השוואת שיטות

	Round-Robin Design	Actor-Partner Interdependence Model
תחום	פסיכולוגיה חברתית	פסיכולוגיה חברתית
משפחה	Process / pipeline	Regression model
שנת המקור	2006	2006
הוגה השיטה	Dyadic methodology (Kenny and colleagues)	David A. Kenny and colleagues
סוג	Data-collection design for dyadic data	Regression model for dyadic interdependence
מקור מכונן	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
כינויים	Round-Robin Dyadic Design, All-with-All Rating Design, Block-Round-Robin Design	APIM, Dyadic Actor-Partner Model, Interdependence Regression Model
קשורות	3	3
תקציר	The round-robin design is a data-collection structure for dyadic research in which every member of a group interacts with, or provides ratings of, every other member, generating the full set of directed pairwise observations. Because each person serves repeatedly as both perceiver and target, the design produces the crossed data structure required to apply the Social Relations Model and to separate perceiver, target, and relationship effects. Round-robin designs are used to study interpersonal perception, attraction, behavior, and reciprocity in groups, families, and teams, and variants such as the block design (members of one set rate members of another) and half-block designs adapt the logic to different settings. The round-robin design is thus the data-gathering counterpart to the analytic models of dyadic data, and the quality of its structure determines what interpersonal questions can be answered.	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.