PSY 250

1. PSY 250 Exam 3 Review

2. Research strategies • Experimental strategies are not determined solely by potential weaknesses – EVERY study has some weakness. • Rather, the difference between true and non or quasi experiments is determined by the amount of control over assignment to groups (between subjects) or order of conditions (within subjects)

3. Research strategies • Correlational designs are NOT between subject designs with discrete categories or groups you are comparing! • This is probably ex post facto or differential groups design • Not every design that compares two variables is correlational!

4. Between vs. within • Between • DIFFERENT individuals give data point at each level of the IV. Therefore, you are comparing the average scores of one group of people to the average scores of (a) DIFFERENT group(s) of people • Within • The SAME individuals participate and give a data point at each condition of the experiment (or each level of the IV). • Thus, you are comparing an individual’s score under one condition to their own scores in a different condition(s)

5. variables • If you are looking at the effects of more than one variable on another variable (DV), then you have multiple IVs (i.e.. a factorial design) • If one IV is between and another is within, you have a mixed factorial design • You are then probably looking at main effects of IVs PLUS their interactions • i.e. does the DV at one level of one IV depend on the level of another IV • E.g. do girls perform better with fewer classmates in a classroom while boys perform better with more classmates in a classroom? (interaction with number of classmates (IV 1) and gender (IV 2)

6. Interactions cont. • If both males and females do better (and about the same amount better) with fewer classmates then gender and number of classmates do not interact • However, if the improvement is much greater for one gender than the other, there may be an interaction

7. Main effects • If males generally perform better than females (or vice versa), but class size does not effect performance, there is a main effect of gender but not class size and no interaction • If both genders do equally well, but both do better in smaller classes, then there is a main effect of class size, but not gender • If males do better than females but both do better in smaller classes, there are main effects of both gender and class size but no interaction • There can be main effects and no interaction AND interactions without main effects

9. Seeing interactions Is there an interaction? What numbers do we compare to see if there is a main effect of: Gender? Class size? Are there main effects?

10. Seeing interactions What numbers would we place in the missing cell to create An interaction? No interaction?

11. 1. Differential Research Design (NE) • Also called ex post facto research • Compares pre-existing groups defined by participant variable • E.g. shyness scores from single child vs. child with siblings • Existence and description of relationships • Similar to correlational design but different data and analysis

12. 2. Posttest-Only Non-equivalent Control Group Design (NE) • Also called static group comparison • Applied settings • Measure effectiveness of treatment with pre-existing participants • Similar but nonequivalent participants used as control condition X O Exp. Grp O Control

13. 3. Pretest – Posttest Non-equivalent Control Group Design (QE) • Stronger version of posttest only design • Both control (C) and experimental (E) groups measured prior to treatment and again after E group receives treatment • Shows if groups are similar on the DV before manipulation of IV • Also controls for time related changes in DV indep. of IV • Reduces threat of both assignment bias and time related threats O X O Exp. Grp. O O Control

14. 1. One-Group Pretest – Posttest Design (NE) • One pre and one post-test measurement • E.g. voter’s confidence in electoral candidate before and after televised debate • O X O

15. 2. Time Series Design (QE) • Treatment is manipulated by researcher • Series of observations for each participant before and after treatment or event • E.g. Measures of stress weekly for 2 months preceding and following introduction of aromatherapy in workplace • O O O X O O O

16. 3. Interrupted Time Series Design (QE) • Treatment is NOT manipulated by researcher • E.g. Depression measured monthly for 3 months before and after Christmas • Works with predictable event like decriminalizing marijuana • For unpredictable events like Katrina, rely on archival data • Can see trends in data before treatment • Can observe long-term changes following treatment • But other changes can coincide with treatment • E.g. cold weather/snowfall and Christmas

17. 4. Equivalent Time – Samples Design (QE) • Treatment is repeatedly administered and removed during series of observations • E.g. introducing music in the workplace – turning it on and off and measuring worker concentration at regular intervals weekly • O O O X O N O X O • Best used when treatment effect is expected to be temporary • Hard to determine causality if treatment effect is permanent

18. Correlational Studies • Simply measures 2 variables [usually two scores (X and Y) from same individual] or scores on 1 variable between 2 related individuals • Criterion (Y) and Predictor (X) variables • Degree and nature of relationship • descriptive or predictive • Correlation coefficients +1.00 to -1.00 • No attempt to explain relationship • No attempt to manipulate or control variables