Bangun Model Kecepatan Teks

Mari berlatih mendefinisikan model. Ingat untuk menamai variabel laten dengan nama yang tidak ada dalam himpunan data Anda saat ini. Namun, variabel manifes harus berupa nama kolom dari himpunan data Anda.

Gunakan himpunan data HolzingerSwineford1939 untuk membuat model baru tentang kecepatan tekstual dengan variabel x4, x5, dan x6, yang merepresentasikan pemahaman bacaan dan pemahaman makna kata. x7, x8, dan x9 merepresentasikan kecepatan berhitung dan penjumlahan. Model ini akan memiliki satu variabel laten yang memprediksi skor pada enam variabel manifes tersebut.

Latihan ini adalah bagian dari kursus

Pemodelan Persamaan Struktural dengan lavaan di R

Petunjuk latihan

Namai model Anda text.model.
Namai variabel laten Anda textspeed.
Gunakan variabel x4, x5, x6, x7, x8, dan x9 sebagai variabel manifes.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Load the lavaan library
library(lavaan)

# Look at the dataset
data(HolzingerSwineford1939)
head(HolzingerSwineford1939[ , 7:15])

# Define your model specification
___ <- ___

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: Spesifikasi Model Exercise 2: Bangun Model Kecepatan Teks

Latihan Saat Ini

Exercise 3: Bangun Model Demokrasi Politik Exercise 4: Analisis Model Exercise 5: Analisis Model Kecepatan Teks Exercise 6: Ringkas Political Democracy Exercise 7: Penilaian Model Exercise 8: Periksa Loading Terstandarisasi Exercise 9: Jelajahi Indeks Kecocokan Exercise 10: Menelaah Demokrasi Politik

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: 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