Again more details may be found in the above mentioned references.
A cohort is a designated/defined group of individuals followed through time often to study incidence of disease in the study group. Examples of sample cohorts may include occupational cohorts, specific groups at risk of a disease or convenience samples (e.g. Nurses or Framingham cohorts). Be careful to distinguish between study, source and target populations.
Cohort studies offer multiple potential advantages: * Can study exposures that are difficult or unthinkable to randomize * Study population often more representative of target pop’l * Allows calculation of incidence rates * Time sequence is generally clear (exposure before outcome) * Efficient as multiple outcomes / exposures can be assessed as new hypothesis are developed over time
Cohort studies can be of 2 formats; 1. Concurrent (prospective) cohort studies: assembled at present time. Advantages: measurement of exposure, outcome, covariates is decided at baseline and can see temporal ordering of exposure and disease. Disadvantages: expensive and time consuming. 2. Historical/non-concurrent/mixed (retrospective) cohort studies: incorporates historical time exposed (at least partially). Advantages: less expensive; linking data registries (e.g. exposure and outcome information). Disadvantages: can only use available information; possibly lower quality measurements; data may not have been collected for research purposes.
In cohort studies, exposed/unexposed groups exist in the source population and are selected by the investigator while in an RCT, a form of closed cohort, treatment/exposure is assigned by the investigator.
Figure 6.5: Observational research design
Figure 6.6: Experimental research design
Practically, the best general approach to achieve valid causal non-experimental designs is to try to emulate the RCT ( Hernan and Robins 2016 ) you would like to do with special attention to the following: * Selection of population * Exposure definition (induction, duration, intensity, cumulative exposures) * Outcome ascertainment with minimization of lost to follow-up
Sometimes it useful to start with the cases! Although important for all research designs, it is obviously essential for case control designs to have an unambiguous, valid case definition preferably using objective and established criteria that avoids any misclassification or other biases. Careful distinction between incident and prevalent cases is also of prime importance. Where the cases are found is a function of the particular research question and setting. Potential sources include hospital registers, vital records (births/deaths), national registries (e.g., for cancer, diabetes) and community clinics.
After case identification, the most important and difficult next step is the selection of the controls. Consideration of a counterfactual model can help operationalize the choice of controls. Controls are drawn from a sample of individuals without the disease, selected from the same reference population that originated the cases and who go through the same selection processes as the cases. Moreover, controls must be individuals who, if they had developed the case disease, would have been included in the case group.
Case control studies may be conducted with an open or closed study population. In a dynamic (open) population, there are two options for selecting controls; i) if the population is in a steady-state, sample randomly from the person-time distribution ii) if not, controls may be selected at the same time as cases occur (i.e., “matched on time”). In a closed study population, there are three options for selecting controls; i) at the end of follow-up ii) at the beginning of follow-up iii) throughout follow-up as cases occur (“matched on time”). Analytically, these distinctions lead to different effect measures, each of which (under various assumptions) parallels an equivalent measure from a full-cohort study.
Figure 6.7: Case control sampling times
The efficiency of the case control design comes from taking a sample, and not all, of the controls. Under that logic, it may be reasonably asked why not take only a sample of the cases? Consider the following example.
MI yes | MI no | |
---|---|---|
Not exposed | 200 | 1800 |
Exposed | 100 | 1900 |
MI yes | MI no | |
---|---|---|
Not exposed | 200 | 180 |
Exposed | 100 | 190 |
MI yes | MI no | |
---|---|---|
Not exposed | 20 | 180 |
Exposed | 10 | 190 |
The OR is 2.11 with 95% CI 1.65 - 2.71. The OR is fairly close to incident risk ratio for the full cohort (RR = 2) since the rare assumption is approximately true, about 10% incidence. If we select only 1/10 of the controls the OR is 2.11 with 95% CI 1.54 - 2.89, a trivial difference. On the other hand, suppose we take a 1/10 sample of the cases, the OR remains unbiased at 2.11 but the 95% CI 0.96 - 4.63 is much larger. It is this lack of precision that mandates the inclusion of all cases in a case / control design. This is easily understood when it is recalled that the standard error of the estimated OR is \[se(\hat{OR}) = \sqrt{\dfrac{1}{a} + \dfrac{1}{b} + \dfrac{1}{c} + \dfrac{1}{d}}\] so the largest component comes from the smallest cell entries and se will be minimized by taking all the cases.
