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Structural Equation Modeling with lavaan in R

Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.

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Course Description

When working with data, we often want to create models to predict future events, but we also want an even deeper understanding of how our data is connected or structured. In this course, you will explore the connectedness of data using using structural equation modeling (SEM) with the R programming language using the lavaan package. SEM will introduce you to latent and manifest variables and how to create measurement models, assess measurement model accuracy, and fix poor fitting models. During the course, you will explore classic SEM datasets, such as the Holzinger and Swineford (1939) and Bollen (1989) datasets. You will also work through a multi-factor model case study using the Wechsler Adult Intelligence Scale. Following this course, you will be able to dive into your data and gain a much deeper understanding of how it all fits together.
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  1. 1

    One-Factor Models

    Free

    In this chapter, you will dive into creating your first structural equation model with lavaan. You will learn important terminology, how to build, and run models. You will create a one-factor model of mental test abilities using the classic Holzinger and Swineford (1939) dataset.

    Play Chapter Now
    Model Specification
    50 xp
    Build Text Speed Model
    100 xp
    Build Political Democracy Model
    100 xp
    Model Analysis
    50 xp
    Analyze Text Speed Model
    100 xp
    Summarize Political Democracy
    50 xp
    Model Assessment
    50 xp
    Examine Standardized Loadings
    100 xp
    Explore Fit Indices
    100 xp
    Examine Political Democracy
    100 xp
  2. 2

    Multi-Factor Models

    In this chapter, you will expand your skills in lavaan to creating multi-factor models. We will improve the one-factor models from the last chapter by creating multiple latent variables in the classic Holzinger and Swineford (1939) dataset.

    Play Chapter Now
    Multifactor Specification
    50 xp
    Create a Zero df Model
    100 xp
    Fix the Zero df Model
    100 xp
    Build a Multi-Factor Model
    100 xp
    Summarize the Multi-Factor Model
    100 xp
    Model Structure
    50 xp
    Three-Factor Model with Zero Correlation
    100 xp
    Create a Direct Path
    100 xp
    Modification Indices
    50 xp
    Check Model Variance
    100 xp
    Examine Modification Indices
    100 xp
    Model Comparison
    50 xp
    Compare Two Models
    100 xp
    Select Specific Fit Indices
    100 xp
  3. 3

    Troubleshooting Model Errors and Diagrams

    Structural equation models do not always run smoothly, and in this chapter, you will learn how to troubleshoot Heywood cases which are common errors. You will also learn how to diagram your model in R using the semPlot library.

    Play Chapter Now
    Heywood Cases on the Latent Variable
    50 xp
    Analyze a Latent Heywood Case
    100 xp
    Fix the Latent Heywood Model
    100 xp
    Heywood Cases on the Manifest Variables
    50 xp
    Analyze a Manifest Heywood Case
    100 xp
    Fix the Manifest Heywood Model
    100 xp
    Create Diagrams with semPaths()
    50 xp
    Basic SEM Diagram
    100 xp
    Edit the Layout
    100 xp
    Edit the Labels
    100 xp
  4. 4

    Full Example and an Extension

    This chapter examines the WAIS-III IQ Scale and its structural properties. You will use your skills from the first three chapters to create various models of the WAIS-III, troubleshoot errors in those models, and create diagrams of the final model.

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    Model the WAIS-III IQ Scale
    50 xp
    Create a Four-Factor Model
    100 xp
    Update the Model
    100 xp
    Diagram the Final Model
    100 xp
    Update the WAIS-III Model
    50 xp
    Add Paths to Improve Fit
    100 xp
    Compare Models
    100 xp
    A Hierarchical Model of IQ
    50 xp
    Create a Hierarchical Model
    100 xp
    Diagram the Hierarchical Model
    100 xp
    Course Wrap Up
    50 xp
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datasets

WAIS-III IQ Data for Hierarchical ModelLatent Variable Heywood Case DataNegative Variance Heywood Case Data

collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Becca Robins

prerequisites

Intermediate Regression in R
Erin Buchanan HeadshotErin Buchanan

Professor at Harrisburg University of Science and Technology

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