Exploratory Factor Analysis: A Five-Step Guide for Novices

By Brett Williams, A. Onsman, Ted Brown. 2010

2,734 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/66929e853f39ff2100c49ba5164548b2457394d7

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In Computer Networks and Communications

In Management Science and Operations Research

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Citations in the corpus

Corporate social responsibility and hotel performance: A view from Tehran, Iran (2019, background): “Factors were extracted at the next stage in order to reduce the large amount of items and determine the smallest number which would give the best representation of all the variables (Williams et al., 2010).”
Motivating Action through Fostering Climate Change Hope and Concern and Avoiding Despair among Adolescents (2015, methodology): “We used the rule of thumb of eigenvalues greater than 1 to determine the number of factors for each of these scales [31].”
Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General (2018, background): “Comrey and Lee (1992); and Comrey et al., 1973) graded a factor analysis sample of 50 as very poor, 100 as poor, 200 as fair, 300 as good, 500 as very good, and 1000 as excellent (quoted also by Costello & Osborne, 2005; DeVellis, 2017; Williams et al., 2010 and others).”
Exploratory Factor Analysis ; Concepts and Theory (2014, methodology): “There is evidence to suggest that the results of exploratory factor analysis are more accurate when each common factor is represented by multiple variables in the analysis (Williams, Brown et al. 2010).”
Development and application of CatFACS: Are human cat adopters influenced by cat facial expressions? (2017, methodology): “Since there were no initial assumptions about the data collected or the nature of the variables, an Exploratory Factor Analyses (EFA, e.g. Budaev, 2010; Williams et al., 2010) was performed with 36 Behavioural Variables in order to reduce data complexity, while retaining as much of the original rele”