• The use of the correlational research method in psychology, including co-variables. 
  • Types of correlation: positive, negative and including the use of scatter diagrams. 
  • Issues surrounding the use of correlations in psychology; issues with cause and effect, other variables.

Correlational Research

Correlational research is a type of research method used by psychologists to study the relationship between two or more variables. The goal of correlational research is to determine if there is a correlation, or a statistical relationship, between the variables being studied.

Psychologists use correlational analyses for several reasons:

  1. Predictive ability: Correlational analyses can be used to predict the likelihood of one variable based on the value of another variable. For example, if there is a positive correlation between stress levels and sleep quality, researchers may be able to predict that individuals with higher levels of stress will have poorer sleep quality.
  2. Identification of relationships: Correlational analyses can be used to identify relationships between variables. This can help researchers understand how different variables are related and can provide insights into the underlying mechanisms that drive these relationships.
  3. Exploratory research: Correlational analyses can be useful for exploratory research, where researchers are trying to identify new relationships or understand a complex phenomenon. It can be a way to generate new ideas or hypotheses to be tested by other methods.
  4. Convenience: Correlational research is relatively easy to conduct, it does not require a manipulation of variables, and it can be done with a relatively small sample size. Additionally, correlational research can be done in naturalistic settings, so it can be less expensive than other methods.

In the case of psychology, the numbers being analysed relate to behaviours (or variables that could affect behaviour) but actually any two variables producing quantitative data could be checked to establish whether a correlations exists.  Each of the two sets of numbers represents a co-variable.  Once data has been collected for each of the co-variables, it can be plotted in a scattergram and/ or statistically analysed to produce a correlation coefficient.  

Scattergrams and coefficients indicate the strength of a relationship between two variables, which highlights the extent to which two variables correspond. The relationship between two variables will always produce a coefficient of between 1 and -1.  

Coefficients with a minus in front of them highlight a negative correlation which means that as one set of numbers is increasing the other set is decreasing or as one decreases the other increases, so the trend in the data from one variable opposes the other.  In contrast, coefficients which are positive indicate that both sets of data are showing the same trend, so as one set of data increases so does the other or as one set decreases the same trends is observed in the second set of data.

Experiments vs Correlations

Experiments allow researchers to establish causality, that is, to determine whether a change in the independent variable causes a change in the dependent variable. In contrast, correlation studies can only show that two variables are related, but it does not establish causality.

Limitations of correlational research

  1. No causal inferences: Correlational research can only establish a relationship between two or more variables, but it cannot establish causality. Researchers cannot infer that one variable causes the other, or that a change in one variable will result in a change in the other.
  2. Third variable problem: Correlational research can be confounded by the presence of a third variable, also known as a confounding variable. This variable may account for the correlation between the two variables of interest, meaning that the correlation may not be due to the relationship between the two variables.
  3. Directionality: Correlational research cannot determine the direction of the relationship between variables. For example, it is not clear whether a high score on a test of anxiety is causing a low score on a test of academic achievement, or whether a low score on the test of academic achievement is causing a high score on the test of anxiety.
  4. Limited to existing data: Correlational research is limited by the existing data, which means that it cannot answer questions about variables that have not been measured or studied.

Past Paper Questions

  • Define what is meant by the correlational research method. (1) October 2016
  • Identify the type of correlation shown in Figure 1. (1) June 2021
  • Haziq carried out a Spearman’s rank test on his data. He used the p=0.05 level of significance rather than the p=0.01 level of significance. Haziq found that at p≤0.05 his correlation was significant. Explain one difference between the p=0.05 and the p=0.01 levels of significance in relation to Haziq’s correlation. (2) June 2021
  • Explain one strength of the correlational research method. (2) October 2016
  • Serenity has carried out a correlation to determine whether there is a relationship between how old her participants are in years and the number of hours they slept on average per night. (a) State the two fully operationalised co-variables in Serenity’s correlation. (2) January 2022
  • Draw a scatter diagram to represent the data shown in Table 1. (3) January 2022
  • Draw a scatter diagram to show the results from this research. (3) October 2016
  • Describe the type of correlation shown in the scatter diagram you have drawn. (2) October 2016
  • State which statistical test you could use to determine whether there is a relationship between the number of consecutive nights worked and the mean number of mistakes made. (1) October 2016
  • Explain the type of correlation the researchers found. (2) January 2017
  • Describe whether the results of the researchers’ investigation were significant at p<0.05 for a directional (one-tailed) test. (2) January 2017
  • State one conclusion that can be drawn from the data in Table 1. (1) January 2017
  • Draw a scatter diagram to represent the data in Table 1. (3) January 2017
  • Draw a scatter diagram to represent the data shown in Table 1. (3) January 2021
    • State the type of correlation from the scatter diagram you have drawn in (a)(i). (1) January 2021
  • Evaluate the use of the correlational research method in psychology. (8) January 2017
  • Arissa wanted to conduct a correlation study for her psychology coursework. She decided to research whether the number of brothers and sisters her participants have affects the number of children her participants have. State a directional (one-tailed) hypothesis for Arissa’s study. (1) June 2017
  • Explain one strength and one weakness of using the correlational research method. (4) October 2017
  • Explain one reason why cause and effect is an issue in correlational research. (2) January 2019