Introduction to Structural Equation Modeling

Instructors: Dan Bauer & Patrick Curran
Software: Mplus, R, and Stata
Lectures: 20 hours
Software Demonstrations: 3-4 hours per program
Evergreen Content: Continually updated
Lifetime Access: Materials never expire

Class Overview Video

Professional: $1299
Student: $999

Savings Opportunity: Get more content and save up to $400 with our Structural Equation Modeling Bundle

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Category: Asynchronous, Workshops

Introduction to Structural Equation Modeling focuses on the application and interpretation of statistical models that are designed for the analysis of multivariate data with latent variables. 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 multiple regression is also limited to a single dependent variable with unidirectional effects. The structural equation model (SEM) generalizes the regression model 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. In this workshop we provide a introduction to SEM that includes path analysis, confirmatory factor analysis, and structural equation models with latent variables. We focus on both establishing a conceptual understanding of the model and how it is applied in practice. This workshop will provide a strong foundation for expansion into more advanced topics such as longitudinal SEM, structural models for discrete data, multiple group analysis, and beyond.

Note: Get lifetime access to both this class and Longitudinal Structural Equation Modeling at a significant savings of $400 off the professional rate (or $300 off the student rate) relative to the total cost when purchased separately through our Structural Equation Modeling Bundle. This gives you a total of 40 hours of pre-recorded lectures plus many more hours of pre-recorded demonstrations in Mplus, R, and Stata.

1:27 Class Content / 7:14 Materials Provided / 9:19 Learning Objectives

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 multiple 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. He is dedicated to teaching and disseminating advanced quantitative methods and has won multiple awards for teaching excellence. Read More

Workshop Details

Goals of the Workshop

Our motivating goal is to provide an interactive, entertaining, and engaging instructional experience that builds a strong foundation in all of the core components of the SEM. We strive to strike an equal balance between core concepts of the underlying statistical model along with the practical application and interpretation of structural equation models fitted to real empirical data. Our workshop is designed to provide participants with the materials and instruction needed to both develop a real understanding of structural equation models and to be able to thoughtfully apply a variety these models to their own data.

Who Should Attend

This workshop is broadly targeted towards research applications in behavioral, health, educational and psychological sciences, although the methods apply to many other disciplines as well.

We recommend that participants have a working knowledge of the multiple regression model. Those who need a refresher may wish to check out episodes on our linear regression playlist on YouTube.

Additionally, like most multivariate statistical models, structural equation models are expressed using vectors and matrices, so some familiarity with linear algebra can be helpful (see our free Matrix Algebra review). Knowledge of linear algebra is not, however, required to fully benefit from the class.

Syllabus

Chapter 1. Introduction, Background, & Multiple Regression
1.1 Introduction
1.2 Linear Regression as a Structural Equation Model
1.3 Limitations of the Multiple Regression Model

Chapter 2. Path Analysis: Introduction
2.1 The Path Analysis Model
2.2 Model Identification
2.3 Model Estimation

Chapter 3. Path Analysis: Advanced Topics
3.1 Assessing Model Fit
3.2 Model Comparisons
3.3 Model Respecification and Modification Indices
3.4 Testing Direct and Indirect Effects
3.5 Underlying Assumptions

Chapter 4. Confirmatory Factor Analysis
4.1 Review of Exploratory Factor Analysis
4.2 Confirmatory Factor Analysis
4.3 Issues and Extensions

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 Estimation with Non-normal Distributions, Power Estimation, and Equivalent Models

Chapter 6. Extensions and Next Steps
6.1 Summary of Core Topics Covered
6.2 Where to Go From Here: Extensions and Next Steps

Appendix. Maximum Likelihood Estimation (A Primer)

Software Considerations

Notes and video recordings of software demonstrations are provided for self-paced learning using R (focused on the lavaan package), Mplus and Stata. Note that R can be downloaded for free.

Lectures are software independent and focus on the conceptualization, specification, and interpretation of the models. Thus, while it is helpful to have some familiarity with one or more of these software programs, this is not necessary to benefit from the course.

What is Provided

Participants receive lifetime access to all course materials, including complete course notes, lecture recordings, software demonstration notes, software demonstration recordings, and data and code for all examples. You can revisit these materials any time you like, without worrying about expiration dates.

All material is also evergreen content, meaning that any course updates or added material will be immediately available to you no matter when you registered for the workshop.

Availability of Recordings

Certificate of Completion

After enrollment, you will have the option to generate a certificate of completion which will display your first and last name (as indicated in your user account), the name of the workshop, and the date upon which the certificate was generated. It can be printed to a PDF and will also be stored in the your user account for future access (after logging in to your account, select My Certificates from the user menu). Heck, you can even print out a hard copy and frame it to impress your friends.

Sample Content

Tuition Support for Students

We offer reduced-price registrations for undergraduate and graduate students who are actively enrolled in a recognized bachelor's, master's or doctoral training program. No application is necessary to qualify for the student tuition rates; simply choose the student rate when beginning the registration process at the top of the page. Confirmation of student status may be requested at a later time.

Reviews

Here is a sample of reviews from our prior offering of this workshop:

Dan and Patrick do an awesome job at breaking down complex material and provide great applicable examples that really help with understanding and clarifying the concepts. The notes and materials provided are excellent and very detailed!

The workshop had a relaxed environment with plenty of banter. It was easy to follow the step-by-step workshop. As a result, I was able to learn a lot in a short amount of time.

Clear examples, software demonstrations/code, nice pacing (I appreciated the background information!).

Using real data to show examples and providing notes detailed for actually conducting the analyses later. The balance of what the theory is with the practical application is what my previous stats training often lacked (too little application).

The key strength was the clarity of communication and the balance between technicalities and intuition. Dr. Curran & Dr. Bauer did not shy away from technical aspects of the content but also communicated about them as intuitively as possible.

Dr. Curran and Dr. Bauer's style of lecture made complex statistical concepts straightforward and non-intimating, the lecture notes were very clear and the ability to ask questions via chat during lecture was amazing.

I have a much better understanding of what data modeling is. Before the workshop, I knew how to do SEM and even wrote a paper currently under review using SEM, but I didn't really understand what I was doing, why I was doing it, how it was being done

High quality materials and explanations. Very engaging to listen to.

The instructors were incredible. The way they taught complex concepts made things that were difficult for me to grasp previously magically fall into place. Concepts were clear and analogies were entertaining and on point!