title: On exploratory factor analysis: a review of recent evidence, an assessment of current practice, and recommendations for future use.
abstract: Exploratory factor analysis (hereafter, factor analysis) is a complex statistical method that is integral to many fields of research. Using factor analysis requires researchers to make several decisions, each of which affects the solutions generated. In this paper, we focus on five major decisions that are made in conducting factor analysis: (i) establishing how large the sample needs to be, (ii) choosing between factor analysis and principal components analysis, (iii) determining the number of factors to retain, (iv) selecting a method of data extraction, and (v) deciding upon the methods of factor rotation. The purpose of this paper is threefold: (i) to review the literature with respect to these five decisions, (ii) to assess current practices in nursing research, and (iii) to offer recommendations for future use. The literature reviews illustrate that factor analysis remains a dynamic field of study, with recent research having practical implications for those who use this statistical method. The assessment was conducted on 54 factor analysis (and principal components analysis) solutions presented in the results sections of 28 papers published in the 2012 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. The main findings from the assessment were that researchers commonly used (a) participants-to-items ratios for determining sample sizes (used for 43% of solutions), (b) principal components analysis (61%) rather than factor analysis (39%), (c) the eigenvalues greater than one rule and screen tests to decide upon the numbers of factors/components to retain (61% and 46%, respectively), (d) principal components analysis and unweighted least squares as methods of data extraction (61% and 19%, respectively), and (e) the Varimax method of rotation (44%). In general, well-established, but out-dated, heuristics and practices informed decision making with respect to the performance of factor analysis in nursing studies. Based on the findings from factor analysis research, it seems likely that the use of such methods may have had a material, adverse effect on the solutions generated. We offer recommendations for future practice with respect to each of the five decisions discussed in this paper.
authors.affiliation: Gaskin Research, Melbourne, Victoria, Australia; Central Queensland University, Institute for Health and Social Science Research, Centre for Mental Health Nursing Innovation, School of Nursing and Midwifery, Melbourne, Victoria, Australia.
authors.forename: Cadeyrn J
authors.initials: CJ
authors.lastname: Gaskin
authors.affiliation: Central Queensland University, Institute for Health and Social Science Research, Centre for Mental Health Nursing Innovation, School of Nursing and Midwifery, Rockhampton, Queensland, Australia. Electronic address: b.happell@cqu.edu.au.
authors.forename: Brenda
authors.initials: B
authors.lastname: Happell
mesh_terms: D055317:Evidence-Based Practice; D005163:Factor Analysis, Statistical; D025341:Principal Component Analysis
keywords: Factor analysis; Measurement; Nursing research; Principal components analysis; Research design; Review literature as topic; Statistics; Statistics as topic