By Timothy A. Brown PsyD
New to This Edition
*Updated all through to include vital advancements in latent variable modeling.
*Chapter on Bayesian CFA and multilevel size models.
*Addresses new issues (with examples): exploratory structural equation modeling, bifactor research, size invariance overview with express symptoms, and a brand new approach for scaling latent variables.
*Utilizes the most recent models of significant latent variable software program packages.
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Additional info for Confirmatory Factor Analysis for Applied Research
Again, EFA is a largely exploratory procedure, but substantive and practical considerations should strongly guide the factor analytic process. , the role of sampling error), the results of an initial EFA should be interpreted cautiously and should be cross-validated (additional EFAs or CFAs should be conducted with independent data sets). The Common Factor Model and EFA 27 Factor Rotation Once the appropriate number of factors has been determined, the extracted factors are rotated, to foster their interpretability.
They may be highly intercorrelated even when the factors are truly orthogonal; Grice, 2001). Alternatively, factor scores can be estimated by multivariate methods that use various aspects of the reduced or unreduced correlation matrix and factor analysis coefficients. The resulting values are called refined factor scores. , Bartlett, 1937; Harman, 1976; McDonald, 1981). Most statistical software packages provide options to compute refined factor scores by one or more of these methods. In the majority of instances, refined factor scores have less bias than coarse factor scores and thus are favored over coarse factor scores as proxies for the factors (Grice, 2001).
The resulting values are called refined factor scores. , Bartlett, 1937; Harman, 1976; McDonald, 1981). Most statistical software packages provide options to compute refined factor scores by one or more of these methods. In the majority of instances, refined factor scores have less bias than coarse factor scores and thus are favored over coarse factor scores as proxies for the factors (Grice, 2001). However, a complicating issue in factor score estimation is the indeterminate nature of the common factor model.