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An Introduction to the Social Relations Model David A. Kenny
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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
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Free Webinar http://davidakenny.net/webinars/Dyad/SRM/ Introduction/Introduction.html
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4 Social Relations Model: Components of Perceptions Bob Steve Sue Ann
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5 Social Relations Model: Components of Perceptions Bob Steve Sue Ann Bob is friendly
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6 Bob Steve Sue Ann Social Relations Model: Components of Perceptions Everyone is friendly Perceiver effect
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7 Social Relations Model: Components of Perceptions Bob Steve Sue Ann Bob is friendly Target effect Hi!! I’m Bob. What’s your name?
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8 Bob Steve Sue Ann Bob looks like friendly to me. Social Relations Model: Components of Perceptions Relationship effect
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The Two Persons
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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.
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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.
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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.
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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.
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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.
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Other Terms AreaActorPartner Person PerceptionPerceiver Target NonverbalReceiver Sender DecoderEncoder PersuasionReceiver Source AggressionPerpetratorVictim Sport’s ScoreOffenseDefense
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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.
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Other Possible Units Animals sharks primates elephants mice Tribes (Brewer & Campbell) or Countries Groups, Organizations, or Teams
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SRM Equation For actor i with partner j in group k: X ijk = m k + a ik + b jk + g ijk
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Reciprocity Equations X ijk = m k + a ik + b jk + g ijk X jik = m k + a jk + b ik + g jik
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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.
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Sources of SRM Nonindependence l Actor l Partner l Dyadic Reciprocity l Actor-Partner Reciprocity
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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.
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SRM Example: Liking LikingActorPartnerRelat.Error Friendship.15.14.40.31 Dating.12.26.32.30 Variance Partitioning Liking Actor-Partner (Generalized) Relationship (Dyad) Friendship.12.58 Dating -.42.13 Reciprocity
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SRM Example: Liking LikingActorPartnerRelat.Error Friendship.15.14.40.31 Likeability.10.14.31.45 Variance Partitioning Liking Actor-Partner (Generalized) Relationship (Dyad) Friendship.12.58 Likeability.28.80 Reciprocity
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SRM Example: Leadership ActorPartnerRelat.Error Leadership.09.43.19.29 Variance Partitioning Actor-Partner (Generalized) Relationship (Dyad) Leadership.14.03 Reciprocity
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SRM Published Papers
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Suggested Readings Back, M. D., & Kenny D. A. (2010). The Social Relations Model: How to understand dyadic processes. Social and Personality Psychology Compass, 4, 855-870. Dyadic Data Analysis, Kenny, Kashy, & Cook, Chapters 8 and 9 http://davidakenny.net/doc/srmbiblio.pdf
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The End!
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