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Descriptive Correlational Design in Research

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Looking for descriptive correlational design definition and meaning? This research paper example explains all the details of this quantitative research method.

Introduction

Why use descriptive correlational design.

Descriptive statistics refers to information that has been analyzed in order to reveal the basic features of data collected or used in a study (Fowler, 2013). They provide researchers with summaries and other critical information regarding study samples and measures. The two main types include measures of central tendency and the measure of spread (Kothari, 2004). A common occurrence when using descriptive data is the emergence of certain patterns that make it easy for researchers to understand and make sense of data. The statistical data can either be used for further research studies or as an independent entity that can be used to make conclusions (Fowler, 2013). Certain research situations involve the use of only descriptive statistics because of the large sample sizes and complexity of data. A study that involves the computation of mean, median, and mode would require descriptive statistics (Yin, 2009).

For instance, they would be sued in a study that aims to find the media score in a class of 100 students with different test results. On the other hand, surveys, case studies, and naturalistic observations can only be successfully conducted using descriptive statistics. An example of research that involved descriptive statistics only is a research study conducted by Andreyeva, Michaud, and Soest (2007) to investigate obesity and health in Europeans aged 50 years and older. The study aimed to study the prevalence of obesity and related health complications among Europeans aged 50 years and above (Andreyeva, Michaud & Soest, 2007). The study involved the collection of data from participants without altering any environmental factors. It was published in the Journal of Public Health in 2007.

Descriptive correlational design is used in research studies that aim to provide static pictures of situations as well as establish the relationship between different variables (McBurney & White, 2009). In correlational research, two variables, such as the height and weight of individuals, are studied to establish their relationship. One of the research topics that can be studied using a descriptive correctional design is the height and weight of college students between the ages of 18 and 25. This study can be tied to their nutrition or frequency of taking meals in a day. The design is appropriate for the aforementioned topic because in conducting the study, the researcher will be required to collect data based on the behavior or attitudes of the participants.

For instance, the number of times the participants eat a certain meal or take a certain beverage. On the other hand, the researcher will be required to establish the relationship between the frequency of taking certain meals or beverages and gains in weight. The researcher could also establish the relationship between the weight and height of the participants. The study design would also enable the researcher to determine changes in the participants’ behaviors or attitudes over time in order to determine how these changes affect the outcomes or possible trends that could emerge in the future (Monsen & Horn, 2007).

Do SAT scores determine the GPA achieved by college students? This research question has both predictor and criterion variables. In this research question, SAT scores represent the predictor variable, and college GPA represents the criterion variable. College GPA is the criterion variable because it is the component being predicted using students’ SAT scores. On the other hand, SAT scores are the predictor variable because they determine the GPA attained in college. The research question seeks to determine whether students’ SAT scores predict the GPA scores they attain in college.

This research paper focused on descriptive correlation design definition and goals. This quantitative research method aims to describe two or more variables and their relationships. Descriptive correlation design can provide a picture of the current state of affairs. For instance, in psychology, it can be a picture of a given group of individuals, their thoughts, behaviors, or feelings.

Andreyeva, T., Michaud, P. C., & Soest, A. (2007). Obesity and Health in Europeans Aged 50 Years and Older. Public Health 121 (1), 497-509.

Fowler, F. J. (2013). Survey Research Methods . New York, NY: SAGE Publications.

Kothari, C. R. (2004). Research Methodology: Methods and Techniques . New York, NY: New Age International.

McBurney, D. & White, T. (2009). Research Methods . New York, NY: Cengage Learning.

Monsen, E. R & Horn, L. V. (2007). Research: Successful Approaches . New York: American Dietetic Association.

Yin, R. K. (2009). Case Study Research: Design and Methods . New York, NY: SAGE Publications.

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Study designs: Part 2 – Descriptive studies

Rakesh aggarwal.

Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Priya Ranganathan

1 Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

One of the first steps in planning a research study is the choice of study design. The available study designs are divided broadly into two types – observational and interventional. Of the various observational study designs, the descriptive design is the simplest. It allows the researcher to study and describe the distribution of one or more variables, without regard to any causal or other hypotheses. This article discusses the subtypes of descriptive study design, and their strengths and limitations.

INTRODUCTION

In our previous article in this series,[ 1 ] we introduced the concept of “study designs”– as “the set of methods and procedures used to collect and analyze data on variables specified in a particular research question.” Study designs are primarily of two types – observational and interventional, with the former being loosely divided into “descriptive” and “analytical.” In this article, we discuss the descriptive study designs.

WHAT IS A DESCRIPTIVE STUDY?

A descriptive study is one that is designed to describe the distribution of one or more variables, without regard to any causal or other hypothesis.

TYPES OF DESCRIPTIVE STUDIES

Descriptive studies can be of several types, namely, case reports, case series, cross-sectional studies, and ecological studies. In the first three of these, data are collected on individuals, whereas the last one uses aggregated data for groups.

Case reports and case series

A case report refers to the description of a patient with an unusual disease or with simultaneous occurrence of more than one condition. A case series is similar, except that it is an aggregation of multiple (often only a few) similar cases. Many case reports and case series are anecdotal and of limited value. However, some of these bring to the fore a hitherto unrecognized disease and play an important role in advancing medical science. For instance, HIV/AIDS was first recognized through a case report of disseminated Kaposi's sarcoma in a young homosexual man,[ 2 ] and a case series of such men with Pneumocystis carinii pneumonia.[ 3 ]

In other cases, description of a chance observation may open an entirely new line of investigation. Some examples include: fatal disseminated Bacillus Calmette–Guérin infection in a baby born to a mother taking infliximab for Crohn's disease suggesting that adminstration of infliximab may bring about reactivation of tuberculosis,[ 4 ] progressive multifocal leukoencephalopathy following natalizumab treatment – describing a new adverse effect of drugs that target cell adhesion molecule α4-integrin,[ 5 ] and demonstration of a tumor caused by invasive transformed cancer cells from a colonizing tapeworm in an HIV-infected person.[ 6 ]

Cross-sectional studies

Studies with a cross-sectional study design involve the collection of information on the presence or level of one or more variables of interest (health-related characteristic), whether exposure (e.g., a risk factor) or outcome (e.g., a disease) as they exist in a defined population at one particular time. If these data are analyzed only to determine the distribution of one or more variables, these are “descriptive.” However, often, in a cross-sectional study, the investigator also assesses the relationship between the presence of an exposure and that of an outcome. Such cross-sectional studies are referred to as “analytical” and will be discussed in the next article in this series.

Cross-sectional studies can be thought of as providing a “snapshot” of the frequency and characteristics of a disease in a population at a particular point in time. These are very good for measuring the prevalence of a disease or of a risk factor in a population. Thus, these are very helpful in assessing the disease burden and healthcare needs.

Let us look at a study that was aimed to assess the prevalence of myopia among Indian children.[ 7 ] In this study, trained health workers visited schools in Delhi and tested visual acuity in all children studying in classes 1–9. Of the 9884 children screened, 1297 (13.1%) had myopia (defined as spherical refractive error of −0.50 diopters (D) or worse in either or both eyes), and the mean myopic error was −1.86 ± 1.4 D. Furthermore, overall, 322 (3.3%), 247 (2.5%) and 3 children had mild, moderate, and severe visual impairment, respectively. These parts of the study looked at the prevalence and degree of myopia or of visual impairment, and did not assess the relationship of one variable with another or test a causative hypothesis – these qualify as a descriptive cross-sectional study. These data would be helpful to a health planner to assess the need for a school eye health program, and to know the proportion of children in her jurisdiction who would need corrective glasses.

The authors did, subsequently in the paper, look at the relationship of myopia (an outcome) with children's age, gender, socioeconomic status, type of school, mother's education, etc. (each of which qualifies as an exposure). Those parts of the paper look at the relationship between different variables and thus qualify as having “analytical” cross-sectional design.

