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⚠️ Caution: Site Under Construction ⚠️
Examples may be incomplete or incorrect.
Linear Mixed Models
Landing_LMM
Table of Contents
1. Introduction
1.1 Overview of the Method
1.2 Key Research Questions
Visual Elements
2. Core Concepts & Theoretical Foundation
2.1 Fundamental Principles
2.2 Comparison with Related Methods
3. Assumptions and Model Specification
3.1 Statistical Assumptions
3.2 Model Structure & Equations
3.3 Data Requirements
Handling of Missing Data
Sample Size Considerations
Assumptions Related to Data Requirements
Comparison to Alternative Methods
When Should You Use an LMM?
4.1 Model Fitting and Estimation
Defining the Research Question
Defining the LMM Model
4.2 Understanding Model Outputs
Interpreting the Results
4.3 Model Fit & Effect Size
Choosing the Best Model
Interpreting Model Comparison
4.4 Graphical Representation
Visualizing Individual Growth Trajectories
4.5 Common Challenges and Pitfalls
Common Mistakes in LMM Analysis
Checking Model Assumptions
Essential Diagnostic Checks
5.1 Handling Missing Data
Best Practices for Handling Missing Data in LMM
Common Methods for Handling Missing Data
5.2 Extensions and Variations
1. Nonlinear Mixed-Effects Models (NLME)
2. Bayesian Linear Mixed Models (Bayesian LMM)
3. Multivariate Linear Mixed Models (MLMM)
4. Generalized Additive Mixed Models (GAMM)
5.3 Alternative Approaches
6.1 Quick Recap
When to Use This Method
7.1 Recommended Books & Articles
7.2 Helpful Tutorials and Courses
Online Tutorials and Guides
Free Online Courses
Community
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