| Description |
1 online resource (xxvi, 521 pages) : illustrations |
| Series |
Methodology in the social sciences
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Methodology in the social sciences.
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| Contents |
Cover -- Half Title Page -- Series Page -- Title Page -- Copyright -- Dedication -- Series Editor's Note -- Preface -- Acknowledgments -- Contents -- Part I. Introduction -- 1. Background -- 1.1 Bayesian Statistical Modeling: The Frequency of Use -- 1.2 The Key Impediments within Bayesian Statistics -- 1.3 Benefits of Bayesian Statistics within SEM -- 1.3.1 A Recap: Why Bayesian SEM? -- 1.4 Mastering the SEM Basics: Precursors to Bayesian SEM -- 1.4.1 The Fundamentals of SEM Diagrams and Terminology -- 1.4.2 LISREL Notation -- 1.4.3 Additional Comments about Notation |
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1.5 Datasets Used in the Chapter Examples -- 1.5.1 Cynicism Data -- 1.5.2 Early Childhood Longitudinal Survey-Kindergarten Class -- 1.5.3 Holzinger and Swineford (1939) -- 1.5.4 IPIP 50: Big Five Questionnaire -- 1.5.5 Lakaev Academic Stress Response Scale -- 1.5.6 Political Democracy -- 1.5.7 Program for International Student Assessment -- 1.5.8 Youth Risk Behavior Survey -- 2. Basic Elements of Bayesian Statistics -- 2.1 A Brief Introduction to Bayesian Statistics -- 2.2 Setting the Stage -- 2.3 Comparing Frequentist and Bayesian Estimation -- 2.4 The Bayesian Research Circle -- 2.5 Bayes' Rule |
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2.6 Prior Distributions -- 2.6.1 The Normal Prior -- 2.6.2 The Uniform Prior -- 2.6.3 The Inverse Gamma Prior -- 2.6.4 The Gamma Prior -- 2.6.5 The Inverse Wishart Prior -- 2.6.6 The Wishart Prior -- 2.6.7 The Beta Prior -- 2.6.8 The Dirichlet Prior -- 2.6.9 Different Levels of Informativeness for Prior Distributions -- 2.6.10 Prior Elicitation -- 2.6.11 Prior Predictive Checking -- 2.7 The Likelihood (Frequentist and Bayesian Perspectives) -- 2.8 The Posterior -- 2.8.1 An Introduction to Markov Chain Monte Carlo Methods -- 2.8.2 Sampling Algorithms -- 2.8.3 Convergence |
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2.8.4 MCMC Burn-In Phase -- 2.8.5 The Number of Markov Chains -- 2.8.6 A Note about Starting Values -- 2.8.7 Thinning a Chain -- 2.9 Posterior Inference -- 2.9.1 Posterior Summary Statistics -- 2.9.2 Intervals -- 2.9.3 Effective Sample Size -- 2.9.4 Trace-Plots -- 2.9.5 Autocorrelation Plots -- 2.9.6 Posterior Histogram and Density Plots -- 2.9.7 HDI Histogram and Density Plots -- 2.9.8 Model Assessment -- 2.9.9 Sensitivity Analysis -- 2.10 A Simple Example -- 2.11 Chapter Summary -- 2.11.1 Major Take-Home Points -- 2.11.2 Notation Referenced -- 2.11.3 Annotated Bibliography of Select Resources |
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Appendix 2.A: Getting Started with R -- Part II. Measurement Models and Related Issues -- 3. The Confirmatory Factor Analysis Model -- 3.1 Introduction to Bayesian CFA -- 3.2 The Model and Notation -- 3.2.1 Handling Indeterminacies in CFA -- 3.3 The Bayesian Form of the CFA Model -- 3.3.1 Additional Information about the (Inverse) Wishart Prior -- 3.3.2 Alternative Priors for Covariance Matrices -- 3.3.3 Alternative Priors for Variances -- 3.3.4 Alternative Priors for Factor Loadings -- 3.4 Example 1: Basic CFA Model -- 3.5 Example 2: Implementing Near-Zero Priors for Cross-Loadings |
| Summary |
This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies datasets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book's examples. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Bayesian statistical decision theory.
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Social sciences -- Statistical methods.
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Bayesian statistical decision theory |
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Social sciences -- Statistical methods |
| Other Form: |
Print version: DePaoli, Sarah. Bayesian structural equation modeling. New York, NY : The Guilford Press, [2021] 9781462547746 (DLC) 2021011543 (OCoLC)1242915271 |
| ISBN |
9781462547807 (electronic bk.) |
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146254780X (electronic bk.) |
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9781462547746 |
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1462547745 |
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