The following figure from ( Knol et al. 2008 ) is a useful summary of the effect measures available from case control studies depending on the nature of the cases (prevalent or incident; level 1), the type of source population (fixed cohort or dynamic population; level 2), the sampling design used to select controls (level 3), and the underlying assumptions (level 4).
Figure 6.8: Effect measures from case control designs
In summary, case control studies have the advantages of being faster to perform and less expensive to conduct than cohort studies but care must be exercised that they, like all study designs, are carefully performed. Proper control selection is essential and must come from the same target population as cases (easiest when performed within an established cohort). Controls must be sampled independently of exposure and there is improved precision with more controls (1,2,3,4) but diminishing returns (SE 0.167, 0.145, 0.138, 0.134). Effect measure precision is improved precision by taking all the cases. Although case control studies are susceptible to recognized biases (Berkson, recall, incidence/prevalence) these can be avoided with necessary care. The routine placement of case-control studies under cohort studies on hierarchies of study designs is not well-founded.
An interesting variant is the case crossover design where each case serves as its own control thereby minimizes confounding for time invariant factors whether observed or unobserved. Exposures must vary over time but have a short induction time, a transient effect (e.g., triggers for heart attacks, asthma episodes) and no cumulative effects. This design is the observational analogue of the crossover randomized trial.
A final variant are case series without any controls. While the early identification of cases may prove sentinel for a new disease (see cases series that lead to first identification of the AIDS epidemic ), the inferential strength of this design is limited due to the lack of any suitable comparator. Moreover arbitrary selection of cases and an embellished narrative can lead to an undervaluing of scientific evidence and great public health danger (see cases series ( Wakefield et al. 1998 ) , later retracted, at the genesis of the vaccine autism falsehood).
Cross sectional studies are most useful for descriptive epidemiology with a primary goal of estimating disease prevalence. As no follow-up is required, cross sectional studies are fast, efficient and can enroll large numbers of participants. However they have little value for causal inference as they provide no information on timing of outcome relative to exposure (temporality) and include only those individuals alive at the time of the study, thereby introducing a prevalence-incidence bias. Due to these limitations, this study design has little value in clinical epidemiology and will not be discussed further.
There are, of course, many variants and other miscellaneous non-experimental designs including difference-in-difference (DID) , regression discontinuity , and quasi-experimental to name but a few.
Conceptually, DID design can be best thought of as a combination of a before & after comparison and a comparison between treated & untreated individuals and are therefore also known as ontrolled before and after studies. These studies minimize bias due to pretreatment differences in outcomes,allow for a flexible control of time invariant confounders and are preferable to an uncontrolled before and after comparison of only treated individuals.
Quasi-experimental designs refer to approaches to effect estimation in which investigators identify (or create) a source of variation in the exposure which is unrelated to the rest of the causal system under study—including the outcome (except through the exposure itself) and the confounders. A classic historical example is John Snow’s cholera work where which household received “dirtier” water from the Southwark and Vauxhall company or “cleaner” water from the Lambeth company was a quasi-random event. The company can thus be seen as an instrumental variable, similar to randomization. Regression discontinuity designs are a special subset of quasi-experimental designs where subjects just above and below a given threshold are essentially identical on all observed and unobserved characteristics yet are arbitrarily assigned different therapies.
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Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton
Non-experimental research is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world).
Most researchers in psychology consider the distinction between experimental and non-experimental research to be an extremely important one. This is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, non-experimental research generally cannot. As we will see, however, this inability to make causal conclusions does not mean that non-experimental research is less important than experimental research. It is simply used in cases where experimental research is not able to be carried out.
As we saw in the last chapter , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable. It stands to reason, therefore, that non-experimental research is appropriate—even necessary—when these conditions are not met. There are many times in which non-experimental research is preferred, including when:
Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question. Recall the three goals of science are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred. If the goal is to describe or to predict, a non-experimental approach is appropriate. But the two approaches can also be used to address the same research question in complementary ways. For example, in Milgram’s original (non-experimental) obedience study, he was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. However, Milgram subsequently conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [1] .
Non-experimental research falls into two broad categories: correlational research and observational research.
The most common type of non-experimental research conducted in psychology is correlational research. Correlational research is considered non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable. More specifically, in correlational research , the researcher measures two variables with little or no attempt to control extraneous variables and then assesses the relationship between them. As an example, a researcher interested in the relationship between self-esteem and school achievement could collect data on students’ self-esteem and their GPAs to see if the two variables are statistically related.
