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Correlation Studies in Psychology Research
Determining the relationship between two or more variables.
Verywell / Brianna Gilmartin
- Characteristics
Potential Pitfalls
Frequently asked questions.
A correlational study is a type of research design that looks at the relationships between two or more variables. Correlational studies are non-experimental, which means that the experimenter does not manipulate or control any of the variables.
A correlation refers to a relationship between two variables. Correlations can be strong or weak and positive or negative. Sometimes, there is no correlation.
There are three possible outcomes of a correlation study: a positive correlation, a negative correlation, or no correlation. Researchers can present the results using a numerical value called the correlation coefficient, a measure of the correlation strength. It can range from –1.00 (negative) to +1.00 (positive). A correlation coefficient of 0 indicates no correlation.
- Positive correlations : Both variables increase or decrease at the same time. A correlation coefficient close to +1.00 indicates a strong positive correlation.
- Negative correlations : As the amount of one variable increases, the other decreases (and vice versa). A correlation coefficient close to -1.00 indicates a strong negative correlation.
- No correlation : There is no relationship between the two variables. A correlation coefficient of 0 indicates no correlation.
Characteristics of a Correlational Study
Correlational studies are often used in psychology, as well as other fields like medicine. Correlational research is a preliminary way to gather information about a topic. The method is also useful if researchers are unable to perform an experiment.
Researchers use correlations to see if a relationship between two or more variables exists, but the variables themselves are not under the control of the researchers.
While correlational research can demonstrate a relationship between variables, it cannot prove that changing one variable will change another. In other words, correlational studies cannot prove cause-and-effect relationships.
When you encounter research that refers to a "link" or an "association" between two things, they are most likely talking about a correlational study.
Types of Correlational Research
There are three types of correlational research: naturalistic observation, the survey method, and archival research. Each type has its own purpose, as well as its pros and cons.
Naturalistic Observation
The naturalistic observation method involves observing and recording variables of interest in a natural setting without interference or manipulation.
Can inspire ideas for further research
Option if lab experiment not available
Variables are viewed in natural setting
Can be time-consuming and expensive
Extraneous variables can't be controlled
No scientific control of variables
Subjects might behave differently if aware of being observed
This method is well-suited to studies where researchers want to see how variables behave in their natural setting or state. Inspiration can then be drawn from the observations to inform future avenues of research.
In some cases, it might be the only method available to researchers; for example, if lab experimentation would be precluded by access, resources, or ethics. It might be preferable to not being able to conduct research at all, but the method can be costly and usually takes a lot of time.
Naturalistic observation presents several challenges for researchers. For one, it does not allow them to control or influence the variables in any way nor can they change any possible external variables.
However, this does not mean that researchers will get reliable data from watching the variables, or that the information they gather will be free from bias.
For example, study subjects might act differently if they know that they are being watched. The researchers might not be aware that the behavior that they are observing is not necessarily the subject's natural state (i.e., how they would act if they did not know they were being watched).
Researchers also need to be aware of their biases, which can affect the observation and interpretation of a subject's behavior.
Surveys and questionnaires are some of the most common methods used for psychological research. The survey method involves having a random sample of participants complete a survey, test, or questionnaire related to the variables of interest. Random sampling is vital to the generalizability of a survey's results.
Cheap, easy, and fast
Can collect large amounts of data in a short amount of time
Results can be affected by poor survey questions
Results can be affected by unrepresentative sample
Outcomes can be affected by participants
If researchers need to gather a large amount of data in a short period of time, a survey is likely to be the fastest, easiest, and cheapest option.
It's also a flexible method because it lets researchers create data-gathering tools that will help ensure they get the information they need (survey responses) from all the sources they want to use (a random sample of participants taking the survey).
Survey data might be cost-efficient and easy to get, but it has its downsides. For one, the data is not always reliable—particularly if the survey questions are poorly written or the overall design or delivery is weak. Data is also affected by specific faults, such as unrepresented or underrepresented samples .
The use of surveys relies on participants to provide useful data. Researchers need to be aware of the specific factors related to the people taking the survey that will affect its outcome.
For example, some people might struggle to understand the questions. A person might answer a particular way to try to please the researchers or to try to control how the researchers perceive them (such as trying to make themselves "look better").
Sometimes, respondents might not even realize that their answers are incorrect or misleading because of mistaken memories .
Archival Research
Many areas of psychological research benefit from analyzing studies that were conducted long ago by other researchers, as well as reviewing historical records and case studies.
For example, in an experiment known as "The Irritable Heart ," researchers used digitalized records containing information on American Civil War veterans to learn more about post-traumatic stress disorder (PTSD).
Large amount of data
Can be less expensive
Researchers cannot change participant behavior
Can be unreliable
Information might be missing
No control over data collection methods
Using records, databases, and libraries that are publicly accessible or accessible through their institution can help researchers who might not have a lot of money to support their research efforts.
Free and low-cost resources are available to researchers at all levels through academic institutions, museums, and data repositories around the world.
Another potential benefit is that these sources often provide an enormous amount of data that was collected over a very long period of time, which can give researchers a way to view trends, relationships, and outcomes related to their research.
While the inability to change variables can be a disadvantage of some methods, it can be a benefit of archival research. That said, using historical records or information that was collected a long time ago also presents challenges. For one, important information might be missing or incomplete and some aspects of older studies might not be useful to researchers in a modern context.
A primary issue with archival research is reliability. When reviewing old research, little information might be available about who conducted the research, how a study was designed, who participated in the research, as well as how data was collected and interpreted.
Researchers can also be presented with ethical quandaries—for example, should modern researchers use data from studies that were conducted unethically or with questionable ethics?
You've probably heard the phrase, "correlation does not equal causation." This means that while correlational research can suggest that there is a relationship between two variables, it cannot prove that one variable will change another.
For example, researchers might perform a correlational study that suggests there is a relationship between academic success and a person's self-esteem. However, the study cannot show that academic success changes a person's self-esteem.
To determine why the relationship exists, researchers would need to consider and experiment with other variables, such as the subject's social relationships, cognitive abilities, personality, and socioeconomic status.
The difference between a correlational study and an experimental study involves the manipulation of variables. Researchers do not manipulate variables in a correlational study, but they do control and systematically vary the independent variables in an experimental study. Correlational studies allow researchers to detect the presence and strength of a relationship between variables, while experimental studies allow researchers to look for cause and effect relationships.
If the study involves the systematic manipulation of the levels of a variable, it is an experimental study. If researchers are measuring what is already present without actually changing the variables, then is a correlational study.
The variables in a correlational study are what the researcher measures. Once measured, researchers can then use statistical analysis to determine the existence, strength, and direction of the relationship. However, while correlational studies can say that variable X and variable Y have a relationship, it does not mean that X causes Y.
The goal of correlational research is often to look for relationships, describe these relationships, and then make predictions. Such research can also often serve as a jumping off point for future experimental research.
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Carpenter S. Visualizing Psychology . 3rd ed. John Wiley & Sons; 2012:14-30.
Pizarro J, Silver RC, Prause J. Physical and mental health costs of traumatic war experiences among civil war veterans . Arch Gen Psychiatry . 2006;63(2):193. doi:10.1001/archpsyc.63.2.193
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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
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Home » Correlational Research – Methods, Types and Examples
Correlational Research – Methods, Types and Examples
Table of Contents
Correlational Research
Correlational Research is a type of research that examines the statistical relationship between two or more variables without manipulating them. It is a non-experimental research design that seeks to establish the degree of association or correlation between two or more variables.
Types of Correlational Research
There are three types of correlational research:
Positive Correlation
A positive correlation occurs when two variables increase or decrease together. This means that as one variable increases, the other variable also tends to increase. Similarly, as one variable decreases, the other variable also tends to decrease. For example, there is a positive correlation between the amount of time spent studying and academic performance. The more time a student spends studying, the higher their academic performance is likely to be. Similarly, there is a positive correlation between a person’s age and their income level. As a person gets older, they tend to earn more money.
Negative Correlation
A negative correlation occurs when one variable increases while the other decreases. This means that as one variable increases, the other variable tends to decrease. Similarly, as one variable decreases, the other variable tends to increase. For example, there is a negative correlation between the number of hours spent watching TV and physical activity level. The more time a person spends watching TV, the less physically active they are likely to be. Similarly, there is a negative correlation between the amount of stress a person experiences and their overall happiness. As stress levels increase, happiness levels tend to decrease.
Zero Correlation
A zero correlation occurs when there is no relationship between two variables. This means that the variables are unrelated and do not affect each other. For example, there is zero correlation between a person’s shoe size and their IQ score. The size of a person’s feet has no relationship to their level of intelligence. Similarly, there is zero correlation between a person’s height and their favorite color. The two variables are unrelated to each other.
Correlational Research Methods
Correlational research can be conducted using different methods, including:
Surveys are a common method used in correlational research. Researchers collect data by asking participants to complete questionnaires or surveys that measure different variables of interest. Surveys are useful for exploring the relationships between variables such as personality traits, attitudes, and behaviors.
Observational Studies
Observational studies involve observing and recording the behavior of participants in natural settings. Researchers can use observational studies to examine the relationships between variables such as social interactions, group dynamics, and communication patterns.
Archival Data
Archival data involves using existing data sources such as historical records, census data, or medical records to explore the relationships between variables. Archival data is useful for investigating the relationships between variables that cannot be manipulated or controlled.
Experimental Design
While correlational research does not involve manipulating variables, researchers can use experimental design to establish cause-and-effect relationships between variables. Experimental design involves manipulating one variable while holding other variables constant to determine the effect on the dependent variable.
