Risky business: factor analysis of survey data – assessing the probability of incorrect dimensionalisation

van der Eijk, Cees and Rose, Jonathan (2015) Risky business: factor analysis of survey data – assessing the probability of incorrect dimensionalisation. PLoS ONE, 10 (3). 0118900/1-0118900/31. ISSN 1932-6203

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This paper undertakes a systematic assessment of the extent to which factor analysis the correct number of latent dimensions (factors) when applied to ordered categorical survey items (so-called Likert items). We simulate 2400 data sets of uni-dimensional Likert items that vary systematically over a range of conditions such as the underlying population distribution, the number of items, the level of random error, and characteristics of items and item-sets. Each of these datasets is factor analysed in a variety of ways that are frequently used in the extant literature, or that are recommended in current methodological texts. These include exploratory factor retention heuristics such as Kaiser’s criterion, Parallel Analysis and a non-graphical scree test, and (for exploratory and confirmatory analyses) evaluations of model fit. These analyses are conducted on the basis of Pearson and polychoric correlations.We find that, irrespective of the particular mode of analysis, factor analysis applied to ordered-categorical survey data very often leads to over-dimensionalisation. The magnitude of this risk depends on the specific way in which factor analysis is conducted, the number of items, the properties of the set of items, and the underlying population distribution. The paper concludes with a discussion of the consequences of overdimensionalisation, and a brief mention of alternative modes of analysis that are much less prone to such problems.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/747179
Additional Information: Article is based on simulated data; all scripts (in R) to generate and analyse the data are available through the website of PLOS One
Keywords: factor analysis, surveys, Likert systems eigenvalues principal component analysis survey data ordered categorical data applied statistics latent variables factor retention criteria
Schools/Departments: University of Nottingham, UK > Faculty of Social Sciences > School of Politics and International Relations
Identification Number: https://doi.org/10.1371/journal.pone.0118900
Depositing User: van der Eijk, Prof Cees
Date Deposited: 27 May 2015 14:28
Last Modified: 04 May 2020 17:04
URI: https://eprints.nottingham.ac.uk/id/eprint/28820

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