Analyze a Latent Heywood Case

A local animal shelter has designed a survey to measure the impact of their Adopt Me program. Viewers rated each dog's picture, background story, and other characteristics to indicate the "adoptableness" of each animal.

The adoptsurvey data contains the six items they rated including pictures, background, loveskids that measure a "good story" latent variable, while energy, wagstail, playful measure an "in person" latent variable. You will build a two-factor model of their survey and examine it for Heywood cases. The lavaan library has been loaded for you.

Este exercicio faz parte do curso

Structural Equation Modeling with lavaan in R

Instruções do exercicio

The goodstory latent is measured by pictures, background, and loveskids.
The inperson latent is measured by energy, wagstail, and playful.
Analyze the two-factor model with the cfa() function.

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

# Look at the data
head(adoptsurvey)

# Build the model
adopt.model <- 'goodstory ___
inperson ___'

# Analyze the model
adopt.fit <- cfa(model = ___, data = ___)

Editar e Executar Código

Este exercicio faz parte do curso

Structural Equation Modeling with lavaan in R

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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

Exercicio Atual

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 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