By Timothy A. Brown PsyD

With its emphasis on functional and conceptual facets, instead of arithmetic or formulation, this obtainable publication has validated itself because the go-to source on confirmatory issue research (CFA). exact, worked-through examples drawn from psychology, administration, and sociology experiences illustrate the approaches, pitfalls, and extensions of CFA method. The textual content indicates tips on how to formulate, application, and interpret CFA types utilizing renowned latent variable software program applications (LISREL, Mplus, EQS, SAS/CALIS); comprehend the similarities and adjustments among CFA and exploratory issue research (EFA); and file effects from a CFA examine. it truly is jam-packed with precious recommendation and tables that define the methods. The better half site (www.guilford.com/brown3-materials) deals information and application syntax records for many of the examine examples, in addition to hyperlinks to CFA-related resources.

New to This Edition
*Updated all through to include vital advancements in latent variable modeling.
*Chapter on Bayesian CFA and multilevel dimension models.
*Addresses new themes (with examples): exploratory structural equation modeling, bifactor research, dimension invariance evaluate with specific signs, and a brand new technique for scaling latent variables.
*Utilizes the newest models of significant latent variable software program packages.

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Items from a questionnaire) by uncovering the smallest number of interpretable factors needed to explain the correlations among them. Whereas the researcher must ultimately specify the number of factors, EFA is an “explor- The Common Factor Model and EFA 19 atory” analysis because no a priori restrictions are placed on the pattern of relationships between the observed measures and the latent variables. This is a key difference between EFA and CFA. , number of factors, patterns of indicator–­factor loadings).

With respect to factor scores, this indeterminacy means that an infinite number of sets of factor scores can be computed from any given factor analysis that will be equally consistent with the same factor loadings (Grice, 2001). , factors defined by several items with strong communalities have better determinacy). If a high degree of indeterminacy is present, the sets of factor scores can vary so widely that an individual ranked high on the dimension in one set may receive a low ranking on the basis of another set.

The unstandardized means of the indicators can also be included in the CFA. Thus, in contrast to EFA, which focuses on completely standardized values, CFA may entail the analysis of both unstandardized variance–­covariance structures and mean structures (as the result of standardization in EFA, indicator means are presumed to be zero). As discussed in Chapter 7, when indicator means are included as input in CFA, the analysis can estimate the means of the factors and the intercepts of the indicators.

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Confirmatory Factor Analysis for Applied Research by Timothy A. Brown PsyD
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