase. A value of ${{M}}_{0}$ = 1 indicates that the relative efficiency has not changed from the base period. In contrast, a value of $\,{{M}}_{0}$ < 1 indicates a decrease in relative efficiency from the base period.

The Malmquist can be further decomposed into technical progress efficiency (TEch) and technical efficiency change (EFch). TEch indicates technological advancement. When TEch > 1, this implies an increase in potential output driven by technological progress, while other input factors remain unchanged. EFch denotes the degree of use of the input factors. When EFch > 1, resource potential is maximised by improving coordination between various resource factors (Lovell, 2003 ). EFch can be further decomposed into pure technical efficiency change (PTEch) and scale efficiency change (SEch). PTEch measures the efficiency changes caused by pure technical changes such as policy systems and management modes. SEch reflects the efficiency changes caused by scale factors such as resource structure and resource scale. The quantitative relationship between them is ${{M}}_{0}$ = TEch * EFch = TEch * (PTEch * SEch) (Samut and Cafri, 2016 ; Jiang et al., 2020 ).

Most of the relative efficiency values obtained according to the superefficient DEA-Malmquist model are between (0,1]. If the least squares method is directly followed for parameter estimation, there is a possibility of distortion in the results obtained (Atkinson and Wilson, 1995 ). To avoid this problem, the Tobit model was introduced in this study with reference to Jamil ( 2013 ). The model was first proposed by Tobit (1958). When the dependent variable is discontinuous data, bias can be eliminated to the maximum extent, and the accuracy of the analysis results can be guaranteed with the help of the Tobit model. The expression of the Tobit model is as follows (Tobin, 1958 ; Barrett, 2012 ).

In this study, Y represents the relative efficiency of university resource allocation. α is a constant term. Xn is the explanatory variable closely related to the research hypothesis. β n is the regression coefficient of the explanatory variables. μ is the random disturbance term. i and t denote the specific decision unit and time, respectively.

Input–output indicator system

In the study of the efficiency of university resource allocation, how to choose indicators and which indicators to choose are key issues that directly affect the scientificity and objectivity of the research conclusions. Due to differences in research objectives, existing studies have not established a unified evaluation standard for this topic (Xie and Yu, 2019 ). This study mainly refers to Nazarko and Saparauskas ( 2014 ) and Ramirez-Gutierrez et al. ( 2020 ) to determine the indicator system for measuring the resource allocation efficiency of China’s “double first-class” universities from the input–output dimension (2020).

Input Indicators

University education resources generally refer to the various types of resources that constitute, maintain and serve the university education system. These resources mainly include human resources, material resources and financial resources (Li and Zhang, 2019 ; Guo, 2023 ). First, human resources mainly refer to all kinds of university staff. Expanding faculty is the focus of human resource investment at universities (Altbach, 2009 ). Referring to the research design of Chang et al. ( 2012 ), this study specifically selects the total number of teachers in universities (I1) to denote human resource input. Second, physical resources mainly refer to the educational facilities provided by the government, enterprises and other social organisations in higher education activities (Regassa et al., 2013 ). Universities enhance students’ and teachers’ research awareness and improve the quality of educational output through good educational facilities (Nzivo and Chen, 2013 ). This study refers to the study of Carrington et al. ( 2005 ) and specifically selects total fixed assets (I2) to reflect physical resource input. Third, financial resources mainly refer to the educational investment made by the main beneficiaries of educational activities. With respect to the actual development of higher education in China, financial education funding from all levels of government is the main source of financial resources for university education activities (Yu et al., 2017 ). Therefore, this study refers to the study of Zhu et al. ( 2023 ) to specifically select education funding (I3) as an indicator of financial resource investment.

Output indicators

Universities are large organisations that involve complex outputs such as teaching, research and social interaction. The output dimensions of these products are diverse (Abbott and Doucouliagos, 2003 ; Nokkala, 2012 ). Therefore, in selecting the output indicators, this study specifically referred to the Chinese government’s institutional documents for comprehensively evaluating “ double first-class” universities. This study decided to respect the characteristics of “double first-class” universities and use the policy objectives and evaluation criteria of “double first-class” construction. We chose to measure the output of resource allocation in five dimensions: talent cultivation, scientific research, international exchanges and cooperation, social services and faculty development. First, graduates are the direct manifestation of universities’ talent cultivation function (Athanassopoulos and Shale, 1997 ). This study refers to Johnes ( 2006 ) and Laureti et al. ( 2014 ), who used the number of graduates (O1) as a direct reflection of the talent cultivation of “ double first-class” universities. Second, published papers, books and projects are important criteria for reflecting the research output of Chinese universities (Kao and Hung, 2008 ; Chang et al., 2012 ; Kuah and Wong, 2013 ). To accurately measure the scientific research output of “double first-class” universities, based on existing studies, three indicators are selected for this study: the number of academic papers published (O2), the number of scientific and technological publications (O3), and the number of scientific research projects funded (O4). Third, in response to the rapidly changing external situation, the government actively promotes higher education institutions to carry out international exchanges and cooperation and to provide social services (Mok and Cheung, 2011 ). “Double first-class” universities actively establish cooperative relationships with domestic and foreign businesses and industries as well as with the community at large through academic exchanges, technology transfer, and other channels (Xu, 2020 ). This study refers to Zhu et al. ( 2023 ) and Anderson et al. ( 2007 ), who chose the number of international cooperation papers (O5) and the actual income for the year from technology transfer (O6) to reflect the outputs of international exchange and cooperation and social services of the “double top” universities, respectively. Fourth, the Chinese government established the National Science Foundation for Distinguished Young Scholars (Jieqing) and the National Science Foundation for Excellent Young Scholars (Youqing) to accelerate the cultivation of a group of national high-level talent. These awards can adequately represent high-quality achievements in the faculty development of “double first-class” universities (Yue et al., 2020 ). Therefore, this study refers to Xu’s ( 2020 ) study and takes the number of Jieqing or Youqing recipients in the faculty (O7) as an important indicator of the achievements of the faculty construction of “double first-class” universities.

System of impact factor indicators

Modern universities must cope with multiple influences from both external and internal environments (Wolszczak-Derlacz and Parteka, 2011 ). Existing studies have constructed a system of influencing factors from the perspectives of the external environment and internal characteristics (Ginder et al., 2021 ; Wang and Fu, 2023 ). This study refers to existing studies to construct a system of factors influencing the resource allocation efficiency of Chinese “double first-class” universities.

In this study, the external environmental factor indicators are selected from the regional economic environment, financial support and policy support. First, regional economic development has a significant impact on university resource allocation activities (Baimuratov et al., 2020 ). This study refers to Rolim et al.‘s (2021) study using the gross area product per capita of the university’s location (A1) to indicate the level of regional economic development. Second, China’s higher education management system stipulates that central and local governments share financial education funding. However, the central government is mainly responsible for funding a small number of universities directly under the central government. A much larger amount of local higher education funding is mainly provided by local governments (Liang et al., 2020 ). Financial support from local governments plays an important role in enhancing the efficiency of university resource allocation (Doh and Kim, 2014 ). This study refers to Caerteling et al. ( 2013 ) and Zhu and Zhang (2023), who used the share of local government appropriation in the funding of university education programmes (A2) to indicate the strength of regional financial support. Third, policy-makers expect policies to be effective in improving the quality of higher education (Daraio et al., 2015 ). However, practical experience proves that policy effects do not follow the designers’ initial assumptions exactly. There is a need to explore the effects of policy implementation that become apparent over time (Volkwein and Tandberg, 2008 ). Therefore, to accurately measure the specific effects of “double first-class” policy implementation, this study refers to Zha et al. ( 2022 ) and Zhu et al. ( 2023 ), who used the time of policy implementation as a time dummy variable and conducted quantitative analyses accordingly. Specifically, the time dummy variable for the introduction of the “double first-class” policy (A3) is used to indicate the time of policy implementation.

Internal characteristic factors

In this study, internal characteristic factor indicators are selected from the faculty title structure and external exchanges. First, teachers’ good performance is an important factor in increasing overall satisfaction and enhancing the quality of university output (Wachtel, 2006 ). Asian countries have a strict hierarchy for faculty performance. The senior title is the highest level of university faculty titles (Horta et al., 2010 ). this study refers to Li ( 2019 ), who used the proportion of teachers with senior titles (A4) in the internal characteristics impact indicator system. Second, the external exchange situation is an important factor in improving the efficiency of university resource allocation (Weerasinghe and Dedunu, 2020 ). The goal of “double first-class” construction emphasises that “double first-class” universities should strive for international recognition (Li and Xue, 2023 ). The Chinese government has issued a number of policies to encourage “double first-class” universities to engage in foreign exchange activities by participating in international academic conferences (Zhou and Wu, 2016 ). The Ministry of Education has officially declared the number of papers exchanged at international conferences outside China and the number of invited presentations at international academic conferences as important indicators of external exchanges in the government’s education statistics yearbook. Influenced by the importance of these statistics, this study refers to Daraio et al. ( 2015 ) Shamohammadi and Oh ( 2019 ) to include the number of papers exchanged at international academic conferences and the number of invited presentations at international academic conferences (A5) as important indicators reflecting the extent of international exchanges. This study expects these indicators to comprehensively reflect the influence of internal and external environmental factors on the resource allocation efficiency of China’s “double first-class” universities. The input‒output indicator system and influence factor indicator system used in this study are shown in Table 1 .

Description of data

Since the launch of the “double first-class” construction project in 2015, the government has invested a large amount in educational resources. Unfortunately, there are very limited public data available. In this study, 13 Chinese “double first-class” universities were selected as the research sample. These universities are typical and representative of Chinese universities as key universities that have been consecutively selected for China’s “double first-class” list (Zhu et al., 2023 ). In addition, due to the influence of the COVID-19 pandemic, the resource allocation of universities after 2020 has changed significantly compared with that in previous years. Therefore, to ensure the comparability of the data, this study analyses and compares the data between 2015 and 2019. All the data are derived from the annual basic statistics of universities of the Ministry of Education of China. Table 2 lists the descriptive statistics of each variable.

Results and discussion

Results of static analyses.

The study finds that the overall level of resource allocation efficiency in the sample universities is high through cross-sectional comparison. A number of universities have achieved DEA efficiency for consecutive years. As shown in Table 3 , the overall average value of resource allocation efficiency is 1.08. A total of 92.31% of the decision-making units have efficiency values that are at or even beyond the production efficiency frontier. This shows that the 13 universities that were continuously selected as part of China’s “double first-class” list have stronger resource allocation capacities and reasonable resource allocation layouts. It is important to note that there are relatively large differences between universities. For example, the resource allocation efficiency of University G exceeds the production frontier by 51%. University G has been ranked number one for five consecutive years. At the same time, the resource allocation efficiency of University D is only 69.82% of the overall level, and University D has not reached an effective allocation for five consecutive years. This shows that there is internal variability in the allocation efficiency of educational resources.

Based on the longitudinal analysis, this study finds that the level of resource allocation efficiency of the 13 “double first-class” universities fluctuates significantly. As shown in Fig. 4 , the efficiency change shows a downward trend and then an upward trend, with 2017 being the turning point. The first stage (2015–2017) shows a slow decline followed by a rapid decline, with an overall decline of 5.6%. The second stage (2017–2019) begins a recovery phase, with an overall increase of 1.8%. The trend of the change in efficiency is closely related to the process through which the government promoted the “double first-class” policy. Specifically, in the early stage of policy implementation, the overall reform practice was exploratory. The traditional mechanism of resource allocation in universities, represented by “high input–high output”, was abandoned. However, the new mechanism for promoting the growth of resource allocation efficiency has not yet been fully defined. This inevitably causes a frictional decline in the value of efficiency. With the continuous promotion of new policies, the “input‒output” structure has been further optimised. Input resources have been effectively utilised. The overall efficiency of resource allocation began to increase.

The folding line shows the changes in resource allocation efficiency of 13 "double first-class" universities in China during 2015–2019.

Achieving optimal resource allocation efficiency by minimising inputs or maximising outputs is one of the important actions by policy-makers. Optimal-scale projection analysis can help in determining the most suitable scale of inputs and outputs to achieve the goal of increased efficiency (Banker et al., 1984 ). In this study, optimal-scale projection analysis was conducted with reference to Assani et al. ( 2018 ) and Haghighatpisheh et al. ( 2022 ). The optimal scale projection characteristics are different for different “double first-class” universities. As shown in Table 4 , the optimal scale projection of the inputs and outputs sides of four universities are both 0. This indicates that the amount of resources invested in these four universities can exactly meet the allocation needs. The potential of these resources has been fully released. Eight universities, B, C and E, have projected values of inputs greater than 0. This means that some of the resources invested in these 8 universities are more than the actual needs. There is redundancy of inputs and waste of resources. The values of both the input-side projection and the output-side projection of University D are >0. This means that University D not only has redundant resources but also faces the problem of insufficient effective outputs. This study further compares the results of the optimal projection analysis and finds that only 30.77% of the universities have reached the standard of resource allocation efficiency. Many “double first-class” universities have different degrees of resource wastage. A mismatch between the actual value and the target value mostly occurs at the input end. “Double first-class” universities should pay close attention to the problems of wasted resources and insufficient output. The main body of resource input needs to optimise the method of resource input.

Results of dynamic analyses

As shown in Table 5 , the total factor productivity of the resource allocation of the “double first-class” universities is 0.973. The average annual growth rate of total factor productivity is −6.3%. This indicates that the production frontier of the resource allocation efficiency of “double first-class” universities has decreased. Based on the longitudinal dimension, this study found that the dynamic fluctuation in the efficiency value during the cycle is obvious. Specifically, taking 2017 as the demarcation point, it shows a wedge-shaped trend of decreasing, then increasing and then decreasing. In the first stage (2015–2017), total factor productivity declined rapidly and reached a minimum level. In the second stage (2017–2019), total factor productivity rebounded rapidly, followed by a small decline.

As shown in Fig. 5 , there is a high degree of consistency between the trend in total factor productivity (TFP) and the trend in the wedge of technical efficiency (EFch). From the perspective of the efficiency decomposition relationship, this study finds that the technical efficiency (EFch, 0.997) and technical progress efficiency (TEch, 0.976) of resource allocation in “double first-class” universities are greater than the total factor productivity (TFP, 0.973). This indicates that both technical efficiency (EFch) and technical progress efficiency (TEch) play important roles in improving total factor productivity (TFP). Compared with technical progress efficiency (TEch, 0.976), technical efficiency (EFch, 0.997) plays a more significant pulling role. Regarding the efficiency analysis relationship, pure technical efficiency (PTEch, 0.999) and scale efficiency (SEch, 0.997) are not lower than the technical efficiency level (EFch, 0.997). This indicates that under the existing level of production technology, “double first-class” universities improve the coordination between resource elements by improving the management level and increasing resource allocation.

The light blue folding line shows the total factor productivity (TFP) and the orange folding line shows the technical progress efficiency (TEch). The grey folding line shows the technical efficiency change (EFch). The yellow folding line shows the pure technical efficiency change (PTEch). The dark blue folding line shows the scale efficiency change (SEch).

As shown in Table 6 , only four “double first-class” universities, A, H, J and K, have a total factor productivity (TFP) of resource allocation greater than 1 in the observation cycle, which means that only a few universities are in a state of balanced development in terms of technical efficiency (EFch) and technical progress efficiency (TEch). Most of the universities in the sample have not released their resource potential to the greatest extent. According to the quantitative relationship between technical efficiency (EFch) and technical progress efficiency (TEch), these universities are further classified into technical efficiency-dominated and technical progress-dominated. Among them, the technical efficiency-dominated universities represented by B and C (EFch is greater than TEch) mainly drive the efficiency of resource allocation by improving the management level and expanding the efficiency of scale. A and H, as the representatives of technical progress-dominated universities (TEch is greater than EFch), mainly rely on innovation and upgrading of the configuration of technology to increase resource allocation efficiency. Overall, the universities in which total factor productivity (TFP) is increasing are technical progress-dominated universities. The universities in which this factor is decreasing are all technical efficiency-dominated universities. This finding suggests that China’s top universities, represented by “double first-class” universities, need to gradually abandon the traditional model of exchanging an input scale for the desired output and gradually transition to the comprehensive and balanced “high technical efficiency–high technical progress” model. To achieve this transition, top universities need to fully exploit the important role of technological progress in enhancing resource allocation.

Results of the analysis of influencing factors

The above efficiency measurements reveal that the resource allocation efficiency of China’s “double first-class” universities is generally high. However, there are significant differences and cyclical fluctuations. Therefore, to explore the potential of resources, this study attempts to investigate the influence of external environmental factors and internal characteristics on the resource allocation efficiency of universities. The study of influencing factors is divided into two steps. In the first step, a Tobit regression model of the influence of external environmental factors and internal characteristic factors on total factor productivity (M1) and technical efficiency (M2) is established. The second step is to refine and decompose the Tobit regression model of external environmental factors and internal characteristic factors on pure technical efficiency (M3) and scale efficiency (M4). Due to the similarity of the values and the same trend of change in technical progress efficiency and total factor productivity, no repeated measurements were carried out. The specific results are shown in Table 7 .

There is a significant positive correlation between the regional economic environment and the total factor productivity (TFP) and technical efficiency (EFch) of resource allocation in “double first-class” universities. To a certain extent, these findings echo those of previous studies by Marquez-Ramos and Mourelle ( 2019 ) and Baimuratov et al. ( 2020 ), which suggested that regional economic development can provide strong economic support for improving the efficiency of resource allocation in universities. This study further clarifies that the economic environment has no correlation with the scale efficiency of resource allocation through the multiefficiency combined regression model. For “double first-class” universities, the regional economic environment can indeed play a positive role. However, it does not simply rely on financial support in exchange for the scale advantage of educational resources. Rather, by optimising the technological innovation environment, cultivating advanced technology and attracting talent, the resource allocation efficiency of “double first-class” universities can be promoted in a comprehensive way.

There is a negative correlation between regional financial support and total factor productivity of resource allocation at the 5% level of significance. This suggests that local government funding may act as a disincentive to increase the efficiency of resource allocation in universities. This study suggests that the reason may lie in the crowding out effect of local government financial funding on other social resources. It is also possible that universities are overly dependent on financial support and may thus lose the motivation to improve resource allocation efficiency (Fukui, 2021 ). In this regard, this study suggests that the government should be advised to strengthen the principle of efficiency in resource input, and universities should also be encouraged to actively seek social resources. The establishment of a multifaceted and coordinated resource input mechanism helps universities to form an incentive and constraint mechanism for resource allocation.

The policy implementation time is negatively related to the total factor productivity of resource allocation at the 5% significance level. Previous studies have mostly viewed policy as a quick tool for improving the efficiency of resource allocation at universities (Fussy, 2018 ; Hendin, 2022 ). This study discovers that efficiency improvement is a systematic and comprehensive long-term transformative endeavour. There is a limit to the immediate boost that a single policy can provide. Specifically, traditional inefficient allocation strategies are quickly discarded in the early stages of a policy’s launch. This may lead to obvious efficiency gains. However, overall policy reform practices are still in the exploratory stage. The new mechanism for promoting the growth of resource allocation efficiency is not yet perfect. This will inevitably lead to a decline in efficiency. As the reforms continue to deepen, the gradual strong policy spillover effect will be effective at promoting resource allocation efficiency.

The proportion of senior teacher is positively correlated with the pure technical efficiency of resource allocation at the 1% significance level. Abundant high-level human capital can help facilitate the accumulation of disciplinary advantages. This can effectively improve the quality of university output and promote the technical efficiency of resource allocation. This study simultaneously compares the effects of financial support and faculty structure on the resource allocation efficiency level of “double first-class” universities. China’s “double first-class” universities are at a new stage of development where physical capital is relatively saturated but human capital is not. The accumulation of high-level human capital and the release of human potential can effectively stimulate dynamic efficiency improvement. This finding is a significant addition to those of previous studies.

The degree of international exchange is positively associated with the total factor productivity of resource allocation at the 1% significance level. This finding, on the one hand, corroborates the previous views of Dalla Corte and Mendes ( 2018 ) and Malesevic Perovic and Kosor ( 2020 ) that international academic exchanges can help universities break development bottlenecks and improve resource allocation capacity. On the other hand, this study also provides an important development strategy for resource allocation in China’s top universities. In the future, China’s “double first-class” universities should continue to actively use international academic platforms to track research frontiers and developments. They should also enhance their resource allocation efficiency by strengthening international resource sharing.

Robustness tests

According to Guo et al. ( 2019 ), measuring the level of university resource allocation and the factors influencing it is a complex task. Studies exploring this issue have several limitations. The relative efficiency values calculated by the DEA model are restricted variables (Rolim et al., 2021 ). To reduce the error, the Tobit model is adopted in this study. However, the Tobit model cannot completely avoid the problem that the results are not robust. Therefore, further testing of the robustness of the regression results is needed. This study refers to Chang et al. ( 2020 ) and Zhang et al. (2023) to further verify the robustness of the study based on cross-validation and lag tests.

Robustness testing seeks to determine the effect of certain parameter modifications in the model on the relative efficiency of the decision units. A typical approach to cross-validation is to add or remove decision units to the model. Regression results are considered robust if parameter perturbations do not significantly affect the results (Picard and Cook, 1984 ). In this study, reference is made to Chang et al. ( 2020 ), who used the cross-validation method. This method reassesses the relative efficiency scores of decision units after excluding one decision unit at a time. The Tobit regression model is recalculated using the new efficiency scores, and the differences between the two model calculations are compared to evaluate the robustness of the model. As shown in Table 8 , the results of the cross-validation remain consistent in terms of significance with the results of the regression that includes the full range of decision units. There are no significant differences in the specific impact effects. This indicates the robustness of the results of the regression analysis.

In addition, the decision to invest in education resources may be influenced by past investments in education (Shen et al., 2023 ). A lag test can eliminate the influence of lagged factors by using the prior independent variable and the lagged dependent variable (Meng et al., 2024 ). This study refers to Zhang and Du ( 2023 ), who introduced lagged one-period data for the dependent variable as an additional independent variable in the regression model and carried out a lag test to test the consistency and validity of the model. As shown in Table 9 , the extended lagged one-period variable had no effect on the findings ( p = 0.811 > 0.05). The regression model is not affected by lagged effects.

