Update the Model

The current model of the WAIS-III indicated a Heywood case between perceptual organization and processing speed. To fix a highly correlated set of latent variables, you should collapse those two variables into one latent variable. You should make a performance variable that combines the manifest variables for the perceptorg and processing latent variables.

The data has been loaded for you and is called IQdata. You can view the data using head(IQdata).

This exercise is part of the course

Structural Equation Modeling with lavaan in R

Exercise instructions

Edit the four-factor model to include one performance variable measured by piccomp, block, matrixreason, digsym, and symbolsearch.
Use the cfa() function to analyze the model for any new errors.
Summarize the model to determine model fit with the standardized solution and fit indices.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Edit the original model
wais.model <- 'verbalcomp =~ vocab + simil + inform + compreh 
workingmemory =~ arith + digspan + lnseq
perceptorg =~ piccomp + block + matrixreason
processing =~ digsym + symbolsearch'

# Analyze the model and include the model and data argument
wais.fit <- cfa(___)

# Summarize the model
summary(___)

Edit and Run Code

This exercise is part of the course

Structural Equation Modeling with lavaan in R

AdvancedSkill Level

4.9+

Start Course for 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.

Exercise 1: Model Specification Exercise 2: Build Text Speed Model Exercise 3: Build Political Democracy Model Exercise 4: Model Analysis Exercise 5: Analyze Text Speed Model Exercise 6: Summarize Political Democracy Exercise 7: Model Assessment Exercise 8: Examine Standardized Loadings Exercise 9: Explore Fit Indices Exercise 10: Examine Political Democracy

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.

Exercise 1: Multifactor Specification Exercise 2: Create a Zero df Model Exercise 3: Fix the Zero df Model Exercise 4: Build a Multi-Factor Model Exercise 5: Summarize the Multi-Factor Model Exercise 6: Model Structure Exercise 7: Three-Factor Model with Zero Correlation Exercise 8: Create a Direct Path Exercise 9: Modification Indices Exercise 10: Check Model Variance Exercise 11: Examine Modification Indices Exercise 12: Model Comparison Exercise 13: Compare Two Models Exercise 14: Select Specific Fit Indices

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.

Exercise 1: Heywood Cases on the Latent Variable Exercise 2: Analyze a Latent Heywood Case Exercise 3: Fix the Latent Heywood Model Exercise 4: Heywood Cases on the Manifest Variables Exercise 5: Analyze a Manifest Heywood Case Exercise 6: Fix the Manifest Heywood Model Exercise 7: Create Diagrams with semPaths()Exercise 8: Basic SEM Diagram Exercise 9: Edit the Layout Exercise 10: Edit the Labels

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.

Exercise 1: Model the WAIS-III IQ Scale Exercise 2: Create a Four-Factor Model Exercise 3: Update the Model

Current Exercise

Exercise 4: Diagram the Final Model Exercise 5: Update the WAIS-III Model Exercise 6: Add Paths to Improve Fit Exercise 7: Compare Models Exercise 8: A Hierarchical Model of IQ Exercise 9: Create a Hierarchical Model Exercise 10: Diagram the Hierarchical Model Exercise 11: Course Wrap Up