Meta-Analysis
Meta-analysis involves combining and analyzing the results of multiple studies to explore the relationships between variables across different contexts and populations. Meta-analysis is useful for identifying patterns and inconsistencies in the literature and can provide insights into the strength and direction of relationships between variables.
Data Analysis Methods
Correlational research data analysis methods depend on the type of data collected and the research questions being investigated. Here are some common data analysis methods used in correlational research:
Correlation Coefficient
A correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables. The correlation coefficient ranges from -1 to +1, with -1 indicating a perfect negative correlation, +1 indicating a perfect positive correlation, and 0 indicating no correlation. Researchers use correlation coefficients to determine the degree to which two variables are related.
Scatterplots
A scatterplot is a graphical representation of the relationship between two variables. Each data point on the plot represents a single observation. The x-axis represents one variable, and the y-axis represents the other variable. The pattern of data points on the plot can provide insights into the strength and direction of the relationship between the two variables.
Regression Analysis
Regression analysis is a statistical method used to model the relationship between two or more variables. Researchers use regression analysis to predict the value of one variable based on the value of another variable. Regression analysis can help identify the strength and direction of the relationship between variables, as well as the degree to which one variable can be used to predict the other.
Factor Analysis
Factor analysis is a statistical method used to identify patterns among variables. Researchers use factor analysis to group variables into factors that are related to each other. Factor analysis can help identify underlying factors that influence the relationship between two variables.
Path Analysis
Path analysis is a statistical method used to model the relationship between multiple variables. Researchers use path analysis to test causal models and identify direct and indirect effects between variables.
Applications of Correlational Research
Correlational research has many practical applications in various fields, including:
- Psychology : Correlational research is commonly used in psychology to explore the relationships between variables such as personality traits, behaviors, and mental health outcomes. For example, researchers may use correlational research to examine the relationship between anxiety and depression, or the relationship between self-esteem and academic achievement.
- Education : Correlational research is useful in educational research to explore the relationships between variables such as teaching methods, student motivation, and academic performance. For example, researchers may use correlational research to examine the relationship between student engagement and academic success, or the relationship between teacher feedback and student learning outcomes.
- Business : Correlational research can be used in business to explore the relationships between variables such as consumer behavior, marketing strategies, and sales outcomes. For example, marketers may use correlational research to examine the relationship between advertising spending and sales revenue, or the relationship between customer satisfaction and brand loyalty.
- Medicine : Correlational research is useful in medical research to explore the relationships between variables such as risk factors, disease outcomes, and treatment effectiveness. For example, researchers may use correlational research to examine the relationship between smoking and lung cancer, or the relationship between exercise and heart health.
- Social Science : Correlational research is commonly used in social science research to explore the relationships between variables such as socioeconomic status, cultural factors, and social behavior. For example, researchers may use correlational research to examine the relationship between income and voting behavior, or the relationship between cultural values and attitudes towards immigration.
Examples of Correlational Research
- Psychology : Researchers might be interested in exploring the relationship between two variables, such as parental attachment and anxiety levels in young adults. The study could involve measuring levels of attachment and anxiety using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying potential risk factors for anxiety in young adults, and in developing interventions that could help improve attachment and reduce anxiety.
- Education : In a correlational study in education, researchers might investigate the relationship between two variables, such as teacher engagement and student motivation in a classroom setting. The study could involve measuring levels of teacher engagement and student motivation using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying strategies that teachers could use to improve student motivation and engagement in the classroom.
- Business : Researchers might explore the relationship between two variables, such as employee satisfaction and productivity levels in a company. The study could involve measuring levels of employee satisfaction and productivity using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying factors that could help increase productivity and improve job satisfaction among employees.
- Medicine : Researchers might examine the relationship between two variables, such as smoking and the risk of developing lung cancer. The study could involve collecting data on smoking habits and lung cancer diagnoses, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying risk factors for lung cancer and in developing interventions that could help reduce smoking rates.
- Sociology : Researchers might investigate the relationship between two variables, such as income levels and political attitudes. The study could involve measuring income levels and political attitudes using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in understanding how socioeconomic factors can influence political beliefs and attitudes.
How to Conduct Correlational Research
Here are the general steps to conduct correlational research:
- Identify the Research Question : Start by identifying the research question that you want to explore. It should involve two or more variables that you want to investigate for a correlation.
- Choose the research method: Decide on the research method that will be most appropriate for your research question. The most common methods for correlational research are surveys, archival research, and naturalistic observation.
- Choose the Sample: Select the participants or data sources that you will use in your study. Your sample should be representative of the population you want to generalize the results to.
- Measure the variables: Choose the measures that will be used to assess the variables of interest. Ensure that the measures are reliable and valid.
- Collect the Data: Collect the data from your sample using the chosen research method. Be sure to maintain ethical standards and obtain informed consent from your participants.
- Analyze the data: Use statistical software to analyze the data and compute the correlation coefficient. This will help you determine the strength and direction of the correlation between the variables.
- Interpret the results: Interpret the results and draw conclusions based on the findings. Consider any limitations or alternative explanations for the results.
- Report the findings: Report the findings of your study in a research report or manuscript. Be sure to include the research question, methods, results, and conclusions.
Purpose of Correlational Research
The purpose of correlational research is to examine the relationship between two or more variables. Correlational research allows researchers to identify whether there is a relationship between variables, and if so, the strength and direction of that relationship. This information can be useful for predicting and explaining behavior, and for identifying potential risk factors or areas for intervention.
Correlational research can be used in a variety of fields, including psychology, education, medicine, business, and sociology. For example, in psychology, correlational research can be used to explore the relationship between personality traits and behavior, or between early life experiences and later mental health outcomes. In education, correlational research can be used to examine the relationship between teaching practices and student achievement. In medicine, correlational research can be used to investigate the relationship between lifestyle factors and disease outcomes.
Overall, the purpose of correlational research is to provide insight into the relationship between variables, which can be used to inform further research, interventions, or policy decisions.
When to use Correlational Research
Here are some situations when correlational research can be particularly useful:
- When experimental research is not possible or ethical: In some situations, it may not be possible or ethical to manipulate variables in an experimental design. In these cases, correlational research can be used to explore the relationship between variables without manipulating them.
- When exploring new areas of research: Correlational research can be useful when exploring new areas of research or when researchers are unsure of the direction of the relationship between variables. Correlational research can help identify potential areas for further investigation.
- When testing theories: Correlational research can be useful for testing theories about the relationship between variables. Researchers can use correlational research to examine the relationship between variables predicted by a theory, and to determine whether the theory is supported by the data.
- When making predictions: Correlational research can be used to make predictions about future behavior or outcomes. For example, if there is a strong positive correlation between education level and income, one could predict that individuals with higher levels of education will have higher incomes.
- When identifying risk factors: Correlational research can be useful for identifying potential risk factors for negative outcomes. For example, a study might find a positive correlation between drug use and depression, indicating that drug use could be a risk factor for depression.
Characteristics of Correlational Research
Here are some common characteristics of correlational research:
- Examines the relationship between two or more variables: Correlational research is designed to examine the relationship between two or more variables. It seeks to determine if there is a relationship between the variables, and if so, the strength and direction of that relationship.
- Non-experimental design: Correlational research is typically non-experimental in design, meaning that the researcher does not manipulate any variables. Instead, the researcher observes and measures the variables as they naturally occur.
- Cannot establish causation : Correlational research cannot establish causation, meaning that it cannot determine whether one variable causes changes in another variable. Instead, it only provides information about the relationship between the variables.
- Uses statistical analysis: Correlational research relies on statistical analysis to determine the strength and direction of the relationship between variables. This may include calculating correlation coefficients, regression analysis, or other statistical tests.
- Observes real-world phenomena : Correlational research is often used to observe real-world phenomena, such as the relationship between education and income or the relationship between stress and physical health.
- Can be conducted in a variety of fields : Correlational research can be conducted in a variety of fields, including psychology, sociology, education, and medicine.
- Can be conducted using different methods: Correlational research can be conducted using a variety of methods, including surveys, observational studies, and archival studies.
Advantages of Correlational Research
There are several advantages of using correlational research in a study:
- Allows for the exploration of relationships: Correlational research allows researchers to explore the relationships between variables in a natural setting without manipulating any variables. This can help identify possible relationships between variables that may not have been previously considered.
- Useful for predicting behavior: Correlational research can be useful for predicting future behavior. If a strong correlation is found between two variables, researchers can use this information to predict how changes in one variable may affect the other.
- Can be conducted in real-world settings: Correlational research can be conducted in real-world settings, which allows for the collection of data that is representative of real-world phenomena.
- Can be less expensive and time-consuming than experimental research: Correlational research is often less expensive and time-consuming than experimental research, as it does not involve manipulating variables or creating controlled conditions.
- Useful in identifying risk factors: Correlational research can be used to identify potential risk factors for negative outcomes. By identifying variables that are correlated with negative outcomes, researchers can develop interventions or policies to reduce the risk of negative outcomes.
- Useful in exploring new areas of research: Correlational research can be useful in exploring new areas of research, particularly when researchers are unsure of the direction of the relationship between variables. By conducting correlational research, researchers can identify potential areas for further investigation.
Limitation of Correlational Research
Correlational research also has several limitations that should be taken into account:
- Cannot establish causation: Correlational research cannot establish causation, meaning that it cannot determine whether one variable causes changes in another variable. This is because it is not possible to control all possible confounding variables that could affect the relationship between the variables being studied.
- Directionality problem: The directionality problem refers to the difficulty of determining which variable is influencing the other. For example, a correlation may exist between happiness and social support, but it is not clear whether social support causes happiness, or whether happy people are more likely to have social support.
