Pilih Indeks Kecocokan Tertentu

Anda juga dapat membandingkan model menggunakan indeks kecocokan AIC atau ECVI, alih-alih fungsi anova(). Indeks kecocokan ini sangat berguna jika model Anda mencakup variabel manifes yang berbeda. Saat membandingkan sekumpulan nilai AIC atau ECVI, model terbaik adalah yang memiliki nilai indeks kecocokan paling kecil. Model yang disimpan dari latihan sebelumnya telah dimuat untuk Anda.

Latihan ini adalah bagian dari kursus

Pemodelan Persamaan Struktural dengan lavaan di R

Petunjuk latihan

Gunakan fitmeasures() untuk menghitung indeks kecocokan untuk setiap model secara terpisah.
Pilih hanya indeks kecocokan aic dan ecvi untuk mempersingkat keluaran.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Analyze the original model
epi.fit <- cfa(model = epi.model, data = epi)

# Find the fit indices for the original model


# Analyze the updated model
epi.fit1 <- cfa(model = epi.model1, data = epi)

# Find the fit indices for the updated model

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: Spesifikasi Multifaktor Exercise 2: Buat Model dengan df Nol Exercise 3: Perbaiki Model df Nol Exercise 4: Membangun Model Multi-Faktor Exercise 5: Meringkas Model Multi-Faktor Exercise 6: Struktur Model Exercise 7: Model Tiga Faktor dengan Korelasi Nol Exercise 8: Buat Jalur Langsung Exercise 9: Indeks Modifikasi Exercise 10: Periksa Varians Model Exercise 11: Periksa Indeks Modifikasi Exercise 12: Perbandingan Model Exercise 13: Membandingkan Dua Model Exercise 14: Pilih Indeks Kecocokan Tertentu

Latihan Saat Ini

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