Open AccessMaximum likelihood estimation of a social relations structural equation model
Author(s) -
Steffen Nestler,
Oliver Lüdtke,
Alexander Robitzsch
Publication year - 2020
Publication title -
psychometrika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.375
H-Index - 76
eISSN - 1860-0980
pISSN - 0033-3123
DOI - 10.1007/s11336-020-09728-z
Subject(s) - structural equation modeling , multivariate statistics , computer science , maximum likelihood , interpersonal communication , perception , econometrics , psychology , mathematics , statistics , social psychology , machine learning , neuroscience
The social relations model (SRM) is widely used in psychology to investigate the components that underlie interpersonal perceptions, behaviors, and judgments. SRM researchers are often interested in investigating the multivariate relations between SRM effects. However, at present, it is not possible to investigate such relations without relying on a two-step approach that depends on potentially unreliable estimates of the true SRM effects. Here, we introduce a way to combine the SRM with the structural equation modeling (SEM) framework and show how the parameters of our combination can be estimated with a maximum likelihood (ML) approach. We illustrate the model with an example from personality psychology. We also investigate the statistical properties of the model in a small simulation study showing that our approach performs well in most simulation conditions. An R package (called srm) is available implementing the proposed methods.