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Overview test file

Autoregressive Cross-Lagged Models Cross-Lagged Panel Models (CLPM)

Latent Change Score Models (LCSM)Latent Transition Analysis (LTA)Residualized Change Scores

Hierarchical Linear Modeling (HLM)Linear Mixed-Effects Models

Latent Growth Curve Models (LGCM)Multilevel Growth Models Multivariate Latent Growth Curve Models Piecewise Growth Models

Growth Mixture Models (GMM)Latent Class Growth Analysis (LCGA)

Discrete-Time Survival Models Survival Analysis with Time-Dependent Covariates

Time-Varying Covariates Models (TVCM)

Generalized Estimating Equations (GEE)Latent Curve Model with Structured Residuals (LCM-SR)Random Intercept Cross-Lagged Panel Model (RI-CLPM)

Cleaning and Transforming Data for Longitudinal Analysis

Diagnostic Plots for Longitudinal Data Models Model Evaluation in Longitudinal Data Analysis

Customizing Plots with ggplot2 ggplot2 Basics

Bootstrapping and Resampling Cross-Validation and Resampling for Longitudinal Models Handling Missing Data

Tutorials Overview

Overview Resources

Overview Difference Scores: Paired Samples T-test Difference Scores: Simple Regression Residualized Change Scores Linear Mixed Models: Random Intercept Linear Mixed Models: Random Intercept and Slope

Overview Signed-Rank Test Marginal Models Generalized Estimating Equations Generalized Linear Mixed Effects Models

Overview Autoregressive Crosslagged Panel Models Latent Change Score Models Latent Growth Curve Models Multivariate Latent Growth Curve Models Latent Class Growth Analysis Growth Mixture Models

R Programming Tools rcontent 1 R Resources

Overview of test content

Advanced Models

Latent Curve Model with Structured Residuals (LCM-SR)

🏗️ Foundations
🤔 When to Use Latent Curve Model with Structured Residuals?

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