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# Structural Equation Modeling (SEM) Using R

This course will teach you how to implement structural equation models (SEM) using R.

## Overview

Structural Equation Modeling (SEM) allows you to go beyond simple single-outcome models, and deal with multiple outcomes and multi-directional causation. You will learn how to create structural equation models using the lavaan package in R. We will cover SEM terminology, such as latent and manifest variables, how to create measurement and structural models, and assess that model for accuracy. In this course, you will apply your knowledge to real datasets to design, build, assess, and update a structural equation model. By the end of the course, you will be able to analyze path models, conduct a confirmatory factor analysis, and diagram your model using the semPlot package.

• Intermediate, Advanced
• 4 Weeks
• Expert Instructor
• Tuiton-Back Guarantee
• 100% Online
• TA Support

## Learning Outcomes

You will learn how to:

• Identify latent, manifest, exogenous and endogenous variables
• Fit SEM models with the R package lavaan
• Produce path diagrams of SEM models with semPlot
• Use confirmatory factor analysis

## Who Should Take This Course

Researches and analysts who want to go beyond simple models and incorporate multi-directionality, multiple outcomes and latent variables, using R.

## Our Instructors

#### Dr. Erin Buchanan Ph.D.

Dr. Erin Buchanan is a Professor at Harrisburg University of Science and Technology where she teaches a variety of statistics courses, data science skills, and natural language processing. Her research focuses on applied statistics, the use and misuse of statistics, and computational linguistics. She runs a statistics YouTube channel and StatsTools.com for everyone to improve their skills.

## Course Syllabus

### Week 1

Terms and Concepts in SEM

• Terminology about models: latent, manifest, exogenous, and endogenous variables
• Understanding model diagrams: squares, circles, and paths
• Hypothesis testing in SEM
• Specification, identification, and degrees of freedom
• Estimation and other considerations

### Week 2

Your First Model and Fit Indices

• lavaan syntax: understanding how to create models
• Path models: regression on regression
• Fit indices: goodness of fit and residual statistics
• Interpreting lavaan output

### Week 3

Measurement Models

• Creating a measurement model: applications to confirmatory factor analysis
• Reflective versus formative modeling approaches
• Latent variables
• Scaling
• Creating diagrams with semPaths

### Week 4

Full Structural Equation Models

• Combine path and measurement models
• Heywood cases
• Modification indices
• Model comparison

## Class Dates

### 2023

10/27/2023 to 11/24/2023
Instructors:

## Prerequisites

You should have some familiarity with statistical modeling (e.g. regression) and the basics of educational measurement and assessment.  You should also be comfortable working in R.

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## Register For This Course

Structural Equation Modeling (SEM) Using R

## Additional Information

#### Time Requirements

About 15 hours per week, at times of your choosing.

#### Homework

Homework in this course consists of short answer problems and includes exercises that require the use of computer software.  In addition to assigned readings, this course also has an end-of-course project, short narrated software demos, example software codes, and supplemental readings available online.

#### Course Text

All necessary course materials will be made available online.  If you would like a text, a good optional choice is Latent Variable Modeling Using R by A. Beaujean.

#### Software

This courses uses the lavaan and SEMPlot packages in R.

#### Literacy, Accessibility, and Dyslexia

At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:

Chrome

Firefox

Safari

• Navidys (for colorblindness, dyslexia, and reading difficulties)
• HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

## Register For This Course

Structural Equation Modeling (SEM) Using R