This paper discusses factor analysis, a statistical tool to determine patterns in data.
Written in 2005; 1,115 words; 6 sources; APA; $ 38.95
Paper Summary:
This paper explains that factor analysis analyzes the patterns of relationship among several variables, with the aim of finding something about the character of the independent variables, which influence them, even though those independent variables, called factors, were not assessed directly. The paper explains that the results gotten by factor analysis are essentially more theoretical and provisional than is true when independent variables are spotted directly. The author stresses that, in order for the factor to be analyzed, the data must be bi-linear; this implies that the row entities and the column entities must be independent of each other.
From the Paper:
"Factor analysis can handle over hundred variables at a time; recompense for random and meaningless mistakes, and unravel difficult interrelationships into their major and distinct reliabilities. But, factor analysis has disadvantages. It is mathematically problematic and requires varied and various relevant factors. Its technical terms include strange words like 'eigenvalues, rotate, simple structure, orthogonal, loadings, and communality'. Its product usually takes up a dozen or so pages in a given report, giving little space for a procedural foreword or clarification of terms."
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