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E-grāmata: Confirmatory Factor Analysis

(Virginia Polytechnic Institute and State University, USA), (Purdue University, USA)
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Measurement connects theoretical concepts to what is observable in the empirical world, and is fundamental to all social and behavioral research. In this volume, J. Micah Roos and Shawn Bauldry introduce a popular approach to measurement: Confirmatory Factor Analysis (CFA). As the authors explain, CFA is a theoretically informed statistical framework for linking multiple observed variables to latent variables that are not directly measurable. The authors begin by defining terms, introducing notation, and illustrating a wide variety of measurement models with different relationships between latent and observed variables. They proceed to a thorough treatment of model estimation, followed by a discussion of model fit.  Most of the volume focuses on measures that approximate continuous variables, but the authors also devote a chapter to categorical indicators. Each chapter develops a different example (sometimes two) covering topics as diverse as racist attitudes, theological conservatism, leadership qualities, psychological distress, self-efficacy, beliefs about democracy, and Christian nationalism drawn mainly from national surveys. Data to replicate the examples are available on a companion website, along with code in R, Stata, and Mplus.

Recenzijas

Confirmatory Factor Analysis is well written and easy to read, The book covers the essentials necessary for understanding and using CFA. It is appropriate for graduate students and professors new to this analysis approach. -- Jerry J. Vaske The authors provide a masterful and fluid overview of confirmatory factor analysis that will guide readers to the best practices whether conducting their own research or evaluating the research of others. -- John Hoffmann This is a well-written and comprehensive text. -- Michael D. Biderman Roos and Bauldry lucidly set out foundations of confirmatory factor analysis (CFA) as applied in the assessment and construction of scales. Beginning with model specification, they discuss identification, estimation, and assessment of CFA models, before developing extensions to assessing measurement invariance and categorical (rather than quantitative) indicators. -- Peter V. Marsden

Series Editor's Introduction xi
Preface xiii
Acknowledgments xv
About the Authors xvi
1 Introduction
1(9)
1.1 Latent and Observed Variables
1(1)
1.2 Reliability and Validity
1(2)
1.3 Confirmatory Factor Analysis
3(5)
1.4 Statistical Software and Code
8(1)
1.5 Outline of the Book
8(1)
1.6 Further Reading
9(1)
2 Model Specification
10(18)
2.1 Forms of CFA Measurement Models
10(16)
2.2 Conclusion
26(1)
2.3 Further Reading
27(1)
3 Identification and Estimation
28(21)
3.1 Identification
28(8)
3.2 Estimation
36(12)
3.3 Conclusion
48(1)
3.4 Further Reading
48(1)
4 Model Evaluation and Respecification
49(20)
4.1 Model Evaluation
50(7)
4.2 Comparing Models
57(5)
4.3 Model Respecification
62(5)
4.4 Conclusion
67(1)
4.5 Further Reading
68(1)
5 Measurement Invariance
69(12)
5.1 Multiple-Groups CFA
70(6)
5.2 MIMIC Models
76(4)
5.3 Conclusion
80(1)
5.4 Further Reading
80(1)
6 Categorical Indicators
81(22)
6.1 Conceptualization of Ordinal Measures
83(2)
6.2 Estimators
85(6)
6.3 Model Fit and Parameter Interpretation
91(9)
6.4 Comparison With IRT Models
100(1)
6.5 Conclusion
101(1)
6.6 Further Reading
102(1)
7 Conclusion
103(4)
7.1 Advanced Topics in CFA
103(2)
7.2 Moving Beyond CFA
105(2)
8 Appendix: Reliability of Scales
107(3)
9 Glossary
110(3)
References 113(5)
Index 118
J. Micah Roos is an associate professor of sociology at Virginia Polytechnic Institute and State University. His research interests include knowledge, science, religion, culture, stratification, measurement, racial attitudes, and quantitative methods. He ties these interests together through a quantitative, measurement approach to the sociology of knowledge and culture, with a focus on stratification along the early life course. Another strand of his work involves applying techniques in confirmatory factor analysis to the problem of differential item functioning or item-level bias. He holds a PhD in Sociology from the University of North Carolina at Chapel Hill. 



Shawn Bauldry is an Associate Professor in the Department of Sociology at Purdue University. He received a Ph.D. in Sociology and an M.S. in Statistics from the University of North Carolina at Chapel Hill in 2012 and has previously taught at the University of Alabama at Birmingham. His research in applied statistics primarily focuses on the development of structural equation models. His research in sociology explores interrelationships between socioeconomic resources and health over the life course and across generations. His work has appeared in a variety of outlets including Sociological Methodology, Sociological Methods & Research, Psychological Methods, Social Forces, and Journal of Health and Social Behavior.
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