Type of factor analysis[edit] Exploratory factor analysis (EFA) is used to identify complex interrelationships among items and group items that are part of unified concepts.[3] The researcher makes no a priori assumptions about relationships among factors.[3] Confirmatory factor analysis (CFA) is a more complex approach that tests the hypothesis that the items are associated with specific factors.[3] CFA uses structural equation modeling to test a measurement model whereby loading on the factors allows for evaluation of relationships between observed variables and unobserved variables.[3] Structural equation modeling approaches can accommodate measurement error, and are less restrictive than least-squares estimation.[3] Hypothesized models are tested against actual data, and the analysis would demonstrate loadings of observed variables on the latent variables (factors), as well as the correlation between the latent variables.[3] Types of factoring[edit] Principal component analysis (PCA): PCA is a widely used method for factor extraction, which is the first phase of EFA.[3] Factor weights are computed in order to extract the maximum possible variance, with successive factoring continuing until there is no further meaningful variance left.[3] The factor model must then be rotated for analysis.[3] Canonical factor analysis, also called Raos canonical factoring, is a different method of computing the same model as PCA, which uses the principal axis method. Canonical factor analysis seeks factors which have the highest canonical correlation with the observed variables. Canonical factor analysis is unaffected by arbitrary rescaling of the data. Common factor analysis, also called principal factor analysis (PFA) or principal axis factoring (PAF), seeks the least number of factors which can account for the common variance (correlation) of a set of variables. Image factoring: based on the correlation matrix of predicted variables rather than actual variables, where each variable is predicted from the others using multiple regression. Alpha factoring: based on maximizing the reliability of factors, assuming variables are randomly sampled from a universe of variables. All other methods assume cases to be sampled and variables fixed. Factor regression model: a combinatorial model of factor model and regression model; or alternatively, it can be viewed as the hybrid factor model,[4] whose factors are partially known.
Posted on: Sat, 15 Mar 2014 04:52:32 +0000
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