The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test
Open accessAbstract
Popular statistical software packages do not have the proper procedures for determining the number of components in factor and principal components analyses. Parallel analysis and Velicer's minimum average partial (MAP) test are validated procedures, recommended widely by statisticians. However, many researchers continue to use alternative, simpler, but flawed procedures, such as the eigenvalues-greater-than-one rule. Use of the proper procedures might be increased if these procedures could be conducted within familiar software environments. This paper describes brief and efficient programs for using SPSS and SAS to conduct parallel analyses and the MAP test.
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“MAP suggests the smallest number of factors, while PA-SMC95 suggests the largest number of factors (O ' connor, 2000).”
From Reliability and validity of the Japanese version of the experiential avoidance in caregiving questionnaire (EACQ) (2023) · cites this paper as O'Connor, 2000
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“Empirically, parallel analysis (O’connor, 2000) indicated retention of five, theoretically consistent factors”
From The metacognitive processes of decentering scale: Development and initial validation of trait and state versions (2020) · cites this paper as O’connor, 2000
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Citations
Cites
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Exploratory and confirmatory factor analysis: Understanding concepts and applications.
Thompson, B. (2004) · doi:10.1037/10694-000 · Cited by 3,469
Henson, R. K., & Roberts, J. K. (2006) · Educational and Psychological Measurement · doi:10.1177/0013164405282485 · Cited by 2,454
Exploratory Factor Analysis: A Guide to Best Practice
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Metadata
- Authors
- O’connor, B. P.
- Affiliations
- Lakehead University (CA)
- Corresponding author
- O’connor, Brian P.
- ORCID
- O’connor, Brian P.
- Venue
- Behavior Research Methods, Instruments, & Computers
- Published
- 2000-09
- Volume
- 32
- Issue
- 3
- Pages
- 396-402
- ISSN
- 0743-3808 (ISSN-L)
- 1532-5970
- OpenAlex
- W2124563089
- Cited by
- 4,116
- References
- 24
- Citations by year
- 2026: 140 · 2025: 161 · 2024: 212 · 2023: 190 · 2022: 234 · 2021: 268 · 2020: 285 · 2019: 245 · 2018: 249 · 2017: 286 · 2016: 227 · 2015: 252 · 2014: 221 · 2013: 207 · 2012: 169
- FWCI
- 12.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods, Instruments, & Computers (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Social Sciences and Humanities Research Council of Canada
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- number of components, parallel analysis, factor analysis, principal component analysis, SAS program
- MeSH terms
- Factor Analysis, Statistical, Psychology, Software
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