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.
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.
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.Multifactor Specification50 xpCreate a Zero df Model100 xpFix the Zero df Model100 xpBuild a Multi-Factor Model100 xpSummarize the Multi-Factor Model100 xpModel Structure50 xpThree-Factor Model with Zero Correlation100 xpCreate a Direct Path100 xpModification Indices50 xpCheck Model Variance100 xpExamine Modification Indices100 xpModel Comparison50 xpCompare Two Models100 xpSelect Specific Fit Indices100 xp
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.Heywood Cases on the Latent Variable50 xpAnalyze a Latent Heywood Case100 xpFix the Latent Heywood Model100 xpHeywood Cases on the Manifest Variables50 xpAnalyze a Manifest Heywood Case100 xpFix the Manifest Heywood Model100 xpCreate Diagrams with semPaths()50 xpBasic SEM Diagram100 xpEdit the Layout100 xpEdit the Labels100 xp
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.Model the WAIS-III IQ Scale50 xpCreate a Four-Factor Model100 xpUpdate the Model100 xpDiagram the Final Model100 xpUpdate the WAIS-III Model50 xpAdd Paths to Improve Fit100 xpCompare Models100 xpA Hierarchical Model of IQ50 xpCreate a Hierarchical Model100 xpDiagram the Hierarchical Model100 xpCourse Wrap Up50 xp
DatasetsWAIS-III IQ Data for Hierarchical ModelLatent Variable Heywood Case DataNegative Variance Heywood Case Data
PrerequisitesIntermediate Regression in R
Erin BuchananSee More
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.