Tambahkan Jalur untuk Meningkatkan Kecocokan

Model tiga faktor untuk WAIS-III menunjukkan kecocokan yang kurang baik saat meninjau indeks kecocokan. Anda dapat menggunakan modification indices untuk melihat perkiraan parameter potensial yang dapat ditambahkan ke model untuk meningkatkan kecocokan. Kesalahan yang berkorelasi (correlated error terms) adalah perkiraan yang umum ditambahkan, karena varians variabel manifes pada faktor yang sama dapat saling berkaitan.

Data telah dimuat untuk Anda dengan nama IQdata. Anda dapat melihat datanya menggunakan head(IQdata).

Latihan ini adalah bagian dari kursus

Pemodelan Persamaan Struktural dengan lavaan di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Examine modification indices 
modificationindices(wais.fit, sort = TRUE)

# Update the three-factor model
wais.model2 <- 'verbalcomp =~ vocab + simil + inform + compreh 
workingmemory =~ arith + digspan + lnseq
perceptorg =~ piccomp + block + matrixreason + digsym + symbolsearch
___'

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pemodelan Persamaan Struktural dengan lavaan di R

SkillTag.level.advancedSkillTag.label

4.8+

Mulai Kursus 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 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: Memodelkan Skala IQ WAIS-III Exercise 2: Membuat Model Empat Faktor Exercise 3: Perbarui Model Exercise 4: Membuat Diagram Model Final Exercise 5: Perbarui Model WAIS-III Exercise 6: Tambahkan Jalur untuk Meningkatkan Kecocokan

Latihan Saat Ini

Exercise 7: Membandingkan Model Exercise 8: Model Hierarkis IQ Exercise 9: Buat Model Hierarkis Exercise 10: Buat Diagram Model Hierarkis Exercise 11: Penutup Kursus