# Introduction to Structural Equation Modeling

Self-Paced Online Course
12-Week Access
Instructors:
Dan Bauer and Patrick Curran
Software Demonstrations:
R

\$49.00

## Course Description

In May of 2020, Dan Bauer and Patrick Curran offered a free-of-charge three-day live streaming course titled Introduction to Structural Equation Modeling. There were 3,000 participants from 38 countries and six continents. Because many people were unable to register at the time, we are now offering the recordings of this three-day class for Just-in-Time self-paced online statistics training at a nominal charge (simply to cover infrastructure costs).

The course focuses on the application and interpretation of statistical models that are designed for the analysis of multivariate data with latent variables, broadly referred to as structural equation models, or SEMs. Although the traditional multiple regression model is a powerful analytical tool within the social sciences, this is also highly restrictive in a variety of ways. Not only are all variables assumed to have no measurement error, but it is also limited to a single dependent variable with unidirectional effects. The SEM generalizes multiple regression to include multiple dependent variables, reciprocal effects, indirect effects, and the estimation and removal of measurement error through the inclusion of latent variables. The SEM is a general framework that allows for the empirical testing of research hypotheses in ways not otherwise possible. This course provides an introduction to the core components of the SEM along with detailed worked examples estimated using the lavaan package in R.

In this welcome video (free to access), Dan and Patrick introduce themselves and briefly describe the goals and structure of the class.

Below is a comprehensive outline of the material that is covered. Click on the button below to proceed with registration.

Course Price: \$49

Chapter 1. Introduction, Background, & Multiple Regression
1.1       Introduction
1.2       A Brief Review of Matrix Algebra
1.3       Review of Multiple Regression
1.4       Linear Regression as a Structural Equation Model
1.5       Limitations of the Multiple Regression Model

Chapter 2.  Path Analysis Part I
2.1       The Path Analysis Model
2.2       Means and Covariance Structures
2.3       Model Identification and Estimation

Chapter 3. Path Analysis: Part II
3.1       Assessing Model Fit
3.2       Model Comparisons
3.3       Model Respecification and Modification Indices
3.4       Testing Direct and Indirect Effects

Chapter 4: Confirmatory Factor Analysis
4.1       Exploratory Factor Analysis
4.2       Confirmatory Factor Analysis

Chapter 5: Structural Equation Models with Latent Variables
5.1       Introduction to Structural Equation Models
5.2       Fitting and Evaluating Structural Equation Models
5.3       Example Structural Equation Model

• ## Introduction to Structural Equation Modeling

Self-Paced Online Course
12-Week Access
Instructors:
Dan Bauer and Patrick Curran
Software Demonstrations:
R

#### Recent Livestream Workshops

Our Spring Workshop Schedule for 2021 will be Announced Soon

See below for details on recently completed workshops

• ## Introduction to Multilevel Modeling

December 2-4, 2020
Online Webinar via Zoom
Instructors: Dan Bauer and Patrick Curran
Software Demonstrations: R, SAS, SPSS, and Stata

• ## Applied Research Design using Mixed Methods

December 7-8, 2020
Online Webinar via Zoom
Instructor:
Greg Guest

• ## Introduction to Longitudinal Structural Equation Modeling

December 9-11, 2020
Online Webinar via Zoom
Instructors:
Dan Bauer and Patrick Curran
Software Demonstrations: Mplus, R, and Stata

• ## Mixture Modeling and Latent Class Analysis

December 14-16, 2020
Online Webinar via Zoom
Instructor:
Dan Bauer
Software Demonstrations: R and Mplus

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