- Third variable problem: The third variable problem refers to the possibility that a third variable, not included in the study, is responsible for the observed relationship between the two variables being studied.
- Limited generalizability: Correlational research is often limited in terms of its generalizability to other populations or settings. This is because the sample studied may not be representative of the larger population, or because the variables studied may behave differently in different contexts.
- Relies on self-reported data: Correlational research often relies on self-reported data, which can be subject to social desirability bias or other forms of response bias.
- Limited in explaining complex behaviors: Correlational research is limited in explaining complex behaviors that are influenced by multiple factors, such as personality traits, situational factors, and social context.
About the author
Muhammad Hassan
Researcher, Academic Writer, Web developer
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7.2 Correlational Research
Learning objectives.
- Define correlational research and give several examples.
- Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of nonexperimental research.
What Is Correlational Research?
Correlational research is a type of nonexperimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are essentially two reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms independent variable and dependent variable do not apply to this kind of research.
The other reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, Allen Kanner and his colleagues thought that the number of “daily hassles” (e.g., rude salespeople, heavy traffic) that people experience affects the number of physical and psychological symptoms they have (Kanner, Coyne, Schaefer, & Lazarus, 1981). But because they could not manipulate the number of daily hassles their participants experienced, they had to settle for measuring the number of daily hassles—along with the number of symptoms—using self-report questionnaires. Although the strong positive relationship they found between these two variables is consistent with their idea that hassles cause symptoms, it is also consistent with the idea that symptoms cause hassles or that some third variable (e.g., neuroticism) causes both.
A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.
Figure 7.2 “Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists” shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.
Figure 7.2 Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists
Data Collection in Correlational Research
Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. However, because some approaches to data collection are strongly associated with correlational research, it makes sense to discuss them here. The two we will focus on are naturalistic observation and archival data. A third, survey research, is discussed in its own chapter.
Naturalistic Observation
Naturalistic observation is an approach to data collection that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). It could involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are often not aware that they are being studied. Ethically, this is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated.
Researchers Robert Levine and Ara Norenzayan used naturalistic observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999). One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in the United States and Japan covered 60 feet in about 12 seconds on average, while people in Brazil and Romania took close to 17 seconds.
Because naturalistic observation takes place in the complex and even chaotic “real world,” there are two closely related issues that researchers must deal with before collecting data. The first is sampling. When, where, and under what conditions will the observations be made, and who exactly will be observed? Levine and Norenzayan described their sampling process as follows:
Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities. (p. 186)
Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.
The second issue is measurement. What specific behaviors will be observed? In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance. Often, however, the behaviors of interest are not so obvious or objective. For example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979). But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.
Naturalistic observation has revealed that bowlers tend to smile when they turn away from the pins and toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.
sieneke toering – bowling big lebowski style – CC BY-NC-ND 2.0.
When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as coding . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This is the issue of interrater reliability. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.
Archival Data
Another approach to correlational research is the use of archival data , which are data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005). In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.
As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988). In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them, were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as college students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as college students, the healthier they were as older men. Pearson’s r was +.25.
This is an example of content analysis —a family of systematic approaches to measurement using complex archival data. Just as naturalistic observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.
Key Takeaways
- Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
- Correlational research is not defined by where or how the data are collected. However, some approaches to data collection are strongly associated with correlational research. These include naturalistic observation (in which researchers observe people’s behavior in the context in which it normally occurs) and the use of archival data that were already collected for some other purpose.
Discussion: For each of the following, decide whether it is most likely that the study described is experimental or correlational and explain why.
- An educational researcher compares the academic performance of students from the “rich” side of town with that of students from the “poor” side of town.
- A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
- A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
- An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
- A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
- A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.
Kanner, A. D., Coyne, J. C., Schaefer, C., & Lazarus, R. S. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine, 4 , 1–39.
Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553.
Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205.
Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110.
Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27.
Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
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Statistics By Jim
Making statistics intuitive
Correlational Study Overview & Examples
By Jim Frost 2 Comments
What is a Correlational Study?
A correlational study is an experimental design that evaluates only the correlation between variables. The researchers record measurements but do not control or manipulate the variables. Correlational research is a form of observational study .
A correlation indicates that as the value of one variable increases, the other tends to change in a specific direction:
- Positive correlation : Two variables increase or decrease together (as height increases, weight tends to increase).
- Negative correlation : As one variable increases, the other tends to decrease (as school absences increase, grades tend to fall).
- No correlation : No relationship exists between the two variables. As one increases, the other does not change in a specific direction (as absences increase, height doesn’t tend to increase or decrease).
For example, researchers conducting correlational research explored the relationship between social media usage and levels of anxiety in young adults. Participants reported their demographic information and daily time on various social media platforms and completed a standardized anxiety assessment tool.
The correlational study looked for relationships between social media usage and anxiety. Is increased social media usage associated with higher anxiety? Is it worse for particular demographics?
Learn more about Interpreting Correlation .
Using Correlational Research
Correlational research design is crucial in various disciplines, notably psychology and medicine. This type of design is generally cheaper, easier, and quicker to conduct than an experiment because the researchers don’t control any variables or conditions. Consequently, these studies often serve as an initial assessment, especially when random assignment and controlling variables for a true experiment are not feasible or unethical.
However, an unfortunate aspect of a correlational study is its limitation in establishing causation. While these studies can reveal connections between variables, they cannot prove that altering one variable will cause changes in another. Hence, correlational research can determine whether relationships exist but cannot confirm causality.
Remember, correlation doesn’t necessarily imply causation !
Correlational Study vs Experiment
The difference between the two designs is simple.
In a correlational study, the researchers don’t systematically control any variables. They’re simply observing events and do not want to influence outcomes.
In an experiment, researchers manipulate variables and explicitly hope to affect the outcomes. For example, they might control the treatment condition by giving a medication or placebo to each subject. They also randomly assign subjects to the control and treatment groups, which helps establish causality.
Learn more about Randomized Controlled Trials (RCTs) , which statisticians consider to be true experiments.
Types of Correlation Studies and Examples
Researchers divide these studies into three broad types.
Secondary Data Sources
One approach to correlational research is to utilize pre-existing data, which may include official records, public polls, or data from earlier studies. This method can be cost-effective and time-efficient because other researchers have already gathered the data. These existing data sources can provide large sample sizes and longitudinal data , thereby showing relationship trends.
However, it also comes with potential drawbacks. The data may be incomplete or irrelevant to the new research question. Additionally, as a researcher, you won’t have control over the original data collection methods, potentially impacting the data’s reliability and validity .
Using existing data makes this approach a retrospective study .
Surveys in Correlation Research
Surveys are a great way to collect data for correlational studies while using a consistent instrument across all respondents. You can use various formats, such as in-person, online, and by phone. And you can ask the questions necessary to obtain the particular variables you need for your project. In short, it’s easy to customize surveys to match your study’s requirements.
However, you’ll need to carefully word all the questions to be clear and not introduce bias in the results. This process can take multiple iterations and pilot studies to produce the finished survey.
For example, you can use a survey to find correlations between various demographic variables and political opinions.
Naturalistic Observation
Naturalistic observation is a method of collecting field data for a correlational study. Researchers observe and measure variables in a natural environment. The process can include counting events, categorizing behavior, and describing outcomes without interfering with the activities.
For example, researchers might observe and record children’s behavior after watching television. Does a relationship exist between the type of television program and behaviors?
Naturalistic observations occur in a prospective study .
Analyzing Data from a Correlational Study
Statistical analysis of correlational research frequently involves correlation and regression analysis .
A correlation coefficient describes the strength and direction of the relationship between two variables with a single number.
Regression analysis can evaluate how multiple variables relate to a single outcome. For example, in the social media correlational study example, how do the demographic variables and daily social media usage collectively correlate with anxiety?
Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research . Nurse Researcher . 2016;23(6):20-25. doi:10.7748/nr.2016.e1382
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January 14, 2024 at 4:34 pm
Hi Jim. Have you written a blog note dedicated to clinical trials? If not, besides the note on hypothesis testing, are there other blogs ypo have written that touch on clinical trials?
January 14, 2024 at 5:49 pm
Hi Stan, I haven’t written a blog post specifically about clinical trials, but I have the following related posts:
Randomized Controlled Trials Clinical Trial about a COVID vaccine Clinical Trials about flu vaccines
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- Correlational Research | Guide, Design & Examples
Correlational Research | Guide, Design & Examples
Published on 5 May 2022 by Pritha Bhandari . Revised on 5 December 2022.
A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them.
A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.
Positive correlation | Both variables change in the same direction | As height increases, weight also increases |
---|---|---|
Negative correlation | The variables change in opposite directions | As coffee consumption increases, tiredness decreases |
Zero correlation | There is no relationship between the variables | Coffee consumption is not correlated with height |
Table of contents
Correlational vs experimental research, when to use correlational research, how to collect correlational data, how to analyse correlational data, correlation and causation, frequently asked questions about correlational research.
Correlational and experimental research both use quantitative methods to investigate relationships between variables. But there are important differences in how data is collected and the types of conclusions you can draw.
Correlational research | Experimental research | |
---|---|---|
Purpose | Used to test strength of association between variables | Used to test cause-and-effect relationships between variables |
Variables | Variables are only observed with no manipulation or intervention by researchers | An is manipulated and a dependent variable is observed |
Control | Limited is used, so other variables may play a role in the relationship | are controlled so that they can’t impact your variables of interest |
Validity | High : you can confidently generalise your conclusions to other populations or settings | High : you can confidently draw conclusions about causation |
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Correlational research is ideal for gathering data quickly from natural settings. That helps you generalise your findings to real-life situations in an externally valid way.
