A Reduced Social Relations Model for Dyad-Constant Dependent Variables

Jorgensen, Terrence D.; Forney, K. Jean

doi:10.1007/978-3-031-04572-1_19

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…[8] (p. 884, Footnote 2) mentions a "tedious procedure" to trick the software into estimating such case-level effects. Researchers are often interested in modeling case-level SRM components as predictors or outcomes [7,40], and even dyad-level covariates might be constant within a dyad rather than asymmetric [33,88]. The two-stage approach described here could be easily extended to accommodate level-specific covariates, including group-level variables, by expanding the level-specific correlation matrices and vectors of SDs to include such variables.…”

Section: Extensionsmentioning

confidence: 99%

Two-Stage Limited-Information Estimation for Structural Equation Models of Round-Robin Variables

Jorgensen,

Bhangale,

Rosseel

2024

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We propose and demonstrate a new two-stage maximum likelihood estimator for parameters of a social relations structural equation model (SR-SEM) using estimated summary statistics (Σ^) as data, as well as uncertainty about Σ^ to obtain robust inferential statistics. The SR-SEM is a generalization of a traditional SEM for round-robin data, which have a dyadic network structure (i.e., each group member responds to or interacts with each other member). Our two-stage estimator is developed using similar logic as previous two-stage estimators for SEM, developed for application to multilevel data and multiple imputations of missing data. We demonstrate out estimator on a publicly available data set from a 2018 publication about social mimicry. We employ Markov chain Monte Carlo estimation of Σ^ in Stage 1, implemented using the R package rstan. In Stage 2, the posterior mean estimates of Σ^ are used as input data to estimate SEM parameters with the R package lavaan. The posterior covariance matrix of estimated Σ^ is also calculated so that lavaan can use it to calculate robust standard errors and test statistics. Results are compared to full-information maximum likelihood (FIML) estimation of SR-SEM parameters using the R package srm. We discuss how differences between estimators highlight the need for future research to establish best practices under realistic conditions (e.g., how to specify empirical Bayes priors in Stage 1), as well as extensions that would make 2-stage estimation particularly advantageous over single-stage FIML.

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