Factor Retention Decisions in Exploratory Factor Analysis: a Tutorial on Parallel Analysis

By James C. Hayton, David G. Allen, Vida Scarpello. 2004

2,992 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Food Science, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/87d65c2bca5ab625e7b21f76715503a036b64faa

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Citations in the corpus

The White Privilege Attitudes Scale: Development and initial validation. (2009, background): “Factors may be retained for exploratory factor analysis if their eigenvalues are larger than the average random eigenvalue produced by the parallel analysis (Hayton et al., 2004).”
From safety culture to safety orientation: Validation and simplification of a safety orientation scale using a sample of seafarers working for Norwegian ship owners (2009, methodology): “Parallel analysis criterion (PC) According to Hayton et al. (2004), PC is one of the most accurate factor retention methods while also being one of the most underutilised in management and organisational research.”
Exploratory Factor Analysis ; Concepts and Theory (2014, methodology): “Both types of misspecifications have been empirically demonstrated to lead to poor factor-loading pattern reproduction and interpretation (Velicer, Eaton et al. 2000; Hayton, Allen et al. 2004) and also they will effect on EFA efficiency and meaning (Ledesma and Valero-Mora 2007).”
Applicability of the Student—Teacher Relationship Scale (STRS) in the Greek Educational Setting (2008, methodology): “Hayton et al. (2004) point out that parallel analysis should be conducted in four steps.”
Establishing Evidence for Internal Structure Using Exploratory Factor Analysis (2017, background): “According to Hayton, Allen, and Scarpello (2004), three important implications are associated with this decision-making process.”
A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve? (2016, methodology): “Parallel analysis involves the generation of many randomized data sets with the same number of variables and cases as the data set of interest (Hayton, Allen, & Scarpello, 2004).”