Data Analysis Outline

1. Data Analysis Outline • What is an experiment? • What are independent vs. dependent variables in an experimental study? • What are our dependent measures/variables in this study?

2. Goals for Today • Learn about the basics of an experiment • Learn how to create a unit-weighted composite variable and how/why it is used in psychology. • Learn how to create composite variables in SPSS. • Learn how to compare the difference between two groups using Cohen’s d.

3. Composite Scores • When we have used multiple ways of assessing a construct (e.g., self-esteem), we often create a composite that captures the these scores.

4. It is assumed that there is variation across people with respect to the latent variable (i.e., self-esteem). • A “latent variable” is one that we assume exists, but that we cannot observe directly. • Most constructs in psychology are latent variables: memory, extraversion, self-esteem, intelligence.

5. It is also assumed that variation in this latent variable causes variation in the observed responses (i.e., the ratings of each item). Item 1 Self-esteem Item 2 Self-esteem Item 3 Self-esteem Item 4 Self-esteem + + + - Latent Self-esteem

6. Reverse Scored Items • Some items are negatively related to the construct of interest. • Ex: “I feel I do not have much to be proud of. ” • These items cannot be weighted in the same fashion as the others when creating a composite variable.

7. Unit-weighted composite • To create a “unit-weighted composite”—the most commonly used composite in personality psychology, do the following: • 1. Reverse-key responses to items that are in the opposite direction of the construct.

8. One way to do this is to use the following formula: • Max - X + Min • Thus, on a 1 (Min) to 5 (Max) scale, like the one we used, we would use the following equation to reverse key the responses: • Rev key response = 5 – X + 1

9. 2. Once the appropriate responses have been reverse keyed, simply average the responses for each person.

11. Qualifications • This method is the simplest, but there are more complex ways of creating composites. • For example, sometimes responses to each variable are standardized before the averaging takes place. • In some work, the different variables are weighted differently. That is, some variables count more than others. • In other work, non-linear relationships might be assumed between the latent variable and an item response (e.g., Item Response Theory models).

12. Mean Differences • The big question in our experiment is whether people’s self-esteem improves after listening to a subliminal recording containing subliminal messages designed to improve self-esteem.

13. Our Experiment • Two conditions: • A. People in the “good” condition were presented with self-affirming subliminal messages, such as “You are a good person.” • B. People in the “bad” condition were presented with self-defacing subliminal messages, such as “No one likes you.”

14. Answering the Question • One way of addressing the question is whether the self-esteem of people in the Condition A is higher than that of people in Condition B. (As measured after hearing the recording.)

15. Everyone has a unique self-esteem score, so we average the scores (i.e., the composite scores) for people in Condition A and separately average the scores for people in Condition B. • We want two statistics: (a) the mean, which tells us the average self-esteem value for a person in Condition X, and (b) the standard deviation (SD), which tells us the amount of variability there is around the mean in that condition.

16. Mean Difference between conditions: • (Mean of Group A – Mean of Group B) • If positive, then Group A > Group B • If negative, then Group A < Group B • If zero, then no difference between conditions.

17. Cohen’s d • If we divide the mean difference by the average SD of the two groups, we obtain a standardized mean difference or Cohen’s d. Pooled standard deviation

18. Cohen’s d expresses the difference between groups relative to the average standard deviation of the scores.

19. Another Way – For Wed. • We could also ask about the amount of change that takes place in self-esteem scores from Time 1 (before the recording) to Time 2 (after the recording). • Create a composite for the Time 1 scores. • Create a new variable in SPSS that represents the Time 2 composite – Time 2 composite scores.