Observational research is non-experimental because it focuses on making observations of behavior in a natural or laboratory setting without manipulating anything. Milgram’s original obedience study was non-experimental in this way. He was primarily interested in the extent to which participants obeyed the researcher when he told them to shock the confederate and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of observational research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the researchers asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories).
When psychologists wish to study change over time (for example, when developmental psychologists wish to study aging) they usually take one of three non-experimental approaches: cross-sectional, longitudinal, or cross-sequential. Cross-sectional studies involve comparing two or more pre-existing groups of people (e.g., children at different stages of development). What makes this approach non-experimental is that there is no manipulation of an independent variable and no random assignment of participants to groups. Using this design, developmental psychologists compare groups of people of different ages (e.g., young adults spanning from 18-25 years of age versus older adults spanning 60-75 years of age) on various dependent variables (e.g., memory, depression, life satisfaction). Of course, the primary limitation of using this design to study the effects of aging is that differences between the groups other than age may account for differences in the dependent variable. For instance, differences between the groups may reflect the generation that people come from (a cohort effect ) rather than a direct effect of age. For this reason, longitudinal studies , in which one group of people is followed over time as they age, offer a superior means of studying the effects of aging. However, longitudinal studies are by definition more time consuming and so require a much greater investment on the part of the researcher and the participants. A third approach, known as cross-sequential studies , combines elements of both cross-sectional and longitudinal studies. Rather than measuring differences between people in different age groups or following the same people over a long period of time, researchers adopting this approach choose a smaller period of time during which they follow people in different age groups. For example, they might measure changes over a ten year period among participants who at the start of the study fall into the following age groups: 20 years old, 30 years old, 40 years old, 50 years old, and 60 years old. This design is advantageous because the researcher reaps the immediate benefits of being able to compare the age groups after the first assessment. Further, by following the different age groups over time they can subsequently determine whether the original differences they found across the age groups are due to true age effects or cohort effects.
The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. But as you will learn in this chapter, many observational research studies are more qualitative in nature. In qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s observational study of the experience of people in psychiatric wards was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semi-public room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256) [2] . Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.
Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure 6.1 shows how experimental, quasi-experimental, and non-experimental (correlational) research vary in terms of internal validity. Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) falls in the middle because it contains some, but not all, of the features of a true experiment. For instance, it may fail to use random assignment to assign participants to groups or fail to use counterbalancing to control for potential order effects. Imagine, for example, that a researcher finds two similar schools, starts an anti-bullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” While a comparison is being made with a control condition, the inability to randomly assign children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying (e.g., there may be a selection effect).
Notice also in Figure 6.1 that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational (non-experimental) studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well-designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in Chapter 5.
A research that lacks the manipulation of an independent variable.
Research that is non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable.
Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.
Studies that involve comparing two or more pre-existing groups of people (e.g., children at different stages of development).
Differences between the groups may reflect the generation that people come from rather than a direct effect of age.
Studies in which one group of people are followed over time as they age.
Studies in which researchers follow people in different age groups in a smaller period of time.
Overview of Non-Experimental Research Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
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European Journal of Training and Development
ISSN : 2046-9012
Article publication date: 6 September 2016
Nonexperimental research, defined as any kind of quantitative or qualitative research that is not an experiment, is the predominate kind of research design used in the social sciences. How to unambiguously and correctly present the results of nonexperimental research, however, remains decidedly unclear and possibly detrimental to applied disciplines such as human resource development. To clarify issues about the accurate reporting and generalization of nonexperimental research results, this paper aims to present information about the relative strength of research designs, followed by the strengths and weaknesses of nonexperimental research. Further, some possible ways to more precisely report nonexperimental findings without using causal language are explored. Next, the researcher takes the position that the results of nonexperimental research can be used cautiously, yet appropriately, for making practice recommendations. Finally, some closing thoughts about nonexperimental research and the appropriate use of causal language are presented.
A review of the extant social science literature was consulted to inform this paper.
Nonexperimental research, when reported accurately, makes a tremendous contribution because it can be used for conducting research when experimentation is not feasible or desired. It can be used also to make tentative recommendations for practice.
This article presents useful means to more accurately report nonexperimental findings through avoiding causal language. Ways to link nonexperimental results to making practice recommendations are explored.