Sometimes, cross-sectional studies are repeated after a time interval in the same population (using the same subjects as were included in the initial study, or a fresh sample) to identify temporal trends in the occurrence of one or more variables, and to determine the incidence of a disease (i.e., number of new cases) or its natural history. Indeed, the investigators in the myopia study above visited the same children and reassessed them a year later. This separate follow-up study[ 8 ] showed that “new” myopia had developed in 3.4% of children (incidence rate), with a mean change of −1.09 ± 0.55 D. Among those with myopia at the time of the initial survey, 49.2% showed progression of myopia with a mean change of −0.27 ± 0.42 D.

Cross-sectional studies are usually simple to do and inexpensive. Furthermore, these usually do not pose much of a challenge from an ethics viewpoint.

However, this design does carry a risk of bias, i.e., the results of the study may not represent the true situation in the population. This could arise from either selection bias or measurement bias. The former relates to differences between the population and the sample studied. The myopia study included only those children who attended school, and the prevalence of myopia could have been different in those did not attend school (e.g., those with severe myopia may not be able to see the blackboard and hence may have been more likely to drop out of school). The measurement bias in this study would relate to the accuracy of measurement and the cutoff used. If the investigators had used a cutoff of −0.25 D (instead of −0.50 D) to define myopia, the prevalence would have been higher. Furthermore, if the measurements were not done accurately, some cases with myopia could have been missed, or vice versa, affecting the study results.

Ecological studies

Ecological (also sometimes called as correlational) study design involves looking for association between an exposure and an outcome across populations rather than in individuals. For instance, a study in the United States found a relation between household firearm ownership in various states and the firearm death rates during the period 2007–2010.[ 9 ] Thus, in this study, the unit of assessment was a state and not an individual.

These studies are convenient to do since the data have often already been collected and are available from a reliable source. This design is particularly useful when the differences in exposure between individuals within a group are much smaller than the differences in exposure between groups. For instance, the intake of particular food items is likely to vary less between people in a particular group but can vary widely across groups, for example, people living in different countries.

However, the ecological study design has some important limitations.First, an association between exposure and outcome at the group level may not be true at the individual level (a phenomenon also referred to as “ecological fallacy”).[ 10 ] Second, the association may be related to a third factor which in turn is related to both the exposure and the outcome, the so-called “confounding”. For instance, an ecological association between higher income level and greater cardiovascular mortality across countries may be related to a higher prevalence of obesity. Third, migration of people between regions with different exposure levels may also introduce an error. A fourth consideration may be the use of differing definitions for exposure, outcome or both in different populations.

Descriptive studies, irrespective of the subtype, are often very easy to conduct. For case reports, case series, and ecological studies, the data are already available. For cross-sectional studies, these can be easily collected (usually in one encounter). Thus, these study designs are often inexpensive, quick and do not need too much effort. Furthermore, these studies often do not face serious ethics scrutiny, except if the information sought to be collected is of confidential nature (e.g., sexual practices, substance use, etc.).

Descriptive studies are useful for estimating the burden of disease (e.g., prevalence or incidence) in a population. This information is useful for resource planning. For instance, information on prevalence of cataract in a city may help the government decide on the appropriate number of ophthalmologic facilities. Data from descriptive studies done in different populations or done at different times in the same population may help identify geographic variation and temporal change in the frequency of disease. This may help generate hypotheses regarding the cause of the disease, which can then be verified using another, more complex design.

DISADVANTAGES

As with other study designs, descriptive studies have their own pitfalls. Case reports and case-series refer to a solitary patient or to only a few cases, who may represent a chance occurrence. Hence, conclusions based on these run the risk of being non-representative, and hence unreliable. In cross-sectional studies, the validity of results is highly dependent on whether the study sample is well representative of the population proposed to be studied, and whether all the individual measurements were made using an accurate and identical tool, or not. If the information on a variable cannot be obtained accurately, for instance in a study where the participants are asked about socially unacceptable (e.g., promiscuity) or illegal (e.g., substance use) behavior, the results are unlikely to be reliable.

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Understanding Descriptive Research Designs and Methods

Siedlecki, Sandra L. PhD, RN, APRN-CNS, FAAN

Author Affiliation: Senior Nurse Scientist and Clinical Nurse Specialist, Office of Nursing Research & Innovation, Nursing Institute, Cleveland Clinic, Ohio.

The author reports no conflicts of interest.

Correspondence: Sandra L. Siedlecki, PhD, RN, APRN-CNS, 3271 Stillwater Dr, Medina, OH 44256 ( [email protected] ).

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Descriptive Research and Qualitative Research

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Descriptive research is a study of status and is widely used in education, nutrition, epidemiology, and the behavioral sciences. Its value is based on the premise that problems can be solved and practices improved through observation, analysis, and description. The most common descriptive research method is the survey, which includes questionnaires, personal interviews, phone surveys, and normative surveys. Developmental research is also descriptive. Through cross-sectional and longitudinal studies, researchers investigate the interaction of diet (e.g., fat and its sources, fiber and its sources, etc.) and life styles (e.g., smoking, alcohol drinking, etc.) and of disease (e.g., cancer, coronary heart disease) development. Observational research and correlational studies constitute other forms of descriptive research. Correlational studies determine and analyze relationships between variables as well as generate predictions. Descriptive research generates data, both qualitative and quantitative, that define the state of nature at a point in time. This chapter discusses some characteristics and basic procedures of the various types of descriptive research.

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Koh, E.T., Owen, W.L. (2000). Descriptive Research and Qualitative Research. In: Introduction to Nutrition and Health Research. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1401-5_12

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Home Market Research

Descriptive Correlational: Descriptive vs Correlational Research

Descriptive research and Correlational research are two important types of research studies that help researchers make ambitious and measured decisions in their respective fields. Both descriptive research and correlational research are used in descriptive correlational research. 

Descriptive research is defined as a research method that involves observing behavior to describe attributes objectively and systematically. A descriptive research project seeks to comprehend phenomena or groups in depth.

Correlational research , on the other hand, is a method that describes and predicts how variables are naturally related in the real world without the researcher attempting to alter them or assign causation between them.

The main objective of descriptive research is to create a snapshot of the current state of affairs, whereas correlational research helps in comparing two or more entities or variables.

What is descriptive correlational research?

Descriptive correlational research is a type of research design that tries to explain the relationship between two or more variables without making any claims about cause and effect. It includes collecting and analyzing data on at least two variables to see if there is a link between them. 

In descriptive correlational research, researchers collect data to explain the variables of interest and figure out how they relate. The main goal is to give a full account of the variables and how they are related without changing them or assuming that one thing causes another.

In descriptive correlational research, researchers do not change any variables or try to find cause-and-effect connections. Instead, they just watch and measure the variables of interest and then look at the patterns and relationships that emerge from the data.

Experimental research involves the independent variable to see how it affects the dependent variable, while descriptive correlational research just describes the relationship between variables. 

In descriptive correlational research, correlational research designs measure the magnitude and direction of the relationship between two or more variables, revealing their associations. At the outset creating initial equivalence between the groups or variables being compared is essential in descriptive correlational research

The independent variable occurs prior to the measurement of the measured dependent variable in descriptive correlational research. Its goal is to explain the traits or actions of a certain population or group and look at the connections between independent and dependent variables.

How are descriptive research and correlational research carried out?

Descriptive research is carried out using three methods, namely:  

• Case studies – Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they don’t have the capacity to make accurate predictions.
• Surveys – A survey is a set of questions that is administered to a population, also known as respondents. Surveys are a popular market research tool that helps collect meaningful insights from the respondents. To gather good quality data, a survey should have good survey questions, which should be a balanced mix of open-ended and close-ended questions .
• Naturalistic Observation – Naturalistic observations are carried out in the natural environment without disturbing the person/ object in observation. It is much like taking notes about people in a supermarket without letting them know. This leads to a greater validity of collected data because people are unaware they are being observed here. This tends to bring out their natural characteristics.

