课程

Structural Equation Modeling with lavaan in R

高级技能水平

更新时间 2022年7月

Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.

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RProbability & Statistics4 小时14 视频45 练习3,750 经验值10,025成就声明

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课程描述

When working with data, we often want to create models to predict future events, but we also want an even deeper understanding of how our data is connected or structured. In this course, you will explore the connectedness of data using using structural equation modeling (SEM) with the R programming language using the lavaan package. SEM will introduce you to latent and manifest variables and how to create measurement models, assess measurement model accuracy, and fix poor fitting models. During the course, you will explore classic SEM datasets, such as the Holzinger and Swineford (1939) and Bollen (1989) datasets. You will also work through a multi-factor model case study using the Wechsler Adult Intelligence Scale. Following this course, you will be able to dive into your data and gain a much deeper understanding of how it all fits together.

先决条件

Intermediate Regression in R

1

One-Factor Models

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

50 经验值

Build Text Speed Model

100 经验值

Build Political Democracy Model

100 经验值

Model Analysis

50 经验值

Analyze Text Speed Model

100 经验值

Summarize Political Democracy

50 经验值

Model Assessment

50 经验值

Examine Standardized Loadings

100 经验值

Explore Fit Indices

100 经验值

Examine Political Democracy

100 经验值

2

Multi-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

50 经验值

Create a Zero df Model

100 经验值

Fix the Zero df Model

100 经验值

Build a Multi-Factor Model

100 经验值

Summarize the Multi-Factor Model

100 经验值

Model Structure

50 经验值

Three-Factor Model with Zero Correlation

100 经验值

Create a Direct Path

100 经验值

Modification Indices

50 经验值

Check Model Variance

100 经验值

Examine Modification Indices

100 经验值

Model Comparison

50 经验值

Compare Two Models

100 经验值

Select Specific Fit Indices

100 经验值

3

Troubleshooting Model Errors and Diagrams

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.

Heywood Cases on the Latent Variable

50 经验值

Analyze a Latent Heywood Case

100 经验值

Fix the Latent Heywood Model

100 经验值

Heywood Cases on the Manifest Variables

50 经验值

Analyze a Manifest Heywood Case

100 经验值

Fix the Manifest Heywood Model

100 经验值

Create Diagrams with semPaths()

50 经验值

Basic SEM Diagram

100 经验值

Edit the Layout

100 经验值

Edit the Labels

100 经验值

4

Full Example and an Extension

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

50 经验值

Create a Four-Factor Model

100 经验值

Update the Model

100 经验值

Diagram the Final Model

100 经验值

Update the WAIS-III Model

50 经验值

Add Paths to Improve Fit

100 经验值

Compare Models

100 经验值

A Hierarchical Model of IQ

50 经验值

Create a Hierarchical Model

100 经验值

Diagram the Hierarchical Model

100 经验值

Course Wrap Up

50 经验值

Structural Equation Modeling with lavaan in R

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