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6.5 OTHER HUMANITIES
Factor analysis examines the inter-correlations that exist between a large number of items (questionnaire responses) and in doing so reduces the items into smaller groups, known as factors. These factors contain correlated variables and are typically quite similar in terms of content or meaning. Unlike other methods discussed in this book, exploratory factor analysis (EFA) does not discriminate between variables on whether they are independent or dependent, but rather it is an interdependence technique that does not specify formal hypotheses. It is in this sense it is ‘exploratory’ in nature as it allows the researcher to determine the underlying dimensions or factors that exist in a set of data. The technique is particularly useful for managerial or academic research in reducing items into discrete dimensions that can be summed or aggregated and subsequently used as input for further multivariate analysis such as multiple regression. It is also used extensively in scale development research to condense a large item pool into a more succinct, reliable and conceptually sound measurement instrument. Factor analytic techniques can typically be classified as either exploratory or confirmatory and the former of these is addressed within this chapter using a research example to demonstrate it's use.
Hooper, D. (2012), ‘Exploratory Factor Analysis’, in Chen, H. (Ed.), Approaches to Quantitative Research – Theory and its Practical Application: A Guide to Dissertation Students, Cork, Ireland: Oak Tree Press.