Iterative principal factor analysis -application on ordinal data
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Date
2012-06Author
Njatha, Moses M
Type
ThesisLanguage
enMetadata
Show full item recordAbstract
This study discusses the procedure of variables reduction that improves assessment of attributes
for a subject of interest. Details of how measured variables can be used to obtain a set of
unobserved underlying factors are provided. These factors though considered obvious it is shown
how they can be quantified. Two ordinal data sets of data are used to discuss the different
considerations of the procedure. Starting with results of principal component analysis the study
uses iterative principal factor analysis. Assuming orthogonal relationship of possible sets of
factors, Varimax rotation is applied to distribute variation among factors. The results show how a
better fit of a common factor model can be obtained with the consideration of changes in
individual communalities. The model considered to provide the best fit explains 62.5% of the
variation and includes measured variables with communalities more than 0.5.
Sponsorhip
University of NairobiPublisher
School of mathematics