Correlational research also uses naturalistic observation to collect data. However, in addition, it uses archival data to gather information. Archival data is collected from previously conducted research of a similar nature. Archival data is collected through primary research.

In contrast to naturalistic observation, information collected through archived is straightforward. For example, counting the number of people named Jacinda in the United States using their social security number.  

Descriptive Research vs Correlational Research

Features of Descriptive Correlational Research

The key features of descriptive correlational research include the following:

01. Description

The main goal, just like with descriptive research, is to describe the variables of interest thoroughly. Researchers aim to explain a certain group or event’s traits, behaviors, or attitudes. 

02. Relationships

Like correlational research, descriptive correlational research looks at how two or more factors are related. It looks at how variables are connected to each other, such as how they change over time or how they are linked.

03. Quantitative analysis

Most methods for analyzing quantitative analysis data are used in descriptive correlational research. Researchers use statistical methods to study and measure the size and direction of relationships between variables.

04. No manipulation

As with correlational research, the researcher does not change or control the variables. The data is taken in its natural environment without any changes or interference.

05. Cross-sectional or longitudinal

Cross-sectional or longitudinal designs can be used for descriptive correlational research. It collects data at one point in time, while longitudinal research collects data over a long period of time to look at changes and relationships over time. 

Examples of descriptive correlational research

For example, descriptive correlational research could look at the link between a person’s age and how much money they make. The researcher would take a sample of people’s ages and incomes and then look at the data to see if there is a link between the two factors.

• Example 1 : A research project is done to find out if there is a link between how long college students sleep and how well they do in school. They keep track of how many hours kids sleep each night and what their GPAs are. By studying the data, the researcher can describe how the students sleep and find out if there is a link between how long they sleep and how well they do in school.
• Example 2 : A researcher wants to know how people’s exercise habits affect their physical health if they are between the ages of 40 and 60. They take notes on things like how often and how hard you work out, your body mass index (BMI), blood pressure, and cholesterol numbers. By analyzing the data, the researcher can describe the participants’ exercise habits and physical health and look for any links between these factors.
• Example 3 : Let’s say a researcher wants to find out if college students who work out feel less stressed. Using a poll, the researcher finds out how many hours students spend exercising each week and how stressed they feel. By looking at the data, the researcher may find that there is a moderate negative correlation between exercise and stress levels. This means that as exercise grows, stress levels tend to go down. 

Descriptive correlational research is a good way to learn about the characteristics of a population or group and the relationships between its different parts. It lets researchers describe variables in detail and look into their relationships without suggesting that one variable caused another. 

Descriptive correlational research gives useful insights and can be used as a starting point for more research or to come up with hypotheses. It’s important to be aware of the problems with this type of study, such as the fact that it can’t show cause and effect and relies on cross-sectional data. 

Still, descriptive correlational research helps us understand things and makes making decisions in many areas easier.

QuestionPro is a very useful tool for descriptive correlational research. Its many features and easy-to-use interface help researchers collect and study data quickly, giving them a better understanding of the characteristics and relationships between variables in a certain population or group. 

The different kinds of questions, analytical research tools, and reporting features on the software improve the research process and help researchers come up with useful results. QuestionPro makes it easier to do descriptive correlational research, which makes it a useful tool for learning important things and making decisions in many fields.

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6 Correlational Design and Analysis

• Published: March 2018
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Interests in how variables may relate to each other and how systems of relationships among variables may be at play often underlie the questions music education researchers pose. This chapter describes basic design and analysis considerations in research that involves the systematic investigation of whether and how variables are related; in other words, correlational research. The chapter poses correlational research as an extension of the book’s previous discussion of descriptive research. The chapter briefly describes the role of correlational studies in advancing theory, presents several issues to consider when designing studies, and provides an introduction to correlation as a statistical concept.

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Chapter 3. Psychological Science & Research

3.5 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Charles Stangor and Jennifer Walinga

Learning Objectives

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.3, are known as research designs . A research design  is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is research designed to provide a snapshot of the current state of affairs . Correlational research  is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research  is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.3).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.4.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.4 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.4 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.5 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median  is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.5 that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.6.

Or they may be more spread out away from it, as seen in Figure 3.7.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.4 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.8, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.9 a scatter plot  is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.9 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.9 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables , and they are said to be independent . Parts (d) and (e) of Figure 3.9 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991).  Multiple regression  is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.10 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.12):

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.13).

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable  is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.14)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship  is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable  in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable  in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.15):

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.16

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.3: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991).  Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978).  Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.  Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings.  (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research  (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.5 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000.

Figure 3.9 long description: Types of scatter plots.

• Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
• Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
• Independent, r=0.00. The plots on the graph are spread out around the centre.
• Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
• Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

Introduction to Psychology Copyright © 2019 by Charles Stangor and Jennifer Walinga is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Knowledge Base
• Methodology
• Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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• Descriptive Research | Definition, Types, Methods & Examples

Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . Revised on June 22, 2023.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods, other interesting articles.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when and where it happens.

Descriptive research question examples

• How has the Amsterdam housing market changed over the past 20 years?
• Do customers of company X prefer product X or product Y?
• What are the main genetic, behavioural and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organization’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event or organization). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalizable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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An overview of research designs relevant to nursing: Part 1: quantitative research designs

This three part series of articles provides a brief overview of relevant research designs in nursing. The first article in the series presents the most frequently used quantitative research designs. Strategies for non-experimental and experimental research designs used to generate and refine nursing knowledge are described. In addition, the importance of quantitative designs and the role they play in developing evidence-based practice are discussed. Nursing care needs to be determined by the results of sound research rather than by clinical preferences or tradition.

research; nursing research; quantitative analysis; methodology; nursing

Esta serie de tres artículos muestra una breve revisión de los diseños de investigación resaltantes para Enfermería. En el primer artículo de la serie son revisados los diseños de investigación cuantitativa mas utilizados en la actualidad para las investigaciones en esta área del conocimiento. Son indicados los tipos de estrategias que tales diseños utilizan para generar y refinar conocimiento siendo descritos los diseños clasificados como no experimentales y experimentales. A modo de conclusión se resalta sobre la importancia de la práctica basada en evidencia para la profesión, de forma que el cuidado de enfermería sea determinado por resultados de investigación sólida y no de acuerdo con preferencias clínicas o tradicionales.

investigación; investigación en enfermería; análisis cuantitativo; metodología; enfermería

Esta série de três artigos apresenta uma breve revisão dos desenhos de pesquisa relevantes para a enfermagem. Neste primeiro artigo da série são revistos os desenhos de pesquisa quantitativa mais utilizados atualmente nas investigações desta área de conhecimento. São apontados os tipos de estratégia que tais desenhos utilizam para gerar e refinar conhecimento e são descritos os desenhos classificados como não-experimentais e experimentais. A guisa de conclusão ressalta-se a importância da prática baseada em evidência para a profissão, de modo que o cuidado de enfermagem seja determinado por resultados de pesquisa sólida e não por preferências clínicas ou por tradição.

pesquisa; pesquisa em enfermagem; análise quantitativa; metodologia; enfermagem

UPDATING ARTICLE

An overview of research designs relevant to nursing: Part 1: Quantitative research designs

Valmi D. Sousa I ; Martha Driessnack II ; Isabel Amélia Costa Mendes III

I PhD, APRN, BC, Assistant Professor, College of Health and Human Services, The University of North Carolina at Charlotte, United States of America, e-mail: [email protected]

II PhD, APRN, BC, Postdoctoral Research Fellow in Clinical Genetics, College of Nursing, The University of Iowa, United States of America, e-mail: [email protected]

III PhD, RN, Full Professor, University of São Paulo at Ribeirão Preto College of Nursing, WHO Collaborating Centre for Nursing Research Development, Brazil, CNPq Researcher 1A, e-mail: [email protected]

Descriptors: research; nursing research; quantitative analysis; methodology; nursing

INTRODUCTION

A research design is the framework or guide used for the planning, implementation, and analysis of a study (1-2) . It is the plan for answering the research question or hypothesis. Different types of questions or hypotheses demand different types of research designs, so it is important to have a broad preparation and understanding of the different types of research designs available. Research designs are most often classified as either quantitative or qualitative . However, it is becoming more common for investigators to combine, or mix, multiple quantitative and/or qualitative designs in the same study (3) .

