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.

Model Specification

Build Text Speed Model

Build Political Democracy Model

Model Analysis

Analyze Text Speed Model

Summarize Political Democracy

Model Assessment

Examine Standardized Loadings

Explore Fit Indices

Examine Political Democracy

One-Factor Models

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.

Multifactor Specification

Create a Zero df Model

Fix the Zero df Model

Build a Multi-Factor Model

Summarize the Multi-Factor Model

Model Structure

Three-Factor Model with Zero Correlation

Create a Direct Path

Modification Indices

Check Model Variance

Examine Modification Indices

Model Comparison

Compare Two Models

Select Specific Fit Indices

Multi-Factor Models

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.

Kasus Heywood pada Variabel Laten

Menganalisis Kasus Heywood Laten

Perbaiki Model Heywood Laten

Kasus Heywood pada Variabel Manifes

Menganalisis Kasus Heywood pada Variabel Manifes

Perbaiki Model Heywood Manifest

Buat Diagram dengan semPaths()

Diagram SEM Dasar

Ubah Tata Letak

Ubah Label

Troubleshooting Model Errors and Diagrams

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.

Model the WAIS-III IQ Scale

Create a Four-Factor Model

Update the Model

Diagram the Final Model

Update the WAIS-III Model

Add Paths to Improve Fit

Compare Models

A Hierarchical Model of IQ

Create a Hierarchical Model

Diagram the Hierarchical Model

Course Wrap Up

Full Example and an Extension

WAIS-III IQ Data for Hierarchical Model

Latent Variable Heywood Case Data

Negative Variance Heywood Case Data

Saat bekerja dengan data, kita sering ingin membuat model untuk memprediksi peristiwa di masa depan, namun kita juga menginginkan pemahaman yang lebih mendalam tentang bagaimana data saling terhubung atau terstruktur. Dalam kursus ini, Anda akan mengeksplorasi keterkaitan data menggunakan structural equation modeling (SEM) dengan bahasa pemrograman R melalui paket lavaan. SEM akan memperkenalkan Anda pada variabel laten dan manifes serta cara membuat model pengukuran, menilai akurasi model pengukuran, dan memperbaiki model yang kurang sesuai. Sepanjang kursus, Anda akan mempelajari himpunan data SEM klasik, seperti Holzinger dan Swineford (1939) serta Bollen (1989). Anda juga akan mengerjakan studi kasus model multi-faktor menggunakan Wechsler Adult Intelligence Scale. Setelah mengikuti kursus ini, Anda akan mampu menyelami data Anda dan memperoleh pemahaman yang jauh lebih mendalam tentang bagaimana semuanya saling terkait.

Intermediate Regression in R

Pelajari cara membuat dan menilai model pengukuran untuk mengonfirmasi struktur skala atau kuesioner.

Pemodelan Persamaan Struktural dengan lavaan di R

Pelajari cara membuat dan mengevaluasi model pengukuran yang digunakan untuk memverifikasi struktur skala atau kuesioner.

Kasus Heywood pada Variabel Manifes

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