There are a few situations where correlational research is an appropriate choice.
To investigate non-causal relationships
You want to find out if there is an association between two variables, but you don’t expect to find a causal relationship between them.
Correlational research can provide insights into complex real-world relationships, helping researchers develop theories and make predictions.
To explore causal relationships between variables
You think there is a causal relationship between two variables, but it is impractical, unethical, or too costly to conduct experimental research that manipulates one of the variables.
Correlational research can provide initial indications or additional support for theories about causal relationships.
To test new measurement tools
You have developed a new instrument for measuring your variable, and you need to test its reliability or validity .
Correlational research can be used to assess whether a tool consistently or accurately captures the concept it aims to measure.
There are many different methods you can use in correlational research. In the social and behavioural sciences, the most common data collection methods for this type of research include surveys, observations, and secondary data.
It’s important to carefully choose and plan your methods to ensure the reliability and validity of your results. You should carefully select a representative sample so that your data reflects the population you’re interested in without bias .
In survey research , you can use questionnaires to measure your variables of interest. You can conduct surveys online, by post, by phone, or in person.
Surveys are a quick, flexible way to collect standardised data from many participants, but it’s important to ensure that your questions are worded in an unbiased way and capture relevant insights.
Naturalistic observation
Naturalistic observation is a type of field research where you gather data about a behaviour or phenomenon in its natural environment.
This method often involves recording, counting, describing, and categorising actions and events. Naturalistic observation can include both qualitative and quantitative elements, but to assess correlation, you collect data that can be analysed quantitatively (e.g., frequencies, durations, scales, and amounts).
Naturalistic observation lets you easily generalise your results to real-world contexts, and you can study experiences that aren’t replicable in lab settings. But data analysis can be time-consuming and unpredictable, and researcher bias may skew the interpretations.
Secondary data
Instead of collecting original data, you can also use data that has already been collected for a different purpose, such as official records, polls, or previous studies.
Using secondary data is inexpensive and fast, because data collection is complete. However, the data may be unreliable, incomplete, or not entirely relevant, and you have no control over the reliability or validity of the data collection procedures.
After collecting data, you can statistically analyse the relationship between variables using correlation or regression analyses, or both. You can also visualise the relationships between variables with a scatterplot.
Different types of correlation coefficients and regression analyses are appropriate for your data based on their levels of measurement and distributions .
Correlation analysis
Using a correlation analysis, you can summarise the relationship between variables into a correlation coefficient : a single number that describes the strength and direction of the relationship between variables. With this number, you’ll quantify the degree of the relationship between variables.
The Pearson product-moment correlation coefficient, also known as Pearson’s r , is commonly used for assessing a linear relationship between two quantitative variables.
Correlation coefficients are usually found for two variables at a time, but you can use a multiple correlation coefficient for three or more variables.
Regression analysis
With a regression analysis , you can predict how much a change in one variable will be associated with a change in the other variable. The result is a regression equation that describes the line on a graph of your variables.
You can use this equation to predict the value of one variable based on the given value(s) of the other variable(s). It’s best to perform a regression analysis after testing for a correlation between your variables.
It’s important to remember that correlation does not imply causation . Just because you find a correlation between two things doesn’t mean you can conclude one of them causes the other, for a few reasons.
Directionality problem
If two variables are correlated, it could be because one of them is a cause and the other is an effect. But the correlational research design doesn’t allow you to infer which is which. To err on the side of caution, researchers don’t conclude causality from correlational studies.
Third variable problem
A confounding variable is a third variable that influences other variables to make them seem causally related even though they are not. Instead, there are separate causal links between the confounder and each variable.
In correlational research, there’s limited or no researcher control over extraneous variables . Even if you statistically control for some potential confounders, there may still be other hidden variables that disguise the relationship between your study variables.
Although a correlational study can’t demonstrate causation on its own, it can help you develop a causal hypothesis that’s tested in controlled experiments.
A correlation reflects the strength and/or direction of the association between two or more variables.
- A positive correlation means that both variables change in the same direction.
- A negative correlation means that the variables change in opposite directions.
- A zero correlation means there’s no relationship between the variables.
A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .
Controlled experiments establish causality, whereas correlational studies only show associations between variables.
- In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
- In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.
In general, correlational research is high in external validity while experimental research is high in internal validity .
A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.
A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.
Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.
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Correlation analysis in clinical and experimental studies
Hélio amante miot.
1 Universidade Estadual Paulista – UNESP, Faculdade de Medicina de Botucatu, Departamento de Dermatologia e Radioterapia, Botucatu, SP, Brasil.
It is common for researchers conducting clinical or biomedical studies to be interested in investigating whether the values of two or more quantitative variables change in conjunction in a given individual or object of study. In other words, whether when the value of one variable increases, the value of another tends to increase/ or, inversely, reduce, progressively. There are many different statistical tests that explore the intensity and direction of this mutual behavior of variables, known as correlation tests. 1 , 2
The first step in analyzing correlations between two quantitative variables should be to look at a scatter plot, in order to discern whether there is a gradual variability between the sets of variables, whether this variation is monotonic (predominantly increasing or decreasing), if it follows a proportional tendency (linear), and whether the underlying distribution of the data is normal. 2 - 4 Different combinations of these premises indicate a need for different techniques for correlation analysis.
Figure 1 illustrates the distribution of values of four hypothetical variables (V1, V2, V3, and V4), which exhibit data that follow a normal distribution (Shapiro-Wilk, p > 0.32).
Variables V1 and V2 exhibit simultaneously increasing values, which are distributed around an underlying imaginary (ideal) straight line, which describes the trajectory of the data. It can be stated that there is a positive linear correlation between V1 and V2. For example, Rossi et al. identified a strong positive correlation (ρ = 0.82; p < 0.01) between cores on the Venous Symptoms Clinical Severity Scale and pain in chronic venous disease. 5
In contrast, variables V1 and V3 exhibit antagonistic behavior: when the values of one increase, the values of the other reduce. It can be stated that there is a negative linear correlation between V1 and V3, just as Ohki and Bellen identified a moderate negative correlation (ρ = -0.65; p < 0.01) between average regional temperature and the incidence of venous thrombosis. 6
It can also be observed that the values for the correlation between V1 and V3 are closer to the imaginary straight line than the values for the correlation between V1 and V2. This invites the conclusion that the relationship between the values of the variables V1 and V3 is stronger than the relationship between V1 and V2, even though the directions are opposite.
Comparisons of the data for V4, whether with V1, V2, or V3, do not reveal gradually increasing or decreasing behavior. This leads to the conclusion that V4 does not exhibit a correlation with the other variables.
The most widely-used technique for evaluating the correlation between two quantitative variables is Pearson’s product-moment correlation coefficient, or Pearson’s r , which requires that both samples follow a normal distribution and that the relationship between the two variables is linear. 2 , 7 Failure to adhere to these prerequisites leads to erroneous conclusions, even when working with large sample sizes.
However, it is very common that samples of clinical and demographic data do not follow a normal distribution (for example, the distributions of income, quality of life indexes, disease severity indexes, years of study, and number of children). The most widely used options for investigating correlations between variables that do not exhibit normal distributions are the Spearman rank order correlation and the Kendall rank correlation coefficient ( Tau -b), which substitute the original data for their ordered ranks. 2 , 7 , 8 These methods are also used in cases in which at least one of the variables has ordinal characteristics (for example, functional class, educational level, cancer staging, social class).
Another advantage of using the Spearman and Kendall nonparametric tests is that they are not restricted to linear correlations, as long as they exhibit monotonic behavior. In other words, they must exhibit a gradual relationship in the same direction (rising or falling) for the whole domain of the data studied.
In Figure 2 , it can be observed that there is no direct proportionality (linear) between the data for V1 and V5; rather there is an increase that is apparently exponential. Since the variation is monotonic (the data for V1 increase as a function of V5), the Spearman and Kendall coefficients can be used to estimate the correlation. In this example, to use the Pearson’s coefficient, it will be necessary to log transform the data to achieve a certain linearity of correlation ( Figure 2 : V1 x V6). It should be noted that the ρ and τ coefficients give the same resultant values for the correlations V1 vs. V5 and V1 vs. V6, since V6 is a transformation of V5 into monotonic data.
In biomedical sciences, the Spearman coefficient (ρ or rho) is the most widely used for evaluating the correlation between two quantitative variables, probably because it is similar to the Pearson method, once the data have been substituted for their ordered ranks. However, care should be taken when generalizing conclusions of interpretations of the correlation between the values of the ranks of data from the variables and the original data.
In contrast, the Kendall Tau-b coefficient (τ or t b ), has mathematical properties that make it more robust to extreme data (outliers), give it a greater capacity for populational inference and a smaller estimation error. While significance (p-value) and direction (+ or -) are similar to those of the Spearman method, the coefficient returns less extreme values and interpretation is different, since it signifies the percentage of observed pairs that take the same direction in the sample (agreement) minus the pairs that do not agree. For example, a τ coefficient of 0.60 signifies that 80% of pairs agree, while 20% disagree (τ = 0.80 - 0.20 = 0.60). 9
Transformation of data (for example, logarithmic, square root, 1/x) in order to obtain a normal distribution to enable Pearson’s coefficient to be tested is a valid option for samples with asymmetrical data distributions ( Figure 2 : V1 x V6). However, it should be borne in mind that, in common with techniques that employ ordered ranks, transformation of data alters the scale between measures and impacts on direct interpretation of the measures of effect. 7
The magnitude of the effect of the correlation between two or more variables is represented by correlation coefficients, which take values from -1 to +1, passing through zero (absence of correlation). Positive coefficients ( r > 0) indicate a direct relationship ( Figure 1 : V1 x V2) between variables; while negative coefficients ( r < 0) indicate an inverse correlation ( Figure 1 : V1 x V3 and V2 x V3).