Quantitative research designs most often reflect a deterministic philosophy that is rooted in the post-positivist paradigm, or school of thought. Post-positivists examine cause, and how different causes interact and/or influence outcomes. The post-positivist paradigm adopts the philosophy that reality can be discovered, however only imperfectly and in a probabilistic sense. The approach is typically deductive - where most ideas or concepts are reduced into variables and the relationship between or among them are tested (1,3) . The knowledge that results is based on careful observation and measurement and interpretation of objective reality.

In contrast, qualitative research designs are rooted in the naturalistic paradigm. The approach to study is inductive, rather than deductive, and begins with the assumption that reality is subjective, not objective, and that multiple realities exist, rather than just one (1,3) . When little is known about a particular phenomenon, experience, or concept, a qualitative design is often used first. Once concepts and/or themes are identified, or grouped into a theory, they can then be tested using a quantitative design or approach. Quantitative research designs primarily involve the analysis of numbers in order to answer the research question or hypothesis, while qualitative designs primarily involve the analysis of words .

RELEVANT QUANTITATIVE RESEARCH DESIGNS

Quantitative research designs adopt objective, rigorous, and systematic strategies for generating and refining knowledge (1-4) . They primarily use deductive reasoning and generalization . Deductive reasoning is the process in which the researcher begins with an established theory or framework, where concepts have already been reduced into variables, and then gathers evidence to assess, or test, whether the theory or framework is supported (1) . Generalization is the extent to which conclusions developed from evidence collected from a sample can be extended to the larger population (1) .

Quantitative research is most often about quantifying relationships between or among variables - the independent or predictor variable (s) and the dependent or outcome variable (s). Broadly, quantitative research designs are classified as either non-experimental or experimental ( Table 1 ). Non-experimental designs are used to describe, differentiate, or examine associations, as opposed to direct relationships, between or among variables, groups, or situations. There is no random assignment, control groups, or manipulation of variables, as these designs use observation only. The most common non-experimental designs are descriptive or correlational studies.

Non-experimental designs are often further classified according to timing of data collection, cross-sectional or longitudinal , or according to the timing of the experience or event being studied, retrospective or prospective (1,5) . In a cross-sectional study , variables are identified one point in time and the relationships between them are determined. In a longitudinal study , data are collected at different points over time. In a retrospective study, an event or phenomenon identified in the present is linked to factors or variables in the past. In a prospective study , or cohort study, potential factors and variables identified in the present are linked to potential outcomes in the future.

NON-EXPERIMENTAL RESEARCH DESIGNS

Non-experimental designs do not have random assignment, manipulation of variables, or comparison groups. The researcher observes what occurs naturally without intervening in any way. There are many reasons for undertaking non-experimental designs. First, a number of characteristics or variables are not subject or amenable to experimental manipulation or randomization. Further, some variables cannot or should not be manipulated for ethical reasons. In some instances, independent variables have already occurred, so no control over them is possible.

Non-experimental designs may resemble the posttest-only experiment. However, there is a natural assignment to the condition or group being studied, as opposed to random assignment, and the intervention or condition (X) is something that has happened naturally, not imposed or manipulated. The most common methods used in non-experimental designs involve exploratory surveys and/or questionnaires. Non-experimental designs are typically classified as either descriptive or correlational ( Table 1 ).

Descriptive Designs

Descriptive, or exploratory studies are used when little is known about a particular phenomenon (1,6) . The researcher observes, describes, and documents various aspects of a phenomenon. There is no manipulation of variables or search for cause and effect related to the phenomenon. Descriptive designs describe what actually exists, determine the frequency with which it occurs, and categorizes the information. Researchers pose Level I research questions( 2,7-8) ( Table 1 ). The results provide the knowledge base for potential hypotheses that direct subsequent correlational, quasi-experimental, and experimental studies. The two most common types of quantitative descriptive designs are: case control and comparative (1,6) .

Case Control Studies . Case control studies involve a description of cases with and without a pre-existing condition or exposure. The cases, subjects, or units of study can be an individual, a family, or a group. Case control studies are more feasible than experiments in cases in which an outcome is rare or takes years to develop. This design is also known as a case report or case study .

Comparative Studies . Comparative studies are also called ex post facto or causal-comparative studies . These studies describe the differences in variables that occur naturally between two or more cases, subjects, or units of study. Researchers who use a comparative design normally pose hypotheses about the differences in variables between or among two or more units. The main difference between this approach and the quasi-experimental design is the lack of researcher control of the variables.

Correlational Designs

Correlational designs involve the systematic investigation of the nature of relationships, or associations between and among variables, rather than direct cause-effect relationships. Correlational designs are typically cross-sectional (1,6) . These designs are used to examine if changes in one or more variable are related to changes in another variable(s). This is referred to as co-variance . Correlations analyze direction, degree, magnitude, and strength of the relationships or associations. The results from correlational studies provide the means for generating hypotheses to be tested in quasi-experimental and experimental studies. Researchers may pose Level I or II research questions (2,7-8) ( Table 1 ). Three of the most common correlational designs include: descriptive , predictive , and model testing correlational design ( 1,6) .

Descriptive Correlational Designs . Descriptive correlational studies describe the variables and the relationships that occur naturally between and among them.

Predictive Correlational Designs . Predictive correlational studies predict the variance of one or more variables based on the variance of another variable (s). As with experimental designs, the study variables are classified as independent (predictor) and dependent (outcome). However, these variables are not manipulated, but occur naturally.

Model Testing Correlational Designs . Model testing correlational studies examine, or pilot test, proposed relationships for a model or theory. As with experimental designs, the study variables are classified as independent (predictor) and dependent (outcome). However, the variables are not manipulated, but occur naturally.

EXPERIMENTAL DESIGNS

Experimental designs typically use random assignment, manipulation of an independent variable(s), and strict controls (1,6,9) . These characteristics provide increased confidence of cause-and-effect relationships. Random assignment means that each subject had equal chance to be assigned to either the control or experimental group. The use of random assignment of subjects attempts to eliminate systematic bias. Random assignment is different from random sampling. Random sampling means that each subject had an equal chance of being selected from a larger group to participate in the study. This approach is often used in survey research to facilitate generalization. However, it is the random assignment to different conditions that distinguishes a true experimental design. To be classified as true experimental, there must be randomization, a control group, and manipulation of a variable when examining the direct causal or predicted relationship between variables. When any one of these requirements is not met, the design is no longer a true experiment and is classified as quasi -experimental. Researchers typically pose Level III research questions (2,7-8) ( Table 1 ).

True-Experimental Designs

True experimental designs examine the cause and effect relationships between independent (predictor) and dependent (outcome) variables under highly controlled conditions. The simplest of all experimental designs is the posttest-only control group . Other common true-experimental designs include the posttest only control group design , pretest-posttest control group design , Soloman four group design , and cross-over design (1,6,9) .

