situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.

Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each

When conducting an exploratory factor analysis, the decision regarding the number of factors to retain following factor extraction is one that the researcher should consider very carefully, as the decision can have a dramatic effect on results. Although there are numerous strategies that can

Factor analysis has historically been used for myriad purposes in the social and behavioral sciences, but an especially important application of this technique has been to evaluate construct validity. Since in the present milieu both exploratory factor analysis (EFA) and confirmatory factor

(ii) development of

FA is not worth the time necessary to understand it and carry it out.-Hills, 1977 Factor analysis should not be used in most practical situations.-Chatfield and Collins, 1980, pg. 89. At the present time, factor analysis still maintains the flavor of an art,

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There are many new developments in the area of exploratory factor analysis. The use of path models to explain exploratory factor analysis, speeds the learning curve in a subject that was previous dominated by matrix algebra (Loehlin, 1998). This approach not only improves understanding

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Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) are both variable reduction techniques and sometimes mistaken as the same statistical method. However, there are distinct differences between PCA and EFA. Similarities and differences between PCA and EFA will be examined