Bewerk de lay-out

De tree-lay-out in semPaths() is de standaardweergave, waarbij rotation is ingesteld op 1. rotation instellen op 2 helpt vaak niet-statistici om modellen te begrijpen, omdat het model van links naar rechts loopt: de latente variabelen links voorspellen de responsen op de manifeste variabelen rechts. Werk de modelafbeelding bij om deze instellingen te gebruiken.

Deze oefening maakt deel uit van de cursus

Structurele vergelijkingsmodellen met lavaan in R

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Oefeninstructies

Voeg het argument layout toe aan de functie semPaths().
Voeg het argument rotation toe aan de functie semPaths().
Stel layout gelijk aan "tree" en rotation gelijk aan 2.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load the semPlot library
library(semPlot)

# Update the default picture
semPaths(object = adopt.fit, 
         ___,
         ___)

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Deze oefening maakt deel uit van de cursus

Structurele vergelijkingsmodellen met lavaan in R

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4.8+

Begin de cursus gratis

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-gevallen bij de latente variabele Exercise 2: Analyseer een latente Heywood-case Exercise 3: Repareer het latente Heywood-model Exercise 4: Heywood-gevallen bij manifeste variabelen Exercise 5: Analyseer een manifeste Heywood-case Exercise 6: Los het manifeste Heywood-model op Exercise 7: Diagrammen maken met semPaths()Exercise 8: Basischdiagram van een SEM Exercise 9: Bewerk de lay-out

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Exercise 10: Labels bewerken

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