Exploratory factor analysis is a statistical technique that is used to reduce data to a smaller set of summary variables and to explore the underlying theoretical structure of the phenomena. It is used to identify the structure of the relationship between the variable and the respondent. Exploratory factor analysis can be performed by using the following two methods:
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There are two methods for driving factor, these two methods are as follows:
Selection of factors to be extracted: Theory is the first criteria to determine the number of factors to be extracted. From theory, we know that the number of factors extracted does make sense. Most researchers use the Eigenvalue criteria for the number of factors to be extracted. Value of the percentage and variance explained method is also used for exploratory factor analysis. We can use the scree test criteria for the selection of factors. In this method, Eigenvalue is plotted on a graph and factors are selected.
Orthogonal rotation: In this method, axis are maintained at 90 degrees, thus the factors are uncorrelated to each other. In orthogonal rotation, the following three methods are available based on the rotation:
A. QUARTIMAX: Rows are simplified so that the variable should be loaded on a single factor.
B. VARIMAX: Used to simplify the column of the factor matrix so that the factor extracts are clearly associated and there should be some separation among the variables.
C. EQUIMAX: The combination of the above two methods. This method simplifies row and column at a single time.
Criteria for Practical and Statistical Significance of Factor Loadings: Factor loading can be classified based on their magnitude:
Greater than + .30 — minimum consideration level
+ .40 — more important
+ .50 — practically significant
Power and significance level: The researcher can determine the statistical power and significance level. For instance, in order to achieve a factor loading of .55 with a power of .80, a sample of 100 is needed.
Factor analysis and SPSS: Factor analysis can be performed in SPSS by clicking on “analysis” from menu, and then selecting “factor” from the data reduction option.