手法を比較

	Social Relations Model	Actor-Partner Interdependence Model
分野	社会心理学	社会心理学
系統	Regression model	Regression model
提唱年	2006	2006
提唱者	David A. Kenny and colleagues	David A. Kenny and colleagues
種類	Variance-decomposition model for dyadic data	Regression model for dyadic interdependence
原典	Kenny, D. A., Kashy, D. A., & Cook, W. L. (2006). Dyadic Data Analysis. Guilford Press. ISBN: 9781572309869	Kenny, D. A., Kashy, D. A., & Cook, W. L. (2006). Dyadic Data Analysis. Guilford Press. ISBN: 9781572309869
別名	SRM, Kenny Social Relations Model, Round-Robin Variance Partition	APIM, Dyadic Actor-Partner Model, Interdependence Regression Model
関連	3	3
概要	The Social Relations Model (SRM), developed by David Kenny and colleagues, is a variance-decomposition framework for analyzing interpersonal perception and behavior in groups. When every member of a group rates (or behaves toward) every other member in a round-robin design, each rating reflects three distinct sources: the perceiver's general tendency to see others a certain way (actor effect), the target's general tendency to be seen that way by others (partner effect), and the unique adjustment a particular perceiver makes for a particular target (relationship effect), plus error. The SRM partitions the total variance into these components and estimates two kinds of reciprocity -- generalized (do people who like others tend to be liked?) and dyadic (do specific pairs uniquely reciprocate?). By separating the perceiver, the target, and their unique relationship, the SRM answers fundamental questions about whether interpersonal judgments lie in the eye of the beholder, the qualities of the person judged, or the chemistry of the dyad.	The Actor-Partner Interdependence Model (APIM), formalized by Kenny, Kashy, and Cook, is the standard framework for analyzing dyadic data in which two people's outcomes depend on both their own and their partner's characteristics. For each member of a dyad, the model estimates an actor effect -- the influence of a person's own predictor on their own outcome -- and a partner effect -- the influence of the partner's predictor on the person's outcome -- while explicitly modeling the statistical non-independence of the two members' scores. For example, a person's relationship satisfaction may depend on their own attachment anxiety (actor effect) and on their partner's attachment anxiety (partner effect). By simultaneously estimating these effects and accounting for the correlation between partners, the APIM avoids the bias of treating dyad members as independent and reveals how individuals in relationships shape each other, making it indispensable for research on couples, families, and other interacting pairs.