Each correlation test has its own coefficient, demanding its own interpretation. In general, for the coefficients Pearson’s r and Spearman’s ρ, values from 0 to 0.3 (or 0 to -0.3) are biologically negligible; those from 0.31 to 0.5 (or -0.31 to -0.5) are weak; from 0.51 to 0.7 (or -0.51 and -0.7) are moderate; from 0.71 to 0.9 (or -0.71 to 0.9) are strong correlations; and correlations > 0.9 (or < -0.9) are considered very strong. 8
One peculiarity of Pearson’s r coefficient is that the square of its value provides an estimate of the percentage of variability in the values of one variable that is explained by the variability in the other. For example, a coefficient of r = 0.7 indicates that 49% of the variability of one variable can be explained by, or is followed by, the variation in the values of the other, in the sample tested.
In clinical and biomedical studies, the majority of coefficients with biological significance fall in the range of 0.5 to 0.8 (or -0.5 to -0.8). This is the result of errors of measurement, laboratory techniques, or variation of instruments, which affect the precision of measurements, and also, and primarily, because biological phenomena are affected by multifactorial influences and complex interactions, in which the variation of a single variable cannot totally explain the behavior of another. 2
Tests of the significance of a correlation between quantitative variables are based on the null hypothesis that there is no correlation between the variables ( r = 0), which makes the p-value subject to influence both from the dimension of the effect and from the sample size. This means that caution is necessary when interpreting coefficients that result in a weak correlation (r < 0.3), but have highly significant p-values, caused by overly-large sample sizes. Calculation of sample sizes for analysis of correlations has been explored in an earlier edition of this periodical. 10
Correlation coefficients have inferential properties and, in scientific texts, should preferably be expressed with their 95% confidence intervals and significance (p-value), for example: ρ = 0.76 (95%CI 0.61-0.91), p < 0.01. 11 , 12 In the case of multiple comparisons, coefficients can be shown on their own, in the form of a matrix, and with their significance indicated to facilitate interpretation of the data, as Brianezi et al. presented the 28 correlations between seven histological parameters in a single table. 13 Special cases involving hundreds or thousands of correlations may demand graphical representation techniques, such as the color heatmaps often used in genome studies, just as Hsu et al. represented 4,930 correlations between (85x58) genomic and metabolomic variables. 14
In the case of ordinal data with few categories (for example, satisfaction scores, quality of life items, socioeconomic status), investigations based on the polychoric test of correlation may be more robust (smaller type I errors) than using the Spearman and Kendall tests. 15 Although rarely used, there are also methods for assessing the correlations between variables of a categorical nature (for example, Cramér’s V coefficient) and between dichotomous and quantitative variables (for example, the point-biserial correlation coefficient), but these approaches are beyond the scope of this text. 7
In special situations in which linear correlations between different variables must be analyzed in conjunction (for example, questionnaire items) in order to understand the overall variation in conjunction of the variables, analysis of the correlation between “n” variables can be assessed using the consistency type Intraclass Correlation Coefficient (ICC). There are different ways to analyze ICCs, which result in indicators of different magnitudes. 16 An ICC (random or two-factor mean, mean measures) returns the same value as Cronbach’s α coefficient, used to measure the internal consistency of scales. 17
The identification of a significant correlation between two or more variables should be interpreted with caution, since statistical analysis does not provide evidence of direct dependence or even of causality between the variables, just that they tend to vary in conjunction. 1 , 18 , 19 However, despite the risk of fallacious conclusions of causality or on the basis of results of correlations between variables, correlation tests are important exploratory techniques for investigation of associations between the behavior of groups of variables, facilitating construction of hypothetical models that should then be confirmed by means of dedicated experiments. Indeed, this occurs with ecological clinical studies that often employ correlation techniques for data analysis and provide a basis for subsequent investigations of the phenomena indicated by correlations between indicators and population groups. 20 - 22 The same applied to genome-wide and proteomic studies, considered exploratory, which study the patterns of correlations of findings with clinical variables in order to indicate models for later confirmation. 19 , 23
Indeed, performing multiple correlation tests on a sequence of variables increases the chances of identifying, by chance, correlations described as “spurious”, which should be evaluated in terms of their biological plausibility and confirmed later using appropriate investigation techniques. Use of techniques for correction of p-values adjusted for multiple correlations is always recommended in these conditions. 7 , 19 , 24 - 27
Another limitation of an inferential nature in correlation analyses is rooted in their incapacity for extrapolation of conclusions to different data intervals or different populations from those studied.
Correlation analyses were not developed a priori with the purpose of predicting values or for inference of the participation of multiple variables in the explanation of a phenomenon and there are regression or multivariate analysis techniques that can be used for this purpose. 7 Although there are partial correlation techniques that adjust the correlation values for the behavior of confounding variables (identical to the standardized β coefficient in multivariate linear regression), and polynomial transformation techniques for correction of non-monotonic correlations, an experienced statistics professional should be consulted for planning and execution of analyses of greater complexity.
Correlation analyses can also be employed to compare parallelism of measures between two different scales for measurement of the same phenomenon, such as psychometric quality-of-life scales, 28 or clinimetric scales, such as pressure ulcer risk scales. However, researchers very often use them erroneously to test agreement of data or consecutive measures of the same phenomenon (for example, test-retest, 29 calibration of measurement instruments, interrater comparisons), even though there are more appropriate methods for these purposes. 30
Finally, strategies for evaluation of correlations between variables should be encouraged in clinical and biomedical research, since they maximize understanding of the phenomena studied. However, due to the peculiarities inherent to the different methods, they must be described in detail in the methodology and in the presentation of results.
Financial support: None.
The study was carried out at Departamento de Dermatologia e Radioterapia, Faculdade de Medicina de Botucatu, Universidade Estadual Paulista (UNESP), Botucatu, SP, Brazil.
- J Vasc Bras. 2018 Oct-Dec; 17(4): 275–279.
Análise de correlação em estudos clínicos e experimentais
É habitual que, em uma pesquisa clínica ou biomédica, seja do interesse do pesquisador investigar se os valores de duas ou mais variáveis quantitativas se modificam de forma conjunta em um mesmo sujeito ou objeto de estudo. Ou seja, quando o valor de uma variável aumenta, o valor de outra tende a aumentar; ou, inversamente, reduza-se progressivamente. Há uma série de testes estatísticos que exploram a intensidade e o sentido desse comportamento mútuo entre variáveis, os chamados testes de correlação 1 , 2 .
O primeiro passo para analisar a correlação entre duas variáveis quantitativas deve ser a visualização do diagrama de dispersão, a fim de identificar se existe uma variabilidade gradual entre os conjuntos de dados, se essa variação é monotônica (predominantemente ascendente ou descendente), se assume uma tendência proporcional (linear) e se a distribuição subjacente dos dados é normal 2 - 4 . Diferentes combinações dessas premissas indicam diferentes técnicas de análise de correlação.
A Figura 1 exemplifica a dispersão dos valores entre quatro variáveis hipotéticas (V1, V2, V3 e V4), as quais apresentam dados aderentes à distribuição normal (Shapiro-Wilk, p > 0,32).
As variáveis V1 e V2 apresentam um crescimento simultâneo dos valores, que se distribuem sobre uma linha reta subjacente imaginária (ideal), a qual descreve a trajetória dos dados. Pode-se afirmar que existe correlação linear positiva entre V1 e V2. Da mesma forma, Rossi e colaboradores identificaram forte correlação positiva (ρ = 0,82; p < 0,01) entre os escores da Escala de Gravidade Clínica dos Sintomas Venosos e da escala de dor em doença venosa crônica 5 .
As variáveis V1 e V3, em contrapartida, apresentam um comportamento antagônico: enquanto os valores de uma aumentam, os da outra reduzem. Pode-se afirmar que existe correlação linear negativa entre V1 e V3, assim como Ohki e Bellen identificaram moderada correlação negativa (ρ = -0,65; p < 0,01) entre a temperatura regional média e a incidência de trombose venosa 6 .
Nota-se ainda que os valores da correlação V1 e V3 aproximam-se mais da linha reta imaginária que os valores da correlação entre V1 e V2. Isso leva à conclusão que a relação entre os valores das variáveis V1 e V3 é mais intensa que a relação entre V1 e V2, apesar dos sentidos serem opostos.
As comparações dos dados da V4, sejam com V1, V2 ou V3, não apresentam um comportamento gradualmente ascendente ou descendente. Isso leva à conclusão que V4 não apresenta correlação com as demais variáveis.
A técnica mais difundida para a avaliação da correlação entre duas variáveis quantitativas é o coeficiente de correlação produto-momento de Pearson, ou r de Pearson, que pressupõe distribuição normal das duas amostras e comportamento linear da relação entre as variáveis 2 , 7 . A falta de observação dessas premissas leva a conclusões equivocadas, mesmo diante de amostragem numerosa.
É, porém, bastante comum que amostras de dados clínicos e demográficos não sigam distribuição normal (por exemplo, distribuição de renda, índices de qualidade de vida, índices de gravidade de uma doença, anos de estudo, número de filhos). As opções mais utilizadas na investigação da correlação entre variáveis que não apresentam distribuição normal são a correlação da ordem de postos de Spearman e o coeficiente de correlação de postos de Kendall ( Tau -b), que substituem os dados originais por postos ordenados ( ranks ) 2 , 7 , 8 . Esses métodos também se impõem em casos que ao menos uma das variáveis apresenta características ordinais (por exemplo, classe funcional, nível de escolaridade, estadiamento de câncer, classe social).