Reio, T.G. (2016), "Nonexperimental research: strengths, weaknesses and issues of precision", European Journal of Training and Development , Vol. 40 No. 8/9, pp. 676-690. https://doi.org/10.1108/EJTD-07-2015-0058
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Not all research is about measuring the effects of an intervention on one group compared to a group that did not receive the intervention. There is another class of quantitative research design called non-experimental research. These research designs can be used to show relationships between variables (correlational design). Non-experimental research can also be used to study he existence or incidence of a phenomenon (descriptive design). Note that in a non-experimental design, the independent variable is not controlled.
Correlational retrospective design. Research studies in this category examine how an event in the past may have an effect in the present, for example, presence of cancer and pulmonary disease rates among residents of New York City after the 9/11 bombings.
Correlational prospective design. In a prospective design, researchers believe that a phenomenon may have a future effect on the population of interest. For example, is the existence of lead-based paint in the home correlated to lower education scores? Newborns living in homes with lead-based paint would be followed by the researches and tested on some regular basis to compare test scores.
Descriptive correlational studies demonstrate the relationship among variables without going as far as showing cause and effect. Prevalence and incidence studies are examples of this type of research. In epidemiology, a prevalence study is used to study the saturation of a condition whereas an incidence study is used to study the rate of new cases in the population.
Statistical analysis
Statistical measures of correlation will depend on the data type. Incidence and prevalence are measured by a defined formula for rate.
Strengths and limitations
Non-experimental research lacks the reliability and validity of quasi-experimental and experimental research designs. However, findings from non-experimental research is the first step in determining whether an experimental design is called for.
While there are many types of quantitative research designs, they generally fall under one of three umbrellas: experimental research, quasi-experimental research, and non-experimental research.
Experimental research designs are what many people think of when they think of research; they typically involve the manipulation of variables and random assignment of participants to conditions. A traditional experiment may involve the comparison of a control group to an experimental group who receives a treatment (i.e., a variable is manipulated). When done correctly, experimental designs can provide evidence for cause and effect. Because of their ability to determine causation, experimental designs are the gold-standard for research in medicine, biology, and so on. However, such designs can also be used in the “soft sciences,” like social science. Experimental research has strict standards for control within the research design and for establishing validity. These designs may also be very resource and labor intensive. Additionally, it can be hard to justify the generalizability of the results in a very tightly controlled or artificial experimental setting. However, if done well, experimental research methods can lead to some very convincing and interesting results.
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Non-experimental research, on the other hand, can be just as interesting, but you cannot draw the same conclusions from it as you can with experimental research. Non-experimental research is usually descriptive or correlational, which means that you are either describing a situation or phenomenon simply as it stands, or you are describing a relationship between two or more variables, all without any interference from the researcher. This means that you do not manipulate any variables (e.g., change the conditions that an experimental group undergoes) or randomly assign participants to a control or treatment group. Without this level of control, you cannot determine any causal effects. While validity is still a concern in non-experimental research, the concerns are more about the validity of the measurements, rather than the validity of the effects.
Finally, a quasi-experimental design is a combination of the two designs described above. For quasi-experimental designs you still can manipulate a variable in the experimental group, but there is no random assignment into groups. Quasi-experimental designs are the most common when the researcher uses a convenience sample to recruit participants. For example, let’s say you were interested in studying the effect of stress on student test scores at the school that you work for. You teach two separate classes so you decide to just use each class as a different group. Class A becomes the experimental group who experiences the stressor manipulation and class B becomes the control group. Because you are sampling from two different pre-existing groups, without any random assignment, this would be known as a quasi-experimental design. These types of designs are very useful for when you want to find a causal relationship between variables but cannot randomly assign people to groups for practical or ethical reasons, such as working with a population of clinically depressed people or looking for gender differences (we can’t randomly assign people to be clinically depressed or to be a different gender). While these types of studies sometimes have higher external validity than a true experimental design, since they involve real world interventions and group rather than a laboratory setting, because of the lack of random assignment in these groups, the generalizability of the study is severely limited.
So, how do you choose between these designs? This will depend on your topic, your available resources, and desired goal. For example, do you want to see if a particular intervention relieves feelings of anxiety? The most convincing results for that would come from a true experimental design with random sampling and random assignment to groups. Ultimately, this is a decision that should be made in close collaboration with your advisor. Therefore, I recommend discussing the pros and cons of each type of research, what it might mean for your personal dissertation process, and what is required of each design before making a decision.