Posttest Only Control Group Design . In posttest only control group design, subjects are randomly assigned (R) to either a control or an experimental group. The groups are not pretested. One group is exposed to a treatment (X) or series of different treatments (X 1 , X 2 ), and then both groups are posttested (O).

Pretest-Posttest Control Group Design . In the pretest-posttest control group design, or classic experiment, subjects are randomly assigned (R) to either a control or experimental group. Both groups are pretested (O). The experimental group is exposed to a treatment (X) or different treatments (X 1 , X 2 ), and then both groups are posttested (O).

Solomon Four-Group Design . In Solomon four-group design, subjects are randomly assigned (R) to one of four different groups. Two of the groups are pretested (O) and two are not. Only one pretested and one not pretested group are then exposed to a treatment (X). All of the groups are postested (O).

Cross-over Design . In the cross-over, or counterbalanced, switchover, or rotation design, subjects are given two treatments, one being the experimental treatment (X E ), the other a control or reference treatment (X C ). The subjects are randomly assigned to one of two groups. One group receives the experimental treatment first and the other group receives the experimental group second. After a period of time, sufficient to allow for any treatment effect to wash out (W), the treatments are crossed over. Multiple cross-over designs involve several treatments.

Quasi-experimental Designs

Quasi-experimental, like true-experimental designs, examine cause-and-effect relationships between or among independent and dependent variables. However, one of the characteristics of true-experimental design is missing, typically the random assignment of subjects to groups. Although quasi-experimental designs are useful in testing the effectiveness of an intervention and are considered closer to natural settings, these research designs are exposed to a greater number of threats of internal and external validity, which may decrease confidence and generalization of study's findings. The most common used quasi-experimental designs are: non-equivalent group pretest-posttest group design , control-group interrupted time series design , single-group interrupted time-series design , and counterbalanced design ( 1,6,9) .

Non-equivalent pretest-posttest control group design . The non-equivalent pretest-posttest control group design is identical in many ways to the pretest-posttest control group design except that subjects are not randomly (NR) assigned to groups. Both groups are pretested (O) and posttested (O). However, only the experimental group is exposed to a treatment (X).

Control-group Interrupted Time Series Design . In the control-group interrupted time series design, groups are measured or tested repeatedly on the same variable over time. Again, there is no random assignment (NR) to groups. The experimental group is exposed to a treatment (X) at some point in the series while the control group is not.

Single-group Interrupted Time-Series Design . With the single-group interrupted time-series design, the researcher measures only one group repeatedly, both before and after exposure to a treatment (X).

Counterbalanced Design . The counterbalanced design is similar to the cross-over experimental design except that subjects are not randomly assigned (NR) to the different groups. All groups are exposed to all treatments. The most common counterbalanced design is the Latin square , where four different treatments are applied to four naturally assembled groups or individuals. Each of the groups or individuals is posttested after each treatment. The number of treatment and groups must be equal. The Latin square is shown here.

SELECTION OF QUANTITATIVE RESEARCH DESIGN

The selection of a research design is based on the research question or hypothesis and the phenomena being studied. A true-experimental design is considered the strongest or most rigorous with regard to establishing causal effects and internal validity . Internal validity is the control of factors within the study that might influence the outcomes besides the experimental intervention or treatment. A non-experimental design is generally the weakest in this respect. However, this does not mean that non-experimental designs are weak designs overall. They are weak only with respect to assessing cause-effect relationships and the establishment of internal validity. In fact, the simplest form of non-experiment, the one-time survey design that consists of one single observation (O), is one of the most common forms of research and, for some research questions, especially descriptive ones, is clearly a strong and most appropriate design.

Research is important to the nursing profession. It is designed to provide new knowledge, improve health care, and challenge current nursing practice with new ideas. Evidence-based nursing practice comes from the idea that the care we provide be determined by sound research rather than by clinician preference or tradition. Understanding how to select the best design to answer a research question or test a hypothesis is the first step in conducting meaningful research. This process assists nurses as they read and critique original research articles. Nursing practice is seldom changed based on one study. It is the accumulation of results from several studies, often using different research designs that provide enough evidence for change.

In the first article of this series, we have presented an introduction and overview to different quantitative research designs, including descriptive, correlational, true-experimental, quasi-experimental designs. Each design offers a unique approach or plan for answering a nursing research question. In the next article, qualitative research designs will be presented and discussed, providing nurses with even more choices of design. Finally, in the third article, the combination, or mixing of designs within one study, will be introduced. At the completion of this series, nurses will have an overview of relevant research designs for nursing research and be able to select an appropriate design as a framework or guide for a potential study.

Recebido em: 21.6.1006

Aprovado em: 6.3.2007

• 1. Burns N, Grove SK. The practice of nursing research: conduct, critique, and utilization. 5 th ed. St Louis: Elsevier; 2005.
• 2. Polit DF, Beck CT, Hungler BP. Essentials of nursing research: methods, appraisal, and utilization. 5 th ed. Philadelphia: Lippincott; 2001.
• 3. Creswell JW. Research design: qualitative, quantitative, and mixed methods approaches. 2 nd ed. Thousand Oaks: Sage Publications; 2003.
• 4. Carvalho V. Cuidando, pesquisando e ensinando: acerca de significados e implicações da prática da enfermagem. Rev Latino-am Enfermagem 2004 setembro/outubro; 12(5):806-15.
• 5. Demo P. Pesquisa qualitativa: busca de equilíbrio entre forma e conteúdo. Rev Latino-am Enfermagem 1998 abril; 6(2):89-104.
• 6. Walker W. The strengths and weaknesses of research designs involving quantitative measures. J Res Nurs 2005; 10(5): 571-82.
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• 8. LoBiondo-Wood G, Haber J. Nursing research: Methods, critical appraisal, and utilization. 5 th ed. St Louis: Mosby; 2002.
• 9. Blink P, Wood M. Advanced design in nursing. Thousand Oaks: Sage Publications; 1998.
• 10. Cassidy CM, Hart JA. Methodological issues in investigations of massage/bodywork therapy: Part III: Qualitative and quantitative design for MBT and the bias of interpretation. J Bodywork and Movement Ther 2003; 7(3): 136-41.
• 11. Shadish WR, Cook TD, Campbell DT. Experimental and quasi-experimental designs for generalized causal inference . New York: Houghton Mifflin Company; 2002.

Publication Dates

• Publication in this collection 12 July 2007
• Date of issue June 2007
• Accepted 06 Mar 2007
• Received 21 June 2006

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• Published: 19 August 2024

The relationship between moral reasoning and nurses’ professional values in undergraduate nursing students: a descriptive-correlational study

• Amir Mohamad Nazari 1 ,
• Fariba Borhani   ORCID: orcid.org/0000-0002-8937-2649 1 ,
• Akbar Zare-Kaseb   ORCID: orcid.org/0000-0002-0014-7104 1 ,
• Vahid Yousofvand 2 &
• Abbas Abbaszadeh 1  

BMC Medical Education volume  24 , Article number:  889 ( 2024 ) Cite this article

Metrics details

Moral reasoning in nursing is crucial in delivering high-quality patient care and fostering increased job satisfaction among nurses. Adhering to professional values is vital to this profession, and nurses must modify their actions to align with these values.

This study aimed to examine the correlation between moral reasoning and professional values among undergraduate nursing students.

Research design

A descriptive correlational design was recruited.

Participants and research context

The research was conducted at three nursing schools located in Tehran, Iran. The sample was recruited through random stratified sampling, specifically targeting undergraduate nursing students. The data collection tool comprised a three-part questionnaire, including a demographic information form, the Nursing Dilemma Test, and the Nurses Professional Values Scale Revised Questionnaire. The distribution of questionnaires encompassed both face-to-face and electronic methods. The analysis of data was conducted using SPSS 16 software. The data was analyzed using the independent samples t-test, Pearson’s correlation coefficient, and linear regression analysis. The P value of 0.05 was considered significant.