How to scientifically measure and effectively improve the efficiency of university resource allocation has become a hot topic. As a key project of China’s higher education development, the construction of “double first-class” universities urgently requires academics to measure and evaluate the corresponding resource allocation efficiency. In this regard, this study takes 13 “double first-class” universities as research objects to measure their resource allocation efficiency and identify the factors affecting their performance. The results of the study provide meaningful references for the management practices of the administrators of these top universities.

Theoretical contributions

The most significant contribution of this study is the specialised analysis of the resource allocation efficiency of China’s “ double first-class” universities. Previous studies have noted the critical importance of improving the resource allocation efficiency of universities. Some scholars have also attempted to measure the resource allocation efficiency of all Chinese universities. However, the results lack purposefulness. This study focuses closely on “double first-class” universities. These universities absorb a large amount of financial funds and policy support. They have a natural responsibility to develop efficiently. Moreover, as the top universities in China, they have a strong influence on other universities. Therefore, this study uses quantitative tools to analyse the resource allocation efficiency of “double first-class” universities. This is an important supplement to the existing research. To meet the needs of exploration, we introduce the superefficient DEA-Malmquist-Tobit model into the field of resource allocation evaluation of Chinese “double first-class” universities. The finding discovers that the development of resource allocation in China’s “double first-class” universities is basically in line with the current general pattern of universities in the world. Moreover, this study further expands upon the existing findings by providing new research objects and new evidence. This study finds that some “double first-class” universities have accumulated advantages in terms of resource allocation. There are signs of widening gaps in resource allocation efficiency between universities. This is a significant addition to the existing findings. It is hoped that this work will lead future scholars to pay attention to the development of resource allocation in “double first-class” universities and other similar universities.

Practical implications

Our findings reaffirm the importance of educational resources in the development of higher education and provide some reference for the resource allocation actions of the administrators of “double first-class” universities. First, this study finds that some “double first-class” universities have problems related to wasted resources and insufficient output. This study suggests that a multilevel and diversified funding mechanism should be established. Under the premise of clarifying the resource demand of universities, targeted and comprehensive resource supply should be carried out to ensure the efficiency of resource allocation and reduce resource wastage. Universities with high conversion rates of resource output should be given priority in terms of resource supply. And the strategy of systematic supply should be used to promote the maximisation of efficiency. Second, through the analysis of the factors influencing resource allocation efficiency, this study finds that each factor has different effects on the resource allocation efficiency of “double first-class” universities. The advantages of positive factors should be fully utilised, and the disadvantages of negative factors should be compensated for. In particular, it is necessary to overcome the limitations of the traditional concept of resource allocation. Efforts should be made to promote the strengthening of exchanges and cooperation and to achieve the sharing of high-quality resources. The government and other resource providers can reasonably control the proportion of local government funding. The traditional resource allocation model should be promoted from resource scale-driven to technological innovation and management optimisation synergistic development change.

Limitations and future research

Although this study has made unremitting efforts to measure the resource allocation efficiency and influencing factors of “double first-class” universities, we are well aware of several limitations of the current study. We systematically summarise two aspects of the study, namely, the data sources and the research perspectives. First, the current study utilises public data released by the government. Using this source ensures the comparability and availability of the data and improves the persuasiveness of the results. However, this approach inevitably hides the individual characteristics of different “double first-class” universities. This makes it very difficult to analyse the resource allocation characteristics of individual universities. Our future research will explore the individual characteristics of the resource allocation of “double first-class” universities in more detail by expanding the data sources. Second, like the majority of scholars, this study argues that universities should focus all their efforts on increasing quantifiable and comprehensive outputs. In fact, universities are important nonprofit organisations. There are certain positive externalities associated with the combined outputs of higher education (Carrington et al., 2007 ). It is not sufficient to focus on quantifiable combined outputs. This study is considering adding analyses of nonprofit and nonquantifiable integrated outputs to future research.

This paper is an exploratory study on the level of resource allocation efficiency and its determining factors in China’s “double first-class” universities. This study provides theoretical references and empirical evidence for evaluating the construction of China’s “double first-class” universities, and it highlights the current problems and future development directions of higher education resource allocation. We firmly believe that future research will more accurately reflect the resource allocation efficiency of universities.

Data availability

All data generated or analysed during this study are included in this published article.

Abbott M, Doucouliagos C (2003) The efficiency of Australian universities: a data envelopment analysis. Econ Educ Rev 22:89–97. https://doi.org/10.1016/S0272-7757(01)00068-1

Article Google Scholar

Agasisti T, Wolszczak-Derlacz J (2016) Exploring efficiency differentials between Italian and Polish universities, 2001–11. Sci Public Policy 43:128–142. https://doi.org/10.1093/scipol/scv026

Altbach PG (2009) Peripheries and centers: research universities in developing countries. Asia Pac Educ Rev 10:15–27. https://doi.org/10.1007/s12564-009-9000-9

Anderson TR, Daim TU, Lavoie FF (2007) Measuring the efficiency of university technology transfer. Technovation 27:306–318. https://doi.org/10.1016/j.technovation.2006.10.003

Angelopoulos K, Malley J, Philippopoulos A (2011) The welfare implications of resource allocation policies under uncertainty: the case of public education spending. J Macroecon 33:176–192. https://doi.org/10.1016/j.jmacro.2010.11.007

Assani S, Jiang JL, Cao RM, Yang F (2018) Most productive scale size decomposition for multi-stage systems in data envelopment analysis. Comput Ind Eng 120:279–297. https://doi.org/10.1016/j.cie.2018.04.043

Athanassopoulos AD, Shale E (1997) Assessing the comparative efficiency of higher education institutions in the UK by the means of data envelopment analysis. Educ Econ 5:117–134

Atkinson SE, Wilson PW (1995) Comparing mean efficiency and productivity scores from small samples: a bootstrap methodology. J Prod Anal 6:37–52

Azadeh A, Kokabi R (2016) Z -number DEA: a new possibilistic DEA in the context of Z -numbers. Adv Eng Inform 30:604–617. https://doi.org/10.1016/j.aei.2016.07.005

Bai XJ, Sun XZ, Chiu YH (2020) Does China’s higher education investment play a role in industrial growth? Technol Soc 63. https://doi.org/10.1016/j.techsoc.2020.101332

Baimuratov UB, Zhanbayev RA, Sagintayeva SS (2020) The Triple Helix model for the conceptual mechanism of cooperation between higher education and business: the regional aspect. Econ Reg 16:1046–1060. https://doi.org/10.17059/ekon.reg.2020-4-3

Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:1078–1092. https://doi.org/10.1287/mnsc.30.9.1078

Bao W, Chen J, Wan TT (2016) The analyses of operating mechanism and efficiency for Project 985’: based on the perspective of international comparison and empirical study. Fudan Educ Forum 14:11–18. https://doi.org/10.13397/j.cnki.fef.2016.03.003

Barrett G (2012) Microeconometrics using stata. Econ Rec 88:595–596. https://doi.org/10.1111/1475-4932.12006

Behr D (2017) Assessing the use of back translation: the shortcomings of back translation as a quality testing method. Int J Soc Res Methodol 20:573–584. https://doi.org/10.1080/13645579.2016.1252188

Berbegal-Mirabent J, Lafuente E, Solé F (2013) The pursuit of knowledge transfer activities: an efficiency analysis of Spanish universities. J Bus Res 66:2051–2059. https://doi.org/10.1016/j.jbusres.2013.02.031

Bonaccorsi A, Daraio C, Simar L (2006) Advanced indicators of productivity of universities an application of robust nonparametric methods to Italian data. Scientometrics 66:389–410. https://doi.org/10.1007/s11192-006-0028-x

Caballero R, Galache T, Gomez T, Molina J, Torrico A (2004) Budgetary allocations and efficiency in the human resources policy of a university following multiple criteria. Econ Educ Rev 23:67–74. https://doi.org/10.1016/S0272-7757(03)00049-9

Caerteling JS, Halman JIM, Song M, Dorée AG, Van der Bij H (2013) How relevant is government championing behavior in technology development? J Prod Innov Manag 30:349–363. https://doi.org/10.1111/j.1540-5885.2012.01003.x

Carrington R, Coelli T, Rao P (2005) The performance of Australian universities: conceptual issues and preliminary results. Econ Pap 24:145–163

Carrington R, Meek VL, Wood FQ (2007) The role of further government intervention in Australian international education. High Educ 53:561–577. https://doi.org/10.1007/s10734-005-8755-0

Chang JS, Han S, Jo S (2020) Road safety performance across local governments: a data envelopment analysis approach. Int J Inj Control Saf Promot 27:447–457. https://doi.org/10.1080/17457300.2020.1810073

Chang TY, Chung PH, Hsu SS (2012) Two-stage performance model for evaluating the managerial efficiency of higher education: application by the Taiwanese tourism and leisure department. J Hosp Leis Sport Tour Educ 11:168–177. https://doi.org/10.1016/j.jhlste.2012.04.003

Chen XS, Yu H, Ding ML, Shu HS (2023) The impact of regional socio-economic development on spatial and temporal differences in the distribution pattern of top-tier education in China. Sustainability 15:15277. https://doi.org/10.3390/su152115277

Chen XX, Shu X (2021) The scientific and technological innovation performance of Chinese world-class universities and its influencing factors. IEEE Access 9:84639–84650. https://doi.org/10.1109/ACCESS.2021.3087659

Chen Y (2005) Measuring super-efficiency in DEA in the presence of infeasibility. Eur J Oper Res 161:545–551. https://doi.org/10.1016/j.ejor.2003.08.060

Article MathSciNet Google Scholar

Chen Z, Yang ZB (2021) Exploring the effect of resource misallocation on the basic research efficiency. Sci Technol Manag Res 41(9):69–76. https://doi.org/10.3969/j.issn.1000-7695.2021.09.010

Cook WD, Liang L, Zha Y, Zhu J (2009) A modified super-efficiency DEA model for infeasibility. J Oper Res Soc 60:276–281. https://doi.org/10.1057/palgrave.jors.2602544

Cordero JM, Garcia-Garcia A, Lau-Cortes E, Polo C (2021) Assessing Panamanian hospitals’ performance with alternative frontier methods. Int Trans Oper Res 30:394–420. https://doi.org/10.1111/itor.13013

Crossouard B (2010) Entrepreneurialism in universities and the knowledge economy. Stud High Educ 35:366–368

Google Scholar

Dai ZM, Guo L, Jiang ZY (2016) Study on the industrial Eco-Efficiency in East China based on the Super Efficiency DEA Model: an example of the 2003–2013 panel data. Appl Econ 48:5779–5785. https://doi.org/10.1080/00036846.2016.1184380

Dalla Corte MG, Mendes FZ (2018) Public policies and internationalization of higher education: south–south cooperation on the agenda. Educ Por Escr 9:350–367. https://doi.org/10.15448/2179-8435.2018.2.30280

Daraio C, Bonaccorsi A, Simar L (2015) Efficiency and economies of scale and specialization in European universities: a directional distance approach. J Informetr 9:430–448. https://doi.org/10.1016/j.joi.2015.03.002

Doh S, Kim B (2014) Government support for SME innovations in the regional industries: The case of government financial support program in South Korea. Res Policy 43:1557–1569. https://doi.org/10.1016/j.respol.2014.05.001

Drucker J (2016) Reconsidering the regional economic development impacts of higher education institutions in the United States. Reg Stud 50:1185–1202. https://doi.org/10.1080/00343404.2014.986083

Du J, Liang L, Zhu J (2010) A slacks-based measure of super-efficiency in data envelopment analysis: a comment. Eur J Oper Res 204:694–697. https://doi.org/10.1016/j.ejor.2009.12.007

Fadda N, Marinò L, Pischedda G, Ezza A (2022) The effect of performance-oriented funding in higher education: evidence from the staff recruitment budget in Italian higher education. High Educ 83:1003–1019. https://doi.org/10.1007/s10734-021-00725-4

Fukui F (2021) Do government appropriations and tax policies impact donations to public research universities in Japan and the USA? High Educ 81:325–344. https://doi.org/10.1007/s10734-020-00543-0

Fussy DS (2018) Policy directions for promoting university research in Tanzania. Stud High Educ 43:1573–1585. https://doi.org/10.1080/03075079.2016.1266611

Ganji SS, Rassafi AA (2019) DEA Malmquist productivity index based on a double- frontier slacks-based model: Iranian road safety assessment. Eur Transp Res Rev 11:4. https://doi.org/10.1186/s12544-018-0339-z

Ginder W, Kwon WS, Byun SE (2021) Effects of internal-external congruence-based CSR positioning: an attribution theory approach. J Bus Ethics 169:355–369. https://doi.org/10.1007/s10551-019-04282-w

Guo F, Luo Y, Liu L, Shi JH, Coates H (2019) Analysing mechanisms for evaluating higher education outcomes in China. High Educ Policy 32:557–575. https://doi.org/10.1057/s41307-019-00140-6

Guo HX (2023) Títul the efficiency of resource allocation in higher education in India based on DEA-Tobit model. Appl Math Nonlinear Sci. https://doi.org/10.2478/amns.2023.1.00287

Guo J, Wu XH, Wu C (2013) Performance evaluation and policy suggestions of S& T input–output activities of China universities based on DEA-Tobit model. Sci Technol Manag Res 33:65–70. https://doi.org/10.3969/j.issn.1000-7695.2013.23.014

Article CAS Google Scholar

Haghighatpisheh H, Kordrostami S, Amirteimoori A, Lotfi FH (2022) Optimal scale sizes in input–output allocative data envelopment analysis models. Ann Oper Res 315:1455–1476. https://doi.org/10.1007/s10479-019-03499-2

Hendin A (2022) Separate but equal? Diversity policy narratives in Israeli higher education. High Educ Policy. https://doi.org/10.1057/s41307-022-00291-z

Horta H, Sato M, Yonezawa A (2010) Academic inbreeding: exploring its characteristics and rationale in Japanese universities using a qualitative perspective. Asia Pac Educ Rev 12:35–44. https://doi.org/10.1007/s12564-010-9126-9

Huang TH, Lin CI, Wu RC (2019) Assessing the marketing and investment efficiency of Taiwan’s life insurance firms under network structures. Q Rev Econ Financ 71:132–147. https://doi.org/10.1016/j.qref.2018.07.002

Jahic H, Pilav-Velic A (2021) Measuring returns on investment in education: lessons for sustainable and innovative education policy. Interdiscip Descr Complex Syst 19:189–209. https://doi.org/10.7906/indecs.19.2.2

Jamil N (2013) A comparison of iterative methods for the solution of non-linear systems of equations. Int J 3:119–130

Jeon J, Kim SY (2018) Is the gap widening among universities? On research output inequality and its measurement in the Korean higher education system. Qual Quant 52:589–606. https://doi.org/10.1007/s11135-017-0652-y

Jiang XH, Ma JX, Zhu HZ, Guo XC, Huang ZG (2020) Evaluating the carbon emissions efficiency of the logistics industry based on a Super-SBM model and the Malmquist index from a strong transportation strategy perspective in China. Int J Environ Res Public Health 17:8459. https://doi.org/10.3390/ijerph17228459

Article PubMed PubMed Central Google Scholar

Johnes J (2006) Measuring teaching efficiency in higher education: An application of data envelopment analysis to economics graduates from UK Universities 1993. Eur J Oper Res 174:443–456. https://doi.org/10.1016/j.ejor.2005.02.044

Article ADS Google Scholar

Kao CA, Hung HT (2008) Efficiency analysis of university departments: an empirical study. Omega—Int J Manag Sci 36:653–664. https://doi.org/10.1016/j.omega.2006.02.003

Kempkes G, Pohl C (2010) The efficiency of German universities: Some evidence from nonparametric and parametric methods. Appl Econ 42:2063–2079. https://doi.org/10.1080/00036840701765361

Kremen VG, Hrynevych LM, Lugovyi VI, Talanova ZV (2022) Quality of education and in novation-driven development: The new Ukrainian school reform in the context of global trends. Sci and Innov 18:29–43. https://doi.org/10.15407/scine18.01.029

Kuah CT, Wong KY (2013) Data envelopment analysis modeling for measuring knowledge management performance in Malaysian higher educational institutions. Inf Dev 29:200–216. https://doi.org/10.1177/0266666912460794

Laureti T, Secondi L, Biggeri L (2014) Measuring the efficiency of teaching activities in Italian universities: an information theoretic approach. Econ Educ Rev 42:147–164. https://doi.org/10.1016/j.econedurev.2014.07.001

Lee HS, Zhu J (2012) Super-efficiency infeasibility and zero data in DEA. Eur J Oper Res 216:429–433. https://doi.org/10.1016/j.ejor.2011.07.050

Li H, Li CM, Bai NR, Qu FY (2018) Driving factors of regional higher education efficiency under the background of Double Top Based on the empirical analysis of DEA-Tobit model. J Tech Econ Manag 27:108–112

Li J, Xue EY (2023) Unpacking policy evaluation and measurement of creating world-class universities in China: an integrated policy analysis. Educ Philos Theory 55:35–44. https://doi.org/10.1080/00131857.2021.1972412

Li L (2019) An empirical study on the efficiency of scientific and technological innovation in Beijing–Tianjin–Hebei universities—SBM Model and Malmquist productivity index based on DEA analysis. Educ Acad J 2:44–53

Li Q, Zhang L (2019) Research on the influence of internet technology popularization on the development efficiency of agricultural insurance—empirical analysis based on SBM-Tobit model. Rural Financ Res 14:139–148

Liang HJ, Liu JS, Wang R, Song YQ, Zhou YY (2020) Research on China’s regional energy efficiency evaluation and influencing factors based on the DEA-Tobit model. Pol J Environ Stud 29:3691–3701. https://doi.org/10.15244/pjoes/115976

Lovakov A, Panova A, Sterligov I, Yudkevich M (2021) Does government support of a few leading universities have a broader impact on the higher education system? Evaluation of the Russian University Excellence Initiative. Res Eval 30:240–255

Lovell CAK (2003) The decomposition of Malmquist productivity indexes. J Product Anal 20:437–458. https://doi.org/10.1023/A:1027312102834

Lu GS, Wen S (2023) Are students’ abilities better developed at double first-class Universities? Evidence from Chinese university graduates. Res High Educ. https://doi.org/10.1007/s11162-023-09758-3

Malesevic Perovic L, Kosor MM (2020) The efficiency of universities in achieving sustainable development goals. Amfiteatru Econ 22:516–532. https://doi.org/10.24818/EA/2020/54/516

Malmquist S (1953) Index numbers and indifference surface. Trab Estat 4:209–242. https://doi.org/10.1007/BF03006863

Marquez-Ramos L, Mourelle E (2019) Education and economic growth: an empirical analysis of nonlinearities. Appl Econ Anal 27:21–45. https://doi.org/10.1108/AEA-06-2019-0005

Mayerle SF, Rodrigues HF, de Figueiredo JN, Chiroli DMD (2022) Optimal student/school/class/teacher/classroom matching to support efficient public school system resource allocation. Socio-Econ Plan Sci 83. https://doi.org/10.1016/j.seps.2022

McClure KR, Vamosiu AC, Titus MA, Gray SM (2023) Rivatization and cost inefficiency at US public research universities. Stud High Educ 48:1498–1515. https://doi.org/10.1080/03075079.2023.2204922

Meng QY, Li Y, Cao QW (2024) The paradox analysis and functional mechanism between R&D efficiency and transformation effect: evidence from key universities in China. Technovation 130:102934. https://doi.org/10.1016/j.technovation.2023.102934

Ministry of Education of China (2015) Several opinions of the Ministry of Education on comprehensively improving the higher education quality. http://www.moe.gov.cn/jyb_xxgk/moe_1777/moe_1778/201511/t20151105_217823.html . Accessed 17 December 2023

Ministry of Education of China (2017) Coordinate development of world-class universities and first-class disciplines construction overall plan. www.gov.cn/xinwen/2017-01/27/content_5163903 . Accessed 17 December 2023

Mok KH, Cheung ABL (2011) Global aspirations and strategizing for world-class status: new form of politics in higher education governance in Hong Kong. J High Educ Policy Manag 33:231–251. https://doi.org/10.1080/1360080X.2011.564998

Nazarko J, Saparauskas J (2014) Application of DEA method in efficiency evaluation of public higher education institutions. Technol Econ Dev Econ 20:25–44. https://doi.org/10.3846/20294913.2014.837116

Nisar M (2015) Higher education governance and performance-based funding as an ecology of games. High Educ 69:289–302. https://doi.org/10.1007/s10734-014-9775-4

Nokkala T (2012) The road to academic excellence: the making of world-class research universities. J Reg Sci 52:729–731. https://doi.org/10.1111/j.1467-9787.2012.00784_15.x

Nzivo CN, Chen CF (2013) International students’ perception of library services and information resources in Chinese academic libraries. J Acad Librariansh. 39:129–137. https://doi.org/10.1016/j.acalib.2012.11.004

Orsini LP, Leardini C, Vernizzi S, Campedelli B (2021) Inefficiency of public hospitals: a multistage data envelopment analysis in an Italian region. BMC Health Serv Res 21:1281. https://doi.org/10.1186/s12913-021-07276-5

Peters MA, Besley T (2018) China’s double first-class university strategy. Educ Philos Theory 50:1075–1079. https://doi.org/10.1080/00131857.2018.1438822

Picard RR, Cook RD (1984) Cross-validation of regression models. J Am Stat Assoc 79:575–583. https://doi.org/10.2307/2288403

Rambeli N, Marikan DAA, Podivinsky JM, Amiruddin R, Ismail I (2021) The dynamic impact of government expenditure in education on economic growth. Int J Bus Soc 22:1487–1507. https://doi.org/10.33736/ijbs.4318.2021

Ramirez-Gutierrez Z, Barrachina-Palanca M, Ripoll-Feliu V (2020) Efficiency in higher education: Empirical study in public universities of Colombia and Spain. Rev Adm Publica 54:468–500. https://doi.org/10.1590/0034-761220190232x

Regassa T, Tolemariam T, Ferede B, Hunde AB, Lemma A (2013) Quality of education: The case of Jimma University. Education 3:267–278. https://doi.org/10.5923/j.edu.20130305.03

Rolim LF, de Almeida ATC, Lombardi SC, dos Anjos OR (2021) Evaluation of expenditure efficiency of the federal institutions of Brazilian higher education. Teor Prat Adm 11:1–16. https://doi.org/10.22478/ufpb.2238-104X.2021v11n1.50628

