8. Factor Analysis - 0905-section2.QX5 4:11 PM Page 172 15...

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172 15 Factor analysis Factor analysis Factor analysis is different from many of the other techniques presented in this book. It is not designed to test hypotheses or to tell you whether one group is significantly different from another. It is included in the SPSS package as a ‘data reduction’ technique. It takes a large set of variables and looks for a way that the data may be ‘reduced’ or summarised using a smaller set of factors or components. It does this by looking for ‘clumps’ or groups among the inter- correlations of a set of variables. This is an almost impossible task to do ‘by eye’ with anything more than a small number of variables. This family of factor analytic techniques has a number of different uses. It is used extensively by researchers involved in the development and evaluation of tests and scales. The scale developer starts with a large number of individual scale items and questions and, by using factor analytic techniques, they can refine and reduce these items to form a smaller number of coherent subscales. Factor analysis can also be used to reduce a large number of related variables to a more manageable number, prior to using them in other analyses such as multiple regression or multivariate analysis of variance. There are two main approaches to factor analysis that you will see described in the literature—exploratory and confirmatory. Exploratory factor analysis is often used in the early stages of research to gather information about (explore) the interrelationships among a set of variables. Confirmatory factor analysis, on the other hand, is a more complex and sophisticated set of techniques used later in the research process to test (confirm) specific hypotheses or theories concerning the structure underlying a set of variables. The term ‘factor analysis’ encompasses a variety of different, although related techniques. One of the main distinctions is between what is termed principal components analysis (PCA) and factor analysis (FA). These two sets of techniques are similar in many ways and are often used interchangeably by researchers. Both attempt to produce a smaller number of linear combinations of the original variables in a way that captures (or accounts for) most of the variability in the pattern of correlations. They do differ in a number of ways, however. In principal components analysis the original variables are transformed into a smaller set of linear combinations, with all of the variance in the variables being used. In factor analysis, however, factors are estimated using a mathematical model, where only the shared variance is analysed (see Tabachnick & Fidell, 2001, Chapter 13, for more information on this).
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Chapter 15 Factor analysis 173 Although both approaches (PCA and FA) often produce similar results, books on the topic often differ in terms of which approach they recommend. Stevens (1996, pp. 362–363) admits a preference for principal components analysis and gives a number of reasons for this. He suggests that it is psychometrically sound
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  • Spring '15
  • Spss, parallel analysis

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