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Stuff I Have Done and Am Doing Now
David A. Kenny
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Stuff I Have Done
Social Relations ModelActor-Partner Interdependence Model
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Social Relations Model
groups or teamsdirected dyadic data: A’s perception of B & B’s of A
gathered from all or mostly all pairs 3
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Round Robin Design
1 2 3 4 5 61 - 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 -
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SRM Equation
For actor i with partner j in group k (e.g., how intelligent i sees fellow member j in group k):
Xijk = mk + aik + bjk + gijk
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actor
group partner
relationship
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Reciprocity Equations
Xijk = mk + aik + bjk + gijk
Xjik = mk + ajk + bik + gjik
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A Very Messy Multilevel
ModelThree 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
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EstimationANOVA modelsSnijders approach creation of many dummy
variables many constraints on the tau
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SRM Example: Leadership
Group
Actor Part.
Relat. Error
Leadership
.00 .09 .43 .19 .29
Variance Partitioning (proportions)
Actor-Partner(Generalized)
Relationship
(Dyad)
Leadership .14 .03
Reciprocity (correlations)
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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.
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Research Question
Metaperception of Liking: How much person 1 thinks 2 likes 1 or P12
Two dyadic predictors:How much person 2 likes person 1 or A21 (accuracy)
How much 1 likes 2 or A12 (assumed
reciprocity) 11
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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
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A’s Perception of B’s Liking of A
Actor Partner Interdependence Model
B’s Perception of A’s Liking of B
Bias
Bias
AccuracyAccuracy
A’s Liking of B
B’s Liking of A
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APIM in Groups: GAPIM (Kenny &
Garcia)Terms
Actor: Effect of own XPartner: Effect of others’ XActor similarity: Similar of the actor to others.Others’ similarity: How are the others
Terms can be combined to createDiversityGroup compositionFrog pond effects 14
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Fixed Relationship
EffectsCould enter in dyadic variables A12 and A21 as predictors within a multilevel model.
Two problemsEstimation messiness of the Snijders approachSome of the effects of dyadic predictors will be at the individual and group levels.To obtain a “pure” dyadic measures is very messy.
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StrategyRemove 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.
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Curry & Emerson 6 groups of 8 personsLiking and metaperception of liking
measured at five timesData from Week 1, the first
measurement, are used.
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EstimatesTerm b (SE) Confidence IntervalAR .363 (.036) .293 to .432Acc .084 (.036) .014 to .153
http://davidakenny.net/doc/sre.pdf
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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
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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
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DyadR Programs
Restructuring Dyadic Datadavidakenny.net/RDDD.htm
Analysisdavidakenny.net/DyadR/DyadRweb.htm
Tests of distinguishabilityAPIM power analysesAPIM analysesOther stuff 21
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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
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Thank You!