Salas-Velasco M (2020) Does greater school resource allocation improve efficiency in education production? Performance assessment of Spanish public sector-funded schools. Int J Educ Manag 34:903–915. https://doi.org/10.1108/IJEM-08-2019-0305

Samut PK, Cafri R (2016) Analysis of the efficiency determinants of health systems in OECD countries by DEA and Panel Tobit. Soc Indic Res 129:113–132. https://doi.org/10.1007/s11205-015-1094-3

Shamohammadi M, Oh DH (2019) Measuring the efficiency changes of private universities of Korea: a two-stage network data envelopment analysis. Technol Forecast Soc Change 148:119730. https://doi.org/10.1016/j.techfore.2019.119730

Shen JK, Feng BJ, Sun XF (2019) A study on the influencing factors of financial resource utilization in research universities: from the perspective of financial resource users. J High Educ Manag 13:78–88. https://doi.org/10.13316/j.cnki.jh

Shen Q, Wu R, Feng YC (2023) The effectiveness of smart city policy on pollution reduction in China: new evidence from a quasi-natural experiment. Environ Sci Pollut Res 30:52841–52857. https://doi.org/10.1007/s11356-023-26010-8

Silva JG, da Araujo KD, Suzart JA, Sousa MD (2023) Budget allocation: Impact on the technical efficiency of academic units of a Brazilian Federal University. Reunir-Rev Adm Contab Sustentabilidade 13:179–189

Tobin J (1958) Estimation of relationships for limited dependent variables. Econometrica 26:24–36

Volkwein JF, Tandberg DA (2008) Measuring up: examining the connections among state structural characteristics, regulatory practices, and performance. Res High Educ 49:180–197. https://doi.org/10.1007/s11162-007-9066-3

Wachtel HK (2006) Student evaluation of college teaching effectiveness: a brief review. Assess Eval High Educ 23:191–212. https://doi.org/10.1080/0260293980230207

Wang CD, Fu B (2023) A study on the efficiency of allocation and its influencing factors on innovation and entrepreneurship education resources in Chinese universities under the Five-in-One model. Inte J Manag Educ 21. https://doi.org/10.1016/j.ijme.2022.100755

Wang DD (2019) Performance-based resource allocation for higher education institutions in China. Socio-Econ Plan Sci 65:66–75. https://doi.org/10.1016/j.seps.2018.01.004

Wang W, Hong Y, Yuan C, Ma Y (2018) Preliminary study on the evaluation system of “double first-class” discipline construction of comprehensive evaluation index based on academic performance. J Southeast Univ (Philos Soc Sci Ed) 20:39–146

Weerasinghe IMS, Dedunu HH (2020) Contribution of academics to university-industry knowledge exchange: a study of open innovation in Sri Lankan universities. Ind High Educ 35:233–243. https://doi.org/10.1177/0950422220964363

Warning S (2004) Performance differences in German higher education: Empirical analysis of strategic groups. Rev Ind Organ 24:393–408. https://doi.org/10.1023/B:REIO.0000037538.48594.2c

Wolszczak-Derlacz J, Parteka A (2011) Efficiency of European public higher education institutions: a two-stage multicountry approach. Scientometrics 89:887–917. https://doi.org/10.1007/s11192-011-0484-9

Wu J, Zhang GG, Zhu QY, Zhou ZX (2020) An efficiency analysis of higher education institutions in China from a regional perspective considering the external environmental impact. Scientometrics 122:57–70. https://doi.org/10.1007/s11192-019-03296-5

Xie SS, Yu GX (2019) Research on financing efficiency of agricultural listed companies in China—an empirical analysis based on DEA model and Malmquist index method. Math Pract Theory 33:162–169

Xu CQ (2020) Towards a framework for evaluating the research performance of Chinese double first-class Universities. Front Educ China 15:369–402. https://doi.org/10.1007/s11516-020-0016-6

Xue M, Harker PT (2002) Note: ranking DMUs with infeasible super-efficiency DEA models. Manag Sci 48:705–770. https://doi.org/10.1287/mnsc.48.5.705.7805

Yang R, Welch A (2012) A world-class university in China? The case of Tsinghua. High Educ 63:645–666. https://doi.org/10.1007/s10734-011-9465-4

Yu ZJ, Yang H, Bai Y, Peng ZN (2017) Research on innovation efficiency and influencing factors in universities from the perspective of achievement type. Sci Res Manag 38:141–149

Yue ML, Li RN, Ou GY, Wu X, Ma TC (2020) An exploration on the flow of leading research talents in China: from the perspective of distinguished young scholars. Scientometrics 125:1559–1574. https://doi.org/10.1007/s11192-020-03562-x

Zha DL, Chen S, Yang X (2022) Empirical research on scientific research efficiency and influencing factors for Double-First Class Universities: based on super-efficient SBM-Malmquist–Tobit model. Educ Econ 38:9–16

Zhang CH, Jiang NY, Su TT, Chen J, Streimikiene D, Balezentis T (2022) Spreading knowledge and technology: Research efficiency at universities based on the Three-Stage-Mcdmnrsdea method with bootstrapping. Technol Soc 68. https://doi.org/10.1016/j.techsoc.2022.101915

Zhang XX, Du XY (2023) Industry and regional peer effects in corporate digital transformation: the moderating effects of TMT characteristics. Sustainability 15:6003. https://doi.org/10.3390/su15076003

Zhou YS, Wu JT (2016) The game plan: four contradictions in the development of world class universities from the global south. Egit Bilim-Educ Sci 41:75–89

Zhu TT, Lu YH, Zhang YJ (2023) Evaluating the scientific and technological innovation efficiency of universities in China: evidence from the global Malmquist–Luenberger Index model. Appl Econ 55:1341–1355. https://doi.org/10.1080/00036846.2022.2097185

Zong XH, Zhang W (2017) Establishing world-class universities in China: deploying a quasi-experimental design to evaluate the net effects of Project 985. Stud High Educ 44:417–431. https://doi.org/10.1080/03075079.2017.1368475

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This work was supported by The National Social Science Foundation of China (20BGL237) and The China Association of Higher Education (23GG0201).

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Biao Chen, Yajing Sun, Yu Tong & Ling Liu

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Chen, B., Chen, Y., Sun, Y. et al. The measurement, level, and influence of resource allocation efficiency in universities: empirical evidence from 13 “double first class” universities in China. Humanit Soc Sci Commun 11 , 955 (2024). https://doi.org/10.1057/s41599-024-03461-z

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Empirical research in clinical ethics: The 'committed researcher' approach

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1 Clinical Ethics Centre, (AP-HP), Paris, France.
2 UVSQ, Versailles, France.
PMID: 32125719
DOI: 10.1111/bioe.12742

After the 'empirical turn' in bioethics, few specific approaches have been developed for doing clinical ethics research in close connection with clinical decision-making on a daily basis. In this paper we describe the 'committed researcher' approach to research in clinical ethics that we have developed over the years. After comparing it to two similar research methodological approaches, the 'embedded researcher' and 'deliberative engagement', we highlight its main features: it is patient-oriented, it is implemented by collegial and multidisciplinary teams, it uses an ethical grid to build the interview guide, and it is geared towards bringing the results to bear on the public debate surrounding the issue at stake. Finally, we position our methodological approach with respect to the 'is vs. ought' distinction. We argue that our 'commitment researcher' approach to clinical ethics research takes concerned people's life-building values as the main data, and compares them to the larger normative framework underlying the medical practice at stake.

Keywords: commitment; committed researcher; deliberative engagement; embedded researcher; empirical bioethics; research in clinical ethics.

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What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 36min read

Ever wondered how we gather the facts, unveil hidden truths, and make informed decisions in a world filled with questions? Empirical research holds the key.

In this guide, we'll delve deep into the art and science of empirical research, unraveling its methods, mysteries, and manifold applications. From defining the core principles to mastering data analysis and reporting findings, we're here to equip you with the knowledge and tools to navigate the empirical landscape.

What is Empirical Research?

Empirical research is the cornerstone of scientific inquiry, providing a systematic and structured approach to investigating the world around us. It is the process of gathering and analyzing empirical or observable data to test hypotheses, answer research questions, or gain insights into various phenomena. This form of research relies on evidence derived from direct observation or experimentation, allowing researchers to draw conclusions based on real-world data rather than purely theoretical or speculative reasoning.

Characteristics of Empirical Research

Empirical research is characterized by several key features:

Observation and Measurement : It involves the systematic observation or measurement of variables, events, or behaviors.
Data Collection : Researchers collect data through various methods, such as surveys, experiments, observations, or interviews.
Testable Hypotheses : Empirical research often starts with testable hypotheses that are evaluated using collected data.
Quantitative or Qualitative Data : Data can be quantitative (numerical) or qualitative (non-numerical), depending on the research design.
Statistical Analysis : Quantitative data often undergo statistical analysis to determine patterns , relationships, or significance.
Objectivity and Replicability : Empirical research strives for objectivity, minimizing researcher bias . It should be replicable, allowing other researchers to conduct the same study to verify results.
Conclusions and Generalizations : Empirical research generates findings based on data and aims to make generalizations about larger populations or phenomena.

Importance of Empirical Research

Empirical research plays a pivotal role in advancing knowledge across various disciplines. Its importance extends to academia, industry, and society as a whole. Here are several reasons why empirical research is essential:

Evidence-Based Knowledge : Empirical research provides a solid foundation of evidence-based knowledge. It enables us to test hypotheses, confirm or refute theories, and build a robust understanding of the world.
Scientific Progress : In the scientific community, empirical research fuels progress by expanding the boundaries of existing knowledge. It contributes to the development of theories and the formulation of new research questions.
Problem Solving : Empirical research is instrumental in addressing real-world problems and challenges. It offers insights and data-driven solutions to complex issues in fields like healthcare, economics, and environmental science.
Informed Decision-Making : In policymaking, business, and healthcare, empirical research informs decision-makers by providing data-driven insights. It guides strategies, investments, and policies for optimal outcomes.
Quality Assurance : Empirical research is essential for quality assurance and validation in various industries, including pharmaceuticals, manufacturing, and technology. It ensures that products and processes meet established standards.
Continuous Improvement : Businesses and organizations use empirical research to evaluate performance, customer satisfaction, and product effectiveness. This data-driven approach fosters continuous improvement and innovation.
Human Advancement : Empirical research in fields like medicine and psychology contributes to the betterment of human health and well-being. It leads to medical breakthroughs, improved therapies, and enhanced psychological interventions.
Critical Thinking and Problem Solving : Engaging in empirical research fosters critical thinking skills, problem-solving abilities, and a deep appreciation for evidence-based decision-making.

Empirical research empowers us to explore, understand, and improve the world around us. It forms the bedrock of scientific inquiry and drives progress in countless domains, shaping our understanding of both the natural and social sciences.

How to Conduct Empirical Research?

So, you've decided to dive into the world of empirical research. Let's begin by exploring the crucial steps involved in getting started with your research project.

1. Select a Research Topic

Selecting the right research topic is the cornerstone of a successful empirical study. It's essential to choose a topic that not only piques your interest but also aligns with your research goals and objectives. Here's how to go about it:

Identify Your Interests : Start by reflecting on your passions and interests. What topics fascinate you the most? Your enthusiasm will be your driving force throughout the research process.
Brainstorm Ideas : Engage in brainstorming sessions to generate potential research topics. Consider the questions you've always wanted to answer or the issues that intrigue you.
Relevance and Significance : Assess the relevance and significance of your chosen topic. Does it contribute to existing knowledge? Is it a pressing issue in your field of study or the broader community?
Feasibility : Evaluate the feasibility of your research topic. Do you have access to the necessary resources, data, and participants (if applicable)?

2. Formulate Research Questions

Once you've narrowed down your research topic, the next step is to formulate clear and precise research questions . These questions will guide your entire research process and shape your study's direction. To create effective research questions:

Specificity : Ensure that your research questions are specific and focused. Vague or overly broad questions can lead to inconclusive results.
Relevance : Your research questions should directly relate to your chosen topic. They should address gaps in knowledge or contribute to solving a particular problem.
Testability : Ensure that your questions are testable through empirical methods. You should be able to gather data and analyze it to answer these questions.
Avoid Bias : Craft your questions in a way that avoids leading or biased language. Maintain neutrality to uphold the integrity of your research.

3. Review Existing Literature

Before you embark on your empirical research journey, it's essential to immerse yourself in the existing body of literature related to your chosen topic. This step, often referred to as a literature review, serves several purposes:

Contextualization : Understand the historical context and current state of research in your field. What have previous studies found, and what questions remain unanswered?
Identifying Gaps : Identify gaps or areas where existing research falls short. These gaps will help you formulate meaningful research questions and hypotheses.
Theory Development : If your study is theoretical, consider how existing theories apply to your topic. If it's empirical, understand how previous studies have approached data collection and analysis.
Methodological Insights : Learn from the methodologies employed in previous research. What methods were successful, and what challenges did researchers face?

4. Define Variables

Variables are fundamental components of empirical research. They are the factors or characteristics that can change or be manipulated during your study. Properly defining and categorizing variables is crucial for the clarity and validity of your research. Here's what you need to know:

Independent Variables : These are the variables that you, as the researcher, manipulate or control. They are the "cause" in cause-and-effect relationships.
Dependent Variables : Dependent variables are the outcomes or responses that you measure or observe. They are the "effect" influenced by changes in independent variables.
Operational Definitions : To ensure consistency and clarity, provide operational definitions for your variables. Specify how you will measure or manipulate each variable.
Control Variables : In some studies, controlling for other variables that may influence your dependent variable is essential. These are known as control variables.

Understanding these foundational aspects of empirical research will set a solid foundation for the rest of your journey. Now that you've grasped the essentials of getting started, let's delve deeper into the intricacies of research design.

Empirical Research Design

Now that you've selected your research topic, formulated research questions, and defined your variables, it's time to delve into the heart of your empirical research journey – research design . This pivotal step determines how you will collect data and what methods you'll employ to answer your research questions. Let's explore the various facets of research design in detail.

Types of Empirical Research

Empirical research can take on several forms, each with its own unique approach and methodologies. Understanding the different types of empirical research will help you choose the most suitable design for your study. Here are some common types:

Experimental Research : In this type, researchers manipulate one or more independent variables to observe their impact on dependent variables. It's highly controlled and often conducted in a laboratory setting.
Observational Research : Observational research involves the systematic observation of subjects or phenomena without intervention. Researchers are passive observers, documenting behaviors, events, or patterns.
Survey Research : Surveys are used to collect data through structured questionnaires or interviews. This method is efficient for gathering information from a large number of participants.
Case Study Research : Case studies focus on in-depth exploration of one or a few cases. Researchers gather detailed information through various sources such as interviews, documents, and observations.
Qualitative Research : Qualitative research aims to understand behaviors, experiences, and opinions in depth. It often involves open-ended questions, interviews, and thematic analysis.
Quantitative Research : Quantitative research collects numerical data and relies on statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys.

Your choice of research type should align with your research questions and objectives. Experimental research, for example, is ideal for testing cause-and-effect relationships, while qualitative research is more suitable for exploring complex phenomena.

Experimental Design

Experimental research is a systematic approach to studying causal relationships. It's characterized by the manipulation of one or more independent variables while controlling for other factors. Here are some key aspects of experimental design:

Control and Experimental Groups : Participants are randomly assigned to either a control group or an experimental group. The independent variable is manipulated for the experimental group but not for the control group.
Randomization : Randomization is crucial to eliminate bias in group assignment. It ensures that each participant has an equal chance of being in either group.
Hypothesis Testing : Experimental research often involves hypothesis testing. Researchers formulate hypotheses about the expected effects of the independent variable and use statistical analysis to test these hypotheses.

Observational Design

Observational research entails careful and systematic observation of subjects or phenomena. It's advantageous when you want to understand natural behaviors or events. Key aspects of observational design include:

Participant Observation : Researchers immerse themselves in the environment they are studying. They become part of the group being observed, allowing for a deep understanding of behaviors.
Non-Participant Observation : In non-participant observation, researchers remain separate from the subjects. They observe and document behaviors without direct involvement.
Data Collection Methods : Observational research can involve various data collection methods, such as field notes, video recordings, photographs, or coding of observed behaviors.

Survey Design

Surveys are a popular choice for collecting data from a large number of participants. Effective survey design is essential to ensure the validity and reliability of your data. Consider the following:

Questionnaire Design : Create clear and concise questions that are easy for participants to understand. Avoid leading or biased questions.
Sampling Methods : Decide on the appropriate sampling method for your study, whether it's random, stratified, or convenience sampling.
Data Collection Tools : Choose the right tools for data collection, whether it's paper surveys, online questionnaires, or face-to-face interviews.

Case Study Design

Case studies are an in-depth exploration of one or a few cases to gain a deep understanding of a particular phenomenon. Key aspects of case study design include:

Single Case vs. Multiple Case Studies : Decide whether you'll focus on a single case or multiple cases. Single case studies are intensive and allow for detailed examination, while multiple case studies provide comparative insights.
Data Collection Methods : Gather data through interviews, observations, document analysis, or a combination of these methods.

Qualitative vs. Quantitative Research

In empirical research, you'll often encounter the distinction between qualitative and quantitative research . Here's a closer look at these two approaches:

Qualitative Research : Qualitative research seeks an in-depth understanding of human behavior, experiences, and perspectives. It involves open-ended questions, interviews, and the analysis of textual or narrative data. Qualitative research is exploratory and often used when the research question is complex and requires a nuanced understanding.
Quantitative Research : Quantitative research collects numerical data and employs statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys. Quantitative research is ideal for testing hypotheses and establishing cause-and-effect relationships.

Understanding the various research design options is crucial in determining the most appropriate approach for your study. Your choice should align with your research questions, objectives, and the nature of the phenomenon you're investigating.

Data Collection for Empirical Research

Now that you've established your research design, it's time to roll up your sleeves and collect the data that will fuel your empirical research. Effective data collection is essential for obtaining accurate and reliable results.

Sampling Methods

Sampling methods are critical in empirical research, as they determine the subset of individuals or elements from your target population that you will study. Here are some standard sampling methods:

Random Sampling : Random sampling ensures that every member of the population has an equal chance of being selected. It minimizes bias and is often used in quantitative research.
Stratified Sampling : Stratified sampling involves dividing the population into subgroups or strata based on specific characteristics (e.g., age, gender, location). Samples are then randomly selected from each stratum, ensuring representation of all subgroups.
Convenience Sampling : Convenience sampling involves selecting participants who are readily available or easily accessible. While it's convenient, it may introduce bias and limit the generalizability of results.
Snowball Sampling : Snowball sampling is instrumental when studying hard-to-reach or hidden populations. One participant leads you to another, creating a "snowball" effect. This method is common in qualitative research.
Purposive Sampling : In purposive sampling, researchers deliberately select participants who meet specific criteria relevant to their research questions. It's often used in qualitative studies to gather in-depth information.

The choice of sampling method depends on the nature of your research, available resources, and the degree of precision required. It's crucial to carefully consider your sampling strategy to ensure that your sample accurately represents your target population.

Data Collection Instruments

Data collection instruments are the tools you use to gather information from your participants or sources. These instruments should be designed to capture the data you need accurately. Here are some popular data collection instruments:

Questionnaires : Questionnaires consist of structured questions with predefined response options. When designing questionnaires, consider the clarity of questions, the order of questions, and the response format (e.g., Likert scale , multiple-choice).
Interviews : Interviews involve direct communication between the researcher and participants. They can be structured (with predetermined questions) or unstructured (open-ended). Effective interviews require active listening and probing for deeper insights.
Observations : Observations entail systematically and objectively recording behaviors, events, or phenomena. Researchers must establish clear criteria for what to observe, how to record observations, and when to observe.
Surveys : Surveys are a common data collection instrument for quantitative research. They can be administered through various means, including online surveys, paper surveys, and telephone surveys.
Documents and Archives : In some cases, data may be collected from existing documents, records, or archives. Ensure that the sources are reliable, relevant, and properly documented.

To streamline your process and gather insights with precision and efficiency, consider leveraging innovative tools like Appinio . With Appinio's intuitive platform, you can harness the power of real-time consumer data to inform your research decisions effectively. Whether you're conducting surveys, interviews, or observations, Appinio empowers you to define your target audience, collect data from diverse demographics, and analyze results seamlessly.

By incorporating Appinio into your data collection toolkit, you can unlock a world of possibilities and elevate the impact of your empirical research. Ready to revolutionize your approach to data collection?

Book a Demo

Data Collection Procedures

Data collection procedures outline the step-by-step process for gathering data. These procedures should be meticulously planned and executed to maintain the integrity of your research.

Training : If you have a research team, ensure that they are trained in data collection methods and protocols. Consistency in data collection is crucial.
Pilot Testing : Before launching your data collection, conduct a pilot test with a small group to identify any potential problems with your instruments or procedures. Make necessary adjustments based on feedback.
Data Recording : Establish a systematic method for recording data. This may include timestamps, codes, or identifiers for each data point.
Data Security : Safeguard the confidentiality and security of collected data. Ensure that only authorized individuals have access to the data.
Data Storage : Properly organize and store your data in a secure location, whether in physical or digital form. Back up data to prevent loss.

Ethical Considerations

Ethical considerations are paramount in empirical research, as they ensure the well-being and rights of participants are protected.

Informed Consent : Obtain informed consent from participants, providing clear information about the research purpose, procedures, risks, and their right to withdraw at any time.
Privacy and Confidentiality : Protect the privacy and confidentiality of participants. Ensure that data is anonymized and sensitive information is kept confidential.
Beneficence : Ensure that your research benefits participants and society while minimizing harm. Consider the potential risks and benefits of your study.
Honesty and Integrity : Conduct research with honesty and integrity. Report findings accurately and transparently, even if they are not what you expected.
Respect for Participants : Treat participants with respect, dignity, and sensitivity to cultural differences. Avoid any form of coercion or manipulation.
Institutional Review Board (IRB) : If required, seek approval from an IRB or ethics committee before conducting your research, particularly when working with human participants.

Adhering to ethical guidelines is not only essential for the ethical conduct of research but also crucial for the credibility and validity of your study. Ethical research practices build trust between researchers and participants and contribute to the advancement of knowledge with integrity.

With a solid understanding of data collection, including sampling methods, instruments, procedures, and ethical considerations, you are now well-equipped to gather the data needed to answer your research questions.

