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

By Timothy A. Brown PsyD

Emphasizing useful 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 options, methods, pitfalls, and extensions of CFA technique. Chock choked with invaluable recommendation and tables that define the tactics, the textual content indicates readers easy methods to behavior exploratory issue research (EFA) and comprehend similarities to and adjustments from CFA; formulate, application, and interpret CFA versions utilizing renowned latent variable software program programs reminiscent of LISREL, Mplus, Amos, EQS, and SAS/CALIS; and document effects from a CFA research. additionally lined are extensions of CFA to conventional IRT research, tools for picking out helpful pattern sizes, and new CFA modeling percentages, together with multilevel issue versions and issue blend types. specific gains comprise a better half online page supplying info and application syntax documents for lots of of the learn examples in order that readers can perform the systems defined within the publication with genuine information. the internet web page additionally contains hyperlinks to extra CFA-related resources.
 

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2, the degrees of freedom associated with the χ2 fit statistic is 2, corresponding to the difference a – b, 10 – 8 = 2; see Chapter 3). However, two factors cannot be extracted, because the number of parameters to be estimated in this model exceeds the number of elements of the input matrix by one, that is, a = (4 ∗ 2) + [(2 ∗ 3) / 2)] + 4 – 4 = 8 + 3 + 4 – 4 = 11. Each aspect of the equation used to solve for a corresponds to specific parameters and mathematical restrictions in the EFA model (cf.

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.

The higher up the axis, the higher the factor loading; if an indicator is in close proximity to one factor axis, it does not load highly on another factor. 4B, orthogonal rotation moved the Factor 2 axis closer to the Y5–Y8 indicators. 4A). 244 for the unrotated and rotated solutions, respectively. 4B), the restriction of maintaining a 90° orientation of the factor axes may not be tenable. Oblique rotations such as promax begin with an orthogonal rotation, but then “break” the 90° angle to allow the factor axes to pass through the clusters of indicators better.

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