Outra vantagem do uso dos testes não paramétricos de Spearman e de Kendall é que eles não se restringem a correlações lineares, desde que apresentem comportamento monotônico. Ou seja, eles devem apresentar uma relação gradual no mesmo sentido (ascendente ou descendente) para todo o domínio de dados estudado.
Na Figura 2 , observa-se que não ocorre proporcionalidade direta (linear) entre a variação dos dados de V1 e V5, mas um incremento aparentemente exponencial. Como a variação é monotônica (os dados de V1 oscilam em um crescente em função de V5), os coeficientes de Spearman e Kendall podem estimar a correlação. Já nesse exemplo, para o emprego do coeficiente de Pearson, seria necessária a transformação logarítmica dos dados para garantir certa linearidade da correlação ( Figura 2 : V1 x V6). Destaque-se que os coeficientes ρ e τ resultaram nos mesmos valores para a correlação V1 versus V5 e para V1 versus V6, já que V6 se tratava de uma transformação de V5 em dados monotônicos.
O coeficiente de Spearman (ρ ou rho) é o mais empregado em ciências biomédicas para avaliar a correlação entre duas variáveis quantitativas, provavelmente porque seja semelhante ao método de Pearson, desde que os dados sejam substituídos por postos ordenados ( ranks ). O que, porém, inspira cuidado na generalização das conclusões quanto à interpretação da correlação entre os valores dos ranks dos dados das variáveis e não dos dados originais.
O coeficiente de Kendall Tau-b (τ ou t b ), em contrapartida, apresenta propriedades matemáticas que o tornam mais robusto a dados extremos ( outliers ), de maior capacidade de inferência populacional e menor erro de estimativa. Apesar da significância (p-valor) e o sentido (+ ou -) se aproximarem do método de Spearman, seu coeficiente apresenta valores menos proeminentes e sua interpretação é diferenciada, significando o percentual de pares observados que assumem o mesmo sentido na amostra (concordantes) menos os pares discordantes. Por exemplo, um coeficiente τ de 0,60 representa que 80% dos pares são concordantes, enquanto 20% são discordantes (τ = 0,80 - 0,20 = 0,60) 9 .
A transformação dos dados (por exemplo, logaritmo, raiz quadrada, 1/x) a fim de obter sua normalização para testar o coeficiente de Pearson é uma alternativa válida em amostras com distribuição dos dados assimétricas ( Figura 2 : V1 x V6). Entretanto, deve-se ter em mente que, da mesma forma que as técnicas que empregam postos ordenados ( ranks ), as transformações dos dados alteram a escala entre as medidas, influenciando a interpretação direta das medidas de efeito 7 .
A magnitude do efeito da correlação entre duas ou mais variáveis é representada pelos coeficientes de correlação, que assumem valores de -1 até +1, passando pelo zero (ausência de correlação). Coeficientes positivos ( r > 0) indicam correlação direta ( Figura 1 : V1 x V2) entre as variáveis; já coeficientes negativos ( r < 0) significam uma correlação inversa ( Figura 1 : V1 x V3 e V2 x V3).
Cada teste de correlação apresenta um coeficiente individualizado, que demanda uma interpretação própria. De forma geral, para os coeficientes r de Pearson e ρ de Spearman, valores entre 0 e 0,3 (ou 0 e-0,3) são biologicamente desprezíveis; entre 0,31 e 0,5 (ou -0,31 e -0,5) são correlações fracas; entre 0,51 e 0,7 (ou -0,51 e -0,7) são moderadas; entre 0,71 e 0,9 (ou -0,71 e 0,9) são correlações fortes; e > 0,9 (ou < -0,9) são consideradas muito fortes 8 .
Uma particularidade do coeficiente r de Pearson é que o quadrado do seu valor representa uma estimativa do percentual de variabilidade dos valores de uma variável que é explicado pela variabilidade da outra. Por exemplo, um coeficiente r = 0,7 significa que 49% da variabilidade de uma variável pode ser explicada, ou é acompanhada pela variação dos valores da outra na amostra testada.
Em estudos clínicos e biomédicos, a maior parte dos coeficientes com significado biológico situa-se entre 0,5 e 0,8 (ou -0,5 e -0,8). Isso decorre tanto da existência de erros de mensuração, técnicas laboratoriais ou variação de instrumentos, que interferem na exatidão e na precisão das medidas, mas, principalmente, porque fenômenos biológicos sofrem influências multifatoriais e interações complexas, em que a variação de apenas uma variável não explica totalmente o comportamento de outra 2 .
Os testes de significância da correlação entre variáveis quantitativas se baseiam na hipótese nula que não existe nenhuma correlação entre as variáveis ( r = 0), sujeitando o p-valor à influência tanto da dimensão do efeito quanto do tamanho amostral. Isso exige cautela na interpretação de coeficientes que resultem em baixa correlação (r < 0,3), mas que apresentem p-valores muito significativos, decorrentes de amostras superdimensionadas. O cálculo do tamanho amostral para análise de correlação já foi explorado anteriormente nesta revista 10 .
Os coeficientes de correlação apresentam propriedades inferenciais e devem ser preferencialmente expressos, em textos científicos, pelo seu intervalo de confiança de 95% e a significância (p-valor), por exemplo: ρ = 0,76 (IC95% 0,61-0,91), p < 0,01 11 , 12 . No caso de múltiplas comparações, o coeficiente pode ser apresentado de forma isolada, na forma de uma matriz, e com sua significância sinalizada para facilitar a leitura dos dados, como Brianezi e colaboradores representaram as 28 correlações de sete parâmetros histológicos em uma tabela 13 . Casos especiais que envolvam centenas ou milhares de correlações podem exigir técnicas de representação gráfica, como os mapas térmicos de cor ( heatmap ) frequentemente usados em estudos genômicos, como Hsu e colaboradores representaram 4.930 correlações entre (85x58) variáveis genômicas e metabolômicas 14 .
Em casos de dados ordinais com poucas categorias (por exemplo, escores de satisfação, itens de qualidade de vida, nível socioeconômico), a exploração baseada no teste de correlação policórica pode ser mais robusta (menor erro tipo I) que os testes de Spearman e Kendall 15 . Ainda, apesar de pouco utilizadas, há também formas de se avaliar a correlação entre variáveis de natureza categóricas (por exemplo, coeficiente V de Cramér) e entre variáveis dicotômicas e quantitativas (por exemplo, coeficiente de correlação ponto-bisserial), todavia, essas abordagens ultrapassam o escopo deste texto 7 .
Em situações especiais em que a correlação linear entre diferentes variáveis deva ser analisada conjuntamente (por exemplo, itens de um questionário) visando a compreensão da variação conjunta global das variáveis, a análise de correlação entre “n” variáveis pode ser avaliada pelo coeficiente de correlação intraclasse (CCI) do tipo consistência. Há diferentes formas de análise do CCI, que levam a indicadores de diferentes magnitudes 16 . O CCI (aleatório ou misto de dois fatores, medidas médias) retorna o mesmo valor do coeficiente α de Cronbach, usado para medir a consistência interna de escalas 17 .
A identificação de uma correlação significativa entre duas ou mais variáveis deve ser interpretada com cautela, visto que a análise estatística não fornece evidências de dependência direta ou mesmo de causalidade entre as variáveis, mas apenas que elas tendem a variar conjuntamente 1 , 18 , 19 . Entretanto, apesar do risco de falácia ao se concluir causalidade a partir de resultados de correlação entre variáveis, os testes de correlação são importantes técnicas exploratórias para a investigação de associação entre o comportamento de grupos de variáveis, favorecendo a elaboração de modelos hipotéticos que devam ser confirmados posteriormente por meio de experimentos dedicados. Aliás, isso ocorre com estudos clínicos ecológicos, que frequentemente empregam técnicas de correlação na análise de seus dados e servem de base para investigação posterior dos fenômenos indicados pela correlação entre indicadores e grupos populacionais 20 - 22 . Ou ainda estudos tipo genome-wide e de proteômica, ditos exploratórios, que estudam os padrões de correlações dos achados com as variáveis clínicas em busca da indicação de modelos a serem comprovados posteriormente 19 , 23 .
Inclusive, quando se realizam múltiplos testes de correlação em uma sequência de variáveis, aumentam-se as chances de se identificar, ao acaso, correlações ditas “espúrias”, que devem ser avaliadas quanto à plausibilidade biológica e comprovadas posteriormente por técnicas adequadas de investigação. O emprego de técnicas de correção dos p-valores ajustadas para as correlações múltiplas é sempre indicado nessas condições 7 , 19 , 24 - 27 .
Outra limitação de natureza inferencial das análises de correlação baseia-se na incapacidade de extrapolação das conclusões para outros intervalos de dados ou para populações diferentes dos estudados.
Análises de correlação não foram desenvolvidas a priori com a finalidade de predição de valores ou da inferência da participação de múltiplas variáveis na explicação de um fenômeno, havendo técnicas de regressão ou de análise multivariadas com essas finalidades 7 . Apesar de existirem técnicas de correlação parcial que ajustam os valores da correlação para o comportamento de variáveis de confusão (idêntico ao coeficiente β padronizado em regressão linear múltipla), assim como técnicas de transformação polinomial para corrigir correlações não monotônicas, um profissional estatístico experiente deve ser consultado para o planejamento e a execução de análises de maior complexidade.
