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Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees
Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects...
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Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models
We present generalized additive latent and mixed models (GALAMMs) for analysis of clustered data with responses and latent variables depending...
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Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R
Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for...
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Multivariate Generalized Linear Models for Twin and Family Data
Multivariate twin and family studies are one of the most important tools to assess diseases inheritance as well as to study their genetic and...
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Computation and application of generalized linear mixed model derivatives using lme4
Maximum likelihood estimation of generalized linear mixed models (GLMMs) is difficult due to marginalization of the random effects. Derivative...
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Generalized Linear Mixed Effects Modeling (GLMM) of Functional Analysis Graphical Construction Elements on Visual Analysis
Multielement designs are the quintessential design tactic to evaluate outcomes of a functional analysis in applied behavior analysis. Protecting the...
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A Tutorial for Deception Detection Analysis or: How I Learned to Stop Aggregating Veracity Judgments and Embraced Signal Detection Theory Mixed Models
Historically, deception detection research has relied on factorial analyses of response accuracy to make inferences. However, this practice overlooks...
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Bayes Factors for Mixed Models: Perspective on Responses
In van Doorn et al. (
2021 ), we outlined a series of open questions concerning Bayes factors for mixed effects model comparison, with an emphasis on...
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Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning
Understanding how the adult human brain learns novel categories is an important problem in neuroscience. Drift-diffusion models are popular in such...
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Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model
Variability in treatment effects is common in intervention studies using cluster randomized controlled trial (C-RCT) designs. Such variability is...
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A Note on the Connection Between Trek Rules and Separable Nonlinear Least Squares in Linear Structural Equation Models
We show that separable nonlinear least squares (SNLLS) estimation is applicable to all linear structural equation models (SEMs) that can be specified...
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Bayes Factors for Mixed Models
Although Bayesian linear mixed effects models are increasingly popular for analysis of within-subject designs in psychology and other fields, there...
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Score-based tests for detecting heterogeneity in linear mixed models
Cross-level interactions among fixed effects in linear mixed models (also known as multilevel models) can be complicated by heterogeneity stemming...
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Kernel Equating Presmoothing Methods: An Empirical Study with Mixed-Format Test Forms
When equating test forms, it is common to presmooth the test score distributions before conducting the equating. In this study, the log-linear and...
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Variational Estimation for Multidimensional Generalized Partial Credit Model
Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for...
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Bayesian Analysis of ANOVA and Mixed Models on the Log-Transformed Response Variable
The analysis of variance, and mixed models in general, are popular tools for analyzing experimental data in psychology. Bayesian inference for these...
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Mechanisms associated with the trajectory of depressive and anxiety symptoms: A linear mixed-effects model during the COVID-19 Pandemic
With the fluctuations in anxious and depressive symptomatology accompanied by the pandemic crises, studies on the trajectories of these symptom...
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Efficient Likelihood Estimation of Generalized Structural Equation Models with a Mix of Normal and Nonnormal Responses
A maximum likelihood estimation routine is presented for a generalized structural equation model that permits a combination of response variables...
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Detecting Latent Variable Non-normality Through the Generalized Hausman Test
This paper extends the generalized Hausman test to detect non-normality of the latent variable distribution in unidimensional IRT models for binary...
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Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models
Graph-based causal models are a flexible tool for causal inference from observational data. In this paper, we develop a comprehensive framework to...
