1. Stuff I Have Done and Am Doing Now David A. Kenny

2. Stuff I Have Done Social Relations Model Actor-Partner Interdependence Model 2

3. Social Relations Model groups or teams directed dyadic data: A’s perception of B & B’s of A gathered from all or mostly all pairs 3

4. Round Robin Design 1 2 3 4 5 6 1 - x x x x x 2 x - x x x x 3 x x - x x x 4 x x x - x x 5 x x x x - x 6 x x x x x - 4

5. SRM Equation For actor i with partner j in group k (e.g., how intelligent i sees fellow member j in group k): X ijk = m k + a ik + b jk + g ijk 5 actor group partner relationship

6. Reciprocity Equations X ijk = m k + a ik + b jk + g ijk X jik = m k + a jk + b ik + g jik 6

7. A Very Messy Multilevel Model Three level model: group, individual, and observation Two crossed random variables at the individual level (actor and partner) Linkage across them: Actor and partners the same people 7

8. Estimation ANOVA models Snijders approach creation of many dummy variables many constraints on the tau matrix 8

9. SRM Example: Leadership GroupActorPart.Relat.Error Leadership.00.09.43.19.29 Variance Partitioning (proportions) Actor-Partner (Generalized) Relationship (Dyad) Leadership.14.03 Reciprocity (correlations) 9

10. Random and Fixed Effects Most SRM analyses (like the above) focus on the random effects. Estimation of fixed effects at the group and individual level within the SRM is relatively straight-forward. Estimation of fixed effects at the relationship level is not so simple. 10

11. Research Question Metaperception of Liking: How much person 1 thinks 2 likes 1 or P 12 Two dyadic predictors: How much person 2 likes person 1 or A 21 (accuracy) How much 1 likes 2 or A 12 (assumed reciprocity) 11

12. A’s Perception of B’s Liking of A Actor Partner Interdependence Model Bias Accuracy A’s Liking of B B’s Liking of A 12

13. A’s Perception of B’s Liking of A Actor Partner Interdependence Model B’s Perception of A’s Liking of B Bias Accuracy A’s Liking of B B’s Liking of A 13

14. APIM in Groups: GAPIM (Kenny & Garcia) Terms Actor: Effect of own X Partner: Effect of others’ X Actor similarity: Similar of the actor to others. Others’ similarity: How are the others Terms can be combined to create Diversity Group composition Frog pond effects 14

15. Fixed Relationship Effects Could enter in dyadic variables A 12 and A 21 as predictors within a multilevel model. Two problems Estimation messiness of the Snijders approach Some of the effects of dyadic predictors will be at the individual and group levels. To obtain a “pure” dyadic measures is very messy. 15

16. Strategy Remove from the data all of the group and individual variance. What remains is purely dyadic, i.e., relational. Akin to the old-fashioned “within” approach for the hierarchically nested design. 16

17. Curry & Emerson 6 groups of 8 persons Liking and metaperception of liking measured at five times Data from Week 1, the first measurement, are used. 17

18. Estimates Term b (SE) Confidence Interval AR.363 (.036).293 to.432 Acc.084 (.036).014 to.153 http://davidakenny.net/doc/sre.pdf 18

19. Current Work: DataToText Methodologists need to become more consumer oriented. Computer output needs to be presented in ways that are more user friendly. What about abuse? 19

20. Current Work: DyadR Developed a series of programs for dyadic analysis in R. Use Rstudio’s shiny interface. Programs do not require that the user instal R. Provide the usual computer output, text, tables, and figures. 20

21. DyadR Programs Restructuring Dyadic Data davidakenny.net/RDDD.htm Analysis davidakenny.net/DyadR/DyadRweb.htm Tests of distinguishability APIM power analyses APIM analyses Other stuff 21

22. Woman’s Perception of Man’s Rel. with Child DyadR APIM Example Man’s Perception of Woman’s Rel. with Child Female Bias Male Bias Male Accuracy Female Accuracy Data gathered by Linda Acitelli and the model adapted from West & Kenny Truth and Bias model. Woman’s Rel. with Child Man’s Rel. with Child 22

23. Thank You!