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

By Timothy A. Brown PsyD

Emphasizing functional and theoretical elements of confirmatory issue research (CFA) instead of arithmetic or formulation, Timothy A. Brown makes use of wealthy examples derived from the psychology, administration, and sociology literatures to supply in-depth therapy of the innovations, systems, pitfalls, and extensions of CFA technique. Chock jam-packed with precious recommendation and tables that define the strategies, the textual content exhibits readers the way to behavior exploratory issue research (EFA) and comprehend similarities to and transformations from CFA; formulate, software, and interpret CFA versions utilizing well known latent variable software program applications resembling LISREL, Mplus, Amos, EQS, and SAS/CALIS; and document effects from a CFA research. additionally coated are extensions of CFA to conventional IRT research, equipment for picking beneficial pattern sizes, and new CFA modeling chances, together with multilevel issue types and issue mix types. distinctive beneficial properties comprise a significant other web content supplying facts and software syntax documents for lots of of the learn examples in order that readers can perform the techniques defined within the booklet with genuine information. the net web page additionally comprises hyperlinks to extra CFA-related resources.

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Thus, because the applied CFA researcher must have a working knowledge of EFA, the methods of conducting an EFA are reviewed in this chapter. This overview is also provided to allow more detailed comparisons of EFA and CFA in later chapters. , psychology, education, sociology, management, public health). The fundamental intent of factor analysis is to determine the number and nature of 12 The Common Factor Model and EFA 13 latent variables or factors that account for the variation and covariation among a set of observed measures, commonly referred to as indicators.

If the remaining observed relationships in the input matrix are reproduced as well by the factor loading estimates in this solution, descriptive fit statistics such the χ2 and RMSEA will indicate that the one-factor model provided a good fit to the data. 2, the SPSS output provides a χ2 test of the fit of the one-factor solution. 90, it could be concluded that the one-factor model provides a reasonable fit to the data. The nonsignificant χ2 test suggests the correlation matrix predicted by the factor model parameters does not differ from the sample correlation matrix.

45. Factor Scores After an appropriate factor solution has been established, the researcher may wish to calculate factor scores using the factor loadings and factor correlations. Factor scores are used for various purposes such as to serve as proxies for latent variables, and to determine a participant’s relative standing on the latent dimension. Conceptually, a factor score is the score that would have been observed for a person if it had been possible to measure the latent factor directly. , averaging or summing) found to have salient loadings on the factor.

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