1. Correlational and Differential Research
Graziano and Raulin
Research Methods: Chapter 7
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Graziano & Raulin (1997)

2. Correlational Research
Quantifies the strength of the relationship between two or more variables
Value of correlational research
Correlations can be used for prediction
Evidence consistent or inconsistent with a theory
Cannot prove a theory, but could negate a theory
Note: Correlations CANNOT establish causation
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3. Correlation and Causation
If A and B are correlated, then …
A could cause B
B could cause A
Another variable could cause both A and B
Must be cautious in drawing conclusions
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4. Differential Research Methods
Compare two or more preexisting groups
Similar to both correlational and experimental research
Same form as experimental research
Conceptually similar to correlational research (variables measured, but not manipulated)
Cross-sectional design in developmental research is differential research
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5. Cross-Sectional and Longitudinal Research
Cross-sectional designs are faster
Can test many age groups simultaneously
But cohort effects can be a problem
Defined as “shared life experiences of people of a given age that lead them to behave similarly to others their age and different from people of other ages”
Longitudinal designs are essentially time-series designs
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6. Artifacts and Confounding Variables
Confounding occurs when two variables vary together
Need to have them vary independently, usually by holding all but one variable constant
Failing to provide this control could result in artifactual findings
Procedures standardized for this reason
Comparing groups is reasonable ONLY IF we standardized the measurement procedures
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7. Correlational versus Differential
Both involve the measurement, but not manipulation, of variables
Therefore, neither is able to establish causation
Differential is higher constraint because
The researcher can select the comparison group(s) to control at least some of the potential confounding variables, thus providing stronger evidence for a theory
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When to Use Each Method
Correlational Method
When we are interested in knowing the strength of a relationship for predictive purposes
Often included to help interpret the primary findings of a study
Differential Research
When the manipulation of an independent variable is impractical, impossible, or unethical
Then we rely on comparing preexisting groups
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9. Doing Correlational Research
Steps in conducting correlational research
Developing the problem statement
Measuring the Variables
Obtaining the Sample
Analyzing the Data
Interpreting the Results
Correlational research is often embedded in larger studies
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10. Developing a Problem Statement
“What is the relationship between variable X and variable Y?”
Often want to correlate available demographic variables with the dependent measures or intercorrelate the dependent measures in higher-constraint research
Useful in detecting confounding variables
Provides hypotheses for later research
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11. Measuring the Variables
Need to use reliable and valid measures
Need to control
Experimenter expectancy
Researchers tending to see what they expect to see
Experimenter reactivity
Researchers unconsciously influencing participants
Measurement reactivity
Participants responding differently because they know they are being observed
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12. Controlling these effects
Experimenter expectancy
Use objective measures whenever possible
Experimenter reactivity
Minimize experimenter contact
Measurement reactivity
Use filler items to distract participants
Use unobtrusive measures when possible
Separate the measurements in time
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13. Sampling Considerations
Want the sample to be representative
Is the observed relationship the same in each subpopulation?
If we suspect such differences, we should compute the correlation in each subpopulation
Moderator Variable: a variable that seems to modify the relationship between other variables
e.g., sex: males and females showing different patterns of relationship between variables
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Analyzing the Data
Correlations range from to +1.00
Size indicates strength of the relationship
Sign indicates direction of the relationship
Many types of correlations
Pearson product-moment correlation
Spearman rank-order correlation
Advanced techniques (multiple correlation, canonical correlation, partial correlation, path analysis)
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Interpreting the Data
Note size and sign of correlation
Indicates strength and direction of relationship
Is the correlation significantly different from zero (i.e., evidence for a relationship)?
Is the p value < alpha?
Coefficient of Determination
r2 indicates the proportion of variance accounted for
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16. Doing Differential Research
Developing the problem statement
Measuring the variables
Selecting appropriate control groups
Obtaining the sample
Analyzing the data
Interpreting the results
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17. Developing a Problem Statement
“Does Group A differ from Group B?”
Developing good problem statements
Select theoretically interesting groups to compare
Compare them on theoretically interesting variables
Best to compare groups that differ on only a single variable if possible
Several comparisons are best
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18. Measuring the Variables
Dependent variable is usually continuous, but could be categorical
Independent variable is categorical or is a continuous variable converted to categories
Unlike experimental research, the independent variable is measured, rather than manipulated
Need operational definitions for
Dependent Variable
Independent variables
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19. Selecting Control Groups
Select control groups to avoid confounding
A variable can confound results only if
a) it affects the scores on the dependent variable
b) the groups differ on this variable
Ideal control group is identical to experimental group on all variables except the variable that defines the groups
Rarely possible, so multiple comparisons groups are typical
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20. Sampling of Participants
Like all research, we want representative sampling to permit generalization
Many factors can bias sampling
Where we have access to participants
How we go about identifying participants
Even factors like time of day that we sample
Participants who drop out of the study can limit generalizability
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Analyzing the Data
Same procedures as those used to analyze experimental research
Type of analysis depends on the number of groups and the level of measurement
Score data: t-test or ANOVA
Ordinal data: Mann-Whitney U-test
Nominal data: Chi square
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22. Interpreting the Results
Reject null hypothesis (of no group difference) if the p < alpha
Difficult to draw a strong conclusion from differential research
Sampling considerations
Unlikely that all potential confounding variables will have been adequately controlled
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Blanchard et al. (2001)
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24. Limitations of these Methods
Problems in determining causation
A correlation does not imply causality
If A and B are correlated, then
A could cause B
B could cause A
Some other variable could cause both
Confounding variables
Without experimental control, it is virtually impossible to avoid confounding variables
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25. Copyright © Allyn & Bacon (2007)
Summary
Both correlational and differential research involve measuring the relationship between variables
Drawing causal inferences is risky
Selecting appropriate control groups in the differential research design can control some, but typically not all, potential confounding variables
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