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 relationships among a set of variables or conditions which may be taken as source variables, accounting for the observed interrelations in the data (Emory, 1992, p. 552). Factor loadings are the coefficients of factors identified in factor analysis, and are used as measures of the degree of the relationships between factors and variables.
There are two basic types of factor analysis, although there are a number of different factor analysis procedures (Emory, 1992, p. 554). The...