1. Research Design & Analysis 2: Class 22 Announcement: Honours conference, Saturday 8:30-12:15 BAC 132 Multiple regression SPSS output –(optional lab assignment) Other Multivariate Designs –text book: Chapter 14 Developmental Designs –text book: Chapter: 6 171-180 –(ch 9: 262-271 in 4th Edition)

2. psyc 2023 class #22 (c) Peter McLeod 2 SPSS Multiple Regression Assignment Output: Enter p=#Ivs N=sample

3. psyc 2023 class #22 (c) Peter McLeod 3 SPSS Multiple Regression Assignment Output: Enter t =B/Std. Error

4. psyc 2023 class #22 (c) Peter McLeod 4 SPSS Multiple Regression Assignment Output: Stepwise alpha R 2 increases with additional variables

5. psyc 2023 class #22 (c) Peter McLeod 5 SPSS Multiple Regression Assignment Output: Stepwise

6. psyc 2023 class #22 (c) Peter McLeod 6 SPSS Multiple Regression Assignment Output: Stepwise

7. psyc 2023 class #22 (c) Peter McLeod 7 SPSS Multiple Regression Assignment Output: Stepwise Tolerance= 1-R 2 IVs if high, (near 1), collinearity is not a problem

8. psyc 2023 class #22 (c) Peter McLeod 8 SPSS Multiple Regression: Correlations

9. psyc 2023 class #22 (c) Peter McLeod 9 Multivariate Designs and Analyses Multiple Regression: goal is to explain as much of the variance in the criterion variable (Y - the DV) based on a set of predictor variables (Xs). Discriminant Analysis: basically Multiple regression, with a categorical dependent variable.

10. psyc 2023 class #22 (c) Peter McLeod 10 Activism Among Black South Africans: C. Motjuwadi M.Sc.

11. psyc 2023 class #22 (c) Peter McLeod 11 Activism Among Black South Africans: C. Motjuwadi M.Sc.

12. psyc 2023 class #22 (c) Peter McLeod 12 Motjuwadi’s Discriminant Analyses Predicting Protest Participation gender, friend support, personal power, perceptions of injustice, & area Predicting political Membership participation, gender Predicting Detention participants, gender, area

13. psyc 2023 class #22 (c) Peter McLeod 13 Multivariate Designs and Analyses Canonical Correlation: looks at the relationship between a set of predictor variables and a set of dependent variables by creating one new predictor variable and one new dependent variable and relates these canonical variates.

14. psyc 2023 class #22 (c) Peter McLeod 14 Multivariate Designs and Analyses Multivariate Analysis of Variance (MANOVA). Used when you have more than one independent variable and more than one dependent variable that you believe are related (i.e., not independent). –Controls for type I error –Considering relations among DVs may be more powerful Log-linear analysis. This non-parametric statistic is basically a multivariate Chi-squared.

15. psyc 2023 class #22 (c) Peter McLeod 15 Log-Linear Example

16. psyc 2023 class #22 (c) Peter McLeod 16 Multivariate Designs and Analyses Path Analysis. Uses multiple regression methods to examine hypothesized causal relationships among variables with only correlational data. See how well your theoretically derived model describes relationships among variables. Can also compare competing theories about the relationships among variables.

17. psyc 2023 class #22 (c) Peter McLeod 17 Possible Causal Relationships ABA B C A B C

18. psyc 2023 class #22 (c) Peter McLeod 18 Possible Causal Relationships: Fig 14-9 PESMSATPESMWHSAT

19. psyc 2023 class #22 (c) Peter McLeod 19 Possible Causal Relationships A C B D

20. psyc 2023 class #22 (c) Peter McLeod 20 Possible Causal Relationships AC BD E

21. psyc 2023 class #22 (c) Peter McLeod 21 Path Diagram: Table 14-7 AC BD E 0.21 0.39 0.12 0.72 0.68 0.12 B  E = (.39*.12)+(.12*.72)+(.39*.68*.72)=.32

22. psyc 2023 class #22 (c) Peter McLeod 22 Causal Antecedents of Attachment

23. psyc 2023 class #22 (c) Peter McLeod 23 Stewart, Taylor, Jang, Cox, Watt, Fedoroff, & Borger (in press) causal modeling of relations among learning history, anxiety sensitivity, and panic attacks

24. psyc 2023 class #22 (c) Peter McLeod 24 Cross-correlation in Developmental Research

25. psyc 2023 class #22 (c) Peter McLeod 25 Multivariate Designs and Analyses Factor analysis is a multivariate form of data reduction. Factor analysis is typically use to extract a relatively small number of underlying dimensions or factors that can account for relationships among measures (see example from text)

26. psyc 2023 class #22 (c) Peter McLeod 26

27. psyc 2023 class #22 (c) Peter McLeod 27 Multivariate Designs and Analyses are all very powerful and some are easy statistics to use, and misuse. To use these the techniques appropriately depends upon careful research design and thought. Remember...

28. psyc 2023 class #22 (c) Peter McLeod 28

29. psyc 2023 class #22 (c) Peter McLeod 29 Data Collections Methods in Developmental Psychology Naturalistic Observations Interviews structured –questionnaires –surveys unstructured –clinical Case Studies Experimental: lab field Quasi- experimental correlational ex post facto

30. psyc 2023 class #22 (c) Peter McLeod 30 Experimental Designs in Developmental Psychology Longitudinal Designs Cross-sectional Designs Cohort-Sequential (Cross-sequential, time-sequential) Designs

31. psyc 2023 class #22 (c) Peter McLeod 31 Longitudinal Designs Examine developmental changes in one cohort followed over time Within-Subjects Quasi-analytic design Advantages: Process of development can be followed with individuals Disadvantages: Large investment of time and money is required (especially if large age span) Subject attrition can be a problem Carryover effects (e.g., learning) Differences among cohorts are not addressed

32. psyc 2023 class #22 (c) Peter McLeod 32 Cross-sectional Designs Examine two (or more) ages (or cohorts) at one time Between-Subjects Quasi-analytic design Advantages: Fast and cheap No subject attrition Disadvantages: Confounds age and cohort effects Unable to examine the process of development within individuals

33. psyc 2023 class #22 (c) Peter McLeod 33 Cohort-Sequential Designs Combination of cross-sectional & longitudinal designs two (or more) cohorts, each studied at two (or more) ages. (Sometimes with additional groups tested once to "fill in" the design.) Mixed Quasi-analytic design Advantages & Disadvantages This is a compromise solution with some of the advantages and disadvantages of cross- sectional & longitudinal designs depends on length of the within cohort component and the number of cohorts.

34. psyc 2023 class #22 (c) Peter McLeod 34 Age, Education and I.Q.

35. psyc 2023 class #22 (c) Peter McLeod 35 Age, Education and I.Q.