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.
E-Step M-Step Estimating a 2-PL Model with EM in Julia Find \(\bar r_{jk}\) and \(\bar n_k\) Solve estimating equations Iterations Stopping criteria Benchmarking Remark \[ \newcommand{\bv}[1]{\boldsymbol{\mathbf{#1}}} \]
Import Data Two-Parameter Logistic Model Estimation with mirt in R Marginal Maximum Likelihood (MML) Estimation Implement MML Estimation in Julia Import R data Compute marginal loglikelihood Optimization Benchmarking The semester is finally over, and for some reason, I wanted to consolidate my understanding of some psychometric models, perhaps because I’ll be teaching a graduate measurement class soon.