Introduction to Structural Equation Modeling

$49.00

Self-Paced Workshop
6-Month Access
Instructors: Dan Bauer & Patrick Curran
Software Demonstrations: R
Clear
Category: Self-Paced, Workshops

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.

Participants who enroll now will have access to video recordings of the workshop for 6 months following registration. In the recordings, Patrick and Dan alternate lecturing with the help of teaching assistant Ethan McCormick who organizes and conveys questions submitted by participants. Each day concludes with a live demonstration of how to fit the models discussed during the day using lavaan in R (Rosseel, 2012). There are a total of 18 hours of lecture material organized into five chapters over the three days. Videos are not downloadable, however each participant also receives access to detailed lecture notes (308 pages), demonstration notes (100 pages), and data and script files that can be downloaded and retained indefinitely. You will have access to all videos and additional files for download once you have registered.

In this welcome video, Dan and Patrick introduce themselves and briefly describe the goals and structure of the class.

Instructors

Daniel J. Bauer, Ph.D.

Dan Bauer is a Professor and the Director of the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina. He teaches primarily graduate-level courses in statistical methods, for which he has won teaching awards. Read More

Patrick J. Curran, Ph.D.

Patrick Curran is a Professor in the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina at Chapel Hill. Patrick has dedicated much of his career to the teaching and dissemination of advanced quantitative methods and has won awards in recognition of teaching excellence. Read More

Syllabus

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

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