Driefactormodel met nulcorrelatie

In deze oefening gebruik je de Eysenck Personality Inventory-gegevensset uit de psychTools-bibliotheek om een driefactormodel van persoonlijkheid te maken. Deze vragenlijst bevat 57 vragen die extraversie, neuroticisme en liegen meten.

Laten we een driefactormodel maken met de latente variabelen: extraversion, neuroticism en lying, met vier manifeste variabelen per item. Onthoud dat wanneer je meerdere latente variabelen maakt, deze endogene variabelen automatisch gecorreleerd zijn. Stel de correlatie tussen de latente variabele extraversion en de latente variabele neuroticism gelijk aan nul, door ~~ te gebruiken in de modelspecificatiecode.

Deze oefening maakt deel uit van de cursus

Structurele vergelijkingsmodellen met lavaan in R

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load the library and data
library(psych)
data(epi)

# Specify a three-factor model with one correlation set to zero
epi.model <- 'extraversion =~ V1 + V3 + V5 + V8
neuroticism =~ V2 + V4 + V7 + V9
lying =~ ___
___'

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Deze oefening maakt deel uit van de cursus

Structurele vergelijkingsmodellen met 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: Specificatie van multifactor Exercise 2: Maak een model met nul vrijheidsgraden Exercise 3: Repareer het Zero df-model Exercise 4: Bouw een multifactormodel Exercise 5: Vat het multifactormodel samen Exercise 6: Modelstructuur Exercise 7: Driefactormodel met nulcorrelatie

Huidige oefening

Exercise 8: Maak een directe pad Exercise 9: Modificatie-indices Exercise 10: Controleer modelvariantie Exercise 11: Onderzoek modificatie-indices Exercise 12: Modellen vergelijken Exercise 13: Vergelijk twee modellen Exercise 14: Selecteer specifieke 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 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