Factor Analysis is a collection of methods that are used in examination of how underlying constructs influence the responses on a number of measured variables. It refers to a mathematical procedure applied in reducing a large amount of data into a structure that can be more easily studied. It aims to summarize the information that is contained in a number of variables and present it in a smaller number of factors that comprise of interrelated variables. This makes it much easier to analyze data.
There are basically to kinds of factor analysis that are; exploratory and confirmatory. Exploratory factor analysis aims at discovering the nature of the constructs influencing a set of responses. Confirmatory factor analysis on the other hand, aims at testing whether a specified set of constructs is having an influence on responses in a way that can be predicted. All these kinds of factor analysis have been constructed based on the Common Factor Model. The model asserts that every observed response is partially influenced by underlying common factors and also partly by underlying unique factors.
It should be noted that the strength of connection between each factor and measure is varied in that; a given factor has greater influence on some than others. Factor analyses are performed through the examination of the pattern of correlations between the observed measures. There are higher chances that measures that are either correlated positively or negatively are influenced by similar factors. On the other hand, those measures that are relatively uncorrelated are likely impacted by different factors.
When applying factor analysis, you may have a bit of a problem in choosing whether to use exploratory or confirmatory since each is ideal for a particular situation. For instance, exploratory factor analysis is the best in instances where you lack a strong theory regarding the constructs underlying responses to your measures. In case you have a strong theory, confirmatory factor analysis would be the most suitable type.
Another thing that should be noted when studying factor analysis is that it is related to principal component analysis. However, the two are not identical. One of the differences between the two is that factor analysis uses regression modeling techniques in testing hypotheses that produce error terms while the other is a descriptive technique for statistical analysis.
Factor analysis has been applied in quite a number of disciplines. However, the most significant one is in psychology whereby it is used in the identification of factors that explain various results on different tests. For instance, intelligence research established that people who core high on tests of verbal ability are equally good on other tests that require verbal abilities. Apart from intelligence research, factor analysis has also been applied in obtaining factors in a variety of domains like personality, beliefs, and attitudes among others. One demerit of factor analysis is that it can only be as good as the data allows. Besides, factor analysis cannot identify causes or causality.
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