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.
- 1
One-Factor Models
FreeIn 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. - 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. - 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. - 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.
Datasets
WAIS-III IQ Data for Hierarchical ModelLatent Variable Heywood Case DataNegative Variance Heywood Case Data
Chester Ismay
Becca RobinsPrerequisites
Multiple and Logistic Regression in R
Erin Buchanan
Professor at Harrisburg University of Science and Technology
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 channel and an associated website for everyone to improve their skills.
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