Factor analysis is a statistical method used to identify underlying factors or dimensions that explain patterns of correlation among a set of observed variables. It is often used in social and behavioral sciences to reduce a large number of variables into a smaller set of factors that are more easily interpreted.
Non-continuous variables, also known as categorical or discrete variables, are variables that can only take on specific values and cannot be measured on a continuous scale. Examples of non-continuous variables include gender, race, education level, and occupation.
Factor analysis can be used on both continuous and non-continuous variables. However, it is particularly useful for non-continuous variables because it allows for the identification of underlying factors that can explain the relationships among these variables. This can provide a deeper understanding of the data and aid in the interpretation of results.
Factor analysis on non-continuous variables is commonly used in social and behavioral sciences to study attitudes, beliefs, and behaviors. It can also be used in market research to identify consumer preferences and in healthcare to understand patient satisfaction and treatment outcomes.
One potential limitation of factor analysis on non-continuous variables is that it assumes a linear relationship between the variables, which may not always be the case. Additionally, the results of factor analysis can be heavily influenced by the choice of rotation method and the number of factors extracted, which may introduce bias into the analysis.