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Social Relations Model: Multiple Variables

Social Relations Model: Multiple Variables

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Social Relations Model: Multiple Variables

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  1. Social Relations Model:Multiple Variables David A. Kenny

  2. Types of Variables in SRM Studies • Dyadic variable • Personality variable • Self variable • Group variable

  3. Multiple Dyadic Variables Bivariate Correlations 4 at the individual level 2 at the dyadic level

  4. Dyadic Variables Individual-level Correlations Actor-Actor Actor-Partner Partner-Actor Partner-Partner

  5. Actor-Actor Correlation If initially a person sees others as Extroverted, does that person still see others as extroverted after interacting with them? Not really: r = .21

  6. Actor-Partner Correlation If initially a person sees others as Extroverted, is that person seen as extroverted after interacting with him or her? Maybe: r = .46

  7. Partner-Actor Correlation If a person is initially seen by others as Extroverted, does that person see others as extroverted after interacting with them? Not really: r = -.02

  8. Partner-Partner Correlation If initially a person is seen by others as Extroverted, is that seen as Extroverted after interacting with him or her? Yes: r = .89

  9. Relationship Intrapersonal If one person, A, initially thinks another person, B, is particularly extroverted, does A still think that B is particularly extroverted after interacting with him or her? Nor really: r = .23).

  10. Relationship Interpersonal If one person, A, initially thinks another person, B, is particularly extroverted, does B think that A is particularly extroverted after interacting with him or her? Not really: r = -.15

  11. Creating a Construct Why? to separate error from relationship variance

  12. Multiple Measures Same measure at different times. Different measures at the same time.

  13. How? Sum or average the scores. Create a construct or a latent variable.

  14. Stable versus Unstable Variance stable variance: variance that correlates across different measures of the construct unstable variance: variance that is unique to the specific measure of the construct

  15. Measurement Model Equal loadings of the different measures: All measures need to have the same units. Equal unstable variance in each measure

  16. Construct Variances Stable Actor Unstable Actor Stable Partner Unstable Partner Stable Relationship Unstable Relationship

  17. Error Variance Very often Unstable Actor and Partner variances are very small. There is only Unstable Relationship variance. Can report error variance as the sum of Unstable Actor, Partner, and Relationship variances.

  18. Example Liking at Two Times (Curry) Stable Unstable Actor .160 .029 Partner .259 .016 Relationship .422 .114 Error .159

  19. Correlated Error Some times, pairs of indicators share method variance. Same time Same instrument Need to remove correlated error effect in computing correlations between two constructs.

  20. A Personality Variable witha Dyadic Variable

  21. Extroversion (personality variable) with Smiling (dyadic variable) Actor Personality Variable Correlation: If Dave is extroverted, does Dave smile more? Partner Personality Variable Correlation: If Dave is extroverted, do others smile more at Dave?

  22. A Personality Variable at the Relationship Level Compute the product of actor’s personality X partner’s personality (both centered) or alternatively the absolute difference. Correlate with relationship effect.

  23. Self Variable as a Special Personality Variable Self Variable: A “dyadic” measurement in which actor and partner are the same person. Can correlated with actor and partner effects.

  24. Group Variable Same score for all group members. Examples gender experimental condition Tests level variances

  25. Suggested Readings Dyadic Data Analysis, Kenny, Kashy, & Cook, Chapter 8 Appendix B in Kenny’s Interpersonal Perception (1994)

  26. Thank You!