Exploratory Factor Analysis: A Five-Step Guide for Novices

Abstract

<jats:p>Factor analysis is a multivariate statistical approach commonly used in psychology, education, and more recently in the health-related professions. This paper will attempt to provide novice researchers with a simplified approach to undertaking exploratory factor analysis (EFA). As the paramedic body of knowledge continues to grow, indeed into scale and instrument psychometrics, it is timely that an uncomplicated article such as this be offered to the paramedic readership both nationally and internationally. Factor analysis is an important tool that can be used in the development, refinement, and evaluation of tests, scales, and measures that can be used in education and clinical contexts by paramedics. The objective of the paper is to provide an exploratory factor analysis protocol, offering potential researchers with an empirically-supported systematic approach that simplifies the many guidelines and options associated with completing EFA.</jats:p>

Exploring visitors' willingness to pay to generate revenues for managing the National Elephant Conservation Center in Malaysia

“…Hence, in present study factor analysis was applied in order to reduction of 23 attitudinal items into small number of identified factors with capability of displaying most of the primarily observed variance (Maier, 2007). The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy of 0.86 and 0.82 for visitors activities and facilities satisfactions respectively, with Bartlett's test of Sphericity significant at the 1% level showed that our data set is suitable to run factor analysis (Hutcheson and Sofroniou, 1999;Williams et al, 2012). Correlation matrix (presented in Appendix 1) with sizable correlations among the variables indicates the suitability of variables to be included in factor analysis.…”

Measuring Information Seeking through Facebook: Scale development and initial evidence of Information Seeking in Facebook Scale (ISFS)

“…Multiple criteria were used to determine retention of factors (Williams, Brown, & Onsman, 2010), including eigenvalues >1, a visual analysis of the produced Scree plot, and interpretability of the factor solutions. The Kaiser-Meyer-Oklin (KMO) measure of sample adequacy was assessed (KMO statistic >.80) and Bartlett's Test of Sphericity (p < .05) indicated that the sample was adequate for EFA.…”

“…The Kaiser-Meyer-Oklin (KMO) measure of sample adequacy was assessed (KMO statistic >.80) and Bartlett's Test of Sphericity (p < .05) indicated that the sample was adequate for EFA. The analysis was conducted over a number of iterations and the total variation explained by the factors was set between 50% and 75% (Williams et al, 2010). Tabachnick and Fidell (2001) cite .32 as a good rule of thumb for the minimum loading of an item, which equates to approximately 10% overlapping variance with the other items in that factor.…”

The Patient Participation Culture Tool for healthcare workers (PaCT-HCW) on general hospital wards: A development and psychometric validation study

“…A correlation-matrix showed no correlations higher than 0.80 indicating there was no multi-collinearity (Mortelmans and Dehertogh, 2008). Within all the components, factorability was higher than 0.30 and significant at the 0.001-level (Williams et al 2012). The loading of all the items, except two, for each component was above 0.40.…”