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An Introduction to the Social Relations Model David A. Kenny.

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1 An Introduction to the Social Relations Model David A. Kenny

2 What is the SRM? model for dyadic measurements componential model normally dyadic data are ordered such that X ij not necessarily equal to X ji focus on random not fixed effects allowance for nonindependence non-sequential model

3 Free Webinar Introduction/Introduction.html

4 4 Social Relations Model: Components of Perceptions Bob Steve Sue Ann

5 5 Social Relations Model: Components of Perceptions Bob Steve Sue Ann Bob is friendly

6 6 Bob Steve Sue Ann Social Relations Model: Components of Perceptions Everyone is friendly Perceiver effect

7 7 Social Relations Model: Components of Perceptions Bob Steve Sue Ann Bob is friendly Target effect Hi!! I’m Bob. What’s your name?

8 8 Bob Steve Sue Ann Bob looks like friendly to me. Social Relations Model: Components of Perceptions Relationship effect

9 The Two Persons

10 Social Relations Model: Rating How Friendly Dave Sees Tom Actor: How friendly Dave sees others in general. Partner: How friendly is Tom seen by others in general. Relationship: How much Dave believes that Tom is especially friendly.

11 Social Relations Model: Liking How Much Dave Likes Tom Actor: How much Dave likes others in general. Partner: How much Tom is liked by others in general. Relationship: How much Dave particularly likes Tom.

12 Social Relations Model: Behavior How Much Dave Gazes at Tom Actor: How much Dave gazes at others in general. Partner: How much Tom is gazed at by others in general. Relationship: How much Dave particularly gazes at Tom.

13 Like Two-Way ANOVA Actor: The “main effect” of the responder. Partner: The “main effect” of the other. Relationship: The interaction of actor and partner with the main effects removed.

14 Focus on Variance We could estimate particular actor, partner, and relationship effects: For example, Dave’s actor effect. Within the SRM the focus in not on a particular effect but on the variance in effects.

15 Other Terms AreaActorPartner Person PerceptionPerceiver Target NonverbalReceiver Sender DecoderEncoder PersuasionReceiver Source AggressionPerpetratorVictim Sport’s ScoreOffenseDefense

16 Variance vs. Effect Actor Variance Definition: How different people are people in their actor effects. Actor Effect Definition: How different a given person’s in how he or she generally responds to others. Example There is not a lot of actor variance in liking; Dave has a large actor effect.

17 Other Possible Units Animals sharks primates elephants mice Tribes (Brewer & Campbell) or Countries Groups, Organizations, or Teams

18 SRM Equation For actor i with partner j in group k: X ijk = m k + a ik + b jk + g ijk

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

20 Reciprocity  Dyadic  Correlation of relationship effects or between g ijk with g jik.  Actor-Partner (Generalized or Individual Reciprocity)  Correlation of actor and partner effects or between a ik with b ik.

21 Sources of SRM Nonindependence l Actor l Partner l Dyadic Reciprocity l Actor-Partner Reciprocity

22 Group as a Source of Nonindependence  Similar Scores for members of the same group  Norms, climate, or environment  However, typically the effect of groups with the exception is one of contrast.  Members are see as different from each other.

23 SRM Example: Liking LikingActorPartnerRelat.Error Friendship Dating Variance Partitioning Liking Actor-Partner (Generalized) Relationship (Dyad) Friendship Dating Reciprocity

24

25 SRM Example: Liking LikingActorPartnerRelat.Error Friendship Likeability Variance Partitioning Liking Actor-Partner (Generalized) Relationship (Dyad) Friendship Likeability Reciprocity

26 SRM Example: Leadership ActorPartnerRelat.Error Leadership Variance Partitioning Actor-Partner (Generalized) Relationship (Dyad) Leadership Reciprocity

27 SRM Published Papers

28 Suggested Readings Back, M. D., & Kenny D. A. (2010). The Social Relations Model: How to understand dyadic processes. Social and Personality Psychology Compass, 4, Dyadic Data Analysis, Kenny, Kashy, & Cook, Chapters 8 and 9

29 The End!


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