1. Correlation and Correlational Research Slides Prepared by Alison L. O’Malley Passer Chapter 5

2. Correlation Correlations reveal the degree of statistical association between two variables, and can be computed in experimental and non- experimental research designs Correlational research establishes whether naturally occurring variables are statistically related How does correlational research differ from experimental research?

3. Correlational Research In correlational research, variables are measured rather than manipulated Manipulation is the hallmark of experimentation which enables researchers to draw causal inferences This distinction between measurement and manipulation drives the oft-cited mantra “correlation does not equal causation”

4. Thinking Critically about Correlational Research What information do you need to know in order to determine whether a study uses an experimental or correlational research design? Generate a research question that lends itself to a correlational research design but not an experimental research design.

5. Direction of Relationship: Positive Two variables tend to increase or decrease together Higher scores on X are associated with higher scores on Y Lower scores on X are associated with lower scores on Y Envision two people in an elevator

6. Direction of Relationship: Negative Two variables tend to move in opposite directions Higher scores on X are associated with lower scores on Y Lower scores on X are associated with higher scores on Y Envision two people on a see-saw

7. Examine the pattern of association between (a) X and Y 1 and (b) X and Y 2

8. Correlation Practice Generate your own example of each of the following: A positive relationship A negative relationship A relationship that is not significantly different than zero

9. Measuring Correlations What scale of measurement are we dealing with? Pearson product-moment correlation coefficient Pearson’s r Variables measured on interval or ratio scale Spearman’s rank-order correlation coefficient Spearman’s rho One or both variables measured on ordinal scale

10. Interpreting Correlations In addition to considering the direction of the relationship (i.e., positive or negative), we need to attend to the strength of the relationship. 0.00+1.00

11. Interpreting Correlation Strength Is the relationship between two variables weak? Moderate? Strong? Guidelines from Cohen (1988) Absolute value Weak.10 -.29 Moderate.30 -.49 Strong>.50

12. Interpreting Correlations Pay close attention to how variables were coded In most (but not all) cases, higher values reflect more of the underlying attribute [Note: this does not apply to nominal data]

13. Interpreting Correlations If a psychological scientist establishes a correlation of.33 between integrity and job performance, can one say that the two variables are 33% related?

14. Interpreting Correlations If a psychological scientist establishes a correlation of.33 between integrity and job performance, can one say that the two variables are 33% related? No. r 2 (coefficient of determination) reveals how much of the differences in Y scores are attributable to differences in X scores.

15. Interpreting Correlations How much “overlap” is there? YYYY YYYYXX ?

16. Interpreting Correlations How much “overlap” is there? YYYY YYYYXX ? If r =.33, then r 2 =.11 11% of the variance in Y is attributable to X

17. Interpreting Correlations: Scatter Plots How are the properties of correlation coefficients – sign and strength – reflected in each of these scatter plots?

18. Correlation ≠ Causation Review the three criteria used to draw causal inferences… Which criterion/criteria is/are impacted by the bidirectionality problem? The third- variable problem?

19. Correlation ≠ Causation

20. Strategies to Reduce Causal Ambiguity 1.Statistical approaches Measure and statistically control for (i.e., partial out) a third variable 2.Research design approaches When possible, conduct longitudinal studies Why are longitudinal studies preferable to cross-sectional studies?

21. Longitudinal Research Designs Prospective design X measured at Time 1, Y measured at Time 2 Rules out bidirectionality problem Cross-lagged panel design Measure X and Y at Time 1 Repeat X and Y measurement at Time 2 Examine pattern of relationships (i.e., cross- lagged correlations) across variables and time

22. Cross-Lagged Panel Design What does it mean when a correlation is “spurious”?

23. Drawing Causal Conclusions How do we rule out all plausible third variables (confounds) using correlational research designs? We can’t… only the control afforded by rigorous experimentation provides strong tests of causation. So what good are correlational studies?

24. Correlation and Prediction A goal of science is to forecast future events In simple linear regression, scores on X can be used to predict scores on Y assuming a meaningful relationship (r) has been established between X and Y in past research

25. Linear Regression E.g., Scores on a job interview (X) can be used to predict job performance (Y) X is the predictor; Y is the criterion Interview scores plugged into regression equation and hiring decisions made based on results This is an illustration of criterion validity

26. Regression Regression line generated through application of regression equation

27. Multiple Regression Multiple predictors are used to predict a criterion measure Strive for as little overlap as possible between predictors (i.e., want to account for unique variance in criterion)

28. Multiple Regression General CAT Criterion Structured Interview Work Sample General CAT Criterion Structured Interview Work Sample Which scenario is preferable? (a) (b)

29. Nonlinear Relationships Pearson’s r is useless in cases where X and Y do not relate in a linear fashion. See the curvilinear relationship below. test performance Alertness sleepy alert panic

30. Range Restriction

31. Special Considerations Make sure to examine your scatterplot Are X and Y related in a linear fashion? Do your data reveal range restriction? What scales of measurement are you dealing with? If the relationship of interest is nonlinear and/or you have range restriction and/or you have nominal data, calculating r will produce inaccurate, misleading results!

32. Correlation is a powerful statistical tool and correlational research can shed light on important questions… But make sure to employ these tools wisely! Unfortunately, the media and even some researchers can report misleading findings. And remember, by itself correlation does not establish causation! Closing Considerations