Factor analysis is a statistical data reduction and analysis technique that strives to explain correlations among multiple outcomes as the result of one or more underlying explanations, or factors. The technique involves data reduction, as it attempts to represent a set of variables by a smaller number.
Factor analysis attempts to discover the unexplained factors that influence the co-variation among multiple observations. These factors represent underlying concepts that cannot be adequately measured by a single variable. For example, various measures of political attitudes may be influenced by one or more underlying factors.
Factor analysis is especially popular in survey research, in which the responses to each question represent an outcome. Because multiple questions often are related, underlying factors may influence subject responses.
Because the purpose of factor analysis is to uncover underlying factors that explain correlations among multiple outcomes, it is important that the variables studied be at least somewhat correlated; otherwise, factor analysis is not an appropriate analytical technique.
Factor analysis requires the use of a computer, usually with a statistical software program, such as SAS or SPSS. The spreadsheet program Excel cannot conduct factor analysis without a program that expands its statistical capabilities.
One program that enables Excel to conduct more complex statistical analysis, such as factor analysis, is XLStat, which can be purchased online.