Social Perception and the Social Relations Model

1996 ◽  
Vol 7 (3) ◽  
pp. 268-275
Author(s):  
Lee Jussim
2010 ◽  
Vol 34 (4) ◽  
pp. 374-383 ◽  
Author(s):  
Noel A. Card ◽  
Philip C. Rodkin ◽  
Claire F. Garandeau

Analyses of children’s peer relations have recently begun considering interpersonal behaviors and perceptions from the perspective of the Social Relations Model. An extension of this model, the Triadic Relations Model (TRM), allows for consideration and analysis of more complex three-person data to understand triadic processes; separate individual, dyadic, and triadic variance; and model co-occurrences among dyadic phenomena. The goal of this article is to provide a didactic introduction to the TRM and its potential for studying peer relations. The TRM is applied to data from nine classes (N = 162) of third and fourth grade boys and girls involving perceptions (peer nominations) of actors’ (aggressors’) behavior toward partners (victims). We report and illustrate interpretation of 7 variance and 16 covariance estimates from this TRM analysis of who perceives whom as bullying whom. In particular, triadic analyses revealed a tendency for children to perceive others as sharing the same aggressors and the same targets for aggression as themselves. We discuss implications of findings for studying aggression, as well as extensions of this model, such as incorporating multiple constructs or connecting the TRM estimates with individual and dyadic variables, and challenges of using the TRM.


2010 ◽  
Vol 32 (3) ◽  
pp. 259-279 ◽  
Author(s):  
Jan De Mol ◽  
Ann Buysse ◽  
William L. Cook

2021 ◽  
pp. 107699862110565
Author(s):  
Steffen Nestler ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch

The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM) framework, we show here how longitudinal SRM data can be analyzed using the SR-SEM. Two examples are presented to illustrate the model, and we also present the results of a small simulation study comparing the SR-SEM approach to a two-step approach. Altogether, the SR-SEM has a number of advantages compared to earlier suggestions for analyzing longitudinal SRM data, making it extremely useful for applied research.


Sign in / Sign up

Export Citation Format

Share Document