Factor analysis is a statistical approach for describing variability in seen, correlated variables in terms of a possibly smaller number of unobserved variables known as factors.
It is possible, for example, that fluctuations in six known variables mostly reflect variations in two unseen (underlying) variables.
Factor analysis looks for such joint fluctuations in response to latent variables that are not noticed.
Factor analysis may be regarded of as a specific form of errors-in-variables models since the observed variables are described as linear combinations of the possible factors plus "error" terms.
It may help to deal with data sets where there are large numbers of observed variables that are thought to reflect a smaller number of underlying/latent variables.
It is one of the most commonly used inter-dependency techniques and is used when the relevant set of variables shows a systematic inter-dependence and the objective is to find out the latent factors that create a commonality.
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<span>1. Pose significant questions that can be investigated empirically.
2. Link research to relevant theory.
3. Use methods that permit direct investigation of the question.
4. The methods used to obtain data and test hypotheses should be benevolent and not malevolent.</span>