Empirical Research Data Analysis

Now comes the exciting phase of data analysis, where the raw data you've diligently collected starts to yield insights and answers to your research questions. We will explore the various aspects of data analysis, from preparing your data to drawing meaningful conclusions through statistics and visualization.

Data Preparation

Data preparation is the crucial first step in data analysis. It involves cleaning, organizing, and transforming your raw data into a format that is ready for analysis. Effective data preparation ensures the accuracy and reliability of your results.

Data Cleaning : Identify and rectify errors, missing values, and inconsistencies in your dataset. This may involve correcting typos, removing outliers, and imputing missing data.
Data Coding : Assign numerical values or codes to categorical variables to make them suitable for statistical analysis. For example, converting "Yes" and "No" to 1 and 0.
Data Transformation : Transform variables as needed to meet the assumptions of the statistical tests you plan to use. Common transformations include logarithmic or square root transformations.
Data Integration : If your data comes from multiple sources, integrate it into a unified dataset, ensuring that variables match and align.
Data Documentation : Maintain clear documentation of all data preparation steps, as well as the rationale behind each decision. This transparency is essential for replicability.

Effective data preparation lays the foundation for accurate and meaningful analysis. It allows you to trust the results that will follow in the subsequent stages.

Descriptive Statistics

Descriptive statistics help you summarize and make sense of your data by providing a clear overview of its key characteristics. These statistics are essential for understanding the central tendencies, variability, and distribution of your variables. Descriptive statistics include:

Measures of Central Tendency : These include the mean (average), median (middle value), and mode (most frequent value). They help you understand the typical or central value of your data.
Measures of Dispersion : Measures like the range, variance, and standard deviation provide insights into the spread or variability of your data points.
Frequency Distributions : Creating frequency distributions or histograms allows you to visualize the distribution of your data across different values or categories.

Descriptive statistics provide the initial insights needed to understand your data's basic characteristics, which can inform further analysis.

Inferential Statistics

Inferential statistics take your analysis to the next level by allowing you to make inferences or predictions about a larger population based on your sample data. These methods help you test hypotheses and draw meaningful conclusions. Key concepts in inferential statistics include:

Hypothesis Testing : Hypothesis tests (e.g., t-tests , chi-squared tests ) help you determine whether observed differences or associations in your data are statistically significant or occurred by chance.
Confidence Intervals : Confidence intervals provide a range within which population parameters (e.g., population mean) are likely to fall based on your sample data.
Regression Analysis : Regression models (linear, logistic, etc.) help you explore relationships between variables and make predictions.
Analysis of Variance (ANOVA) : ANOVA tests are used to compare means between multiple groups, allowing you to assess whether differences are statistically significant.

Chi-Square Calculator :

t-Test Calculator :

One-way ANOVA Calculator :

Inferential statistics are powerful tools for drawing conclusions from your data and assessing the generalizability of your findings to the broader population.

Qualitative Data Analysis

Qualitative data analysis is employed when working with non-numerical data, such as text, interviews, or open-ended survey responses. It focuses on understanding the underlying themes, patterns, and meanings within qualitative data. Qualitative analysis techniques include:

Thematic Analysis : Identifying and analyzing recurring themes or patterns within textual data.
Content Analysis : Categorizing and coding qualitative data to extract meaningful insights.
Grounded Theory : Developing theories or frameworks based on emergent themes from the data.
Narrative Analysis : Examining the structure and content of narratives to uncover meaning.

Qualitative data analysis provides a rich and nuanced understanding of complex phenomena and human experiences.

Data Visualization

Data visualization is the art of representing data graphically to make complex information more understandable and accessible. Effective data visualization can reveal patterns, trends, and outliers in your data. Common types of data visualization include:

Bar Charts and Histograms : Used to display the distribution of categorical data or discrete data .
Line Charts : Ideal for showing trends and changes in data over time.
Scatter Plots : Visualize relationships and correlations between two variables.
Pie Charts : Display the composition of a whole in terms of its parts.
Heatmaps : Depict patterns and relationships in multidimensional data through color-coding.
Box Plots : Provide a summary of the data distribution, including outliers.
Interactive Dashboards : Create dynamic visualizations that allow users to explore data interactively.

Data visualization not only enhances your understanding of the data but also serves as a powerful communication tool to convey your findings to others.

As you embark on the data analysis phase of your empirical research, remember that the specific methods and techniques you choose will depend on your research questions, data type, and objectives. Effective data analysis transforms raw data into valuable insights, bringing you closer to the answers you seek.

How to Report Empirical Research Results?

At this stage, you get to share your empirical research findings with the world. Effective reporting and presentation of your results are crucial for communicating your research's impact and insights.

1. Write the Research Paper

Writing a research paper is the culmination of your empirical research journey. It's where you synthesize your findings, provide context, and contribute to the body of knowledge in your field.

Title and Abstract : Craft a clear and concise title that reflects your research's essence. The abstract should provide a brief summary of your research objectives, methods, findings, and implications.
Introduction : In the introduction, introduce your research topic, state your research questions or hypotheses, and explain the significance of your study. Provide context by discussing relevant literature.
Methods : Describe your research design, data collection methods, and sampling procedures. Be precise and transparent, allowing readers to understand how you conducted your study.
Results : Present your findings in a clear and organized manner. Use tables, graphs, and statistical analyses to support your results. Avoid interpreting your findings in this section; focus on the presentation of raw data.
Discussion : Interpret your findings and discuss their implications. Relate your results to your research questions and the existing literature. Address any limitations of your study and suggest avenues for future research.
Conclusion : Summarize the key points of your research and its significance. Restate your main findings and their implications.
References : Cite all sources used in your research following a specific citation style (e.g., APA, MLA, Chicago). Ensure accuracy and consistency in your citations.
Appendices : Include any supplementary material, such as questionnaires, data coding sheets, or additional analyses, in the appendices.

Writing a research paper is a skill that improves with practice. Ensure clarity, coherence, and conciseness in your writing to make your research accessible to a broader audience.

2. Create Visuals and Tables

Visuals and tables are powerful tools for presenting complex data in an accessible and understandable manner.

Clarity : Ensure that your visuals and tables are clear and easy to interpret. Use descriptive titles and labels.
Consistency : Maintain consistency in formatting, such as font size and style, across all visuals and tables.
Appropriateness : Choose the most suitable visual representation for your data. Bar charts, line graphs, and scatter plots work well for different types of data.
Simplicity : Avoid clutter and unnecessary details. Focus on conveying the main points.
Accessibility : Make sure your visuals and tables are accessible to a broad audience, including those with visual impairments.
Captions : Include informative captions that explain the significance of each visual or table.

Compelling visuals and tables enhance the reader's understanding of your research and can be the key to conveying complex information efficiently.

3. Interpret Findings

Interpreting your findings is where you bridge the gap between data and meaning. It's your opportunity to provide context, discuss implications, and offer insights. When interpreting your findings:

Relate to Research Questions : Discuss how your findings directly address your research questions or hypotheses.
Compare with Literature : Analyze how your results align with or deviate from previous research in your field. What insights can you draw from these comparisons?
Discuss Limitations : Be transparent about the limitations of your study. Address any constraints, biases, or potential sources of error.
Practical Implications : Explore the real-world implications of your findings. How can they be applied or inform decision-making?
Future Research Directions : Suggest areas for future research based on the gaps or unanswered questions that emerged from your study.

Interpreting findings goes beyond simply presenting data; it's about weaving a narrative that helps readers grasp the significance of your research in the broader context.

With your research paper written, structured, and enriched with visuals, and your findings expertly interpreted, you are now prepared to communicate your research effectively. Sharing your insights and contributing to the body of knowledge in your field is a significant accomplishment in empirical research.

Examples of Empirical Research

To solidify your understanding of empirical research, let's delve into some real-world examples across different fields. These examples will illustrate how empirical research is applied to gather data, analyze findings, and draw conclusions.

Social Sciences

In the realm of social sciences, consider a sociological study exploring the impact of socioeconomic status on educational attainment. Researchers gather data from a diverse group of individuals, including their family backgrounds, income levels, and academic achievements.

Through statistical analysis, they can identify correlations and trends, revealing whether individuals from lower socioeconomic backgrounds are less likely to attain higher levels of education. This empirical research helps shed light on societal inequalities and informs policymakers on potential interventions to address disparities in educational access.

Environmental Science

Environmental scientists often employ empirical research to assess the effects of environmental changes. For instance, researchers studying the impact of climate change on wildlife might collect data on animal populations, weather patterns, and habitat conditions over an extended period.

By analyzing this empirical data, they can identify correlations between climate fluctuations and changes in wildlife behavior, migration patterns, or population sizes. This empirical research is crucial for understanding the ecological consequences of climate change and informing conservation efforts.

Business and Economics

In the business world, empirical research is essential for making data-driven decisions. Consider a market research study conducted by a business seeking to launch a new product. They collect data through surveys , focus groups , and consumer behavior analysis.

By examining this empirical data, the company can gauge consumer preferences, demand, and potential market size. Empirical research in business helps guide product development, pricing strategies, and marketing campaigns, increasing the likelihood of a successful product launch.

Psychological studies frequently rely on empirical research to understand human behavior and cognition. For instance, a psychologist interested in examining the impact of stress on memory might design an experiment. Participants are exposed to stress-inducing situations, and their memory performance is assessed through various tasks.

By analyzing the data collected, the psychologist can determine whether stress has a significant effect on memory recall. This empirical research contributes to our understanding of the complex interplay between psychological factors and cognitive processes.

These examples highlight the versatility and applicability of empirical research across diverse fields. Whether in medicine, social sciences, environmental science, business, or psychology, empirical research serves as a fundamental tool for gaining insights, testing hypotheses, and driving advancements in knowledge and practice.

Conclusion for Empirical Research

Empirical research is a powerful tool for gaining insights, testing hypotheses, and making informed decisions. By following the steps outlined in this guide, you've learned how to select research topics, collect data, analyze findings, and effectively communicate your research to the world. Remember, empirical research is a journey of discovery, and each step you take brings you closer to a deeper understanding of the world around you. Whether you're a scientist, a student, or someone curious about the process, the principles of empirical research empower you to explore, learn, and contribute to the ever-expanding realm of knowledge.

How to Collect Data for Empirical Research?

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Research Guides

Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

Introduction
Database Tools
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Calfee & Chambliss (2005) (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions." Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43).

The evidence collected during empirical research is often referred to as "data."

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research:

A research question , which will determine research objectives.
A particular and planned design for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
The gathering of primary data , which is then analysed.
A particular methodology for collecting and analysing the data, such as an experiment or survey.
The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
The ability to recreate the study and test the results. This is known as reliability .
The ability to generalize from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element.

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016) (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

Quantitative research -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
Qualitative research -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study.

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology:

Characteristics of Quantitative Research

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes.

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.), Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge. http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing. https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained [video]. YouTube. https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016). Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell. http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .

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Last Updated: Apr 2, 2024 11:25 AM
URL: https://libguides.memphis.edu/empirical-research

Empirical Research: A Comprehensive Guide for Academics

Empirical research relies on gathering and studying real, observable data. The term ’empirical’ comes from the Greek word ’empeirikos,’ meaning ‘experienced’ or ‘based on experience.’ So, what is empirical research? Instead of using theories or opinions, empirical research depends on real data obtained through direct observation or experimentation.

Why Empirical Research?

Empirical research plays a key role in checking or improving current theories, providing a systematic way to grow knowledge across different areas. By focusing on objectivity, it makes research findings more trustworthy, which is critical in research fields like medicine, psychology, economics, and public policy. In the end, the strengths of empirical research lie in deepening our awareness of the world and improving our capacity to tackle problems wisely. 1,2

Qualitative and Quantitative Methods

There are two main types of empirical research methods – qualitative and quantitative. 3,4 Qualitative research delves into intricate phenomena using non-numerical data, such as interviews or observations, to offer in-depth insights into human experiences. In contrast, quantitative research analyzes numerical data to spot patterns and relationships, aiming for objectivity and the ability to apply findings to a wider context.

Steps for Conducting Empirical Research

When it comes to conducting research, there are some simple steps that researchers can follow. 5,6

Create Research Hypothesis: Clearly state the specific question you want to answer or the hypothesis you want to explore in your study.
Examine Existing Research: Read and study existing research on your topic. Understand what’s already known, identify existing gaps in knowledge, and create a framework for your own study based on what you learn.
Plan Your Study: Decide how you’ll conduct your research—whether through qualitative methods, quantitative methods, or a mix of both. Choose suitable techniques like surveys, experiments, interviews, or observations based on your research question.
Develop Research Instruments: Create reliable research collection tools, such as surveys or questionnaires, to help you collate data. Ensure these tools are well-designed and effective.
Collect Data: Systematically gather the information you need for your research according to your study design and protocols using the chosen research methods.
Data Analysis: Analyze the collected data using suitable statistical or qualitative methods that align with your research question and objectives.
Interpret Results: Understand and explain the significance of your analysis results in the context of your research question or hypothesis.
Draw Conclusions: Summarize your findings and draw conclusions based on the evidence. Acknowledge any study limitations and propose areas for future research.

Advantages of Empirical Research

Empirical research is valuable because it stays objective by relying on observable data, lessening the impact of personal biases. This objectivity boosts the trustworthiness of research findings. Also, using precise quantitative methods helps in accurate measurement and statistical analysis. This precision ensures researchers can draw reliable conclusions from numerical data, strengthening our understanding of the studied phenomena. 4

Disadvantages of Empirical Research

While empirical research has notable strengths, researchers must also be aware of its limitations when deciding on the right research method for their study.4 One significant drawback of empirical research is the risk of oversimplifying complex phenomena, especially when relying solely on quantitative methods. These methods may struggle to capture the richness and nuances present in certain social, cultural, or psychological contexts. Another challenge is the potential for confounding variables or biases during data collection, impacting result accuracy.

Tips for Empirical Writing

In empirical research, the writing is usually done in research papers, articles, or reports. The empirical writing follows a set structure, and each section has a specific role. Here are some tips for your empirical writing. 7

Define Your Objectives: When you write about your research, start by making your goals clear. Explain what you want to find out or prove in a simple and direct way. This helps guide your research and lets others know what you have set out to achieve.
Be Specific in Your Literature Review: In the part where you talk about what others have studied before you, focus on research that directly relates to your research question. Keep it short and pick studies that help explain why your research is important. This part sets the stage for your work.
Explain Your Methods Clearly : When you talk about how you did your research (Methods), explain it in detail. Be clear about your research plan, who took part, and what you did; this helps others understand and trust your study. Also, be honest about any rules you follow to make sure your study is ethical and reproducible.
Share Your Results Clearly : After doing your empirical research, share what you found in a simple way. Use tables or graphs to make it easier for your audience to understand your research. Also, talk about any numbers you found and clearly state if they are important or not. Ensure that others can see why your research findings matter.
Talk About What Your Findings Mean: In the part where you discuss your research results, explain what they mean. Discuss why your findings are important and if they connect to what others have found before. Be honest about any problems with your study and suggest ideas for more research in the future.
Wrap It Up Clearly: Finally, end your empirical research paper by summarizing what you found and why it’s important. Remind everyone why your study matters. Keep your writing clear and fix any mistakes before you share it. Ask someone you trust to read it and give you feedback before you finish.

References:

Empirical Research in the Social Sciences and Education, Penn State University Libraries. Available online at https://guides.libraries.psu.edu/emp
How to conduct empirical research, Emerald Publishing. Available online at https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research
Empirical Research: Quantitative & Qualitative, Arrendale Library, Piedmont University. Available online at https://library.piedmont.edu/empirical-research
Bouchrika, I. What Is Empirical Research? Definition, Types & Samples in 2024. Research.com, January 2024. Available online at https://research.com/research/what-is-empirical-research
Quantitative and Empirical Research vs. Other Types of Research. California State University, April 2023. Available online at https://libguides.csusb.edu/quantitative
Empirical Research, Definitions, Methods, Types and Examples, Studocu.com website. Available online at https://www.studocu.com/row/document/uganda-christian-university/it-research-methods/emperical-research-definitions-methods-types-and-examples/55333816
Writing an Empirical Paper in APA Style. Psychology Writing Center, University of Washington. Available online at https://psych.uw.edu/storage/writing_center/APApaper.pdf

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Identifying Empirical Research Articles

Identifying empirical articles.

Searching for Empirical Research Articles

What is Empirical Research?

An empirical research article reports the results of a study that uses data derived from actual observation or experimentation. Empirical research articles are examples of primary research. To learn more about the differences between primary and secondary research, see our related guide:

Primary and Secondary Sources

By the end of this guide, you will be able to:

Identify common elements of an empirical article
Use a variety of search strategies to search for empirical articles within the library collection

Look for the IMRaD layout in the article to help identify empirical research. Sometimes the sections will be labeled differently, but the content will be similar.

I ntroduction: why the article was written, research question or questions, hypothesis, literature review
M ethods: the overall research design and implementation, description of sample, instruments used, how the authors measured their experiment
R esults: output of the author's measurements, usually includes statistics of the author's findings
D iscussion: the author's interpretation and conclusions about the results, limitations of study, suggestions for further research

Parts of an Empirical Research Article

Parts of an empirical article.

The screenshots below identify the basic IMRaD structure of an empirical research article.

Introduction

The introduction contains a literature review and the study's research hypothesis.

The method section outlines the research design, participants, and measures used.

Results

The results section contains statistical data (charts, graphs, tables, etc.) and research participant quotes.

The discussion section includes impacts, limitations, future considerations, and research.

Learn the IMRaD Layout: How to Identify an Empirical Article

This short video overviews the IMRaD method for identifying empirical research.

Next: Searching for Empirical Research Articles >>
Last Updated: Nov 16, 2023 8:24 AM

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Définition de empirical en anglais

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all that glitters is not gold idiom
alternate reality
fantastical
fever dream
not so much idiom
nothing could be further from the truth idiom
nothing could have been further from my mind/thoughts idiom
parallel universe
post-factual
unverifiable

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«empirical» en anglais américain.

empirically

empirical dans le dictionnaire Anglais des Affaires

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(of a person or a thing) socially awkward or not fashionable, but in a way that makes you love or like it or them

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anglais-chinois (simplifié) Chinese (Simplified)–English
anglais-chinois (traditionnel) Chinese (Traditional)–English
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anglais-norvégien norvégien-anglais
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Introduction to Empirical Research

Databases for finding empirical research, guided search, google scholar, examples of empirical research, sources and further reading.

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Introductory Video This video covers what empirical research is, what kinds of questions and methods empirical researchers use, and some tips for finding empirical research articles in your discipline.

Guided Search: Finding Empirical Research Articles This is a hands-on tutorial that will allow you to use your own search terms to find resources.

Study on radiation transfer in human skin for cosmetics
Long-Term Mobile Phone Use and the Risk of Vestibular Schwannoma: A Danish Nationwide Cohort Study
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empirical translation | English-French dictionary

empirically , empiricist , empiricism , empire

Additional comments:


moment

formula

studies lend credence to his ideas.

'empirical' also found in translations in French-English dictionary
		treatment

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Smith has called this the singular empirical discovery of his career.	Smith a appelé cela la découverte empirique singulière de sa carrière.
There is absolutely no empirical validation associated with his results.	Il n'y a absolument aucune validation empirique associée à ses résultats.
The results show a concordance between the deferent empirical methods applied.	Les résultats obtenus montrent une concordance entre les méthodes empiriques appliquées.
The empirical properties of the robust estimators are studied in simulations.	Les propriétés empiriques des estimateurs robustes sont étudiées dans des simulations.
The second impediment relates to unresolved theoretical and empirical issues.	Le deuxième obstacle tient à des questions théoriques et empiriques non résolues.
The procedure has been developed by both empirical and theoretical means.	La procédure a été établie par des moyens empiriques et théoriques.

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Lack of risk management at insolvency consulting companies: an empirical study in germany 2024.

1. Introduction

2. literature review, 3. materials and methods.

“Every lawyer knows the practically unrealisable demands of case law on his diligence and omniscience, which ultimately amount to strict liability regardless of fault. Nevertheless, for most lawyers, ‘risk management’ is limited to ‘paying attention’, effectively monitoring deadlines and reading the NJW [Neu Juristische Wochenschrift; author’s note] every week”.

“[…] tax advisors, tax agents, auditors, sworn accountants and lawyers who prepare annual financial statements for a client must draw the client’s attention to the existence of a possible reason for insolvency in accordance with Sections 17 to 19 of the German Insolvency Code and the associated duties of the managers and members of the supervisory bodies if there are obvious indications of this and they must assume that the client is not aware of the possible insolvency and the associated duties”.

Business with dubious independence;
Risk transactions;
Failure to contest or assert claims;
Transactions below market price;
Cover-up and concealment measures;
The management of criminal assets in the insolvency estate.
Perception of the benefits of risk management in the industry and personally.
Perceptions of increasing risks due to legal measures and support during the pandemic and other influencing factors.
Utilisation of risk management and specific tools and methods.
Perceptions of risks and risk-based factors in insolvency counselling.
Attitudes and plans for risk management in the future.
Areas in which risk management would be useful.

4.1. Established Instruments of Strategic Risk Management

Establishing the context;
Risk identification;
Risk analysis;
Risk treatment;
Risk avoidance;
Risk reduction;
Risk sharing;
Risk transfer;
Risk monitoring and review;
Risk documentation.

“Preventive controls are relevant to actions that are taken before the event occurs. The nature of detective controls means that they relate to circumstances after the event has occurred”.

