A Beginner's Guide to Structural Equation Modeling

Höfundar: Randall E. Schumacker; Tiffany A. Whittaker (Útgáfa: 6)
A Beginner's Guide to Structural Equation Modeling

Kaup valmöguleikar

The sixth edition of A Beginner’s Guide to Structural Equation Modeling has been redesigned to consider the medium-term needs of a beginner in structural equation modeling (SEM) to guide them through their research. This new update includes thorough insights on theory testing, data analysis, and results interpretation; a focus on using LISREL, Mplus, and R programs; and an increased focus on SEM terminology, output, model types, and analyses.

It also includes two new chapters on reproducing results in SEM journal articles and conducting a Monte Carlo analysis. Examples with real data make theory easier to understand and allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models. Exercises at the end of each chapter strengthen the utility of the book for beginners. This book is intended for beginners in SEM and designed for introductory graduate courses in SEM taught in psychology, education, business, and the social and health care sciences.

Nánar um bókina

Útgefandi
Taylor & Francis
ISBN
9781040643273
Print ISBN
9781041073796
Format
ePub
Útgáfa
6
Höfundar
Randall E. Schumacker; Tiffany A. Whittaker
Tungumál
English
Útgefið
2026-09-07
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Cover
  • Half Title
  • Title
  • Copyright
  • Dedication
  • Contents
  • About the Authors
  • Preface
  • Support Material for the Book
  • Learning SEM
  • Book Approach
  • New to the 6th Edition
  • Acknowledgments
  • Chapter 1: Introduction
  • What Is Structural Equation Modeling?
  • History of Structural Equation Modeling
  • Why Conduct Structural Equation Modeling?
  • Structural Equation Modeling Software
  • SEM in Statistical Packages
  • AMOS (SPSS)
  • PROC CALIS (SAS)
  • SEM (STATA)
  • SEPATH (Statistica)
  • SEM Stand-alone Software
  • EQS
  • JMP – SAS Interface
  • LISREL/LISREL-SIMPLIS
  • Mplus
  • SEM Free Software
  • OpenMX – R interface
  • R
  • Latent GOLD
  • Software Considerations
  • Exercises
  • References
  • Chapter 2: SEM Modeling Steps
  • SEM Modeling Steps Explained
  • Model Specification
  • Model Identification
  • Model Estimation
  • Model Testing
  • Table of Model Fit Indices
  • Parameter Statistical Significance
  • Model Comparison
  • Information Criteria for Non-nested Models
  • Model Modification
  • Modification Indices
  • Expected Parameter Change
  • MIs and EPCs in LISREL, Mplus, and R
  • Summary
  • Chapter Footnote
  • Exercises
  • References
  • Chapter 3: Data Complexity
  • Data Access
  • Sample Size and Power
  • Measurement Scale
  • Restriction of Range
  • Skewness
  • Missing Data
  • Outliers
  • Non-normality
  • Summary
  • Exercises
  • References
  • PDF Article References
  • Chapter 4: Correlation and Regression
  • Types of Correlation Coefficients
  • Factors Affecting Correlation Coefficients
  • Nonlinearity
  • Missing Data
  • Level of Measurement and Restriction of Range
  • Non-normality
  • Outliers
  • Multiple Regression and Correlation
  • Bivariate, Part, and Partial Correlations
  • Multicollinearity and Suppressor Variables
  • Covariance and Correlation Matrix Conversion
  • Cov2Cor and Cor2Cov Functions in R
  • Correlation Matrix or Covariance Matrix Usage
  • Standardized or Unstandardized Results
  • Correction for Attenuation
  • Multiple Regression Model
  • Multiple Regression Limitations
  • Model Specification
  • Measurement Error
  • Additive Equation
  • Chapter Footnote
  • Regression Model With Intercept Term
  • Exercises
  • References
  • Chapter 5: Path Models
  • Path Model
  • Diagram Conventions
  • LISREL-SIMPLIS Achievement Path Model Program
  • Mplus Achievement Path Model Program
  • R Program for Achievement Path Model
  • R Achievement Path Model Program
  • Indirect Effects
  • Understanding Direct and Indirect Effects
  • Reproducing the Correlation Matrix
  • Total Effects and Correlation
  • Correlation Reproduction Standardized Example
  • Decomposition Using an Unstandardized Example
  • Path Model Example
  • Model Specification
  • Model Identification
  • Model Estimation
  • Model Testing
  • Residual Matrix Output
  • Testing Indirect Effects
  • Bootstrapping Standard Errors of Indirect Effects
  • R Bootstrap Example
  • Reporting Path Model Results
  • Path Model Assumptions and Limitations
  • Summary
  • Exercises
  • References
  • Chapter 6: Measurement Models Part 1
  • Exploratory Factor Analysis
  • Sample Size
  • Number of Factors
  • Rotation Methods
  • Factor Scores
  • EFA vs PCA
  • LISREL-SIMPLIS EFA Example
  • Mplus EFA Program
  • EFA Program in R With the Psych Package
