In this post, I demonstrate why rescaling a coefficient (i.e., multiplied/divided by a constant) is different from “standardizing” a coefficient (i.e., multiplied/divided by the sample standard deviation, which is a random variable) in SEM.

We investigated the performance of two single indicator methods: latent moderated structural equation (LMS) and reliability-adjusted product indicator (RAPI) methods, on testing interaction effects with congeneric measures, which vary in factor …

Define True Model and Simulate Some Data Define Log-Likelihood Function Defining \(\mathcal{l}(\boldsymbol{\mathbf{\Sigma}}; S)\) in R: MLE Asymptotic Standard Errors Using expected information Observed information (using Hessian) MLM/MLMV MLR Bibliography In our lab meeting I’m going to present the article by Maydeu-Olivares (2017), which talked about standard errors (SEs) of the maximum likelihood estimators (MLEs) in SEM models.

We briefly review the selection accuracy analysis for partial invariance and provide a user-friendly R script (also available as a Web application) that takes parameter estimates as input, automatically produces summary statistics for evaluating selection accuracy, and generates a graph for visualizing the results. Hypothetical and real data examples are provided to illustrate the use of the R script.

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