Analyzing Longitudinal Social Relations Model Data Using the Social Relations Structural Equation Model. (April 2022)

• Record Type: Journal Article
• Authors: Nestler, Steffen; Lüdtke, Oliver; Robitzsch, Alexander
• Is Part Of: Journal of educational and behavioral statistics. Volume 47:Number 2(2022)
• Page Start: 231
• Page End: 260
• Publication Date: 2022-04
• Subjects: social relations model -- latent growth model -- autoregressive model -- longitudinal data -- structural equation model; Educational statistics -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 370.2
• DOI: 10.3102/10769986211056541
• Languages: English
• ISSNs: 1076-9986

Abstract

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.