Social Relations Model: Estimation (Indistinguishable) David A. Kenny

Strategies Multilevel ANOVA

MLM Strategy Better statistically than the ANOVA approach Allows for missing data One setup for all designs Can estimate non-saturated models (e.g., model with group variances set to zero). Can more easily estimate the effects of multiple fixed variables.

With SPSS, HLM and R’s nlme Cannot estimate the full SRM. Must assume zero actor-partner covariance positive dyadic reciprocity

With SAS and MLwiN A method developed by Tom Snijders Can estimate the full SRM. Need to create dummy variables and force many equality constraints.

ANOVA Strategy Oldest Uses Expected Mean Squares Two Major Programs TripleR SOREMO

TripleR Schmukle, Schönbrodt, & Back project.org/web/packages/Tripl eR/index.html 94/Round_robin_analyses_in_R _How_to_use_TripleR

TripleR Schmukle, Schönbrodt, & Back project.org/web/packages/Tripl eR/index.html 94/Round_robin_analyses_in_R _How_to_use_TripleR

SOREMO FORTRAN program originally written in the early 1980s. WINSOREMO makes the running of SOREMO much easier.

Estimation Strategy Computes estimates of actor, partner, and relationship effects. Computes their variance. Adjust the variances by irrelevant components; e.g., variance of actor effects contains relationship variance (Expected Mean Squares)

Getting the Data Ready One line per each cell of the design Ordered as follows:,,,, …, All variables on that line Fixed format Personality variable before dyadic variables No missing data

Decisions Same group sizes? Self data? Personality variables? Constructs? Reverse Variables?

Output Univariate Multivariate

Univariate Output Variance Partitioning RELATIVE VARIANCE PARTITIONING VARIABLE ACTOR PARTNER RELATIONSHIP CONTRIBUTE.335*.345*.320 INFLUENCE.191*.443*.365 EXHIBIT.177*.498*.325 CONTROL.242*.371*.386 PREFER.173*.270*.557

Multivariate Output Matrix: Actor by Actor ACTOR BY ACTOR CORRELATION MATRIX CONTRIBUTE INFLUENCE EXHIBIT CONTROL PREFER CONTRIBUTE INFLUENCE EXHIBIT CONTROL PREFER Matrices for Actor, Partner, Actor X Partner, Relationship Intrapersonal, and Relationship Interpersonal

Construct Variance Partitioning STABLE CONSTRUCT VARIANCE VARIABLE ACTOR PARTNER RELATIONSHIP LEADERSHIP UNSTABLE CONSTRUCT VARIANCE VARIABLE ACTOR PARTNER RELATIONSHIP LEADERSHIP

Anomalous Results with ANOVA Estimation Negative Variances Out-of-range Correlations

Negative Variances Ordinarily impossible Happens in SRM analyses Can treat the variance as if it were zero.

Out-of-range Correlations A correlation greater than +1 or less than -1. Two possibilities Correlation very near one. Variance due to the component near zero.

Summary of Results Using Different Programs TermSOREMOSPSS MLM Mean3.868 Actor Variance Partner Variance Group Variance A-P Covariance Error Variance Error Covariance

Suggested Readings Appendix B in Kenny’s Interpersonal Perception (1994) Kenny & Livi (2009), pp

Thank You!