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Methodology

• Correlational Research | When & How to Use

Correlational Research | When & How to Use

Published on July 7, 2021 by Pritha Bhandari . Revised on June 22, 2023.

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.

Table of contents

Correlational vs. experimental research, when to use correlational research, how to collect correlational data, how to analyze correlational data, correlation and causation, other interesting articles, 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 data collection methods and the types of conclusions you can draw.

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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.

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 behavioral 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 research bias .

In survey research , you can use questionnaires to measure your variables of interest. You can conduct surveys online, by mail, by phone, or in person.

Surveys are a quick, flexible way to collect standardized 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 behavior or phenomenon in its natural environment.

This method often involves recording, counting, describing, and categorizing actions and events. Naturalistic observation can include both qualitative and quantitative elements, but to assess correlation, you collect data that can be analyzed quantitatively (e.g., frequencies, durations, scales, and amounts).

Naturalistic observation lets you easily generalize 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 analyze the relationship between variables using correlation or regression analyses, or both. You can also visualize 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 summarize 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.

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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.

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

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

Research bias

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

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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Bhandari, P. (2023, June 22). Correlational Research | When & How to Use. Scribbr. Retrieved June 24, 2024, from https://www.scribbr.com/methodology/correlational-research/

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• Knowledge Base
• Methodology
• 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.

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.

Prevent plagiarism, run a free check.

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, December 05). Correlational Research | Guide, Design & Examples. Scribbr. Retrieved 24 June 2024, from https://www.scribbr.co.uk/research-methods/correlational-research-design/

Is this article helpful?

Pritha Bhandari

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Chapter 15 Correlational Research_How to Design and Evaluate Research in Education 8th.pdf

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Correlation - Levels of Measurement Introduction For this study, a correlation test was utilized for predictive analytics, to predict as well as estimate whether student’s self-ability to master science depend on their teacher’s intelligence (IQ). Correlation is a statistical technique used by researchers to determine the relationship between two or more variables. Variables In order to achieve the objective of the study, two variables were obtained from a university Longitudinal Study. The variables were: The first variable (dependent) is the aggregate of student’s science self-competence, whereas the second variable (independent) is the IQ score for the science lecturer. The study presumes that the high levels of science lecturer’s intelligence, the better outcome to student’s self-efficacy in the subject. The study is particularly interested in determining if lecturer’s scale of brainpower in science can influence their learner’s self-ability in the subject. The outcome of this study can be used to inform decision-maker in schools during the selection process of the science instructors. Levels of Measurement Level of measurement for the two variables is continuous. Data for students was obtained as exam scores (measured between 0-100), it’s therefore ratio. Conversely, lecturers data was captured based on their completion time (estimated in hours) for IQ scale, hence it’s measured in interval (time intervals). Therefore, the level of measurement for this study is ratio/interval. Type of Correlation Used A bivariate (Pearson) correlation test was used to determine whether there is a relationship between students’ science self-efficacy and lecturer’s IQ score (intelligence level). The test establishes the strength (strong or weak) and the direction (positive or negative) of the relationship between two variables (Salkind, 2013). The value of the Pearson correlation coefficient (r) lies between -1 to +1, and 0. A negative value implies no association (complete inverse relationship), for instance, an increase in variable X facilitates a decrease in variable Y. Hypothesis and Research Research question: Is there a significant positive correlation between lecturer’s science IQ score and student’s science self-capability? Null hypothesis: There is no significant positive correlation between lecturer’s science IQ score and students’ science self-ability. Results Output To test the study hypothesis, a bivariate Pearson Correlation test was performed as presented in Table-set 1.1 below. The analysis was performed using MS Excel, 2016 version. Table-set 1.1: Correlations (Teacher IQ scale and student’s science self-efficacy) SUMMARY OUTPUT Regression Statistics Multiple R 0.84 R Square 0.07 Adjusted R Square 0.06 Standard Error 0.07 Observations 56.00 ANOVA df SS MS F Significance F Regression 1.00 962.2 962.215 43.345 3E-06 Residual 54.00 399.6 22.199 Total 55.00 1361.8 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 7.78 0.0 5.540 0.000 5E+00 10.7 Scale of science lec IQ 0.02 0.0 6.584 0.000 1E-02 0.0 Results Interpretation The Pearson correlation results indicate a statistically significant and positive relationship between lecturer IQ score and their students’ science self-ability, r= 0.84, p=0.00. This shows that the P-value is smaller than the significance level used 0.5. Therefore, the null hypothesis is rejected. The study concludes that; there is a significant (strong) positive correlation between lecturer’s science IQ score and students’ science self-ability. The implication of the Study The study results imply that students with better aggregates in science self-efficacy must have been instructed by lecturers who have high levels of IQ (intelligence) in a science subject. What is more, the implication is that highly professional and brainy lecturers have required confidence, skills, experience, and competence to train students and make them understand so as to gain self-efficacy. The findings of this study resonate with Kunter et al., (2013) research, which advanced that professional competence of lecturers has an effect on the instructional quality and student development. References Salkind, N. J. (2013). Types of Correlation. In Statistics for people who (think they) hate statistics: Excel 2010 edition (p. 148-149). Kansas: SAGE Publications. Kunter, Mareike & Klusmann, Uta & Baumert, Jürgen & Richter. (2013). Professional Competence of Teachers: Effects on Instructional Quality and Student Development. Journal of Educational Psychology. 105. 805–820. 10.1037/a0032583.

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Descriptive Correlational Study to Examine Variables and Relationships Proposed in Conceptual Framework of Virtual Transitioning Program Developed for Foreign Educated Nurses (FEN)

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Identification of a mimotope of a complex gp41 Human Immunodeficiency VIrus epitope related to a non-structural protein of Hepacivirus previously implicated in Kawasaki disease

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Background We have previously isolated a highly mutated VH1-02 antibody termed group C 76-Q13-6F5 (6F5) that targets a conformational epitope on gp41. 6F5 has the capacity to mediate Ab dependent cell cytotoxicity (ADCC). When the VH1-02 group C 76 antibodies variable chain sequence was reverted to germline (76Canc), this still retained ADCC activity. Due to this ability for the 76Canc germline antibody to functionally target this epitope, we sought to identify a protein target for vaccine development. Methods Initially, we interrogated peptide targeting by screening a microarray containing 29,127 linear peptides. Western blot and ELISAs were used to confirm binding and explore human serum targeting. Autoimmune targeting was further interrogated on a yeast-displayed human protein microarray. Results 76Canc specifically recognized a number of acidic peptides. Meme analysis identified a peptide sequence similar to a non-structural protein of Hepacivirus previously implicated in Kawasaki disease (KD). Binding was confirmed to top peptides, including the Hepacivirus-related and KD-related peptide. On serum competitions studies using samples from children with KD compared to controls, targeting of this epitope showed no specific correlation to having KD. Human protein autoantigen screening was also reassuring. Conclusions This study identifies a peptide that can mimic the gp41 epitope targeted by 76C group antibodies (i.e. a mimotope). We show little risk of autoimmune targeting including any inflammation similar to KD, implying non-specific targeting of this peptide during KD. Development of such peptides as the basis for vaccination should proceed cautiously.

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