The DOI matches its Crossref record; 17 of the 20 references it cites were found in a registry or an index.
Minimizing Sample Size When Using Exploratory Factor Analysis for Measurement
Abstract
Traditional protocol for the determination of an adequate sample size is power analysis. Such a protocol is not useful when the primary hypothesis focuses on psychometric measurement properties. Traditional psychometrics advises that there should be 10 respondents per item. Both hypothetical and real research examples illustrate the usefulness of subsample analysis in determining that a sample size of at least 50 and not more than 100 subjects is adequate to represent and evaluate the psychometric properties of measures of social constructs. The "10 respondents per item" advice builds a sample size disincentive into the research design; it also represents "sample size overkill." Sample-size overkill occurs when the research design specifies a number of cases needed, which is in excess of the number actually needed for a desired inference.
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“Sapnas & Zeller (2002) demonstrate that sample sizes between 50 and 100 can be adequate when items are highly inter-related.”
From The Brief Serenity Scale (2009) · cites this paper as Sapnas & Zeller (2002)
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Cites
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3 of the 20 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
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Exploratory Factor Analysis: A Five-Step Guide for Novices
Williams, B. A., Onsman, A., & Brown, T. (2010) · Australasian Journal of Paramedicine · doi:10.33151/ajp.8.3.93 · Cited by 2,967
Exploratory Factor Analysis With Small Sample Sizes
de Winter, J., Dodou, D., & Wieringa, P. A. (2009) · Multivariate Behavioral Research · doi:10.1080/00273170902794206 · Cited by 1,211
Exploratory Factor Analysis; Concepts and Theory
Taherdoost, H., Sahibuddin, S., & Jalaliyoon, N. (2014) · HAL · Cited by 394
The development and initial validation of the cyberchondria severity scale (CSS)
McElroy, E., & Shevlin, M. (2014) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2013.12.007 · Cited by 383
Dimensionality of the Pittsburgh Sleep Quality Index: A systematic review
Manzar, M. D., BaHammam, A. S., Hameed, U. A., Spence, D. W., Pandi-Perumal, S. R., Moscovitch, A., & Streiner, D. L. (2018) · Health and Quality of Life Outcomes · doi:10.1186/s12955-018-0915-x · Cited by 204
Metadata
- Authors
- Sapnas, K. G., & Zeller, R. A.
- Affiliations
- University of Miami (US)
- Kent State University (US)
- Venue
- Journal of Nursing Measurement
- Publisher
- Springer Publishing Company
- Published
- 2002-09
- Volume
- 10
- Issue
- 2
- Pages
- 135-154
- ISSN
- 1061-3749 (ISSN-L)
- 1945-7049
- OpenAlex
- W2088701782
- Cited by
- 405
- References
- 20
- Citations by year
- 2026: 24 · 2025: 26 · 2024: 30 · 2023: 34 · 2022: 28 · 2021: 19 · 2020: 22 · 2019: 17 · 2018: 27 · 2017: 22 · 2016: 28 · 2015: 20 · 2014: 24 · 2013: 17 · 2012: 15
- FWCI
- 5.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Nursing Measurement (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Miami
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Health Sciences › Nursing › Research and Theory › Health Education and Validation
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- sample size determination, exploratory factor analysis, psychometric assessment, power analysis, measurement properties
- MeSH terms
- Computer Simulation, Factor Analysis, Statistical, Humans, Monte Carlo Method, Psychometrics, Likelihood Functions, Sample Size
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