Edit the Labels

The adoption group wants to perfect their survey for future use. To help them understand the results, you can add labels to the paths to indicate which items were the most related to their latent variables.

You can shade those labels by strength to add more data visualization to the model picture. You should see that the goodstory manifest variables are much stronger than the inperson variables.

Diese Übung ist Teil des Kurses

Structural Equation Modeling with lavaan in R

Anleitung zur Übung

Use the whatLabels argument to show the standardized loadings.
Use the what argument to color by the standardized loadings.
Use the edge.color argument to color the model blue.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Load the library
library(semPlot)

# Update the default picture
semPaths(object = adopt.fit,
         layout = "tree",
         rotation = 2,
         ___,
         edge.label.cex = 1,
         ___,
         ___)

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

Structural Equation Modeling with lavaan in R

Hohe SchwierigkeitSchwierigkeitsgrad

4.9+

Kurs kostenlos starten

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

Aktuelle Übung

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