Confirmatory Factor Analysis for Applied Research, Second EditionWith its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities and differences between CFA and exploratory factor analysis (EFA); and report results from a CFA study. It is filled with useful advice and tables that outline the procedures. The companion website (www.guilford.com/brown3- New to This Edition *Updated throughout to incorporate important developments in latent variable modeling. *Chapter on Bayesian CFA and multilevel measurement models. *Addresses new topics (with examples): exploratory structural equation modeling, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables. *Utilizes the latest versions of major latent variable software packages. |
Contents
1 Introduction | 1 |
2 The Common Factor Model and Exploratory Factor Analysis | 10 |
3 Introduction to CFA | 35 |
4 Specification and Interpretation of CFA Models | 88 |
5 Model Revision and Comparison | 139 |
6 CFA of MultitraitMultimethod Matrices | 186 |
7 CFA with Equality Constraints Multiple Groups and Mean Structures | 206 |
HigherOrder Factor Analysis Scale Reliability Evaluation and Formative Indicators | 287 |
Missing NonNormal and Categorical Data | 333 |
10 Statistical Power and Sample Size | 380 |
11 Recent Developments Involving CFA Models | 400 |
| 431 | |
Author Index | 445 |
| 449 | |
About the Author | 462 |
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Confirmatory Factor Analysis for Applied Research, Second Edition Timothy A. Brown Limited preview - 2015 |
Common terms and phrases
Agoraphobia applied research approach Bayesian CFA model CFA solution Chapter computed confidence interval correlated errors correlation matrix covariance matrix data set distribution eigenvalues equation equivalent error covariances example Extraversion factor analysis factor correlations factor covariance factor loadings factor model factor solution factor variances Figure fit indices fixed to zero freely estimated parameters groups CFA indicator intercepts input matrix instance invariance evaluation latent means latent variable LISREL marker indicator measurement error measurement invariance measurement model method effects metric MIMIC models missing data misspecification ML estimation model e.g. model fit modification indices Mplus MTMM Muthén Neuroticism number of factors observed measures order factor output P-Value pairwise deletion parameter estimates predicted prior provides regression relationships residual variances RMSEA Root Mean Square sample scale reliability Social software programs specification SRMR standard errors standardized residuals substantive syntax Table tion unstandardized values variance-covariance matrix ε ε