4.2. Descriptive Analysis of the Results of the Research Survey

4.3. inferential statistical analysis of the results, 5. discussion and conclusions, 5.1. context and necessity of risk management for insolvency advisors, 5.2. empirical findings and recommendations for action, 5.3. testing the hypotheses, 5.4. answering the research questions, 5.5. limitation of the study, 5.6. european perspectives, 5.7. summary and outlook, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

Baumann, Susanne, Iris Erber, and Magdalena Gattringer. 2016. Selection of risk identification instruments. ACRN Oxford Journal of Finance and Risk Perspectives 5: 27–41. [ Google Scholar ]
Becker, Marco. 2021. Zombie-Unternehmen in der Corona-Pandemie oder Phänomen der Verdeckt überschuldeten Unternehmen in Deutschland . IUCF Working Paper, No. 3. Kiel and Hamburg: ZBW—Leibniz Information Centre for Economics. [ Google Scholar ]
Beißel, Stefan. 2017. Rahmenwerke für das IT-GRC-Management. In IT-GRC-Management—Governance, Risk und Compliance. Grundlagen und Anwendungen . Edited by Matthias Knoll and Strahringer Susanne. Wiesbaden: Springer Vieweg, pp. 65–81. [ Google Scholar ] [ CrossRef ]
Berman, Noah. 2024. How Houthi Attacks in the Red Sea Threaten global Shipping. Council on Foreign Relations , January 12. Available online: https://www.cfr.org/in-brief/how-houthi-attacks-red-sea-threaten-global-shipping (accessed on 1 June 2024).
Bömer, Michael, and Johann Weiß. 2024. Die Bedeutung der deutschen Wirtschaft für Europa . Edited by Vereinigung der Bayerischen Wirtschaft e. V. München. Available online: https://www.vbw-bayern.de/Redaktion/Frei-zugaengliche-Medien/Abteilungen-GS/Volkswirtschaft/2024/Downloads/Studie_Die-Bedeutung-der-deutschen-Wirtschaft-f%C3%BCr-Europa_final.pdf (accessed on 1 June 2024).
Brauweiler, Hans-Christian. 2019. Risikomanagement in Unternehmen . Ein grundlegender Überblick für die Management-Praxis. 2, Erweiterte und Ergänzte Auflage. Wiesbaden: Springer. [ Google Scholar ] [ CrossRef ]
Bülte, Jens. 2018. Strafbarkeitsrisiken für Insolvenzberater und Berater . ZIS-Abendsymposium am 6.2.2018—Strafbarkeit und Insolvenz. Mannheim: Fakultät für Rechtswissenschaften und Volkswirtschaftslehre. [ Google Scholar ]
Ceritoğlu, A. Bahar. 2017. Risikomanagement in Lieferketten—Supply-Chain-Risikomanagement. In Risikomanagement in Unternehmen. Interkulturelle Betrachtungen zwischen Deutschland, Österreich und der Türkei. Edited by Stephan Schöning, Eva Handan Göğüş and Helmut Pernsteiner. Wiesbaden: Springer Gabler, pp. 269–96. [ Google Scholar ] [ CrossRef ]
Coutinho, Leonor, Andreas Kappeler, and Alessandro Turrini. 2023. Insolvency Frameworks across the EU: Challenges after COVID-19 . Discussion Paper 182, February 2023. Luxembourg: Publications Office of the European Union. Available online: https://economy-finance.ec.europa.eu/ecfin-publications_en (accessed on 1 June 2024).
Creditreform Wirtschaftsforschung. 2023a. Insolvenzen in Deutschland. 1. Half year 2023 . Neuss: Creditreform. [ Google Scholar ]
Creditreform Wirtschaftsforschung. 2023b. Unternehmensinsolvenzen in Europa, 2022 . Neuss: Creditreform. [ Google Scholar ]
Diller, Martin. 2018. Risikomanagement in der Anwaltskanzlei—ein verdrängtes Thema?! Fehlerkultur in der Anwaltschaft ist mehr als Fehler vermeiden. Anwaltsblatt. Volume 68, Issue 7/2018. Deutscher Anwaltverein e.V. (ed.). Deutscher Anwaltverlag und Institut der Anwaltschaft. Available online: https://anwaltsblatt.anwaltverein.de/files/anwaltsblatt.de/Archiv/2018/anwbl_2018_07_heft.pdf (accessed on 1 June 2024).
de Ruijter, A., and F. Guldenmund. 2016. The bowtie method: A review. Safety Science 88: 211–18. [ Google Scholar ] [ CrossRef ]
Ebert, Christof. 2013. Risikomanagement Kompakt . Risiken und Unsicherheiten bewerten und Beherrschen. Berlin and Heidelberg: Springer-Vieweg. [ Google Scholar ] [ CrossRef ]
European Parliament and European Council. 2019. Preventive Restructuring Frameworks, on Debt Relief and on Prohibitions on Activities, as well as on Measures to Increase the Efficiency of Restructuring, Insolvency and Discharge Procedures and Amending Directive (EU) 2017/1132 (Directive on Restructuring and Insolvency) . Brussels: European Parliament and European Council. Available online: https://eur-lex.europa.eu/eli/dir/2019/1023/oj (accessed on 1 June 2024).
European Parliament and European Council. 2022. Proposal for a Directive of the European Parliament and of the Council on the Harmonisation of Certain Aspects of Insolvency Law” (COM (2022) 702 final—2022/0408 (COD)) . Brussels: European Parliament and European Council. Available online: https://eur-lex.europa.eu/resource.html?uri=cellar:8adadc6c-76e9-11ed-9887-01aa75ed71a1.0020.02/DOC_1&format=PDF (accessed on 1 June 2024).
Fehl-Weileder, Elske. 2022. Insolvency statistics. In Insolvency and Restructuring in Germany . Yearbook 2023. Achern: Schultze & Braun, pp. 54–63. ISBN 978-3-9822268-8-0. [ Google Scholar ]
Gassen, Joachim, and Urška Kosi. 2021. Insolvenzen: Ein Kollateralschaden der Pandemie? In TRR 266 Accounting for Transparency . Berlin and Paderborn: Humboldt Universität and Universität Paderborn. [ Google Scholar ]
Gleißner, Werner, and Kay H. Hofmann. 2020. Unerwartete Unternehmensinsolvenz trotz testiertem Risikomanagement?—Lessons Learned aus der Insolvenz der Gerry Weber International AG. Der Betriebswirt 3: 139–54. [ Google Scholar ] [ CrossRef ]
Grabner, Thomas. 2019. Operations Management . Auftragserfüllung bei Sach- und Dienstleistungen. Wiesbaden: Springer. [ Google Scholar ] [ CrossRef ]
Gudehus, Timm. 2015. Dynamische Märkte . Grundlagen und Anwendungen der Analytischen Ökonomie. 2., neu Bearbeitete und Erweiterte Auflage. Wiesbaden: Springer. [ Google Scholar ] [ CrossRef ]
Greve, Gustav. 2019. Organizational Burnout , 4th ed. Das versteckte Phänomen ausgebrannter Organisationen. Wiesbaden: Springer. [ Google Scholar ]
Hallak, Issam. 2023. Briefing EU Legislation in Progress: Harmonising Certain Aspects of Insolvency Law in the EU . EPRS European Parliamentary ResearchService. Available online: https://www.europarl.europa.eu/RegData/etudes/BRIE/2023/745671/EPRS_ BRI(2023)745671_EN.pdf#:~:text=URL%3A%20https%3A%2F%2Fwww.europarl.europa.eu%2FRegData%2Fetudes%2FBRIE%2F2023%2F745671%2FEPRS_BRI%282023%29745671_EN.pdf%0AVisible%3A%200%25%20 (accessed on 1 June 2024).
Heesen, Bernd, and Vinzenth Wieser-Linhart. 2018. Basiswissen Insolvenz . Schneller Einstieg in Insolvenzprävention und Risikomanagement. Wiesbaden: Springer. [ Google Scholar ] [ CrossRef ]
Hefner, Veronika. 2020. Juristische Personen als Insolvenzverwalter? Spanien und Deutschland im Rechtsvergleich , 1st ed. Baden: NOMOS. [ Google Scholar ]
Hesse, Jannika, and Martin Schneider. 2022. Unternehmensinsolvenzen: Auswirkungen der Hilfsmaßnahmen und Prognose bis 2023. Konjunktur aktuell, Ausgabe März. 2022. Available online: https://www.oenb.at/Publikationen/Volkswirtschaft/konjunktur-aktuell.html (accessed on 1 June 2024).
Hopkin, Paul. 2017. Fundamentals of Risk Management . Understanding, evaluating and implementing effective risk management. London, New York and New Delhi: Kogan Page. [ Google Scholar ]
Hunziker, Stefan, Ute Vanini, Mirjam Durrer, Philipp Henrizi, and Anjuli Unruh. 2020. Die Rolle der Risk Manager in der COVID-19 Krise. ERM Report 2020. Rotkreuz: Institut für Finanzdienstleistungen Zug IFZ der Hochschule Luzern. [ Google Scholar ]
Huth, Michael, Sascha Düerkop, and Frank Romeike. 2017. RIMA-KIL—Risikomanagement für Kritische Infrastrukturen in der Logistik. Discussion Paper No 19 4/2017. Fulda: University of Applied Sciences. [ Google Scholar ]
International Organization for Standardization. 2013. ISO/IEC 27001:2013—Information Security Management System. Available online: https://www.pqm.sk/certifikacia-manazerskych-systemov/akreditovane-systemy/system-manazerstva-informacnej-bezpecnosti-iso-iec-27001/?gad_source=1&gclid=CjwKCAjwhvi0BhA4EiwAX25uj6lZvwJZka5fV29l2guZmrfvXYqo-Jfgpc8gs-O_34Jfse-_cqMFkBoCMpkQAvD_BwE (accessed on 1 June 2024).
International Organization for Standardization. 2015. ISO 9001:2015—Quality Management Systems. Available online: https://www.dnv.sk/siteassets/images/pdf-documents/sk-whitepaper-iso-9001-2015-dnv-gl---dowload.pdf (accessed on 1 June 2024).
International Organization for Standardization. 2018. ISO 31000:2018—Risk Management—Guidelines Management du Risque—Lignes Directrices. Available online: https://shahrdevelopment.ir/wp-content/uploads/2020/03/ISO-31000.pdf (accessed on 1 June 2024).
Kuntsche, Peter, and Kirstin Börchers. 2017. Qualitäts- und Risikomanagement im Gesundheitswesen . Basis- und integrierte Systeme, Managementsystemübersichten und praktische Umsetzung. Berlin: Springer. [ Google Scholar ] [ CrossRef ]
Littich, Alexander, and Janika Sievert. 2019. Strafbarkeit von Beratern und InsolvenzBeratern. Wegweiser für eine risikofreie Beratung . Landshut: ECOVIS L + C. [ Google Scholar ]
Mohammadzai, Gulwali, Khalid Pashtoon, and Ilhamuddin Aini. 2023. Compliance Requirement for Dealing with Risks, Governance and IT Compliance. Integrated Journal for Research in Arts and Humanities 3: 150–56. [ Google Scholar ] [ CrossRef ]
Müller, Klaus-Rainer. 2015. Handbuch Unternehmenssicherheit . Umfassendes Sicherheits-, Kontinuitätsund Risikomanagement mit System. 3., aktualisierte und erweiterte Auflage. Wiesbaden: Springer. [ Google Scholar ] [ CrossRef ]
Müller, Stefan. 2021a. Insolvenzen in der Corona-Krise. IWH Policy Notes No. 2/2021. Halle (Saale): Leibniz-Institut für Wirtschaftsforschung Halle (IWH). [ Google Scholar ]
Müller, Stefan. 2021b. Unternehmensinsolvenzen seit Ausbruch der Pandemie. Wirtschaft im Wandel 27: 35–38. [ Google Scholar ]
Rack, Manfred. 2012. Risikomanagement ohne Organisationsverschulden . Ein Handbuch zum Managementsystem für gute Unternehmensführung. Frankfurt am Main: Eigenverlag. [ Google Scholar ]
Reinhart, Carmen. 2022. From Health Crisis to Financial Distress. IMF Economic Review 70: 4. [ Google Scholar ] [ CrossRef ]
Romeike, Frank. 2017. Management von Rohstoffrisiken. In Risikomanagement in Unternehmen. Interkulturelle Betrachtungen zwischen Deutschland, Österreich und der Türkei. Edited by Stephan Schöning, Eva Handan Göğüş and Helmut Pernsteiner. Wiesbaden: Springer Gabler, pp. 117–74. [ Google Scholar ] [ CrossRef ]
Sandqvist, Pauliina, and Timo Wollmershäuser. 2021. Analyse der Firmeninsolvenzen infolge der Corona-Pandemie und Wirkungsabschätzung finanzpolitischer Maßnahmen . Studie im Auftrag des Bundesministeriums der Finanzen im Rahmen des Forschungsauftrags fe 3/19: Rahmenvertrag Wissenschaftliche (Kurz-) Expertisen zu Grundsatzfragen der Finanz-, Steuer- und Wirtschaftspolitik. München: ifo Zentrum für Makroökonomik und Befragungen. [ Google Scholar ]
Scherer, Josef. 2012. Good Governance und ganzheitliches strategisches und operatives Management: Die Anreicherung des “unternehmerischen Bauchgefühls” mit Risiko-, Chancen- und Compliancemanagement. Corporate Compliance Zeitschrift (CCZ) 6: 201–11. [ Google Scholar ]
Scherer, Josef. 2019. (Compliance-) Risk Management System 4.0—The Digital Transformation of Norms, Guidelines and Standards . Frankfurt am Main: Frankfurter Institut für Risikomanagement und Regulierung, FIRM Yearbook. [ Google Scholar ]
Scherer, Josef, and Klaus Fruth, eds. 2019. Das interessiert Kapitalgeber: Antifragilität und der “Achilleskörper” des Ordentlichen Kaufmanns—Vermeidung der persönlichen Haftung für Missmanagement am Beispiel “Governance, Risk und Compliance (“GRC”)” und Geschäftsprozessdigitalisierung . Frankfurt am Main: Verlag für Governance, Management, Risk & Compliance, Association for Risk Management and Regulation. [ Google Scholar ]
Scherer, Josef, Frank Romeike, and Shari Gursky. 2021. Mehr Risikokompetenz für eine neue Welt. JMG Journal für Medizin- und Gesundheitsrecht 2021: 159–65. [ Google Scholar ] [ CrossRef ]
Schürholz, Markus, and Eike-Christian Spitzner. 2019. Hardware für KI. In Künstliche Intelligenz . Technologie|Anwendung|Gesellschaft. Edited by Volker Wittpahl. Berlin and Heidelberg: Springer-Vieweg, pp. 36–47. [ Google Scholar ] [ CrossRef ]
Sorano, Enrico, and Giovanni Lombardo. 2016. Risk management and performance in public administration, as a way to better monitor the effectiveness and efficiency of processes and administrative procedures. In Risk Management: Perspectives and Open Issues. A Multi-Disciplinary Approach . Edited by Valter Cantino, Paola De Vincentiis and Gabriella Racca. New York: Mc Graw-Hill Education, pp. 280–92. ISBN 978-0-0771-8017-1. [ Google Scholar ]
Sorger, Helmut. 2008. Entscheidungsorientiertes Risikomanagement in der Industrieunternehmung . Frankfurt am Main: Internationaler Verlag der Wissenschaften. [ Google Scholar ]
Troßmann, Ernst, and Alexander Baumeister. 2017. Besonderheiten des Risikomanagements bei Auftragsfertigung. In Risikomanagement in Unternehmen . Interkulturelle Betrachtungen zwischen Deutschland, Österreich und der Türkei. Edited by Stephan Schöning, Eva Handan Göğüş and Helmut Pernsteiner. Wiesbaden: Springer Gabler, pp. 297–311. [ Google Scholar ] [ CrossRef ]
United Nations. 2023. World Economic Situation and Prospects 2023 . New York: United Nations. [ Google Scholar ]
von Oppen, Andreas. 2019. Positionspapier zur Richtlinie (EU) 2019/1023 über präventive Restrukturierungsrahmen, über Entschuldung und über Tätigkeitsverbote sowie über Maßnahmen zur Steigerung der Effizienz von Restrukturierungs-, Insolvenz- und Entschuldungsverfahren und zur Änderung der Richtlinie (EU) 2017/1132. Available online: https://die-dk.de/media/files/2019_09_19_DK_Positionspapier_RestruktRiLi.pdf (accessed on 19 September 2019).
Wälder, Konrad, and Olga Wälder. 2017. Methoden zur Risikomodellierung und des Risikomanagements. Wiesbaden: Springer. [ Google Scholar ] [ CrossRef ]
Weinbeer, Alexander. 2019. Die Größten Haftungsrisiken des Anwalts . Bonn: Typische Fälle von A bis Z. Deutscher Anwaltverlag. [ Google Scholar ]
Wessing, Jürgen. 2018. Compliance in Anwaltskanzleien. ZIS—Zeitschrift für Internationale Strafrechtsdogmatik. 13th Year. Issue 9/2018. pp. 368–374. Available online: https://www.zis-online.com/dat/artikel/2018_9_1225.pdf (accessed on 1 June 2024).
Zimmermann, Volker. 2022. Vielfältige Hemmnisse bremsen die Digitalisierungsaktivitäten deutscher Unternehmen. Ifo Schnelldienst 75: 8–11. [ Google Scholar ]

	2022	2021	2020	2019	2018	Change 2021 in Percent
Belgium	9260	6533	7203	10,598	9878	+41.7
Denmark	7818	8339	5614	8474	7155	−6.2
Germany	14,660	14,130	16,040	18,830	19,410	+3.8
Finland	2656	2473	2135	2597	2534	+7.4
France	41,215	27,470	31,036	51,201	53,887	+50.0
Greece	46	108	102	107	84	−57.4
Great Britain	23,104	14,820	13,298	18,256	18,773	+55.9
Ireland	500	401	575	568	767	+24.7
Italy	7164	9017	7650	11,161	11,259	−20.6
Luxembourg	1054	1199	1199	1263	1195	−12.1
Netherlands	1854	1536	2703	3209	3145	+20.7
Norway	3040	2688	4100	5013	5010	+13.1
Austria	4913	3076	3106	5235	5224	+59.7
Portugal	3869	4770	5000	5071	5888	−18.9
Sweden	7266	6901	7695	7776	7599	+5.3
Switzerland	6799	5127	4893	6009	6878	+32.6
Spain	4755	4098	4097	4464	4131	+16.0
Total	139,973	112,686	116,446	159,832	162,777	+24.2

Option	Answers	Ratio
1 to 3 advisors	39	34.51%
4 to 10 advisors	41	36.28%
More than 10 advisors	33	29.2%

Question	Option	Answers	Ratio
Q. 4 In your opinion, how many of the insolvency advisors in Germany use risk management?	0–24%	77	68.14%
	25–49%	28	24.78%
	50–74%	7	6.19%
	75–100%	1	0.88%

Statistic for Test:
	In your opinion, how many of the insolvency advisors in Germany use risk management?
	131.142
	3
	0.000
For 0 cells (0.0%), fewer than 5 frequencies are expected. The smallest expected cell frequency is 28.3.

Q. 9: In which areas of your work do you use risk management?	In no area	; p < 0.005	Most selected in small law companies
	Identification of compliance risks	; p < 0.005	Most selected in large law companies
	Data protection	; p < 0.005	Most selected in large law companies
Q. 10: Which standards, internal regulations and quality seal requirements do you use as a guide for your consulting activities?	ISO 31000:2018 ( )	; p < 0.005	Most selected in large law companies
	ISO 9001:2015 ( )	5,836; p < 0.005	Most selected in large law companies
	ISO/IEC 27001:2013 ( )	; p < 0.005	Most selected in large law companies
	VID-CERT	; p < 0.005	Most selected in large law companies
	InsO Excellence	; p < 0.005	Most selected in large law companies
	None	; p < 0.005	Most selected in individual law companies
	Other	; p < 0.005	Most selected in large law companies
Q. 11: Which of the following specific risk management tools and methods do you use?	Quantitative risk analysis	; p < 0.005	Most selected in large law companies
	Risk scoring models	; p < 0.005	Most selected in large law companies
	None	; p < 0.005	Most selected in small law companies
Q. 12: Which of the following methods of risk identification and assessment do you use?	Risk matrix	3,056; p < 0.005	Most selected in large law companies
	FMEA	0,483; p < 0.005	Most selected in large law companies
	None	; p < 0.005	Most selected in small law companies
Q. 13: Which of the following methods do you use to identify unknown risks and generate solutions?	Mind mapping	; p < 0.005	Most selected in large law companies
	Monte Carlo simulations	; p < 0.005	Most selected in large law companies
	Other	; p < 0.005	Most selected in large law companies
	None	; p < 0.005	Most selected in small law companies
Q. 16: How do you ensure in your advisory work that you receive all relevant information from clients?	Clarification	; p < 0.005	Most selected in individual law companies
	Communication and information guidelines	; p < 0.005	Most selected in large law companies
	Questionnaire, checklist	; p < 0.005	Most selected in large law companies
	Status meeting	; p < 0.005	Most selected in large law companies
Q. 17: How do you deal with the risk that clients might (unconsciously or consciously) withhold potentially critical information?	Compliance check	; p < 0.005	Most selected in large law companies
	Emergency plan	; p < 0.005	Most selected in large law companies
	Other	; p < 0.005	Most selected in large law companies
	None	; p < 0.005	Most selected in small law companies
Q. 18: How do you ensure that your clients act fairly towards you and provide you with the support in the advisory process?	Transparent remuneration system	; p < 0.005	Most selected in large law companies
	Due diligence	3,770; p < 0.005	Most selected in large law companies
	Adaptation of the counselling approach	; p < 0.005	Most selected in individual law companies
	None	; p < 0.005	Most selected in large law companies

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Seehaus, S.R.; Peráček, T. Lack of Risk Management at Insolvency Consulting Companies: An Empirical Study in Germany 2024. Adm. Sci. 2024 , 14 , 160. https://doi.org/10.3390/admsci14080160

Seehaus SR, Peráček T. Lack of Risk Management at Insolvency Consulting Companies: An Empirical Study in Germany 2024. Administrative Sciences . 2024; 14(8):160. https://doi.org/10.3390/admsci14080160

Seehaus, Sascha Rudolf, and Tomáš Peráček. 2024. "Lack of Risk Management at Insolvency Consulting Companies: An Empirical Study in Germany 2024" Administrative Sciences 14, no. 8: 160. https://doi.org/10.3390/admsci14080160

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Empirical Research: Definition, Methods, Types and Examples

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Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and qualitative market research methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
Qualitative research: Qualitative research methods are used to gather non numerical data. It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

Observational method: Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Learn More: Data Collection Methods: Types & Examples

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

It is used to authenticate traditional research through various experiments and observations.
This research methodology makes the research being conducted more competent and authentic.
It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
The level of control in such a research is high so the researcher can control multiple variables.
It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

LEARN ABOUT: Social Communication Questionnaire

Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

LEARN ABOUT: 12 Best Tools for Researchers

With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

Create a single source of real data with a built-for-insights platform. Store past data, add nuggets of insights, and import research data from various sources into a CRM for insights. Build on ever-growing research with a real-time dashboard in a unified research management platform to turn insights into knowledge.