Análises de correlação podem também ser empregadas na comparação do paralelismo das medidas entre duas escalas diferentes para a medida do mesmo fenômeno, como escalas psicométricas de qualidade de vida 28 , ou clinimétricas, como escalas de risco para úlcera por pressão. Contudo, muitas vezes, elas são utilizadas por pesquisadores no sentido equivocado de se testar a concordância dos dados ou medidas consecutivas do mesmo fenômeno (por exemplo, teste-reteste 29 , calibragem de instrumentos de medida, comparação entre juízes), enquanto existem métodos mais adequados para essas finalidades 30 .
Finalmente, as estratégias de avaliação da correlação entre variáveis devem ser incentivadas em pesquisa clínica e biomédica, uma vez que maximizam a compreensão do fenômeno estudado. Entretanto, devido às particularidades inerentes aos seus diversos métodos, elas devem ser detalhadamente descritas na metodologia e na apresentação dos resultados.
Fonte de financiamento: Nenhuma.
O estudo foi realizado no Departamento de Dermatologia e Radioterapia, Faculdade de Medicina de Botucatu, Universidade Estadual Paulista (UNESP), Botucatu, SP, Brasil.
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Correlational Research: What it is with Examples
Our minds can do some brilliant things. For example, it can memorize the jingle of a pizza truck. The louder the jingle, the closer the pizza truck is to us. Who taught us that? Nobody! We relied on our understanding and came to a conclusion. We don’t stop there, do we? If there are multiple pizza trucks in the area and each one has a different jingle, we would memorize it all and relate the jingle to its pizza truck.
This is what correlational research precisely is, establishing a relationship between two variables, “jingle” and “distance of the truck” in this particular example. The correlational study looks for variables that seem to interact with each other. When you see one variable changing, you have a fair idea of how the other variable will change.
What is Correlational research?
Correlational research is a type of non-experimental research method in which a researcher measures two variables and understands and assesses the statistical relationship between them with no influence from any extraneous variable. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.
Correlational Research Example
The correlation coefficient shows the correlation between two variables (A correlation coefficient is a statistical measure that calculates the strength of the relationship between two variables), a value measured between -1 and +1. When the correlation coefficient is close to +1, there is a positive correlation between the two variables. If the value is relative to -1, there is a negative correlation between the two variables. When the value is close to zero, then there is no relationship between the two variables.
Let us take an example to understand correlational research.
Consider hypothetically, a researcher is studying a correlation between cancer and marriage. In this study, there are two variables: disease and marriage. Let us say marriage has a negative association with cancer. This means that married people are less likely to develop cancer.
However, this doesn’t necessarily mean that marriage directly avoids cancer. In correlational research, it is not possible to establish the fact, what causes what. It is a misconception that a correlational study involves two quantitative variables. However, the reality is two variables are measured, but neither is changed. This is true independent of whether the variables are quantitative or categorical.
Types of correlational research
Mainly three types of correlational research have been identified:
1. Positive correlation: A positive relationship between two variables is when an increase in one variable leads to a rise in the other variable. A decrease in one variable will see a reduction in the other variable. For example, the amount of money a person has might positively correlate with the number of cars the person owns.
2. Negative correlation: A negative correlation is quite literally the opposite of a positive relationship. If there is an increase in one variable, the second variable will show a decrease, and vice versa.
For example, being educated might negatively correlate with the crime rate when an increase in one variable leads to a decrease in another and vice versa. If a country’s education level is improved, it can lower crime rates. Please note that this doesn’t mean that lack of education leads to crimes. It only means that a lack of education and crime is believed to have a common reason – poverty.
3. No correlation: There is no correlation between the two variables in this third type . A change in one variable may not necessarily see a difference in the other variable. For example, being a millionaire and happiness are not correlated. An increase in money doesn’t lead to happiness.
QuestionPro recently published a blog about correlation matrix . Explore to learn about it.
Characteristics of correlational research
Correlational research has three main characteristics. They are:
- Non-experimental : The correlational study is non-experimental. It means that researchers need not manipulate variables with a scientific methodology to either agree or disagree with a hypothesis. The researcher only measures and observes the relationship between the variables without altering them or subjecting them to external conditioning.
- Backward-looking : Correlational research only looks back at historical data and observes events in the past. Researchers use it to measure and spot historical patterns between two variables. A correlational study may show a positive relationship between two variables, but this can change in the future.
- Dynamic : The patterns between two variables from correlational research are never constant and are always changing. Two variables having negative correlation research in the past can have a positive correlation relationship in the future due to various factors.
Data collection
The distinctive feature of correlational research is that the researcher can’t manipulate either of the variables involved. It doesn’t matter how or where the variables are measured. A researcher could observe participants in a closed environment or a public setting.
Researchers use two data collection methods to collect information in correlational research.
01. Naturalistic observation
Naturalistic observation is a way of data collection in which people’s behavioral targeting is observed in their natural environment, in which they typically exist. This method is a type of field research. It could mean a researcher might be observing people in a grocery store, at the cinema, playground, or in similar places.
Researchers who are usually involved in this type of data collection make observations as unobtrusively as possible so that the participants involved in the study are not aware that they are being observed else they might deviate from being their natural self.
Ethically this method is acceptable if the participants remain anonymous, and if the study is conducted in a public setting, a place where people would not normally expect complete privacy. As mentioned previously, taking an example of the grocery store where people can be observed while collecting an item from the aisle and putting in the shopping bags. This is ethically acceptable, which is why most researchers choose public settings for recording their observations. This data collection method could be both qualitative and quantitative . If you need to know more about qualitative data, you can explore our newly published blog, “ Examples of Qualitative Data in Education .”
02. Archival data
Another approach to correlational data is the use of archival data. Archival information is the data that has been previously collected by doing similar kinds of research . Archival data is usually made available through primary research .
In contrast to naturalistic observation, the information collected through archived data can be pretty straightforward. For example, counting the number of people named Richard in the various states of America based on social security records is relatively short.
Use the correlational research method to conduct a correlational study and measure the statistical relationship between two variables. Uncover the insights that matter the most. Use QuestionPro’s research platform to uncover complex insights that can propel your business to the forefront of your industry.
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Frequently asked questions
What’s the difference between correlational and experimental research.
Controlled experiments establish causality, whereas correlational studies only show associations between variables.
- In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
- In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.
In general, correlational research is high in external validity while experimental research is high in internal validity .
Frequently asked questions: Methodology
Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.
Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .
Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.
Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.
Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.
A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”
To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.
Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.
While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.
Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.
Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.
- Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
- Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .
You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.
- Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related
Content validity shows you how accurately a test or other measurement method taps into the various aspects of the specific construct you are researching.
In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.
The higher the content validity, the more accurate the measurement of the construct.
If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.
Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.
When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.
For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).
On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.
A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.
Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.
Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.
Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .
This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .
Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.
Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .
Snowball sampling is best used in the following cases:
- If there is no sampling frame available (e.g., people with a rare disease)
- If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
- If the research focuses on a sensitive topic (e.g., extramarital affairs)
The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.
Reproducibility and replicability are related terms.
- Reproducing research entails reanalyzing the existing data in the same manner.
- Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data .
- A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
- A successful replication shows that the reliability of the results is high.
Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.
The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).
Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.
A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.
The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.
Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.
On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.
Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.
However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.
In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.
A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.
Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.
Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .
A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.
The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .
An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .
It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.
While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.
Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.
Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.
Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.
Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.
You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .
When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.
Construct validity is often considered the overarching type of measurement validity , because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.
Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.
There are two subtypes of construct validity.
- Convergent validity : The extent to which your measure corresponds to measures of related constructs
- Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs
Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.
The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.
Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.
You can think of naturalistic observation as “people watching” with a purpose.
A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.
In statistics, dependent variables are also called:
- Response variables (they respond to a change in another variable)
- Outcome variables (they represent the outcome you want to measure)
- Left-hand-side variables (they appear on the left-hand side of a regression equation)
An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
Independent variables are also called:
- Explanatory variables (they explain an event or outcome)
- Predictor variables (they can be used to predict the value of a dependent variable)
- Right-hand-side variables (they appear on the right-hand side of a regression equation).
As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.
Overall, your focus group questions should be:
- Open-ended and flexible
- Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
- Unambiguous, getting straight to the point while still stimulating discussion
- Unbiased and neutral
A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:
- You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
- You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
- Your research question depends on strong parity between participants, with environmental conditions held constant.
More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .
Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .
Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.
This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.
The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.
There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.
A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:
- You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
- Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.
An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.
Unstructured interviews are best used when:
- You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
- Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
- You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
- Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.
The four most common types of interviews are:
- Structured interviews : The questions are predetermined in both topic and order.
- Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
- Unstructured interviews : None of the questions are predetermined.
- Focus group interviews : The questions are presented to a group instead of one individual.
Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .
In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.
Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.
Deductive reasoning is also called deductive logic.
There are many different types of inductive reasoning that people use formally or informally.
Here are a few common types:
- Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
- Statistical generalization: You use specific numbers about samples to make statements about populations.
- Causal reasoning: You make cause-and-effect links between different things.
- Sign reasoning: You make a conclusion about a correlational relationship between different things.
- Analogical reasoning: You make a conclusion about something based on its similarities to something else.
Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.
Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.
In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.
Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.
Inductive reasoning is also called inductive logic or bottom-up reasoning.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.
A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).
Triangulation can help:
- Reduce research bias that comes from using a single method, theory, or investigator
- Enhance validity by approaching the same topic with different tools
- Establish credibility by giving you a complete picture of the research problem
But triangulation can also pose problems:
- It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
- Your results may be inconsistent or even contradictory.
