Exploring Factor Analysis as a Data Management Technique in Educational Survey Research

The statistical tool, factor analysis is used to extract some new factors, smaller in number, from a large amount of correlated factors to make them more manageable to further analysis. It is considered as an instrument for developing the questionnaire and proceed with that for further analysis like regression, multivariate analysis etc. Addition of more variables in the questionnaire is sometime puzzling and leads to wrong direction. So irrelevant questions can be removed and remaining to be expressed in a manageable form. The present study aims to identify the factors linked with academic performance of high school students. The Kaiser - Meyer- Olkin statistic and Bartlett’s test of sphericity are applied to test the factorability of data. The number of extracted factors are determined on the basis of scree plot and total eigen value respect to each component. The orthogonal technique, varimax rotation method is applied that minimize the number of variables with high loadings on each other and try to make small loadings even smaller. The Cronbach’s Alpha Coefficient measure the internal consistency of data. Convergent validity is confirmed by calculating average variance extracted > 0.4 with composite reliability > 0.6. The results confirms that application of factor analysis detect the irrelevant items and extract the valuable factors from the questionnaire which helps the researchers and administrations, teachers to take care of a few factors and make necessary actions based on that rather than considering a large amount of parameters which generally create confusions.

Factor analysis, Academic performance, Kaiser-Meyer - Olkin, Factor loading

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