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Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief.

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology." Ask yourself: Could I recreate this study and test these results?

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Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

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Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
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Open access
Published: 27 July 2024

The effect of mobile social media on the mental health status of Chinese international students: an empirical study on the chain mediation effect

Chenglong Miao ORCID: orcid.org/0009-0004-0723-9011 1 &
Shuai Zhang ORCID: orcid.org/0009-0002-4920-2669 1

BMC Psychology volume 12 , Article number: 411 ( 2024 ) Cite this article

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Metrics details

To explore the impact of mobile social media on the psychological well-being of Chinese international students and analyze the mechanisms of influence to enhance their overall psychological health and social interactions in a foreign environment.

Convenience sampling was employed, using questionnaires on Mobile Social Media, Psychological Resilience, Body Image, Health Goal Setting, Physical Activity Level, and Mental Health Status as measurement tools. Data were gathered from 378 Chinese international students across 33 universities in South Korea, including Kangwon National University, Myongji University, Kunsan National University, Seoul National University, and Chonbuk National University. Confirmatory factor analysis, correlation analysis, common method bias testing, and chain mediation effect analysis were conducted using SPSS and AMOS 23.0.

Mobile social media has significant indirect effects on the mental health of international students through various factors: psychological resilience and physical activity level (effect ‘adg’ = 0.080, 95% CI [0.029, 0.144]), body image and physical activity level (effect ‘beg’ = 0.122, 95% CI [0.044, 0.247]), and health goal setting and physical activity level (effect ‘cfg’ = 0.255, 95% CI [0.123, 0.428]).

The study shows that mobile social media benefits the mental health of Chinese international students by enhancing psychological resilience, physical activity, body image perception, and health goal setting. Collaboration between educational institutions and social media platforms is recommended to promote physical activity among international students. This collaboration can involve sharing encouraging messages, joining health communities, setting goals, and providing accessible exercise resources. Utilizing mobile apps or social media for tracking progress and goal-setting can also improve self-management skills.

Peer Review reports

Introduction

In the digital age, mobile social media has profoundly reshaped social behaviors and information acquisition, becoming integral to daily life. Statistics from the Chinese Ministry of Education show that nearly 6.56 million Chinese students studied abroad from 1978 to 2019, making them significant users of international mobile social media. However, these students face unique cultural, linguistic, social, and academic challenges, impacting their mental health [ 1 ].

While existing research highlights the impact of mobile social media on mental health, it predominantly focuses on domestic students, neglecting Chinese international students [ 2 ]. This group encounters cultural differences, academic pressure, and social difficulties abroad. High psychological resilience can help them manage stress, adapt more quickly, and reduce mental health issues [ 3 , 4 ]. Moreover, body image, influenced by diverse cultural aesthetics, plays a role in self-esteem and anxiety levels [ 5 ]. Setting health goals and engaging in moderate physical activity are also beneficial for mental health [ 6 , 7 ].

This study explores the combined impact of mobile social media, psychological resilience, body image, health goal setting, and physical activity on the mental health of Chinese international students. By constructing a comprehensive model, we aim to reveal the interactions among these factors, providing new insights and strategies to improve the mental health of Chinese international students. This research fills a gap in the literature and offers valuable theoretical and practical references for future studies on international student mental health.

Literature review and research hypotheses

Chained mediation effect of psychological resilience and physical activity level, impact of mobile social media on psychological resilience.

Mobile social media refers to online social networks and platforms used for disseminating information through social interactions [ 8 ]. These media platforms provide individuals with the means to interact with others through mobile applications or mobile browsers, allowing them to connect, share, and communicate anytime and anywhere, thereby enhancing the flexibility and convenience of social interactions.

Psychological resilience refers to an individual's ability to maintain a stable mental health state when facing stress, difficulties, setbacks, or adversity in life, and to quickly recover and adapt from adverse experiences [ 9 ]. Psychological resilience can enhance an individual's ability to cope with life stress and adversity, alleviate negative emotions, promote positive emotions, accelerate recovery from adversity, and improve mental health [ 10 ].

The impact of mobile social media on psychological resilience is complex and multifaceted, influenced not only by individuals' usage patterns on social media but also by the characteristics of the social media platforms themselves [ 11 , 12 ]. The platforms provided by mobile social media enable individuals to stay connected with friends, family, and social networks [ 13 ]. This social support can have positive psychological effects when facing challenges and stress, promoting the development of psychological resilience. By sharing experiences and emotions on social media and receiving support and understanding from others, individuals can better cope with life's difficulties [ 14 ].

Psychological resilience and its relationship with physical activity level

Physical activity level refers to the extent of an individual's participation in physical exercise in daily life, including frequency, intensity, duration, and type of activities [ 15 , 16 ]. It reflects the individual's level of engagement in exercise and physical activities, covering various forms of physical activities ranging from light daily activities to more intense exercise training [ 16 ].

Research indicates that various traits of psychological resilience, such as positive attitude, self-regulation, adaptive thinking, social support, goal setting, self-efficacy, and flexibility, can influence individuals' levels of physical activity from multiple perspectives [ 17 ]. Firstly, psychological resilience plays an important role when facing fatigue. Budisavljevic et al. (2023) found that compared to oncologists with lower psychological resilience, oncologists in Croatia with higher psychological resilience experienced significantly lower levels of fatigue [ 18 ]. Secondly, individuals with high psychological resilience have higher stress thresholds [ 19 ] and are often able to recover more quickly from setbacks due to injury or training [ 20 ]. These individuals can adjust their ways of thinking and actively face difficulties, thus adapting better to new exercise requirements. Additionally, individuals with high psychological resilience can often set clearer goals and maintain focus, achieving their goals through positive self-regulation [ 21 ], which is crucial for improving physical activity levels. Lastly, athletes with high psychological resilience are better equipped to handle competitive pressure during competitions. They can remain calm, focused, and less susceptible to external distractions, thereby enhancing their athletic performance [ 22 , 23 ].

Chained mediation effect of body image and physical activity level

Body image is the sum of an individual's perceptions, attitudes, and emotional responses towards their own physical appearance, shape, size, and function. It encompasses subjective views of one's appearance and emotional experiences and self-evaluations related to one's own body [ 24 , 25 ].

Impact of mobile social media on body image

Research reveals that mobile social media plays a significant role in shaping individuals' body image [ 26 ]. Some social media accounts are dedicated to promoting positive body image messages, encouraging acceptance of diverse body types and appearances, suggesting that mobile social media may serve as a platform for fostering positive body awareness and self-esteem [ 27 ]. Additionally, mobile social media accounts related to health and fitness can provide individuals with valuable health information, exercise inspiration, and encouragement, motivating them to actively engage in physical exercise and promoting physical health [ 28 ]. Furthermore, the diverse content on mobile social media, including various body types, appearances, and lifestyles, helps broaden individuals' awareness of body diversity and fosters a more inclusive and open-minded body concept [ 29 ].

However, some scholars argue that the widespread dissemination of idealized and beautified body standards on mobile social media often has negative effects on individuals [ 30 , 31 ]. It is precisely because of these idealized body images presented on mobile social media that individuals often experience psychological health issues such as body dissatisfaction, low self-esteem, and anxiety [ 32 , 33 ]. In psychological and sociological research, potential negative impacts of mobile social media on individuals' body image have been identified [ 34 ]. Social interactions such as photo editing, body comparisons, comments, and likes on mobile social media can profoundly influence individuals' body perception and self-esteem [ 35 ]. Additionally, the body standards promoted on mobile social media have led some individuals to experience excessive body pressure, resulting in unhealthy behaviors such as extreme dieting and excessive exercise in order to conform to the idealized body ideals advocated by mobile social media [ 36 , 37 , 38 ].

Impact of body image on physical activity level

The relationship between body image and physical activity level is complex. Body image can influence individuals' confidence and motivation for physical exercise [ 39 ]. This is because confidence and motivation play crucial roles in physical exercise [ 40 ], and body image is one of the key factors closely associated with them [ 41 ]. Positive body image is closely related to higher confidence and positive motivation. When individuals feel satisfied and confident about their bodies, they are generally more willing to actively engage in physical exercise because they believe in their ability to make progress and enjoy the benefits of exercise [ 42 ].

Positive body image not only affects the psychological aspect but also directly influences the execution and performance of exercise, promoting balance in physical, emotional, psychological, and spiritual abilities [ 43 , 44 , 45 ]. Conversely, negative body image may lead to tension and anxiety, thereby adversely affecting exercise performance [ 46 , 47 , 48 ]. Additionally, positive self-perceptions may inspire individuals to engage more actively in physical activity, thereby enhancing physical fitness levels; conversely, negative body image may serve as a barrier to participating in physical activity, hindering physical fitness improvement.

In the end, individual satisfaction with one's body can largely be seen as a key driving force for promoting positive physical activity behavior, while dissatisfaction with one's body may serve as a barrier to engaging in physical activity [ 49 , 50 ].

Chained mediation effect of health goal setting and physical activity level

Impact of mobile social media on health goal setting.

Health goal setting refers to the specific, measurable goals or plans individuals establish in pursuit of improved health conditions, often involving adopting a series of positive health behaviors, lifestyle changes, or achieving specific health indicators [ 51 ].

Mobile social media has various impacts on setting and achieving health goals in today's society. Firstly, social media platforms provide individuals with convenient access to acquiring and sharing health-related information [ 52 , 53 ]. Individuals can easily access knowledge on nutrition, exercise, mental health, and more, and share their health goals and experiences with others. This flow of information encourages individuals to pay more attention to and understand health issues, helping them set clearer health goals [ 54 ]. Secondly, mobile social media provides users with a community platform, enabling them to share their health goals with friends, family, or like-minded individuals. By sharing goals, progress, and challenges, users can receive support and encouragement from their social networks, thereby being more motivated to pursue their health goals. Additionally, some mobile social media platforms combine users' health data and personal preferences to provide personalized health advice [ 55 ], helping users set health goals tailored to their needs and lifestyles, and offering practical suggestions to achieve these goals.

However, mobile social media may also bring about some adverse effects on health goal setting, which require attention and vigilance. Research indicates that the use of mobile social media may lead individuals to face challenges in social comparison and anxiety [ 56 ]. This could be because mobile social media often inundate users with others' health achievements and beauty standards, potentially causing users to feel dissatisfied with their own bodies and health, leading to social comparison and body anxiety. Consequently, some individuals may set unrealistic or extreme health goals, disregarding individual differences, merely to conform to the standards on mobile social media. Additionally, it is important to note that despite the abundance of health-related information on mobile social media, its credibility poses certain challenges. Individuals may be influenced by irresponsible health advice, resulting in the setting and practice of incorrect health goals, and even potentially having negative impacts on physical health.

Impact of health goal setting on physical activity level

The establishment of health goals plays a crucial role in enhancing physical activity levels. This is because clear health goals provide individuals with a clear direction, making it easier for them to understand why they should engage in physical activity and what outcomes to expect. This clarity helps to inspire individuals to participate in and sustain their motivation for physical activity [ 57 ].

Impact of setting health goals on enhancing physical activity levels can be explained from multiple perspectives. Firstly, setting achievable health goals tends to stimulate positive exercise motivation, making individuals more willing to engage in physical activity [ 58 , 59 ]. With clear health goals, individuals are generally more likely to maintain interest in exercising and experience satisfaction from achieving their goals, thereby enhancing the enthusiasm for physical activity. Secondly, health goal setting facilitates the planning and implementation of more effective physical activity plans. Setting goals encourages individuals to choose appropriate modes, intensities, and frequencies to better achieve their health goals, thereby improving the effectiveness of physical activity and producing more significant health benefits. Thirdly, health goal setting helps cultivate healthy behavioral habits [ 60 ]. By incorporating physical activity into individuals' life goals, it gradually becomes a conscious lifestyle rather than a temporary effort, which helps ensure the long-term maintenance of physical activity levels. Lastly, health goal setting is closely related to the level of psychological well-being [ 61 ]. Achieving health goals through physical activity can alleviate issues such as stress, anxiety, and depression, thereby promoting overall psychological health. Additionally, during the implementation of health goals, social support has a positive impact on physical activity levels, so individuals typically persist in achieving their health goals through support from friends, family, or social networks.

Impact of physical activity level on mental health status

Level of mental health is a relatively subjective concept, typically used to describe an individual's overall state at the psychological level [ 62 ]. According to the World Health Organization (WHO), mental health not only refers to the absence of mental illness, but also encompasses various aspects such as adaptability to life, quality of social relationships, stability of emotional experiences, and capacity for self-realization [ 63 ].

The impact of physical activity level on mental health status can be elucidated from several aspects. Firstly, higher levels of physical activity are associated with improved mental health and elevated mental health status. Physical activity activates the reward circuitry [ 64 ] and increases peripheral dopamine [ 65 ], norepinephrine, and serotonin levels, promoting the release of endorphins, which help enhance mood, alleviate anxiety, and depression [ 66 ]. Secondly, regular physical activity helps regulate the circadian rhythm, improve sleep quality, alleviate insomnia issues, and provide an outlet for releasing negative emotions, thus aiding in emotional regulation. Lastly, through physical activity, individuals can improve body shape, enhance self-esteem, and confidence, which can have a positive impact on mental health [ 67 ].

Based on this, the research model (Fig. 1 ) and research hypotheses H1, H2, H3 are proposed.

Research Model. MSM: Mobile Social Media; PR: Psychological Resilience; BI: Body Image; HGS: Health Goal Setting; PAL: Physical Activity Level; MHS: Mental Health Status; Effect 'adg': MSM → PR → PAL → MHS; Effect 'beg': MSM → BI → PAL → MHS; Effect 'cfg': MSM → HGS → PAL → MHS

H1: There is a significant chained mediation effect (effect ‘adg’) of psychological resilience and physical activity level of Chinese international students between mobile social media and mental health status. H2: There is a significant chained mediation effect (effect ‘beg’) of body image and physical activity level of Chinese international students between mobile social media and mental health status. H3: There is a significant chained mediation effect (effect ‘cfg’) of health goal setting and physical activity level of Chinese international students between mobile social media and mental health status.

Methodology

Sample size calculation.

The sample size was calculated using the formula for descriptive cross-sectional studies, determined by the Leslie Kish formula for single proportion sample [ 68 ], as follows:

n = the required sample size

Z = 1.96 (corresponding to a 95% confidence level)

p = 0.5 (expected sample proportion)

E = 0.05 (expected margin of error)

The calculated sample size is approximately 384.16. According to Tehranineshat et al. (2021), the non-response rate for all items should be between 0 and 5% [ 69 ].

Participants

This study employed convenience sampling to distribute questionnaires. Traditional random sampling methods may result in low response rates and answers that are not truthful. While theoretically all studies should use random sampling, in practice, this is nearly impossible. This is especially true for hard-to-reach populations. Convenience sampling offers advantages such as rapid implementation, low cost, and high flexibility [ 70 ]. Therefore, convenience sampling was used, selecting 378 Chinese international students from 33 universities in South Korea, including Kangwon National University, Myongji University, Kunsan National University, Seoul National University, and Jeonbuk National University. To ensure the quality of the questionnaire data, this study employed anonymous completion and paid questionnaire survey methods (3,000 Korean won or equivalent coffee voucher per person), utilizing Google Forms and the SurveyStar website to distribute and collect 383 questionnaires. Five invalid questionnaires, including duplicates and responses deemed unreliable, were excluded, resulting in 378 valid questionnaires, with an effective rate of 98.69%. The deadline for questionnaire collection was December 26, 2023.

Among the 378 Chinese international students, the average age was 22.81 years (SD = 3.53 years), with 164 males, accounting for 43.4%. The mobile social media platforms used included TikTok, Little Red Book, microblog, WeChat Video Account, Bilibili, Fit in Korea, Fitness Fast, MyV, Wave, and Strong On.

Measurements

The measurement tools in this study were adapted from relevant scales developed by previous researchers. The Adolescent Mobile Social Media Scale developed by Wang, Lei (2015) was adapted into a scale for mobile social media usage among international students (3 items) [ 71 ]; the Children's Psychological Resilience Scale developed by Gardiner et al. (2019) was adapted into a scale for psychological resilience among international students (3 items) [ 72 ]; the Patient Body Image Scale developed by Hopwood et al. (2001) was adapted into a scale for body image among international students (4 items) [ 73 ]; the Health Belief Model Scale developed by Villar et al. (2017) was adapted into a scale for health goal setting among international students (3 items) [ 74 ]; the Exercise Benefits Scale developed by Sechrist et al. (1987) was adapted into a scale for physical activity level among international students (3 items) [ 75 ]; and the Psychological Well-being Scale developed by Ryff (1989) was adapted into a scale for mental health status among international students (3 items) [ 76 ]. The translation and localization process of the questionnaire followed internationally recognized guidelines for cross-cultural adaptation, including translation, back-translation, expert committee review, and pre-testing steps [ 77 , 78 , 79 ]. The item content is scored on a 7-point Likert scale, where 1 represents 'completely disagree' and 7 represents 'completely agree'. The validity of the scales was assessed using confirmatory factor analysis in SPSS, and Cronbach’s α coefficients ranged from 0.808 to 0.872, indicating good reliability. Preliminary results indicate that the measurement tools are highly applicable to the international student population.

Data analysis

This study utilized the Windows versions of SPSS and Amos 23.0 statistical software for analysis. Firstly, to eliminate the influence of common method bias, we conducted a common method bias test. Subsequently, we utilized Confirmatory Factor Analysis (CFA) to validate the structural validity of the measurement tools used. Then, we conducted descriptive statistical analysis on the data, including computing measures such as mean, standard deviation, and frequency distribution. In order to explore the relationships between variables, correlation analysis was also performed. Finally, we employed chain mediation effect analysis to investigate the comprehensive impact of mobile social media, psychological resilience, body image, health goal setting, and physical activity level on the mental health status of Chinese international students.

Common method bias and fit test

The study employed self-reported data, which may lead to common method bias. To address potential common method bias, data collection was conducted anonymously. We performed Harman's single-factor test to assess the potential impact of common method bias on study outcomes [ 80 ]. The results indicated that the first factor accounted for 36% of the variance, which is below the threshold of 40%. Therefore, we believe that common method bias is not significant in this study, further confirming the reliability of the model.

The sample size of 378 and the number of latent variables being 6. Although having a sample size over 200 and a large number of variables can lead to an excessively large χ 2 value and poor fit, the fit indices of this model calculated by AMOS 23.0 all meet the standards. The results (Table 1 ) demonstrate that the model fit is satisfactory.

Confirmatory factor analysis and correlation analysis

The study conducted Confirmatory Factor Analysis (CFA) on all measurement indicators. Following the suggestion by Joreskog & Sorbom (1989), items with factor loadings below 0.45 were recommended for removal [ 81 ]. After conducting this operation, the following are the results of the CFA analysis in Table 2 : In this study, the factor loadings (standardized loadings Std.) ranged from 0.633 to 0.946, indicating significant associations between all measurement indicators and their respective latent factors. The Composite Reliability (CR) ranged from 0.792 to 0.905, indicating high internal consistency of each latent factor and strong correlations among their constituent variables. The Average Variance Extracted (AVE) ranged from 0.535 to 0.760, indicating that latent factors explained a large portion of the variance in their constituent variables, demonstrating high accuracy of latent factors in measuring observed variables.

In summary, all six variables in this study exhibited good reliability and convergent validity, meeting the criteria proposed by Hair et al. (1998), namely, factor loadings (Std.) greater than 0.50, composite reliability (CR) greater than 0.60, and average variance extracted (AVE) greater than 0.50 [ 82 ].

According to the results in Table 3 , the score ranges (M) of the six latent variables fall between 5.668 and 6.228, indicating that all latent variables from mobile social media to mental health status are within the positive evaluation range. The absolute range of skewness values is between 0.397 and 1.498, all of which are less than 2; the absolute range of kurtosis values is between 0.030 and 2.733, all of which are less than 8.00. Therefore, according to conventional judgment criteria, this data can be considered as normalized data. Additionally, through the application of the Average Variance Extracted (AVE) method to analyze discriminant validity, it was found that the square root of the AVE for each variable exceeds the standardized correlation coefficient between it and the other variables, indicating good discriminant validity among the variables. The results on the diagonal in Table 3 demonstrate this, further confirming the reliability of this model.

Mediation analysis

According to Fig. 2 , the direct effect of mobile social media on psychological resilience (γ = 0.656, P < 0.001), body image (γ = 0.726, P < 0.001), and health goal setting (γ = 0.688, P < 0.001) is significant. The direct effect of psychological resilience on physical activity level (γ = 0.189, P < 0.001), body image on physical activity level (γ = 0.262, P < 0.001), and health goal setting on physical activity level (γ = 0.578, P > 0.05) is significant. The direct effect of physical activity level on mental health status (γ = 0.642, P < 0.001) suggests that psychological resilience, body image, and health goal setting play positive chained mediating roles between mobile social media and mental health status. To calculate the mediation effect more accurately, this study used structural equation modeling to analyze and test the mediation effect. First, the standard errors of the chained mediation effect were estimated using the Bootstrap method, and then the significance level of the chained mediation effect was further calculated. The results (Table 4 ) show that the total effect of mobile social media on mental health status is 0.457, with a standard error of 0.060 and an absolute value of Z value of 7.617, meeting the standard greater than 1.96. The 95% confidence interval [0.344, 0.583] does not include 0, indicating a significant total effect.

Diagram of the Chained Mediation Model. MSM: Mobile Social Media; PR: Psychological Resilience; BI: Body Image; HGS: Health Goal Setting; PAL: Physical Activity Level; MHS: Mental Health Status

H1: Mobile social media has a significant indirect effect on the mental health status of international students through psychological resilience and physical activity level (effect ‘adg’ = 0.080), with a 95% CI [0.029, 0.144] excluding 0, indicating significance.

H2: Mobile social media has a significant indirect effect on the mental health status of international students through body image and physical activity level (effect ‘beg’ = 0.122), with a 95% CI [0.044, 0.247] excluding 0, indicating significance.

H3: Mobile social media has a significant indirect effect on the mental health status through health goal setting and physical activity level (effect ‘cfg’ = 0.255), with a 95% CI [0.123, 0.428] excluding 0, indicating significance.

