2.  Graduate symposium deadline Friday  CSBS student conference April 25 (deadline April 10)  Thesis defenses  Outline due on Friday

3.  What is internal validity? How can it be increased?  What are the relationships between internal, external, and construct validity?  What is needed to establish causality?

4.  History  Maturation  Testing  Instrumentation  Mortality  Regression to the mean  What are each of these (examples) and how can they be decreased/controlled?

5.  Why does it happen?  When will it happen more?  How can you estimate it?

6.  Selection  Selection-history  Selection-maturation  Selection-testing  Selection-instrumentation  Selection-mortality  Selection-regression  What are each of these (examples) and how can they be decreased/controlled?

7.  Diffusion of treatment  Compensatory rivalry  Resentful demoralization  Compensatory equalization of treatment  What are each of these (examples) and how can they be decreased/controlled?

8.  X O  R O X O R O O N O X O N O O N O

9.  vs. random selection  What is the strongest design in terms of internal validity threats?  Why would you want to add a pretest? Or not?  Are there times that you wouldn’t want to randomly assign?

10.  Independent vs. dependent variables  Within vs. between-participants designs: advantages?  What are control variables?  What can you do to reduce confounds in research?

11.  What are they and why do we care?  What are levels vs. variables?  What kind of FD is this? How many main effects are possible? How many interactions? How many groups? How many people at n=10?  IVs: Presence of an audience, difficulty of task (easy, medium, or hard). DV = nervousness  IVs: Gender, Drug vs. CBT vs. control, previous experience with treatment (yes or no). DV=reduction in symptoms

12.  People who were alone helped more often than those who were with a confederate. Seminary students also helped more often than business students.  Men and women helped equally overall, but men were more likely to help women than to help men.

14.  Null results  Main effect  Interaction  Block design  Covariate  Solomon 4-group  Switching design  ANOVA vs. ANCOVA vs. regression

15.  If a factorial design is good, is a bigger one better?  When would you want to include a variable as an IV vs. a covariate vs. something you match on vs. a control variable?  How do you decide which variables to include?

17.  What is a moderator?  How is a moderator different from a/an  IV?  DV?  Confound?  Mediator?  Covariate?  How can you design a study to test a moderator (various ways)?

18.  What is a mediator?  How is a mediator different from a/an  IV?  DV?  Confound?  Moderator?  Covariate?  How can you design a study to test a mediator (various ways)?

19.  A study finds that convicted criminals are more likely than noncriminals to score low (negatively) on the Attitudes toward Women Scale. Further research shows, however, that this is only true for violent criminals. There is no relationship between nonviolent criminal activity and negative attitudes toward women. Identify the mediator or moderator.  2. A researcher finds that by increasing self-focus in children, she can decrease their likelihood of cheating on a test. By decreasing cheating, in turn, she finds that academic self-efficacy increases. Identify the mediator or moderator.  3. Dr. Laylor finds a relationship between physical attractiveness and self- confidence. He later determines that the primary cause of this relationship is the positive feedback physically attractive people receive from others. Identify the mediator or moderator.  4. Boys who are popular with the same-sex tend to also be popular with the opposite-sex, whereas girls who are popular with the same-sex tend to be less popular with the opposite-sex. Identify the mediator or moderator.  5. Identify and explain at least two potential mediators and two potential moderators for the following relationship: School size and academic achievement.

20.  Table 1—why do some areas of psychology use more mediators than others?

21.  Case 1: Cat IV, Cat Mod:  2 x 2 ANOVA  Case 2: Cont IV, Cat Mod:  Could do correlations sep and compare BUT  Better to do regression and compare unstandardized Betas  Or SEM software

22.  Case 3: Cat IV, Cont Mod:  Figure out how level of moderator affects IV-DV relationship  If linear, do hierarchical regression, showing that XZ adds to the effects of X and Z on Y  Case 4: Both Cont:  Use Case 2 if step function or  Use Case 3 if linear, quadratic  How do you know which one is the moderator vs. IV?

23.  Causal steps (Baron & Kenny)  IV related to M M = i + aX + e  IV related to DV Y = i + c1X + e  M and IV related to DV Y = i + c2X + bM + e  C1 greater than C2 (look at size and sig)  Limitations:  Not good for multilevel, probit, logistic, survival  Need to test for whether C1 > C2 (Sobel test)  Low power esp when IV and DV aren’t related  Overestimates effect of IV on DV if error in M

24.  Used to test whether C1 > C2  Good for sample sizes of 50+ with 1 M  Or 100-200 for >1 M

25.  Use multiple measures of M and use SEM  Distribution of the product  PRODCLIN  Better Type I error rates, higher power  Computer-intensive methods  Aka resampling  Fewer assumptions

26.  Residuals are independent in equations 2 and 3  No XM interaction in equation 3  Direction is correct (DV doesn’t cause M)  Measurement is perfect, esp. in M  No unmeasured variables that cause X, Y, or M  IV related to M M = i + aX + e  IV related to DV Y = i + c1X + e  M and IV related to DV Y = i + c2X + bM + e

27.  Complete vs. partial mediation  Use hierarchical regression  Test C2 significance  Inconsistent mediation  Multilevel mediation  Can increase T1 error if you ignore  Categorical DV mediation  Use logistic or probit regression  Multiple mediators

28.  Longitudinal mediation  Moderated mediation  Mediated moderation  Mediated baseline by treatment moderation

29.  How do you know which variable is the mediator?  How can a moderator lead you to a mediator?  How can a mediator lead you to a moderator?  Can a variable be both a mediator and a moderator at the same time?  Are there variables that are always going to be one or the other?  Does a mediator have to be correlational?  Remember that one study does not a mediator/moderator make

30.  Think of sections as headings in your thesis  Keep headings parallel  Always have at least 2 in each group  Think about the best logical order for things, and keep the order consistent throughout the paper

31.  Quasi-experiments  2 book chapters plus other chapter