  • Pattern and Structure Matrices
  • Confirmatory Factor Analysis
  • CFA Example
  • Lavaan Computer Output
  • CFA With Missing Continuous Data
  • LISREL-SIMPLIS CFA Model With Missing Data
  • Mplus Program-CFA Model With Missing Data
  • CFA With Mean Structure
  • LISREL-SIMPLIS Modified Program
  • CFA Caveats
  • CFA With Missing Ordinal Indicators
  • LISREL-SIMPLIS Program With Missing Ordinal Indicators
  • lavaan Program With Missing Ordinal Indicators
  • Model Comparisons
  • Summary
  • Exercise
  • References
  • Chapter 7: Measurement Models Part 2
  • Second-Order Factor Model
  • Model Specification
  • Model Identification
  • Model Estimation
  • Model Testing
  • Model Modification
  • Model Interpretation
  • Bifactor Model
  • Model Specification
  • Model Identification
  • Model Estimation
  • Model Testing
  • Model Modification
  • Model Interpretation
  • Model Comparisons Between the Second-Order and Bifactor Models
  • R Program – Second-Order Factor Model
  • R Program Bifactor Model
  • Summary
  • Exercise
  • References
  • Chapter 8: Multiple Group Models
  • Brief Summary of Multiple Group Modeling
  • Multiple Group Path Analysis Model
  • Model Identification (Baseline Multiple Group Model – No Equality Constraints)
  • Model Testing Baseline Multiple Group Model – No Equality Constraints
  • Model Identification Multiple Group Model – With Equality Constraints
  • Model Estimation Multiple Group Model – With Equality Constraints
  • Model Testing Multiple Group Model – With Equality Constraints
  • Model Modification
  • Model Modification – Partial Invariance Model
  • Multiple Group Model Interpretation
  • Multiple Group CFA Measurement Model
  • Measurement Invariance
  • Model Testing in Separate Groups
  • Model Identification – Configural CFA Model – No Equality Constraints
  • Model Estimation – Configural CFA Model – No Equality Constraints
  • Model Testing – Configural CFA Model – No Equality Constraints
  • Model Identification – Metric Multiple Group CFA Model
  • Model Estimation – Metric Multiple Group CFA Model
  • Model Testing Metric Multiple Group CFA Model
  • Model Modification Metric Multiple Group CFA Model
  • Model Identification – Scalar (Strong Invariance) Multiple Group CFA Model
  • Model Estimation – Scalar (Strong Invariance) Multiple Group CFA Model
  • Model Testing – Scalar (Strong Invariance) Multiple Group CFA Model
  • Final Model Interpretation
  • Strict Invariance Testing
  • Structural Model Group Differences
  • Multiple Group Models With Ordinal Indicators
  • Invariance Testing Cautions
  • Summary
  • Exercise
  • References
  • Chapter 9: Structural Equation Models Part 1
  • Structural Equation Models
  • Structural Equation Model Example
  • Model Specification – SEM Educational Achievement
  • Model Identification – SEM Educational Achievement
  • Model Estimation – SEM Educational Achievement
  • Model Testing – SEM Educational Achievement
  • Model Modification – SEM Educational Achievement
  • Structural Equation Model With Covariate Variables (MIMIC Model)
  • R lavaan Program
  • SEM Model Interpretation
  • SEM Longitudinal Model – Exercise Behavior
  • Model Identification – SEM Longitudinal Model
  • Model Estimation – SEM Longitudinal Model
  • Model Testing – SEM Measurement Model
  • Summary
  • Exercises
  • References
  • Chapter 10: Structural Equation Models Part 2
  • Hypothesis Testing
  • Parameter Significance Test
  • Power and Sample Size – RMSEA
  • Power (RMSEA) – R code
  • Sample Size (RMSEA) – R code
  • Model Fit Chi-Square
  • Two-Step Versus Four-Step SEM Model Approach
  • Best Practices in SEM
  • Model Specification
  • Model Identification
  • Model Estimation
  • Model Testing
  • Model Modification
  • Summary
  • Exercise
  • References
  • Chapter 11: Reproducing SEM Article Results
  • First SEM Journal Article Example
  • First LISREL_SIMPLIS Program
  • First SEM Article Results
  • First Article Interpretation
  • Second SEM Journal Article Example
  • Second LISREL- SIMPLIS Program
  • Second Article Interpretation
  • Third SEM Journal Article Example
  • Third LISREL-SIMPLIS Program
  • Third Article Interpretation
  • Summary
  • Exercise
  • References
  • Chapter 12: SEM Monte Carlo Methods
  • Method 1 – Generate Population Data From Random Numbers
  • LISREL SIMPLIS Program – Covariance Matrix
  • R Program Method 1
  • Method 2 – Generate Population Data From Covariance Matrix
  • Cholesky Decomposition Approach
  • Pattern Matrix Approach
  • Method 3 – Generate Covariance Matrix From Population Model
  • R Program to Compute Population Covariance Matrix
  • Summary
  • Exercise
  • References
  • Basic Matrices in SEM
  • Index