FACTOR ANALYSIS AND MULTIVARIATE ANALYSIS

This research presents an overview of factor analysis and multivariate analysis procedures. Additionally, the advantages and disadvantages of each set of procedure are identified.

Many research studies generate vast quantities of data. These data more often than not are multidimensional and are characterized by multicollinearity (Summers, Peters, and Armstrong, 1993, p. 555). In most instances, if the data are to be used effectively, it is necessary to reduce the number of explanatory variables to more manageable proportions.

Factor analysis is a general descriptor for a group of specific computational procedures (Emory, 1992, p. 559). Each of the procedures included in the group, however, are intended to reduce a large number of measures to a smaller number which provides a more efficient and powerful measure of the same thing. The three general objectives of factor analysis, as follows:

1. Factor analysis studies the correlations of a large number of variables by clustering those variables into factors in a way that the variables included in each factor are highly correlated.

2. Factor analysis seeks to interpret each factor identified according the variables included in the factor.

3. Factor analysis attempts to summarize many variables in to a few factors.

Factor analysis is a statistical procedure with data-reduction capabilities that is used to determine the underlying pattern of

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factor, the models may be further subdivided on the basis of the correlation or absence thereof of the factors. Within the variants of the multivariate model of factor analysis, any scores given weights and subsequently added together are defined as factors of the resulting variables. These weights are referred to as factor coefficients, or factor loadings. There are three instances wherein multivariate models of factor analysis may not provide the best representation of the factor and variable relationships (Emory, 1992, p. 570). These situations are where relationships are nonlinear, where the relationship between the variable and the factor is not stable through all levels of the factor, and where the relationships of several factors to a single variable are virtually interchangeable. The full component model produces exact relationships (Emory, 1992, p. 575). The problem with the full component model is the mass of data and ties required for its execution. It often represents an impractical approach to a solution to a problem. The assumptions required for the full components models are that the variables may be calculated from the factors by multiplying each factor by the appropriate weight, and then summing across al

Category: Misc - F