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mdbroda

Michael Broda

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MVS Module 6 Handout - Intro to Logistic Regression
An overview of running and interpreting logistic regressions in R.
MVS Module 12 Demonstration - Intro to SEM, Part 2
Using lavaan to estimate mediation models, both with observed variables and with latent variables. Visualizing models using semPlot.
MVS Module 11 Demonstration - Structural Equation Modeling, Part 1
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.
MVS Module 10 Demonstration - Intro to CFA
An overview of specifying, estimating, and interpreting various confirmatory factor analysis (CFA) models in R using the lavaan package.
MVS Module 9 Demonstration - Exploratory Factor Analysis
Calculating coefficient alpha. Performing PCA and FA using the psych package and interpreting the results.
MVS Module 8 Demonstration - Intro to Cluster Analysis
A comparison of hierarchical and k-means clustering in R.
MVS Module 7 Demonstration - Working with Missing Data
Exploring missing data, testing missing data mechanisms, and using the mice package to perform multiple imputation.
MVS Module 6 Demonstration - Intro to Logistic Regression
Running, interpreting, and assessing model fit of the logistic regression model with binary, continuous, and multi-categorical predictors.
MVS Module 5 Demonstration - Assessing Regression Assumptions
Assessing regression assumptions graphically and statistically, and addressing possible assumption violations using bootstrapping.
MVS Module 4 Demonstration - Advanced ANOVA
An overview of ANCOVA and repeated measures ANOVA in R.
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MVS Module 3 Demonstration - Advanced Regression
Running, interpreting, and visualizing moderator analysis in R.
MVS Module 02 - Review of Regression and ANOVA
A review of basic ANOVA and regression analyses using base R and tidyverse tools.
MVS Module 1 CR
Code to accompany the module 1 content review for MVS class.
MVS Module 1 Demonstration - Intro to R and R Studio
Getting started with R and R Studio, loading datasets, and exploring and visualizing data.
MLM Module 11 Demonstration - Cross-Classified Linear Mixed Models
An overview of estimating, interpreting, and visualizing results from a cross-classified linear mixed model using the `lme4` package.
MLM Module 10 Demonstration - Checking Model Assumptions
Using tidy tools to obtain residuals, Cook's Distances, and predicted values from MLM, and then using these diagnostics to assess model assumptions.
MLM Module 9 - Generalized Linear Mixed Models (GLMMs)
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.
MLM Module 8, Part 2 Demonstration - Advanced Growth Models
Demonstration of growth models with nonlinear trends, and growth models with heterogeneous variance structures. Sample visualizations for nonlinear growth models.
MLM Module 8 Demonstration - Intro to 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.
MLM Week 7 Demo - Intro to 3-Level Models
An overview of cleaning data using the tidyverse and then running 3-level models in R using the lme4 package.
MLM Module 5 Demonstration - Random Slopes and Cross-Level Interactions
Running and interpreting random slope models, and running, interpreting, and visualizing cross-level interactions.
MLM Module 5 Content Review - Key
Overview of running and interpreting the models for the module 5 content review.
Module 4 Demonstration
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.
MLM Module 3 Demonstration
Working with packages, creatings variable and value labels, and running and interpreting a null multilevel model.
Module 2 Handout - Review of Linear Regression
Quick review of simple and multiple linear regression in R.
Module 1 Demonstration
Demonstration of code for getting data into Rstudio, generating descriptive statistics, and visualizing variables with histograms.
Module 1 Content Review
This is the key for the Module 1 Content Review for my Multilevel Modeling course.