An overview of running and interpreting logistic regressions in R.

Using lavaan to estimate mediation models, both with observed variables and with latent variables. Visualizing models using semPlot.

Cleaning and coding with a large secondary dataset. Using `lavaan` to replicate common statistical techniques in a path analysis framework. Running a basic SEM model.

An overview of specifying, estimating, and interpreting various confirmatory factor analysis (CFA) models in R using the lavaan package.

Calculating coefficient alpha. Performing PCA and FA using the psych package and interpreting the results.

A comparison of hierarchical and k-means clustering in R.

Exploring missing data, testing missing data mechanisms, and using the mice package to perform multiple imputation.

Running, interpreting, and assessing model fit of the logistic regression model with binary, continuous, and multi-categorical predictors.

Assessing regression assumptions graphically and statistically, and addressing possible assumption violations using bootstrapping.

An overview of ANCOVA and repeated measures ANOVA in R.

Running, interpreting, and visualizing moderator analysis in R.

A review of basic ANOVA and regression analyses using base R and tidyverse tools.

Code to accompany the module 1 content review for MVS class.

Getting started with R and R Studio, loading datasets, and exploring and visualizing data.

An overview of estimating, interpreting, and visualizing results from a cross-classified linear mixed model using the `lme4` package.

Using tidy tools to obtain residuals, Cook's Distances, and predicted values from MLM, and then using these diagnostics to assess model assumptions.

In this demonstration, I show how to implement a GLMM for a binary outcome using the lme4 package in R. I also show how to generate odds ratios and predicted probabilities for coefficient estimates.

Demonstration of growth models with nonlinear trends, and growth models with heterogeneous variance structures. Sample visualizations for nonlinear growth models.

Fitting null, null growth, and 2-level growth models in R using the lme4 package and visualizing growth models using the ggplot2 package.

An overview of cleaning data using the tidyverse and then running 3-level models in R using the lme4 package.

Running and interpreting random slope models, and running, interpreting, and visualizing cross-level interactions.

Overview of running and interpreting the models for the module 5 content review.

Calculating scale scores and reliability statistics; running a series of conditional random intercept models using the lme4 package; creating a model summary using the modelsummary package.

Working with packages, creatings variable and value labels, and running and interpreting a null multilevel model.

Quick review of simple and multiple linear regression in R.

Demonstration of code for getting data into Rstudio, generating descriptive statistics, and visualizing variables with histograms.

This is the key for the Module 1 Content Review for my Multilevel Modeling course.