The findings of this study suggest that mobile social media has a positive impact on the mental health status of Chinese international students through psychological resilience and physical activity level. Furthermore, it influences their mental health status positively via body image and physical activity level. Therefore, this study advocates for the establishment of a positive body image as a crucial factor in cultivating healthy self-confidence, positive motivation, superior athletic performance, high levels of fitness, and long-term exercise habits. Additionally, mobile social media positively affects the mental health status of Chinese international students through health goal setting and physical activity level.

Mobile social media may have a positive impact on the mental health status of Chinese international students through psychological resilience and physical activity level due to the following reasons:

(1) Providing positive motivation and support. Mobile social media can serve as a platform to motivate and support international students to engage in physical activity [ 83 ]. Through social media, students can receive encouragement and support from others, share exercise experiences and achievements, thereby stimulating their interest and motivation to participate in physical activities; (2) Establishing a social network for healthy lifestyles. Mobile social media can help international students establish friendships and communities that care about healthy lifestyles, promoting interaction and communication among them [ 84 , 85 ]. In such social networks, physical activity may be perceived as an important component of a healthy lifestyle, and students are likely to be influenced and encouraged by their peers to actively engage in physical activities [ 86 , 87 ]; (3) Providing exercise information and resources [ 88 ]. Mobile social media platforms typically offer abundant exercise information and resources, including exercise training videos, fitness plans, and sports equipment purchases. Through these information and resources, students can acquire knowledge and skills about physical activities, understand different ways and methods of exercise, thereby increasing their likelihood of participating in physical activities [ 89 ]; (4) Promoting self-monitoring and goal setting [ 90 , 91 ]. Mobile social media can be a tool for students to self-monitor and set goals. By sharing their exercise progress and goals on social media, students can develop a sense of responsibility for themselves, improve self-management skills, and better adhere to physical activities; (5) The positive impact of physical activity. Physical activity has been proven to be beneficial for mental health [ 92 ], such as releasing stress and tension from the body, improving physical and mental fitness. Chinese international students can improve not only their physical health but also their confidence and positive emotions through participation in physical activities, thereby promoting the improvement of mental health status.

Mobile social media may have a positive impact on the mental health status of Chinese international students through body image and physical activity level for the following reasons:

(1) Positive motivational effect of body image: Body images on mobile social media platforms often portray positive, healthy, and energetic images, which may motivate individuals to actively engage in physical activity to achieve or maintain a body image that aligns with societal aesthetics and expectations [ 93 ]; (2) Social pressure and identity needs: Individuals on mobile social media platforms may receive affirmation and approval from others, leading to social pressure and identity needs that prompt individuals to adopt behaviors that conform to societal expectations, including actively participating in physical activity to maintain or improve body image; (3) Promotion of healthy lifestyles on social media: Information and promotions about healthy lifestyles, including fitness exercises and outdoor activities, are often disseminated on mobile social media platforms [ 94 ]. This dissemination of information may spark individuals' interest and motivation, encouraging them to actively engage in physical activity to achieve both physical health and a positive body image; (4) Motivational and encouraging role of social media: On social media platforms, individuals may receive motivation and encouragement from others [ 95 ], such as friends, idols, or influencers sharing their experiences and achievements in physical activity. These positive motivations and encouragements can inspire individuals to participate in physical activity [ 96 , 97 ].

Mobile social media may positively influence the mental health status of Chinese international students through health goal setting and physical activity level for the following reasons:

(1) The motivating effect of goal setting: Mobile social media may provide information and inspiration for health goal setting, such as fitness enthusiasts sharing their experiences in setting and achieving health goals, or promoting healthy lifestyle campaigns. This information and activity can inspire international students and motivate them to set and pursue health goals, including increasing physical activity; (2) Social pressure and need for recognition: On social media, international students may receive encouragement and recognition from others [ 98 ], such as friends, idols, or influencers on social networks sharing their experiences and achievements in setting health goals. These positive encouragement and recognition can stimulate the students' enthusiasm and prompt them to set and pursue health goals, which may include increasing physical activity; (3) The guiding role of goal setting: Health goal setting can help international students clarify the health goals they want to achieve and develop corresponding plans and actions [ 99 ]. On mobile social media, students can access relevant guidance and resources for health goal setting, enabling them to engage in physical activity more purposefully, thereby enhancing the effectiveness and sustainability of their activities [ 100 ]; (4) Social support and cooperative competition: On social media, students can share their health goal setting and progress with others and receive support and encouragement [ 101 ]. Additionally, engaging in cooperative competition or collective challenges with others can also promote the participation of Chinese international students in physical activity, enhancing their enthusiasm and motivation.

Practical implications

This study provides valuable insights for interventions aimed at enhancing the mental health of Chinese international students. Recommendations include promoting positive body image on mobile social media to encourage physical activity participation and facilitating health goal setting through relevant resources. Additionally, schools and communities can organize sports activities and utilize mobile social media for promotion to bolster student engagement.

Theoretical implications

The findings shed light on how mobile social media influences the mental health of Chinese international students, enriching our understanding of this relationship. This study offers theoretical support for mental health interventions and contributes to the fields of social psychology, health communication, and cross-cultural psychology, fostering academic exchange and collaboration.

Research limitations

Although this study yielded meaningful results, it also has some limitations. First, this study employed a cross-sectional design. Conducting a longitudinal study would provide deeper insights into the mechanisms by which mobile social media affects the mental health of Chinese international students. Second, the findings are based on data from Chinese international students, and it remains unclear whether these results are applicable to international students from other countries. Future research could collect data from international students in various countries to enhance the generalizability of the findings.

Mobile social media can indirectly impact the mental health status of international students by fostering psychological resilience and encouraging physical activity. Through avenues such as providing positive motivation and support, establishing social networks for healthy lifestyles, offering exercise information and resources, and promoting self-monitoring and goal setting, social media can help students overcome barriers to exercise and engage more effectively in physical activity, thereby enhancing their mental health status. Additionally, mobile social media positively influences the physical activity level of Chinese international students by shaping positive body images, providing information, motivation, and social pressure for healthy lifestyles, thereby contributing to their improved mental health status. Moreover, mobile social media positively affects the physical activity level of Chinese international students by providing inspiration, guidance, social support, and recognition for health goal setting, ultimately leading to enhanced mental health status.

Educational institutions should actively collaborate with social media platforms to promote the mental health and physical activity of international students. Creating dedicated health communities or pages on social media can provide positive motivation, support, and information, thereby encouraging students to adopt healthy lifestyles. Additionally, providing information and resources related to physical activity, such as fitness classes and workout videos, can make it easier for students to find inspiration and guidance for exercise. Social media activities that encourage students to set health goals and offer self-monitoring tools and guidance can help them effectively achieve these goals. Furthermore, enhancing the promotion of positive body images can inspire students to engage in physical activity through positive body image portrayals. Encouraging students to share their exercise experiences, achievements, and goals with each other can help build a supportive and encouraging social network. Implementing these measures will contribute to improving the overall health and adaptability of international students.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Mo YN, Ko KS. International Chinese students’ experiences of participating in mandala-based art therapy in Korea: a phenomenological study. Front Psychol. 2023;14:1263754. https://doi.org/10.3389/fpsyg.2023.1263754 .

Article PubMed PubMed Central Google Scholar

Amoah PA, Mok EWC. COVID-19 and well-being of non-local students: Implications for international higher education governance. High Educ Pol. 2022;35(3):651–72. https://doi.org/10.1057/s41307-022-00270-4 .

Article Google Scholar

Shu W, Xing BY, Ruan WX, Gao LY, Miao QF. Exploring the relationship between professional identity and psychological resilience of organ donation coordinators in Zhejiang Province (China). Front Public Health. 2021;9: 659871. https://doi.org/10.3389/fpubh.2021.659871 .

Anyan F, Hjemdal O. Adolescent stress and symptoms of anxiety and depression: Resilience explains and differentiates the relationships. J Affect Disord. 2016;203:213–20. https://doi.org/10.1016/j.jad.2016.05.031 .

Article PubMed Google Scholar

Waghachavare VB, Quraishi SR, Dhumale GB, Gore AD. A cross-sectional study of correlation of body image anxiety with social phobia and their association with depression in the adolescents from a rural area of Sangli District in India. Int J Prev Med. 2014;5(12):1626.

PubMed PubMed Central Google Scholar

Zhao Z, Zhao S, Wang Q, Zhang Y, Chen C. Effects of physical exercise on mobile phone addiction in college students: The chain mediation effect of psychological resilience and perceived stress. Int J Environ Res Public Health. 2022;19(23):15679. https://doi.org/10.3390/ijerph192315679 .

Strasser B, Fuchs D. Role of physical activity and diet on mood, behavior, and cognition. Neurol Psychiatry Brain Res. 2015;21(3):118–26.

Macquarie dictionary online. 6th ed. Macquarie Dictionary Publishers, an imprint of Pan Macmillan Australia Pty Ltd, 2003. Available from https://www.macquariedictionary.com.au/ (accessed 15 August 2010).

Chen F, Fan W, Li Y. Influence of psychological supervision on athletes’ compliance, mental elasticity characteristics and acute stress disorder in traumatic fracture rehabilitation training. Iran J Public Health. 2022;51(1):133. https://doi.org/10.18502/ijph.v51i1.8304 .

Ungar M, Theron L. Resilience and mental health: How multisystemic processes contribute to positive outcomes. Lancet Psychiatr. 2020;7(5):441–8. https://doi.org/10.1016/S2215-0366(19)30434-1 .

Asghar MZ, Arif S, Barbera E, Seitamaa-Hakkarainen P, Kocayoruk E. Support through Social Media and Online Class Participation to Enhance Psychological Resilience. Int J Environ Res Public Health. 2021;18(22):11962.

Mu W, Zhu D, Wang Y, Li F, Ye L, Wang K, Zhou M. Three-Wave Longitudinal Survey on the Relationship between Neuroticism and Depressive Symptoms of First-Year College Students: Addictive Use of Social Media as a Moderated Mediator. Int J Environ Res Public Health. 2020;17(17):6074.

Forbes HR. Is COVID-19 social media’s levelling up moment? April 24. 2020.

Google Scholar

Zautra AJ, Hall JS, Murray KE. Resilience: A new definition of health for people andcommunities. Reich JW, Zautra AJ: Hall JS Handbook of adult resilience. New York, the Guilford Pres; 2010.

Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Bauman A. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation. 2007;116(9):1081. https://doi.org/10.1161/CIRCULATIONAHA.107.185649 .

Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, Oja P. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exercise. 2003;35(8):1381–95.

Quinn L, Morgan D. From disease to health: physical therapy health promotion practices for secondary prevention in adult and pediatric neurologic populations. J Neurol Phys Ther. 2017;41(Suppl 3 IV STEP Spec Iss):S46. https://doi.org/10.1097/NPT.0000000000000166 .

Budisavljevic A, Kelemenic-Drazin R, Silovski T, Plestina S, Plavetic ND. Correlation between psychological resilience and burnout syndrome in oncologists amid the Covid-19 pandemic. Support Care Cancer. 2023;31(4):207. https://doi.org/10.1007/s00520-023-07660-3 .

Ozdemir NK. Qualitative exploration of career adaptability of Turkish adolescents. Aust J Career Dev. 2019;28(2):83–91. https://doi.org/10.1177/1038416218821451 .

Zhang N, Huang J, Yao J. Athletes’ self-compassion and emotional resilience to failure: the mediating role of vagal reactivity. Front Psychol. 2023;14:1192265. https://doi.org/10.3389/fpsyg.2023.1192265 .

Wang T, Gao D. How does psychological resilience influence subjective career success of Internet marketers in china? A moderated mediation model. Front Psychol. 2022;13: 921721. https://doi.org/10.3389/fpsyg.2022.921721 .

Crocker PR. A confirmatory factor analysis of the positive affect negative affect schedule (PANAS) with a youth sport sample. J Sport Exerc Psychol. 1997. https://doi.org/10.1123/jsep.19.1.91 .

Seligman ME, Nolen-Hoeksema S, Thornton N, Thornton KM. Explanatory style as a mechanism of disappointing athletic performance. Psychol Sci. 1990;1(2):143–6. https://doi.org/10.1111/j.1467-9280.1990.tb00084.x .

Cash TF, Fleming EC, Alindogan J, Steadman L, Whitehead A. Beyond body image as a trait: The development and validation of the Body Image States Scale. Eat Disord. 2002;10(2):103–13. https://doi.org/10.1080/10640260290081678 .

Miragall M, Etchemendy E, Cebolla A, Rodríguez V, Medrano C, Baños RM. Expand your body when you look at yourself: The role of the posture in a mirror exposure task. PLoS ONE. 2018;13(3): e0194686. https://doi.org/10.1371/journal.pone.0194686 .

Mabe AG, Forney KJ, Keel PK. Do you “like” my photo? Facebook use maintains eating disorder risk. Int J Eat Disord. 2014;47(5):516–23. https://doi.org/10.1002/eat.22254 .

Clark O, Lee MM, Jingree ML, O’Dwyer E, Yue Y, Marrero A, Mattei J. Weight stigma and social media: evidence and public health solutions. Front Nutr. 2021;8:739056. https://doi.org/10.3389/fnut.2021.739056

Carrotte ER, Vella AM, Lim MS. Predictors of “liking” three types of health and fitness-related content on social media: a cross-sectional study. J Med Internet Res. 2015;17(8): e205. https://doi.org/10.2196/jmir.4803 .

Lucibello KM, Vani MF, Koulanova A, DeJonge ML, Ashdown-Franks G, Sabiston CM. # quarantine15: A content analysis of Instagram posts during COVID-19. Body Image. 2021;38:148–56. https://doi.org/10.1016/j.bodyim.2021.04.002 .

Shan Y, Chen KJ, Lin JS. When social media influencers endorse brands: The effects of self-influencer congruence, parasocial identification, and perceived endorser motive. Int J Advert. 2020;39(5):590–610. https://doi.org/10.1080/02650487.2019.1678322 .

Michael SL, Wentzel K, Elliott MN, Dittus PJ, Kanouse DE, Wallander JL, Schuster MA. Parental and peer factors associated with body image discrepancy among fifth-grade boys and girls. J Youth Adoles. 2014;43:15–29. https://doi.org/10.1007/s10964-012-9899-8 .

Pearl RL, Puhl RM, Brownell KD. Positive media portrayals of obese persons: impact on attitudes and image preferences. Health Psychol. 2012;31(6):821. https://doi.org/10.1037/a0027189 .

Pearl RL, Puhl RM. Weight bias internalization and health: a systematic review. Obes Rev. 2018;19(8):1141–63. https://doi.org/10.1111/obr.12701 .

Krupa-Kotara K, Grajek M, Rozmiarek M, Malchrowicz-Mośko E, Staśkiewicz W, León-Guereño P, Castañeda-Babarro A. The Role of Social Media in Internalizing Body Knowledge—A Cross-Sectional Study among Women with Different Food Preferences. Int J Environ Res Public Health. 2023;20(3):2069. https://doi.org/10.3390/ijerph20032069 .

Mills JS, Musto S, Williams L, Tiggemann M. “Selfie” harm: Effects on mood and body image in young women. Body Image. 2018;27:86–92. https://doi.org/10.1016/j.bodyim.2018.08.007 .

White J, Halliwell E. Examination of a sociocultural model of excessive exercise among male and female adolescents. Body Image. 2010;7(3):227–33. https://doi.org/10.1016/j.bodyim.2010.02.002 .

Boepple L, Ata RN, Rum R, Thompson JK. Strong is the new skinny: A content analysis of fitspiration websites. Body Image. 2016;17:132–5. https://doi.org/10.1016/j.bodyim.2016.03.001 .

Wick MR, Harriger JA. A content analysis of thinspiration images and text posts on Tumblr. Body Image. 2018;24:13–6. https://doi.org/10.1016/j.bodyim.2017.11.005 .

Raglin JS. Exercise and mental health: Beneficial and detrimental effects. Sports Med. 1990;9:323–9. https://doi.org/10.2165/00007256-199009060-00001 .

Castillo-Rodriguez A, Hernández-Mendo A, Alvero-Cruz JR, Onetti-Onetti W, Fernández-García JC. Level of precompetitive anxiety and self-confidence in high level Padel players. Front Psychol. 2022;13: 890828. https://doi.org/10.3389/fpsyg.2022.890828 .

Bauer AG, Berkley-Patton J, Bowe-Thompson C, Ruhland-Petty T, Berman M, Lister S, Christensen K. Peer Reviewed: Do Black Women’s Religious Beliefs About Body Image Influence Their Confidence in Their Ability to Lose Weight?. Preventing chronic disease. 2017;14. https://doi.org/10.5888/pcd14.170153

Firoozjah MH, Homayouni A, Nazari S, Pourazar M. Effect of motor skills development on psychological and social traits of students of mazandaran province during the covid-19 pandemics. Sport Sciences for Health. 2023;19(1):301–8. https://doi.org/10.1007/s11332-022-00962-9 .

Cappe E, Bolduc M, Poirier N, Popa-Roch MA, Boujut E. Teaching students with Autism Spectrum Disorder across various educational settings: The factors involved in burnout. Teach Teach Educ. 2017;67:498–508.

Li AW, Goldsmith CAW. The effects of yoga on anxiety and stress. Altern Med Rev. 2012;17(1):21.

PubMed Google Scholar

Halliwell E, Jarman H, Tylka TL, Slater A. Evaluating the impact of a brief yoga intervention on preadolescents’ body image and mood. Body Image. 2018;27:196–201.

Cash TF, Fleming EC. The impact of body image experiences: development of the body image quality of life inventory. Int J Eat Disord. 2002;31(4):455–60. https://doi.org/10.1002/eat.10033 .

Cash TF, Jakatdar TA, Williams EF. The Body Image Quality of Life Inventory: Further validation with college men and women. Body Image. 2004;1(3):279–87. https://doi.org/10.1016/S1740-1445(03)00023-8 .

Yu UJ. Deconstructing college students’ perceptions of thin-idealized versus nonidealized media images on body dissatisfaction and advertising effectiveness. Cloth Text Res J. 2014;32(3):153–69. https://doi.org/10.1177/0887302X14525850 .

Dong BL, Mao LJ. Influence of exercise involvement, exercise commitment and subjective experience on exercise habits of undergraduates: A mixed model. J Tianjin Univ Sport. 2018;33:492–9.

He L, Li Y, Chen Z. The effect of subjective exercise experience on exercise behavior and amount of exercise in children and adolescents: the mediating effect of exercise commitment. Int J Environ Res Public Health. 2022;19(17):10829. https://doi.org/10.3390/ijerph191710829 .

Ide-Okochi A, He M, Murayama H, Samiso T, Yoshinaga N. Noncompliance with Hypertension Treatment and Related Factors among Kumamoto Earthquake Victims Who Experienced the COVID-19 Pandemic during Postearthquake Recovery Period. Int J Environ Res Public Health. 2023;20(6):5203. https://doi.org/10.3390/ijerph20065203 .

Arnoux PH, Xu A, Boyette N, Mahmud J, Akkiraju R, Sinha V. 25 tweets to know you: a new model to predict personality with social media. In: Proceedings of the international AAAI conference on web and social media. Vol. 11, No. 1. 2017. pp. 472–475.

Sue J, Conor L, Laura D, Andrew G, Shaun L. "I can't get no sleep": discussing #insomnia on Twitter. The SIGCHI Conference on Human Factors in Computing Systems; May 5–12, 2012; New York, NY, USA. ACM; 2012.

Chen J, Wang Y. Social media use for health purposes: systematic review. J Med Internet Res. 2021;23(5):e17917. https://doi.org/10.2196/17917 .

Herbert C, Marschin V, Erb B, Meißner D, Aufheimer M, Bösch C. Are you willing to self-disclose for science? Effects of privacy awareness and trust in privacy on self-disclosure of personal and health data in online scientific studies—an experimental study. Frontiers in big Data. 2021;4: 763196. https://doi.org/10.3389/fdata.2021.763196 .

Jiang S, Ngien A. The effects of Instagram use, social comparison, and self-esteem on social anxiety: A survey study in Singapore. Soc Media Soc. 2020;6(2):205.

Kaholokula, J. K. A., Look, M. A., Wills, T. A., de Silva, M., Mabellos, T., Seto, T. B., … & Kā-HOLO Project. Kā-HOLO Project: a protocol for a randomized controlled trial of a native cultural dance program for cardiovascular disease prevention in Native Hawaiians. BMC Public Health. 2017;17:1–12. https://doi.org/10.1186/s12889-017-4246-3 .

Sokolova K, Perez C. You follow fitness influencers on YouTube. But do you actually exercise? How parasocial relationships, and watching fitness influencers, relate to intentions to exercise. J Retailing Consum Serv. 2021;58:102276.

Durau J, Diehl S, Terlutter R. Motivate me to exercise with you: The effects of social media fitness influencers on users’ intentions to engage in physical activity and the role of user gender. Digital Health. 2022;8:20552076221102770. https://doi.org/10.1177/20552076221102769 .

Gao, M. (2022). [Retracted] Experimental Study on the Effect of Taijiquan on the Physical Function and Mental Health of College Students. Applied Bionics Biomech. 2022(1):7346946.

Beatty C, Malik T, Meheli S, Sinha C. Evaluating the therapeutic alliance with a free-text CBT conversational agent (Wysa): a mixed-methods study. Frontiers in Digital Health. 2022;4: 847991. https://doi.org/10.3389/fdgth.2022.847991 .

Zhu Y, Holden M. Housing and psychosocial well-being during the COVID-19 pandemic. Habitat Int. 2023;135: 102812. https://doi.org/10.1016/j.habitatint.2023.102812 .

World Health Organization. World mental health report: Transforming mental health for all. 2022.

Lynch WJ, Peterson AB, Sanchez V, Abel J, Smith MA. Exercise as a novel treatment for drug addiction: a neurobiological and stage-dependent hypothesis. Neurosci Biobehav Rev. 2013;37(8):1622–44. https://doi.org/10.1016/j.neubiorev.2013.06.011 .

Meeusen R, Piacentini MF, Van Den Eynde S, Magnus L, De Meirleir K. Exercise performance is not influenced by a 5-HT reuptake inhibitor. Int J Sports Med. 2001;22(05):329–36. https://doi.org/10.1055/s-2001-15648 .