There are four main types of triangulation :
- Data triangulation : Using data from different times, spaces, and people
- Investigator triangulation : Involving multiple researchers in collecting or analyzing data
- Theory triangulation : Using varying theoretical perspectives in your research
- Methodological triangulation : Using different methodologies to approach the same topic
Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.
However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.
Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.
Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.
Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.
In general, the peer review process follows the following steps:
- First, the author submits the manuscript to the editor.
- Reject the manuscript and send it back to author, or
- Send it onward to the selected peer reviewer(s)
- Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
- Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.
Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.
You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.
Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.
Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.
Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.
Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.
Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.
Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.
Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.
For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.
After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.
Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.
These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.
Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.
Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.
Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.
In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.
Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.
These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.
Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .
You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.
You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.
Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.
Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.
Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .
These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.
In multistage sampling , you can use probability or non-probability sampling methods .
For a probability sample, you have to conduct probability sampling at every stage.
You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.
Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.
But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .
These are four of the most common mixed methods designs :
- Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions.
- Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
- Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
- Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.
Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.
In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.
This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.
No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.
To find the slope of the line, you’ll need to perform a regression analysis .
Correlation coefficients always range between -1 and 1.
The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.
The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.
These are the assumptions your data must meet if you want to use Pearson’s r :
- Both variables are on an interval or ratio level of measurement
- Data from both variables follow normal distributions
- Your data have no outliers
- Your data is from a random or representative sample
- You expect a linear relationship between the two variables
Quantitative research designs can be divided into two main categories:
- Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
- Experimental and quasi-experimental designs are used to test causal relationships .
Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.
A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.
The priorities of a research design can vary depending on the field, but you usually have to specify:
- Your research questions and/or hypotheses
- Your overall approach (e.g., qualitative or quantitative )
- The type of design you’re using (e.g., a survey , experiment , or case study )
- Your sampling methods or criteria for selecting subjects
- Your data collection methods (e.g., questionnaires , observations)
- Your data collection procedures (e.g., operationalization , timing and data management)
- Your data analysis methods (e.g., statistical tests or thematic analysis )
A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.
Questionnaires can be self-administered or researcher-administered.
Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.
Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.
You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.
Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.
Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.
A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.
The third variable and directionality problems are two main reasons why correlation isn’t causation .
The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.
The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.
Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.
Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.
While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .
A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.
A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.
Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.
A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .
A correlation reflects the strength and/or direction of the association between two or more variables.
- A positive correlation means that both variables change in the same direction.
- A negative correlation means that the variables change in opposite directions.
- A zero correlation means there’s no relationship between the variables.
Random error is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .
You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.
Systematic error is generally a bigger problem in research.
With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.
Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.
Random and systematic error are two types of measurement error.
Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).
Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).
On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.
- If you have quantitative variables , use a scatterplot or a line graph.
- If your response variable is categorical, use a scatterplot or a line graph.
- If your explanatory variable is categorical, use a bar graph.
The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.
Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.
The difference between explanatory and response variables is simple:
- An explanatory variable is the expected cause, and it explains the results.
- A response variable is the expected effect, and it responds to other variables.
In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:
- A control group that receives a standard treatment, a fake treatment, or no treatment.
- Random assignment of participants to ensure the groups are equivalent.
Depending on your study topic, there are various other methods of controlling variables .
There are 4 main types of extraneous variables :
- Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
- Experimenter effects : unintentional actions by researchers that influence study outcomes.
- Situational variables : environmental variables that alter participants’ behaviors.
- Participant variables : any characteristic or aspect of a participant’s background that could affect study results.
An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.
A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.
In a factorial design, multiple independent variables are tested.
If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.
Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .
Advantages:
- Only requires small samples
- Statistically powerful
- Removes the effects of individual differences on the outcomes
Disadvantages:
- Internal validity threats reduce the likelihood of establishing a direct relationship between variables
- Time-related effects, such as growth, can influence the outcomes
- Carryover effects mean that the specific order of different treatments affect the outcomes
While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .
- Prevents carryover effects of learning and fatigue.
- Shorter study duration.
- Needs larger samples for high power.
- Uses more resources to recruit participants, administer sessions, cover costs, etc.
- Individual differences may be an alternative explanation for results.
Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.
In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.
In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.
The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.
Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.
In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.
To implement random assignment , assign a unique number to every member of your study’s sample .
Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.
Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.
In contrast, random assignment is a way of sorting the sample into control and experimental groups.
Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.
In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.
“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.
Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.
Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .
If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .
A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.
Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.
Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.
If something is a mediating variable :
- It’s caused by the independent variable .
- It influences the dependent variable
- When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.
A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.
A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.
There are three key steps in systematic sampling :
- Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
- Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
- Choose every k th member of the population as your sample.
Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .
Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.
For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.
You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.
Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.
For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.
In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).
Once divided, each subgroup is randomly sampled using another probability sampling method.
Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.
However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.
There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.
- In single-stage sampling , you collect data from every unit within the selected clusters.
- In double-stage sampling , you select a random sample of units from within the clusters.
- In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.
Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.
The clusters should ideally each be mini-representations of the population as a whole.
If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,
If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.
The American Community Survey is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.
Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.
Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .
Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings.
A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.
Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .
If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.
- In a single-blind study , only the participants are blinded.
- In a double-blind study , both participants and experimenters are blinded.
- In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.
Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .
A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.
However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).
For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.
An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.
Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.
Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.
The type of data determines what statistical tests you should use to analyze your data.
A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.
To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.
In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).
The process of turning abstract concepts into measurable variables and indicators is called operationalization .
There are various approaches to qualitative data analysis , but they all share five steps in common:
- Prepare and organize your data.
- Review and explore your data.
- Develop a data coding system.
- Assign codes to the data.
- Identify recurring themes.
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .
There are five common approaches to qualitative research :
- Grounded theory involves collecting data in order to develop new theories.
- Ethnography involves immersing yourself in a group or organization to understand its culture.
- Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
- Phenomenological research involves investigating phenomena through people’s lived experiences.
- Action research links theory and practice in several cycles to drive innovative changes.
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
Operationalization means turning abstract conceptual ideas into measurable observations.
For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.
Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.
When conducting research, collecting original data has significant advantages:
- You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
- You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )
However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.
There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.
In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.
In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .
In statistical control , you include potential confounders as variables in your regression .
In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.
A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.
Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.
To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.
Yes, but including more than one of either type requires multiple research questions .
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .
To ensure the internal validity of an experiment , you should only change one independent variable at a time.
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!
You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .
- The type of soda – diet or regular – is the independent variable .
- The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.
Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.
In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.
Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .
Probability sampling means that every member of the target population has a known chance of being included in the sample.
Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .
Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .
Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.
Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.
A sampling error is the difference between a population parameter and a sample statistic .
A statistic refers to measures about the sample , while a parameter refers to measures about the population .
Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.
Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.
The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).
The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.
Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .
Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.
Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.
Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.
The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .
Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.
Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.
Longitudinal study | Cross-sectional study |
---|---|
observations | Observations at a in time |
Observes the multiple times | Observes (a “cross-section”) in the population |
Follows in participants over time | Provides of society at a given point |
There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .
Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
The research methods you use depend on the type of data you need to answer your research question .
- If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
- If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
- If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.
A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.
In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.
Discrete and continuous variables are two types of quantitative variables :
- Discrete variables represent counts (e.g. the number of objects in a collection).
- Continuous variables represent measurable amounts (e.g. water volume or weight).
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).
Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .
You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .
In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:
- The independent variable is the amount of nutrients added to the crop field.
- The dependent variable is the biomass of the crops at harvest time.
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .
Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:
- A testable hypothesis
- At least one independent variable that can be precisely manipulated
- At least one dependent variable that can be precisely measured
When designing the experiment, you decide:
- How you will manipulate the variable(s)
- How you will control for any potential confounding variables
- How many subjects or samples will be included in the study
- How subjects will be assigned to treatment levels
Experimental design is essential to the internal and external validity of your experiment.
I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .
External validity is the extent to which your results can be generalized to other contexts.
The validity of your experiment depends on your experimental design .
Reliability and validity are both about how well a method measures something:
- Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
- Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).
If you are doing experimental research, you also have to consider the internal and external validity of your experiment.
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .
In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
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Correlational research is ideal for gathering data quickly from natural settings. That helps you generalize your findings to real-life situations in an externally valid way. There are a few situations where correlational research is an appropriate choice.
Correlational research and experimental research are two different research approaches used in social sciences and other fields of research. Correlational Research is a research approach that examines the relationship between two or more variables.
A correlational study is a type of research design that looks at the relationships between two or more variables. Correlational studies are non-experimental, which means that the experimenter does not manipulate or control any of the variables.
Correlational Research is a type of research that examines the statistical relationship between two or more variables without manipulating them. It is a non-experimental research design that seeks to establish the degree of association or correlation between two or more variables. Types of Correlational Research.
Correlational research is a type of nonexperimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables.
What is a Correlational Study? A correlational study is an experimental design that evaluates only the correlation between variables. The researchers record measurements but do not control or manipulate the variables. Correlational research is a form of observational study.
Correlational and experimental research both use quantitative methods to investigate relationships between variables. But there are important differences in how data is collected and the types of conclusions you can draw.
The most widely-used technique for evaluating the correlation between two quantitative variables is Pearson’s product-moment correlation coefficient, or Pearson’s r, which requires that both samples follow a normal distribution and that the relationship between the two variables is linear. 2, 7 Failure to adhere to these prerequisites leads to e...
What is Correlational research? Correlational research is a type of non-experimental research method in which a researcher measures two variables and understands and assesses the statistical relationship between them with no influence from any extraneous variable.
What’s the difference between correlational and experimental research? Controlled experiments establish causality, whereas correlational studies only show associations between variables. In an experimental design, you manipulate an independent variable and measure its effect on a dependent variable.