1. Within-Subjects (Repeated Measures) Designs Each participant receives all conditions in the experiment Main advantage: increased power Since the number of observations is increased, fewer P's needed

2. Problem: Six Carryover Effects Learning Fatigue Habituation Sensitization Contrast Adaptation

3. How to Deal with Carryover? Practice Try to make the experiment brief Lengthen the time between conditions Try to disguise the hypothesis and variables Counterbalancing –Simple counterbalancing –Latin-Square Designs: For multiple conditions

4. Types of Within-Subject Designs Single-factor two-level design –Simplest design: 1 IV, two levels –Conditions counterbalanced across P's. Single-factor multi-level design –1 IV, > two levels Multifactor (factorial) within-subject designs

5. Comparison of Within and Between Designs Should use Between Design if: –carryover effects could affect results –you have access to large number of P's –if worried about tipping off P's about hypothesis –small effects are not a concern Should use Within Design if: –carryover effects are not a problem –P's are few –multiple exposure to variables is not a problem –small effects ARE a concern

6. Hypotheticals: Which Design Would be the Best? Situation A: Memory Enhancer - testing effects of two types of "smart drugs" Situation B: "Quick-Editing" video style and stress (muscular tension) Situation C: EEG controlled computer cursor

7. Combined Within and Between Subject Designs

8. Mixed (split-plot) Design –Originates in agricultural research Nested Design –Levels of within-subject variables are nested under the between-subject levels. However, the levels of the WS factor are not the same under each BS group

9. Pretest-Posttest Designs Pretest; introduce treatment; Posttest Problem: cannot counterbalance in this situation –Must include control groups –e.g., pretest-treatment-posttest vs. pretest-blank- posttest

10. Covariate Analysis Covariates can be factored out

11. Quasi Experimental Designs Experiments that do not use random assignment Useful for assessing effects of real-life treatments situations Problem: accounting for extraneous variables

12. 3 Types of Quasi-Experimental Designs Time-Series Equivalent Time Samples Non-Equivalent Control Group

13. Time-Series Design Variation of pretest-posttest design - uses several pretests and posttests Then, administer the IV some point If changes are noted from pretest to pretest, then you can estimate how much of the effect is NOT due to the IV

14. Equivalent Time Samples Design Alternate the IV with a non-treatment condition, followed by observations after each IV or after each non- treatment

15. Non-Equivalent Control Group Design Simple experiment without random assignment using a control group to compare to the experimental group

16. Conclusions about Quasi- Experiments Advantage: Allows evaluation under naturally-occurring conditions (good external validity) Disadvantages: –Little control over extraneous variables –Little control over timing of events

17. Developmental Designs Age is a variable

18. 1. Cross-Sectional Design P's drawn from several age groups Pro: obtain data in a short amount of time Con: "Generation Effect" can occur

19. 2. Longitudinal Design A single group is followed over time Pro: circumvents the generation effect Con: “Cross-Generational effects" can occur Con: subject mortality Con: multiple-observation effects

20. 3. Cohort-Sequential Design Age groups born at different times are examined to identify possible generational effects