Infants and Ancestors in Social Relations

Abstract. In family systems, it is possible for one to put oneself at risk by eliciting aversive, high-risk behaviors from others ( Cook, Kenny, & Goldstein, 1991 ). Consequently, it is desirable that family assessments should clarify the direction of effects when evaluating family dynamics. In this paper a new method of family assessment will be presented that identifies bidirectional influence processes in family relationships. Based on the Social Relations Model (SRM: Kenny & La Voie, 1984 ), the SRM Family Assessment provides information about the give and take of family dynamics at three levels of analysis: group, individual, and dyad. The method will be briefly illustrated by the assessment of a family from the PIER Program, a randomized clinical trial of an intervention to prevent the onset of psychosis in high-risk young people.

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Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

Social Psychology ◽

10.1027/1864-9335/a000356 ◽

2019 ◽

Vol 50 (1) ◽

pp. 24-37

Author(s):

Ben Porter ◽

Camilla S. Øverup ◽

Julie A. Brunson ◽

Paras D. Mehta

Keyword(s):

Structural Equation Modeling ◽

Social Relations ◽

Latent Variables ◽

Structural Equation ◽

Multilevel Structural Equation Modeling ◽

Equation Modeling ◽

Complete Understanding ◽

Social Relations Model ◽

Flexible Framework ◽

Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

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