Ethical considerations

The Ethics Research Center of Shahid Beheshti University of Medical Sciences approved the study.

Data analysis showed that moral reasoning was directly correlated to professional values ( r  = 0.528, p  < 0.001). The mean scores of Principled Thinking (P.T.), Practical Consideration (P.C.), and Familiarity with similar moral dilemmas of the NDT scale were 42.55 (SD = 12.95), 15.72 (SD = 6.85), 16.08 (SD = 6.67), respectively. Also, the total score of professional values of students was 90.63 (SD = 28.80).

The findings indicated that moral reasoning and interest in nursing predict students’ professional identity. Thus, any effort to enhance interest in the profession can contribute to developing students’ professional identity. This can involve incentivizing, enhancing the professional reputation at the community and university levels, and valuing student preferences and necessities.

Peer Review reports

The medical environment of nursing practice has been constantly evolving and becoming more complex. The continuous renewal of science and technology, the change in the disease spectrum, and the aging population have specifically impacted traditional nursing work [ 1 ]. On the other hand, the shortage of nursing human resources and the different disease cognition between nurses and patients have often led nurses into an ethical dilemma due to the confusion of roles, moral conflicts, and value conflicts [ 2 , 3 , 4 ].

Research findings in various nursing schools suggest a growing disregard among nursing students for ethical considerations in their everyday nursing practices. The nursing curriculum has recently been exposed to its lack of emphasis on ethics. Nursing schools do not explicitly cover the concept of professional ethics in any of their theoretical or clinical courses [ 5 ]. So, it seems that compared to professional nurses, nursing students are inexperienced and possess inadequate aptitude and the courage to confront and manage ethical dilemmas in clinical settings [ 6 ].

Nursing students face ethical problems in clinical settings, ranging from violating patient’s rights and dignity to insecure care delivery [ 7 ]. Despite learning about different moral theories and principles during their education, nursing students may find it daunting to apply those principles due to minimal support and guidance in clinical settings [ 8 , 9 ]. Encountering dilemmas in clinical settings can lead to student emotional distress, affecting their clinical learning and professional development [ 6 , 10 ]. Thus, strategies are needed to improve nurses’ and nursing students’ moral reasoning abilities to minimize the likelihood of these problems [ 7 ].

Moral reasoning refers to the cognitive process of recognizing an ethical dilemma and selecting the suitable course of action, enabling nurses to make informed decisions [ 11 ]. Moral reasoning in nursing necessitates nurses to evaluate and make appropriate decisions to tackle the daily challenges they face in the clinical environment [ 12 ].

Kohlberg’s research serves as the foundation for most studies on moral reasoning [ 13 ]. A classification of ethical development into six stages was suggested to assess advancement in attaining absolute universal justice. Some suggest that individuals’ moral reasoning aligns with their advancement in each stage [ 14 ].

Kohlberg’s model classifies moral reasoning into three levels: pre-conventional, conventional, and post-conventional, each comprising two stages. During the pre-conventional phase, individuals prioritize their interests and engage in actions focused on self-gratification or avoiding unfavorable outcomes. Within the conventional stage, individuals frequently use accepted social rules and principles to make decisions. At the post-conventional stage, individuals consciously align their actions with established ethical principles and prioritize ethical and compassionate decision-making [ 15 ].

Professional values are the performance standards accepted by the professional and specialist groups [ 16 ]. These values are the basis of nursing performance, the director of the nurses’ interaction with the patients, colleagues, other professionals, and the public, and as a guideline for ethical behavior to provide secure and humanitarian care [ 17 , 18 ]. Values are goals and beliefs that create behaviors and are a basis for decision-making and practice [ 19 , 20 ]. The acquisition and internalization of professional values are necessary in care settings for professional development, and they provide a common framework for meeting professional expectations and standards [ 21 , 22 ].

Considering that nursing involves scientific knowledge in addition to human and professional values, students must thoroughly understand these values to provide ethical care and engage in moral reasoning across various scenarios [ 23 ]. Professional nursing values play an essential role in shaping nursing professionals’ competence. They encompass human dignity, integrity, altruism, and justice and guide nursing standards, practice, and evaluation [ 24 , 25 ]. Therefore, developing nursing professional values can promote care quality, increase patient understanding, and increase job satisfaction and retention of nursing staff; it also helps the professional socialization process [ 21 , 26 ].

Prior research has been conducted in this specific domain, and it is worth noting that these studies possess certain limitations. The investigation conducted by Hajilo et al. [ 27 ] explored the association between ethical reasoning and professional values in nursing students. The results of their study revealed no significant correlation between the two factors. The researchers highlighted certain limitations in their research conducted amidst the coronavirus pandemic. Hence, they propose that the study be replicated in clinical settings with a larger sample size and random sampling to ensure better generalizability. Our study seeks to fill the gaps in this research by utilizing a larger sample size and examining and comparing these variables in nursing students from various semesters.

Moral reasoning and professional identity have been the subjects of only a limited number of Iranian studies, which have not fully addressed this topic’s various dimensions. Considering the importance placed on moral reasoning and the professional values of nursing students, building upon the hypothesis, if there is a relationship between these variables, promoting moral reasoning can be considered a significant factor in cultivating nursing students’ professional values. Thus, this study examined the relationship between moral reasoning and professional identity among nursing students.

Study design

The present study employed a descriptive-correlational design.

Sample and setting

The research was conducted at three nursing schools located in Tehran, Iran. The sample was recruited through random stratified sampling, specifically targeting undergraduate nursing students. The G*power software was utilized to determine the minimum sample size. The criteria for determining sample size were alpha = 0.05, power = 0.80, and a correlation coefficient of 0.2 with a 10% drop-out rate. The number of samples allocated to each faculty was computed based on the student population at the time of data collection [ 28 , 29 ].

C = 0.5*ln[(1 + r)/(1-r)]

N=[(z α +z β )/C] 2 +3.

Inclusion and exclusion criteria

The inclusion criteria were: (1) undergraduate nursing students who were studying in all semesters and (2) willingness and consent to participate in the study, and the exclusion criteria were: (1) returning incomplete questionnaires and (2) being students of other universities which was not in our inclusion criteria for setting and was transferred to this school.

Measurements

The research methodology involved the utilization of a demographic questionnaire, the Nursing Dilemma Test (NDT) [ 30 ], and the Nurses Professional Values Scale-Revised (NPVS-R) [ 31 ] as assessment tools. The demographic factors under investigation were determined through a comprehensive review of related studies and by consulting experts in the field.

Nursing dilemma test (NDT)

The NDT was established in 1981 at the University of Minnesota by Patricia Crisham [ 14 ]. NDT further examines nurses’ moral reasoning, decision-making capabilities, practical considerations, and familiarity with moral dilemmas. The NDT comprises six scenarios that specifically address ethical dilemmas in healthcare. These scenarios cover a range of situations, including (1) dealing with a newborn who has anomalies, (2) the issue of administering medication forcefully, (3) handling an adult’s request for assisted death, (4) orienting new nurses, (5) addressing medication errors, and (6) managing the treatment of an uninformed terminally ill adult [ 32 ].

Part A of the NDT focuses on the assessment of moral decision-making. In each of the six scenarios, participants are tasked with envisioning themselves as the nurse, and it is recommended that they respond to the question: “What actions should the nurse undertake? The choices for the participants include “Should act,” “Cannot decide,” or “Should not act.” The nurse who acts has made a moral decision. The ethical decision-making of the nurses is shown by the percentage of their chosen actions in each scenario.