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There are two main types of nonexperimental research designs: comparative design and correlational design. In comparative research, the researcher examines the differences between two or more groups on the phenomenon that is being studied. For example, studying gender difference in learning mathematics is a comparative research.
Non-experimental research is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world). ... Using this design, developmental psychologists compare groups of people ...
Non-experimental research is the type of research that lacks an independent variable. Instead, the researcher observes the context in which the phenomenon occurs and analyzes it to obtain information. Unlike experimental research, where the variables are held constant, non-experimental research happens during the study when the researcher ...
Key Takeaways. Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both. There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables.
Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure 7.1 shows how experimental, quasi-experimental, and correlational research vary in terms of internal validity. Experimental research tends ...
So when we can't randomize…the role of design for non-experimental studies. •Should use the same spirit of design when analyzing non-experimental data, where we just see that some people got the treatment and others the control •Helps articulate 1) the causal question, and 2) the timing of covariates, exposure, and outcomes.
When to Use Non-Experimental Research. As we saw earlier, experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable.It stands to reason, therefore, that non-experimental research is appropriate—even necessary—when ...
Non-experimental research is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world). Most researchers in psychology consider the distinction between experimental ...
Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) falls in the middle because it contains some, but not all, of the features of a true experiment.
Humans. Research Design*. To support evidence-based nursing practice, the authors provide guidelines for appraising research based on quality, quantity, and consistency. This article, the second of a three-part series, focuses on nonexperimental research designs.
Nonexperimental Designs Definition. Nonexperimental designs are research methods that lack the hallmark features of experiments, namely manipulation of independent variables and random assignment to conditions. The gold standard for scientific evidence in social psychology is the randomized experiment; however, there are many situations in ...
Observational research is an example of non-experimental research, which is classified as a qualitative research method. Cross-section; Experimental research is usually single-sectional while non-experimental research is cross-sectional. That is, when evaluating the research subjects in experimental research, each group is evaluated as an entity.
6.3: Correlational Research. Correlational research is a type of non-experimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical ...
Non-experimental research is a type of research design that is based on observation and measuring instead of experimentation with randomly assigned participants. What characterizes this research design is the fact that it lacks the manipulation of independent variables. Because of this fact, the non-experimental research is based on naturally ...
Over the last three decades, a research design has emerged to evaluate the performance of nonexperimental (NE) designs and design features in field settings. It is called the within-study comparison (WSC) approach or the design replication study.
Among non-experimental designs involving individuals, there are essentially 3 different ways at arriving at conclusions by. 1) reference to population follow-up (cohort) 2) joint assessment of exposure among cases and non-cases (case-control) 3) reference to one particular time (cross-sectional) Since all study designs, including the non ...
Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) falls in the middle because it contains some, but not all, of the features of a true experiment.
Nonexperimental research, defined as any kind of quantitative or qualitative research that is not an experiment, is the predominate kind of research design used in the social sciences. How to unambiguously and correctly present the results of nonexperimental research, however, remains decidedly unclear and possibly detrimental to applied ...
Non-Experimental Design Studies using descriptive design do not include a control group for comparison. Not all research is about measuring the effects of an intervention on one group compared to a group that did not receive the intervention. There is another class of quantitative research design called non-experimental research. These research designs can be used […]
The category of non-experimental designs is the most heterogeneous of the three classification categories (experimental, quasi-experimental, and non-experimental). Examples of the most common non-experimental designs are listed in Table 1. Although, in general, this category has the lowest level of scientific rigor, each design within this category varies as to its own individual level of ...
Non-experimental research. Non-experimental research is a broad term that covers "any study in which the researcher doesn't have quite as much control as they do in an experiment". Obviously, control is something that scientists like to have, but as the previous example illustrates, there are lots of situations in which you can't or shouldn't try to obtain that control.
Non-experimental designs are useful for a few different types of research, including explanatory questions in program evaluation. Certain types of non-experimental design are also helpful for researchers when they are trying to develop a new assessment or scale. Other times, researchers or agency staff did not get a chance to gather any ...
Without this level of control, you cannot determine any causal effects. While validity is still a concern in non-experimental research, the concerns are more about the validity of the measurements, rather than the validity of the effects. Finally, a quasi-experimental design is a combination of the two designs described above.