Comparison of Factor Analysis Options Using the Home/ Employment Orientation ScaleVarimax rotation is a statistical technique used at one level of factor analysis as an attempt to clarify the relationship among factors. Generally, the process involves adjusting the coordinates of data that result from a principal components analysis. The adjustment, or rotation, is intended to maximize the variance shared among items. By maximizing the shared variance, results more discretely represent how data correlate with each principal component. To maximize the variance generally means to increase the squared correlation of items related to one factor, while decreasing the correlation on any other factor.
THE RELATIONS OF THE NEWER MULTIVARIATE STATISTICAL METHODS TO FACTOR ANALYSIS
Factor analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors. This technique extracts maximum common variance from all variables and puts them into a common score. As an index of all variables, we can use this score for further analysis. Factor analysis is part of general linear model GLM and this method also assumes several assumptions: there is linear relationship, there is no multicollinearity, it includes relevant variables into analysis, and there is true correlation between variables and factors. Several methods are available, but principle component analysis is used most commonly.
Factor analysis is a commonly used technique for evaluating the strength of the relationship of individual items of a scale with the latent concept, assessing content or construct validity of an instrument, determining plausible structures underlying a set of variables, and combining a set of variables into one composite score. In using the technique, the analyst must make decisions about the type of extraction and rotation to request and about the number of factors to retain. In contrast, the PAF, IF, and AF extractions seek to uncover hypothetical factors that are estimated from the observed data but that are not completely defined by those data. Each of these extraction methods differ mathematically, based on manipulations of the correlation matrix to be analyzed. In addition, each subsequent factor is tested for significance before extraction Nunnally, Thus, nonsignificant factors are not extracted and interpretation is simplified.
Use the link below to share a full-text version of this article with your friends and colleagues. Learn more. A survey of developments in multivariate analysis during the last thirty years shows that some, though not all, of the purposes for which factor analysis has been used may now be better accomplished by other procedures, e.
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