The second section provides six statements for consideration when approaching the scenarios, encompassing the ethical dilemma. The participants must select the most significant statement from these six and arrange them according to personal importance. The aim of the responses given in this section of the test is to evaluate the levels of “Principled Thinking” (P.T.) and “Practical Consideration” (P.C.). The possible minimum P.T. score on the test is 18, while the maximum P.T. score is 66. The minimum possible P.C. score achievable on the test is 6, while the maximum P.C. score is 36. The P.T. demonstrates the significance of including moral principles in making ethical decisions within the nursing field. When making ethical decisions, the P.C. considers environmental factors like patient load, resource availability, institutional policies, nurses’ perception of administrative support, and doctors’ decision-making authority [ 33 ].

The assessment tool NDT - Part C measures nurses’ Familiarity (F) level with comparable moral dilemmas in each scenario, employing a 5-point scale. Items include: “I have decided in a similar dilemma” (score = 1), “I know someone else in a similar dilemma” (score = 2), “I do not know anyone in a similar dilemma, but the dilemma is conceivable” (score = 3), “It is difficult to imagine the dilemma as it seems remote” (score = 4), and “It is difficult to take the dilemma seriously as it seems unreal” (score = 5). The scoring system for moral dilemmas in NDT categorizes familiarity as a total score between 6 and 17 and unfamiliarity as a score between 18 and 30 (Table  1 ).

The reliability and validity of the questionnaire have been verified by its author, and it has been widely used by researchers [ 14 , 34 , 35 , 36 ]. Borhani et al. [ 37 ] and Mogadasian et al. [ 38 ] reported Cronbach’s alpha coefficients of 0.82 and 0.95, respectively, for the Persian version of the NDT.

Nurses professional values scale-revised (NPVS-R)

Weiss and Shank formulated the questionnaire utilized in this study in 2009 [ 39 ]. It comprises 26 elements from established nursing professional ethics codes, encompassing caring, trust, professionalism, justice, and activism.

The dimension of caring consists of 9 items, specifically items 16, 17, 18, 20, 21, 22, 23, 24, and 25. In the context of trust, there are five items to consider: 1, 2, 9, 14, and 15. The dimension of professionalism comprises four items, namely 5, 6, 7, and 8. The dimension of justice consists of three items, specifically items 3, 12, and 13. Finally, the dimension of activism encompasses items 4, 10, 11, 19, and 26.

The scoring is established on a five-point Likert scale, encompassing the spectrum from “unimportant” to “most important.” The scoring system assigns a score of 1 to the option “unimportant,” a score of 2 to “slightly important,” a score of 3 to “moderately important,” a score of 4 to “important,” and a score of 5 to “most important.” The range of scores for this questionnaire spans from 26 to 130, and a higher score signifies a higher level of familiarity among nurses with professional values. When scoring, a score below 43 signifies low-level professional values, between 43 and 86 indicates medium-level professional values, and above 86 represents high-level professional values.

By employing Cronbach’s alpha method, Weiss and Shank evaluated the tool’s total reliability, revealing a favorable coefficient of 0.92 for the tool [ 40 ]. In a study conducted by Parvan et al. 2012, the Persian version of the questionnaire was evaluated for its validity. The validity of the Persian version of the questionnaire was examined by Parvan et al. after its translation, with the results indicating good face and content validity. Moreover, the Persian adaptation of the questionnaire displayed a Cronbach’s alpha coefficient of 0.91 [ 41 ].

Data gathering

Data gathering lasted from June 1 to November 30, 2023. After securing ethical approval, the researchers presented in three nursing faculties of medical sciences universities. Participants were briefed about the research objectives face-to-face and online and filled out an informed written consent form. Then, participants were asked to complete the paper-based questionnaires. Once filled, researchers collected the questionnaires. Also, to increase the students’ participation, an electronic link to the questionnaires was provided to them. Students did not feel coerced into completing the questionnaires because the researchers were not among their teachers.

Statistical analysis

Data analysis was performed using IBM SPSS Statistics 16.0. We used descriptive statistics (frequency distribution, mean, and standard deviation) and analytical statistics, including the analysis of variance (ANOVA), independent t-test, Pearson’s correlation coefficient, and linear regression analysis. After screening the data for the assumptions of various parametric tests, correlations between moral reasoning and professional values were explored. Also, Multiple regression analysis was conducted to examine the best explanatory variables of professional values. The maximum alpha bias level for testing the hypotheses was fixed at 0.05.

Participants

Two hundred (83.3%) of the 240 distributed questionnaires were returned from study subjects. The Tehran University of Medical Sciences accounted for 40% of the total sample size, while Shahid Beheshti and Iran universities each held a 30% share.

Sample profile

The mean age of participants in this study was 21.34 (SD = 2.01). Most of the participants (55.5%) were male, were single (93.0%), and were interested in nursing (71.5%). Regarding the academic semester, the fourth semester had the highest percentage of participants, at 18.5%, whereas the seventh semester had the lowest rate, at 8% (Table  2 ).

Moral reasoning and professional values

The study’s findings showed that the mean scores of Principled Thinking (P.T.), Practical Consideration (P.C.), and Familiarity with similar moral dilemmas of the NDT scale were 42.55 (SD = 12.95), 15.72 (SD = 6.85), 16.08 (SD = 6.67), respectively. Also, the total score of professional values of students was 90.63 (SD = 28.80). The scores of professional values dimensions are presented in detail in Table  3 .

According to nursing students’ answers to the ethical scenarios of the NDT test, difficulty in decision-making regarding the resuscitation of a child with an anomaly was observed in 18.5% of cases among nursing students. In terms of mandatory drug prescription and providing honest answers to people’s questions at the end of life, the corresponding figures were 18% and 19%. The issue with the least amount of uncertainty pertains to the request of an adult to end their life, a situation which students overwhelmingly oppose, with a rate of 86%. In this particular scenario, a mere 9.5% of individuals remained undecided. The details are presented in Table  4 .

Correlation between major study variables

Principled thinking (P.C.) was directly correlated to professional value’s total ( r  = 0.528), caring ( r  = 0.504), activism ( r  = 0.531), trust ( r  = 0.515), professionalism ( r  = 0.496), and justice ( r  = 0.452), scores. This correlation was all significant ( p  < 0.01) and moderate (0.4 <  r  < 0.59).

Practical consideration (P.C.) had significant, negative, and moderate correlation with professional value’s total ( r  = − 0.539), caring ( r  = − 0.521), activism ( r  = − 0.527), trust ( r  = − 0.546), professionalism ( r  = − 0.490), and justice ( r  = − 0.450) scores.

Familiarity (F) with moral dilemmas didn’t correlate with the total score of professional value or any other subscales of NPVS-R (Table  5 ).

Factors influencing professional identity

Multivariate regression (enter method) determined that the practical consideration (P.C.) of the NDT scale is the most effective dimension in predicting the level of professional value of nursing students (β = -0.356, p  < 0.001). The overall predictive value of the P.T., P.C., and F scales to professional identity was 33.9% (R2 = 0.339, Adjusted R2 = 0.329) (Table  6 ).

Among the socio-demographic variables, multivariate regression showed that being interested in nursing (β = -0.120, p  = 0.045), marital Status (β = 0.090, p  = 0.139), and sex (β = 0.035, p  = 0.564) respectively, have had the most significant effect on the professional value of nursing students. Of the abovementioned factors, only being interested in nursing significantly impacted professional values. This relationship was indirect, as higher interest was associated with weaker professional values. Together, these factors (P.T., P.C., F, marital Status, interest in nursing, and sex) explained about 36.2% of the variance in the professional value of nursing students (Table  7 ).

Moral decision-making

The initial component of each scenario in the NDT questionnaire assesses students’ moral decision-making capacity. Based on our study, it is evident that students demonstrated indecisiveness in fewer than 20% of cases for all six scenarios. The greatest challenge arose in determining the appropriate and truthful approach towards end-of-life patients, with the least complexity encountered when addressing the request for euthanasia by an adult patient. This implies that student nurses can readily decide about euthanasia. As per our results, nursing students rejected the patient’s request in 86% of cases. This may pertain to the cultural aspects of euthanasia within the society under examination.