Basso JC, Suzuki WA. The effects of acute exercise on mood, cognition, neurophysiology, and neurochemical pathways: A review. Brain Plasticity. 2017;2(2):127–52. https://doi.org/10.3233/BPL-160040 .

Puce L, Marinelli L, Girtler NG, Pallecchi I, Mori L, Simonini M, Trompetto C. Self-perceived psychophysical well-being of young competitive swimmers with physical or intellectual impairment. Percept Mot Skills. 2019;126(5):862–85. https://doi.org/10.1177/0031512519865849 .

Price PC, Jhangiani R, Chiang ICA. Research methods in psychology. BCCampus. 2015.

Tehranineshat B, Rakhshan M, Torabizadeh C, Fararouei M, Gillespie M. Development and assessment of the psychometric properties of a compassionate care questionnaire for nurses. BMC Nurs. 2021;20(1):190.

Guest G, Bunce AE, Johnson L. How Many Interviews Are Enough? Field Methods. 2006;18:59–82.

Wang W, Lei L. The structure and characteristics of adolescents’ mobile social media usage. Psychol Res. 2015;8(5):57–63.

Gardiner E, Mâsse LC, Iarocci G. A psychometric study of the Family Resilience Assessment Scale among families of children with autism spectrum disorder. Health Qual Life Outcomes. 2019;17:1–10.

Hopwood P, Fletcher I, Lee A, Al Ghazal S. A body image scale for use with cancer patients. Eur J Cancer. 2001;37(2):189–97.

Ezparza-Del Villar OA, Montañez-Alvarado P, Gutiérrez-Vega M, Carrillo-Saucedo IC, Gurrola-Peña GM, Ruvalcaba-Romero NA, García-Sánchez MD, Ochoa-Alcaraz SG. Factor structure and internal reliability of an exercise health belief model scale in a Mexican population. BMC Public Health. 2017;17:1–9.

Sechrist KR, Walker SN, Pender NJ. Development and psychometric evaluation of the exercise benefits/barriers scale. Res Nurs Health. 1987;10(6):357–65.

Ryff CD. Happiness is everything, or is it? Explorations on the meaning of psychological well-being. J Pers Soc Psychol. 1989;57:1069–81.

Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine. 2000;25(24):3186–91.

Brislin RW. Back-translation for cross-cultural research. J Cross Cult Psychol. 1970;1(3):185–216.

Yu DS, Lee DT, Woo J. Issues and challenges of instrument translation. West J Nurs Res. 2004;26(3):307–20.

Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879.

Joreskog KG, Sorbom D. LISREL7: A guide to the pro gram and Applicatio n[M]. Chicago, IL: SPSS Inc; 1989.

Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate Data Analysis. 5th ed. Upper Saddle River: Prentice Hall International; 1998. p. 1–663.

Cao, C., Meng, Q., & Zhang, H. A longitudinal examination of WeChat usage intensity, behavioral engagement, and cross-cultural adjustment among international students in China. Higher Educ. 2023;1–23. https://doi.org/10.1007/s10734-023-01029-5

Li X. Weaving social media into a business proposal project. Bus Commun Q. 2012;75(1):68–75.

Alalwan N. Actual use of social media for engagement to enhance students’ learning. Educ Inf Technol. 2022;27(7):9767–89.

Kohl HW, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, Kahlmeier S. The pandemic of physical inactivity: global action for public health. The lancet. 2012;380(9838):294–305.

Gesualdo C, Pinquart M, Chamorro Coneo A, Mebarak Chams M. Health Behaviors of Colombian First-Semester University Students in Association with Behaviors of Close Social Ties, Living Arrangement, and Time Spent with Peers. Int J Environ Res Public Health. 2023;20(7):5370.

Lustria MLA. Can interactivity make a difference? Effects of interactivity on the comprehension of and attitudes toward online health content. J Am Soc Inform Sci Technol. 2007;58(6):766–76.

Rahman S, Ramakrishnan T, Ngamassi L. Impact of social media use on student satisfaction in Higher Education. High Educ Q. 2020;74(3):304–19.

Varnfield M, Karunanithi M, Lee CK, Honeyman E, Arnold D, Ding H, Walters DL. Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: results from a randomised controlled trial. Heart. 2014;100(22):1770–9.

Sangster J, Furber S, Allman-Farinelli M, Phongsavan P, Redfern J, Haas M, Bauman A. Effectiveness of a pedometer-based telephone coaching program on weight and physical activity for people referred to a cardiac rehabilitation program. J Cardiopulm Rehabil Prev. 2015;35(2):124–9. https://doi.org/10.1097/HCR.0000000000000082 .

Engelen L. Does active design influence activity, sitting, wellbeing and productivity in the workplace? A systematic review. Int J Environ Res Public Health. 2020;17(24):9228.

Antonucci TC, Ashton-Miller JA, Brant J, Falk EB, Halter JB, Hamdemir L, ... Webster NJ. The right to move: A multidisciplinary lifespan conceptual framework. Curr Gerontol Geriatr Res. 2012(1):873937.

Puigdomenech E, García X, Valderas JM, Eiroa-Orosa FJ, Fernández-Villa T, Molina AJ, ... Espallargues M. Relationship between depression and the use of mobile technologies and social media among adolescents: Umbrella review. Relationship between depression and the use of mobile technologies and social media among adolescents: Umbrella review. 2020;22(8):1–10.

Naidoo U, Flack PS, Rathiram V, Moodley L, Karrim SB, Buthelezi N, Ndanganeni V. Speech–language therapy educator reflections on the planning and implementation of education and training during the COVID-19 pandemic. South African Journal of Communication Disorders. 2022;69(2):908.

Shen Y, Phan N, Xiao X, Jin R, Sun J, Piniewski B, Dou D. Dynamic socialized gaussian process models for human behavior prediction in a health social network. Knowl Inf Syst. 2016;49:455–79.

Al-Adwan AS, Albelbisi NA, Aladwan SH, Horani O, Al-Madadha A, Al Khasawneh MH. Investigating the impact of social media use on student’s perception of academic performance in higher education: evidence from Jordan. Journal of Information Technology Education: Research. 2020;19:953–75.

Zhang CA, Tang L, Liu Z. How social media usage affects psychological and subjective well-being: testing a moderated mediation model. BMC psychology. 2023;11(1):286. https://doi.org/10.1186/s40359-023-01311-2 .

Addis BA, Eyowas FA, Bogale TW, Aynalem ZB, Guadie HA. “Time wasted by health professionals is time not invested in patients”: time management practice and associated factors among health professionals at public hospitals in Bahir Dar, Ethiopia: a multicenter mixed method study. Front Public Health. 2023;11:1159275. https://doi.org/10.3389/fpubh.2023.1159275 .

Bennett Ed. Futurescan 2001: Healthcare Trends and Implications 2011–2016. Chicago, IL, USA: Society for Healthcare Strategy and Market Development; 2011. Social media and hospitals: from trendy to essential; pp. 43–48.

Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C. A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res. 2013;15(4):e1933. https://doi.org/10.2196/jmir.1933 .

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C.M: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, visualization. C.M: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, visualization. S.Z: Conceptualization, validation, formal analysis, investigation, data curation, writing—original draft preparation, writing—review and editing, supervision. S.Z: Conceptualization, validation, formal analysis, investigation, data curation, writing—original draft preparation, writing—review and editing, supervision.

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Miao, C., Zhang, S. The effect of mobile social media on the mental health status of Chinese international students: an empirical study on the chain mediation effect. BMC Psychol 12 , 411 (2024). https://doi.org/10.1186/s40359-024-01915-2

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Introduction: What is Empirical Research?

Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief.

Key characteristics to look for:

Specific research questions to be answered
Definition of the population, behavior, or phenomena being studied
Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools used in the present study
Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

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Empirical dynamics of traffic moving jams: Insights from Kerner's three-phase traffic theory

Chen, Qiucheng
Zhu, Shunying
Chen, Guojun

This study explores the dynamics of moving traffic jams at consecutive merging bottlenecks using empirical trajectory data from China's expressway. It builds on existing phase-transition point methods of Kerner's three-phase traffic theory by incorporating changepoint detection to distinguish between wide moving jams (J) and synchronized flow (S) within the expanded general congested pattern (EGP). The findings indicate that the dissolution of J in the outer lane precedes and influences the disintegration of J in the adjacent inner lane. To investigate this phenomenon, a Bayesian network (BN) was employed for causal analysis to identify key factors. Additionally, wavelet-based energy analysis was conducted on individual vehicles to examine fluctuations triggered by lane-changing maneuvers (LCMs) and their impact on the dynamics of moving jams. The results reveal that traffic flow at downstream merging bottlenecks and the lane-changing rate are crucial for J disintegration. Furthermore, the J propagated beyond the upstream merging bottleneck becomes more unstable due to the compounded effects of consecutive merging bottlenecks. The study also shows that LCMs affect the propagation of moving jams in EGP, and that the induced fluctuations may persist even after phase transitions occur. Consistent with earlier studies on German and US highways, empirical data from China show that non-regular moving jam dynamics in congested traffic, caused by LCMs and vehicle merging at adjacent bottlenecks, can be explained by complex sequences of J->S and S->J transitions. These sequences, which determine the empirical non-regular moving jam dynamics, exhibit qualitatively the same spatiotemporal features observed in microscopic single-vehicle data from German and US highways. Applying the phase-transition points method to empirical vehicle trajectories on China's expressways effectively reveals the spatiotemporal features of non-regular moving jam dynamics (dynamics of traffic oscillations) and traffic congestion. This research provides quantitative insights into the dynamics of traffic moving jams and the impact of LCMs, offering new strategies for mitigating congestion at complex traffic bottlenecks.

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Bayesian network;
Wavelet analysis;
Three-phase traffic theory;
Traffic moving jams

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Empirical Research

Empirical research is the process of testing a hypothesis using experimentation, direct or indirect observation and experience.

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Definition of Research
Research Basics
What is Research?
Steps of the Scientific Method
Purpose of Research

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1 Research Basics
2.1 What is Research?
2.2 What is the Scientific Method?
2.3 Empirical Research
3.1 Definition of Research
3.2 Definition of the Scientific Method
3.3 Definition of Science
4 Steps of the Scientific Method
5 Scientific Elements
6 Aims of Research
7 Purpose of Research
8 Science Misconceptions

The word empirical describes any information gained by experience, observation, or experiment . One of the central tenets of the scientific method is that evidence must be empirical, i.e. based on evidence observable to the senses.

Philosophically, empiricism defines a way of gathering knowledge by direct observation and experience rather than through logic or reason alone (in other words, by rationality). In the scientific paradigm the term refers to the use of hypotheses that can be tested using observation and experiment. In other words, it is the practical application of experience via formalized experiments.

Empirical data is produced by experiment and observation, and can be either quantitative or qualitative.

Objectives of Empirical Research

Empirical research is informed by observation, but goes far beyond it. Observations alone are merely observations. What constitutes empirical research is the scientist’s ability to formally operationalize those observations using testable research questions.

In well-conducted research, observations about the natural world are cemented in a specific research question or hypothesis. The observer can make sense of this information by recording results quantitatively or qualitatively.

Techniques will vary according to the field, the context and the aim of the study. For example, qualitative methods are more appropriate for many social science questions and quantitative methods more appropriate for medicine or physics.

However, underlying all empirical research is the attempt to make observations and then answer well-defined questions via the acceptance or rejection of a hypothesis, according to those observations.

Empirical research can be thought of as a more structured way of asking a question – and testing it. Conjecture, opinion, rational argument or anything belonging to the metaphysical or abstract realm are also valid ways of finding knowledge. Empiricism, however, is grounded in the “real world” of the observations given by our senses.

Reasons for Using Empirical Research Methods

Science in general and empiricism specifically attempts to establish a body of knowledge about the natural world. The standards of empiricism exist to reduce any threats to the validity of results obtained by empirical experiments. For example, scientists take great care to remove bias, expectation and opinion from the matter in question and focus only on what can be empirically supported.

By continually grounding all enquiry in what can be repeatedly backed up with evidence, science advances human knowledge one testable hypothesis at a time. The standards of empirical research – falsifiability, reproducibility – mean that over time empirical research is self-correcting and cumulative.

Eventually, empirical evidence forms over-arching theories, which themselves can undergo change and refinement according to our questioning. Several types of designs have been used by researchers, depending on the phenomena they are interested in.

The Scientific Cycle

Empirical research is not the only way to obtain knowledge about the world, however. While many students of science believe that “empirical scientific methods” and “science” are basically the same thing, the truth is that empiricism is just one of many tools in a scientist’s inventory.

In practice, empirical methods are commonly used together with non-empirical methods, and qualitative and quantitative methods produce richer data when combined. The scientific method can be thought of as a cycle, consisting of the following stages:

Observation Observation involves collecting and organizing empirical data. For example, a biologist may notice that individual birds of the same species will not migrate some years, but will during other years. The biologist also notices that on the years they migrate, the birds appear to be bigger in size. He also knows that migration is physiologically very demanding on a bird.
Induction Induction is then used to form a hypothesis . It is the process of reaching a conclusion by considering whether a collection of broader premises supports a specific claim. For example, taking the above observations and what is already known in the field of migratory bird research, the biologist may ask a question: “is sufficiently high body weight associated with the choice to migrate each year?” He could assume that it is and stop there, but this is mere conjecture, and not science. Instead he finds a way to test his hypothesis. He devises an experiment where he tags and weighs a population of birds and watches to observe whether they migrate or not.
Deduction Deduct ion relies on logic and rationality to come to specific conclusions given general premises. Deduction allows a scientist to craft the internal logic of his experimental design. For example, the argument in the biologist’s experiment is: if high bird weight predicts migration, then I would expect to see those birds who I measure at higher weights to migrate, and those who do not to opt out of migration. If I don’t see that birds with higher weight migrate more often than those who don’t, I can conclude that bird weight and migration are not connected after all.”
Testing Test the hypothesis entails returning to empirical methods to put the hypothesis to the test. The biologist, after designing his experiment, conducting it and obtaining the results, now has to make sense of the data. Here, he can use statistical methods to determine the significance of any relationship he sees, and interpret his results. If he finds that almost every higher weight bird ends up migrating, he has found support (not proof) for his hypothesis that weight and migration are connected.
Evaluation An often-forgotten step of the research process is to reflect and appraise the process. Here, interpretations are offered and the results set within a broader context. Scientists are also encouraged to consider the limitations of their research and suggest avenues for others to pick up where they left off.

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COMMENTS

empirical research
Many translated example sentences containing "empirical research" - French-English dictionary and search engine for French translations.
La recherche empirique : méthodes et pratiques
The author attributes this weakness to the widening gap between empirical and theoretical research since the last fifty years in these disciplines. Having described two obstacles to cumulativity currently encountered, the chapter shows how the classic inductive method combines the empirical and theoretical approaches guaranteeing natural ...
Empirical research
A scientist gathering data for her research. Empirical research is research using empirical evidence. It is also a way of gaining knowledge by means of direct and indirect observation or experience. Empiricism values some research more than other kinds. Empirical evidence (the record of one's direct observations or experiences) can be analyzed ...
PDF Society for Linguistic Anthropology
detail empirical research aimed at assessing the implications of having one language as opposed to another. that is, the classic linguistic relativity proposal that structural differences among languages in their categories of meaning have an impact on thought. The discussion indicates some of the reasons for the paucity or empirical research,
French translation of 'empirical research'
French Translation of "EMPIRICAL RESEARCH" | The official Collins English-French Dictionary online. Over 100,000 French translations of English words and phrases.
Intersectionality and Gender-Based Violence: An Empirical Multi-Level
The purpose of this article is thus to provide empirical evidence on gender-based violence in higher education and research. It is based on the largest cross-cultural survey of its kind in Europe, including 46 research performing organization across 15 countries carried out between January 2022 and May 2022 (Lipinsky et al., 2022). The survey ...
Empirical Studies in Translation: Methodological …
On a conceptual level (when defining the problem and formulating testable theoretic hypotheses empirically and validating these hypotheses through the results obtained) despite inherent difficulties, in Translation Studies research there are still guidelines and procedures to follow in common with all scientific fields that have opted for an empirical approach to solving problems.
EMPIRICAL in French
EMPIRICAL translate: empirique. Learn more in the Cambridge English-French Dictionary.
Empirical Research
Mcleod noted that empirical research, as a tool for investigation within the field of psychology, began in the 1800s with behaviorists who assert that psychology is a scientific discipline, which requires scientific principles in investigating human behavior, stressed its use.They further claimed that there are unseen factors that influence human behavior.
The measurement, level, and influence of resource allocation ...
Li Q, Zhang L (2019) Research on the influence of internet technology popularization on the development efficiency of agricultural insurance—empirical analysis based on SBM-Tobit model. Rural ...
empirical
empirical adj. (from experience) empirique adj. Andy has empirical knowledge of medicine; he was a nurse for two years. Andy a des connaissances empiriques en médecine ; il a été infirmier pendant deux ans. empirical adj. (practical, not theoretical) empirique adj. Empirical research shows that the method works.
Empirical research in clinical ethics: The 'committed researcher
Abstract. After the 'empirical turn' in bioethics, few specific approaches have been developed for doing clinical ethics research in close connection with clinical decision-making on a daily basis. In this paper we describe the 'committed researcher' approach to research in clinical ethics that we have developed over the years.
What is Empirical Research? Definition, Methods, Examples
Empirical research is the cornerstone of scientific inquiry, providing a systematic and structured approach to investigating the world around us. It is the process of gathering and analyzing empirical or observable data to test hypotheses, answer research questions, or gain insights into various phenomena.
What Is Qualitative Research? An Overview and Guidelines
This guide explains the focus, rigor, and relevance of qualitative research, highlighting its role in dissecting complex social phenomena and providing in-depth, human-centered insights. The guide ...
Empirical Research: Defining, Identifying, & Finding
Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods). Ruane (2016) (UofM login required) gets at the basic differences in approach between quantitative and qualitative research: Quantitative research -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data ...
Empirical Research: A Comprehensive Guide for Academics
Tips for Empirical Writing. In empirical research, the writing is usually done in research papers, articles, or reports. The empirical writing follows a set structure, and each section has a specific role. Here are some tips for your empirical writing. 7. Define Your Objectives: When you write about your research, start by making your goals clear.
What Is Empirical Research? Definition, Types & Samples in 2024
Empirical research is defined as any study whose conclusions are exclusively derived from concrete, verifiable evidence. The term empirical basically means that it is guided by scientific experimentation and/or evidence. Likewise, a study is empirical when it uses real-world evidence in investigating its assertions.
City University of Seattle Library: Identifying Empirical Research
Identifying Empirical Research Articles. Look for the IMRaD layout in the article to help identify empirical research.Sometimes the sections will be labeled differently, but the content will be similar. Introduction: why the article was written, research question or questions, hypothesis, literature review; Methods: the overall research design and implementation, description of sample ...
EMPIRICAL
EMPIRICAL définition, signification, ce qu'est EMPIRICAL: 1. based on what is experienced or seen rather than on theory: 2. based on what is experienced or…. En savoir plus.
Empirical Research
Strategies for Empirical Research in Writing is a particularly accessible approach to both qualitative and quantitative empirical research methods, helping novices appreciate the value of empirical research in writing while easing their fears about the research process. This comprehensive book covers research methods ranging from traditional ...
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Empirical studies lend credence to his ideas. exp. Des études empiriques ajoutent foi à ses idées. ***. 'empirical' also found in translations in French-English dictionary. traitement probabiliste. n. empirical treatment.
Lack of Risk Management at Insolvency Consulting Companies: An ...
The supplementary aim of the research work is to generate an empirical inventory of the implementation and utilisation of risk management by insolvency advisors. As a sub-goal, the research formulates the presentation of methods and instruments and their implementation for risk minimisation. The empirical stocktaking performed by this research ...
Empirical Research: Definition, Methods, Types and Examples
Steps for conducting empirical research. Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment. Step #1: Define the purpose of the research
What is "Empirical Research"?
Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components: Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous ...
The effect of mobile social media on the mental health status of
Purpose To explore the impact of mobile social media on the psychological well-being of Chinese international students and analyze the mechanisms of influence to enhance their overall psychological health and social interactions in a foreign environment. Methods Convenience sampling was employed, using questionnaires on Mobile Social Media, Psychological Resilience, Body Image, Health Goal ...
Empirical Research in the Social Sciences and Education
Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components: Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous ...
Empirical research in clinical ethics: The 'committed researcher
After the 'empirical turn' in bioethics, few specific approaches have been developed for doing clinical ethics research in close connection with clinical decision-making on a daily basis. In this paper we describe the 'committed researcher' approach to research in clinical ethics that we have developed over the years.
Empirical dynamics of traffic moving jams: Insights from ...
This study explores the dynamics of moving traffic jams at consecutive merging bottlenecks using empirical trajectory data from China's expressway. It builds on existing phase-transition point methods of Kerner's three-phase traffic theory by incorporating changepoint detection to distinguish between wide moving jams (J) and synchronized flow (S) within the expanded general congested pattern ...
Empirical Research
Empirical Research. Empirical research is the process of testing a hypothesis using experimentation, direct or indirect observation and experience. The word empirical describes any information gained by experience, observation, or experiment. One of the central tenets of the scientific method is that evidence must be empirical, i.e. based on ...

La recherche empirique : méthodes et pratiques

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Empirical Studies in Translation: Methodological and Epistemological Questions [1] Études empiriques en traduction : questions de méthodologie et d´épistémologie

Article body

1. The “Scientific Method”: A Phases Model

1.1. The Theoretical-Conceptual Level

1.2. The Methodological Level: Applying Accurate Criteria

1.3. The Aim of Translation Studies Research

2. Transparency in Translation Research Procedures

2.1. Justifying the Relevance of a Study

2.2. Introducing the Referential Framework

2.3. Formulating Well-Defined Hypotheses

2.3.1. Criteria

2.3.2. Three Levels of Hypotheses

2.4. Deciding the Research Focus and the Strategy for Gathering Data

2.5. Defining the Variables of a Study and its Indicators

2.6. Defining the “Universe” of the Study and Extracting a Sample

2.7. Determining the Tools for Gathering Data

2.8. Gathering Data

2.9. Carrying Out a Statistical Analysis of the Data

2.10. Interpreting the Results and Communicating Them to the Scientific Community

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The measurement, level, and influence of resource allocation efficiency in universities: empirical evidence from 13 “double first class” universities in China

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