Introductory Multivariate Data Analysis

Introduction

This course gives an applied introduction to the most important statistical techniques for leadership and organizational psychology students. Students are given hands-on experience by working with data, using descriptive statistics to motivate models, and using models to turn data into actionable knowledge. The course will focus on “learning by doing”. The course will cover the theory and application of various multivariate statistical methods, as multiple and multivariate regression, classification methods, exploratory and confirmatory factor analysis and an introduction to structural equation modeling.

Course content

Introduction 

• Dataset
• Software
• Sample
• Population
• Descriptive statistics
• Measurement levels

Variance, covariance, correlation

Review of probability and statistical inference

The linear regression model

• Simple regression
• Multiple regression
• Dummy variables (Anove and Ancovava)

Measurement level

Classification Analysis

• Logistic regression
• Discrimant analysis

Exploratory factor analysis and Principal Component Analysis 

Confirmatory factor analysis

• Measurement Models
• Reliability
• MTMM Models

Structural Equation Modeling

• Multivariate Regression analysis
• Path Analytsis models
• Path Models with latent variables
• Inference for non-normal data and Likert-type data
• Model assessment and model modification
• Multi group models