The primary factors linked to positivity and supportiveness stemmed from (a) the patient’s experience of extreme and uncontrollable pain, unbearable suffering, or other distressing situations, (b) the legal aspects of euthanasia, and (c) the patient’s right to choose their death. The negative and unsupportive attitude of nurses was influenced by various factors, such as religion, moral dilemmas, the role of gender in healthcare, and poor palliative care [ 42 ]. The findings of our study in this field have been validated by a recent study conducted in Iran. Additionally, the researchers discovered that nurses with elevated ethical reasoning exhibit a more unfavorable stance on euthanasia [ 43 ].

• Moral reasoning

Moreover, the outcomes of our study demonstrated that the students possess a remarkable level of moral reasoning, enabling them to effectively navigate and resolve moral difficulties frequently encountered in clinical settings. These findings align with the results of a comparable study in this particular domain. The study revealed that the students’ moral reasoning skills exceeded the average level [ 44 ]. Similar results were observed in another study on nurses [ 45 ].

Our study indicates a significant connection between students’ moral reasoning and professional values. Higher professional values were linked to a more favorable level of professional reasoning. This is entirely consistent with the outcomes of comparable research [ 46 , 47 ]. Nursing students who possess elevated professional values exhibit higher confidence when faced with ethical decision-making [ 48 ]. Considering the positive relationship between these two constructs, nursing education can help improve the other by strengthening each. The study’s findings prove that moral reasoning can be a significant stimulus for enhancing professional values. Ethical reasoning exercises, such as simulating scenarios in a simulated environment, can help improve professional values. Conversely, there was a study that did not observe any substantial link between moral reasoning and the professional values ​​of students [ 27 ].

The average score of practical considerations was at the average level, which indicates the importance of environmental factors and organizational climate for students’ ethical decision-making and clinical activities. One of the factors that can contribute to the influence of the work environment on the ethical decision-making and clinical performance of students is their limited exposure and incomplete familiarity with the work environment’s rules. This aligns with the findings of the study conducted by Sari et al. According to their statement, students exhibit less susceptibility to environmental regulations when making ethical decisions than nurses or students of higher semesters [ 44 ]. However, it is worth noting that in two separate studies, students’ practical considerations were higher than average, presenting a slight disparity with the results obtained in the present study [ 27 , 49 ].

The current study revealed a significant inverse correlation between practical considerations and students’ professional values. Thus, students who can decide and engage in moral thinking independently of the influence of environmental rules and organizational atmosphere uphold higher professional values. This is consistent with the results of a similar study [ 27 ].

Familiarity with moral dilemmas

The mean score of students’ familiarity with situations shows that students are slightly familiar with different moral challenges. Students’ lack of clinical experience and inadequate preparation for ethical dilemmas contribute to this issue. Findings from related research in this field yielded similar outcomes [ 27 ]. The findings indicated that students require extensive work experience to comprehensively understand ethical issues in clinical settings, which were inadequately addressed during academic semesters [ 44 ].

• Professional values

The results obtained from the present study show that the average score of students’ professional values was significantly high. Students show a heightened focus on the dimension of patient care and assign considerable importance to it, as per the reported priorities of professional values. This issue highlights the significance of cautiousness in nursing education programs within college and clinical settings. The results of similar studies have been the same [ 50 , 51 , 52 ]. Concerning the dimensions, the research conducted by Poorchangizi et al. emphasized the significance of the caring dimension, aligning with our study. However, their study also highlighted the importance of the justice dimension, which contradicts our findings [ 51 ]. Moreover, this study also revealed that students exhibited a notably more positive perception of the significance of professional values than nurses [ 51 ].

Predictors of professional values

The main variables, P.C. and P.T., demonstrated acceptable predictive efficacy in predicting the professional value. However, demographic variables make a modest contribution to the prediction. When considering the demographic variables, it is evident that only interest in nursing plays a significant role in predicting professional values. The order of effect is as follows: P.C., P.T., interest in nursing, F, marital status, and sex. This means that the professional values ​​of students are more influenced by the rules of the environment and organizational climate than by their decision-making and moral thinking. One of the notable points in this study is the negative relationship between interest in nursing and professional values. This could be because students who entered this field with interest had more expectations from this field. They wavered about professional values while entering the clinical environment and distancing themselves from the ideals. The findings of previous studies exploring the correlation between main variables and demographics have aligned with the findings of our research [ 51 , 53 , 54 ]. Among all the studies conducted, only Pourchengizi et al.‘s research shows a notable association between age and the professional values of students [ 51 ].

A limitation in correlational studies like this is the inability to demonstrate causation. Future research should be conducted with a larger sample size of nursing students from various faculties and a design that investigates cause-and-effect relationships.

A further restriction of this study concerns the scenarios posed in the NDT questionnaire. The responses and associated interpretations can be subjected to the impact of the cultural context. Generalizing the results of these scenarios to other communities can be challenging. A blended approach was adopted to collect students’ data to optimize time and minimize time wastage. This can contribute to the potential response differences between these modes and impact the results.

Despite an in-depth examination of the current literature and consultation with experts in the field, no specific confounding factors could be determined due to the limited number of relevant studies. Consequently, we analyzed the correlation between demographic characteristics and the main variables of the study. In the regression model, we included only those cases that demonstrated a substantial relationship with the primary variable below the level of 0.02. It is recommended that future researchers strive to identify confounding factors and mitigate their impact on the relationship between the main variables, thereby enhancing the generalizability of the results.

Implications for nursing education

Limited research has been conducted on the influence of educational factors, particularly curriculum, on students’ professional identity and moral reasoning. It appears that, given the unique circumstances of the nursing profession, particularly in light of the COVID-19 pandemic, modifying the student curriculum would enhance their circumstances. Nursing educators must be qualified to successfully guide students in cultivating a suitable professional identity.

The findings from our study suggest that nurses showed a high level of moral reasoning and uphold professional values. Also, results revealed a noteworthy correlation between students’ moral reasoning and professional values. The predictive value of moral reasoning in determining professional value was satisfactory. When considering socio-demographic variables, an interest in nursing was found to have a significant effect on professional values. Our research findings indicate that enhancing professional identity and moral reasoning can improve students’ circumstances. Furthermore, generating interest in the nursing profession can impact the professional identity of these students.

Data availability

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

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Medical Ethics and Law Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Amir Mohamad Nazari, Fariba Borhani, Akbar Zare-Kaseb & Abbas Abbaszadeh

PhD Candidate in Nursing, Student Research Committee, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran

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Contributions

A.N. and A.Z. wrote the main manuscript text and A.Z. prepared Tables 1, 2, 3, 4, 5, 6 and 7. Statistical analysis was done by V.Y. All authors reviewed the manuscript.

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Correspondence to Fariba Borhani or Akbar Zare-Kaseb .

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This study was approved by the Medical Ethics and Law Research Center of Shahid Beheshti University of Medical Sciences (IR.SBMU.RETECH.REC.1402.347). Informed written consent was obtained, and they were also assured of the confidentiality of their information. In order to maintain confidentiality, participants were clearly told that they do not need to reveal personal information like their name, last name, or any other identifying details.

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Nazari, A.M., Borhani, F., Zare-Kaseb, A. et al. The relationship between moral reasoning and nurses’ professional values in undergraduate nursing students: a descriptive-correlational study. BMC Med Educ 24 , 889 (2024). https://doi.org/10.1186/s12909-024-05888-z

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Received : 15 June 2024

Accepted : 12 August 2024

Published : 19 August 2024

DOI : https://doi.org/10.1186/s12909-024-05888-z

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