Introduction to Multivariate Data and Analysis
  • Introduction to Multivariate Analysis (MVA) Course
  • Materials for Section 1 Introduction to MV Data and Analysis
  • What is "Multivariate Analysis" ?
  • Missing Values and the Measure Dataset
  • Other Multivariate Datasets
  • Covariance, Correlation and Distance (part 1)
  • Covariance, Correlation and Distance (part 2)
  • Covariance, Correlation and Distance (part 3)
  • The Multivariate Normal Density Function
  • Setting Up Normality Plots
  • Drawing Normality Plots
  • Covariance, Correlation and Normality Exercises
Visualizing Multivariate Data
  • Materials and Exercises for Visualizing Multivariate Data Section
  • Covariance and Correlation Matrices with Missing Data (part 1)
  • Covariance and Correlation Matrices with Missing Data (part 2)
  • Univariate and Multivariate QQPlots of Pottery Data
  • Converting Covariance to Correlation Matrices
  • Plots for Marginal Distributions
  • Outlier Identification
  • Chi, Bubble, and other Glyph Plots
  • Scatterplot Matrix
  • Kernel Density Estimators
  • 3-Dimensional and Trellis (Lattice Package) Graphics
  • More Trellis (Lattice Package) Graphics
  • Bivariate Boxplot and ChiPlot Visualizations Exercises
Principal Components Analysis (PCA)
  • Materials for Principal Components Analysis (PCA) Section
  • Bivariate Boxplot Visualization Exercise Solution
  • ChiPlot Visualization Exercise Solution
  • What is a "Principal Components Analysis" (PCA) ?
  • PCA Basics with R: Blood Data (part 1)
  • PCA Basics with R: Blood Data (part 2)
  • PCA with Head Size Data (part 1)
  • PCA with Head Size Data (part 2)
  • PCA with Heptathlon Data (part 1)
  • PCA with Heptathlon Data (part 2)
  • PCA with Heptathlon Data (part 3)
  • PCA Criminal Convictions Exercise
Multidimensional Scaling (MDS)
  • Materials for Multidimensional Scaling Section
  • PCA Criminal Convictions Exercise Solution
  • Introduction to Multidimensional Scaling
  • Classical Multidimensional Scaling (part 1)
  • Classical Multidimensional Scaling (part 2)
  • Classical Multidimensional Scaling: Skulls Data
  • Non-Metric Multidimensional Scaling Example: Voting Behavior
  • Non-Metric Multidimensional Scaling Example: WW II Leaders
  • Multidimensional Scaling Exercise: Water Voles
Cluster Analysis
  • Materials for Cluster Analysis Section
  • MDS Water Voles Exercise Solution
  • Introduction to Cluster Analysis
  • Hierarchical Clustering Distance Techniques
  • Hierarchical Clustering of Measures Data
  • Hierarchical Clustering of Fighter Jets
  • K-Means Clustering of Crime Data (part 1)
  • K-Means Clustering of Crime Data (part 2)
  • Clustering of Romano-British Pottery Data
  • K-Means Classifying of Exoplanets
  • Model-Based Clustering of Exoplanets
  • Finite Mixture Model-Based Analysis
  • Cluster Analysis Neighborhood and Stripes Plots
  • K-Means Cluster Analysis Crime Data Exercise
Exploratory Factor Analysis (EFA)
  • Materials for Exploratory Factor Analysis (EFA) Section
  • K-Means Crime Data Exercise Solution
  • Introduction to Exploratory Factor Analysis (EFA)
  • The factanal() Function Explained
  • EFA Life Data Example
  • EFA Drug Use Data Example
  • Comparing EFA with Confirmatory Factor Analysis (CFA)
  • EFA Exercise
Introduction to Structural Equation Modeling (SEM), QGraph, and SIMSEM
  • Introduction to the SEM, QGraph and SIMSEM Course Section with Materials
  • Exploratory Factor Analysis (EFA) Exercise Solution
  • Specify and Estimate Drug Use SEM Model
  • Specify and Estimate Alienation SEM Model
  • QGraph Visualizations
  • SIMSEM Package Simulation Capabilities (part 1)
  • SIMSEM Package Simulation Capabilities (part 2)