CoChat
Sign Up
Cited by 416Psychometric Methodologies and TestingHealth Education and ValidationStatistical Methods in Epidemiology

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

Sapnas, Kathryn G., Zeller, Richard A.·2002·Journal of Nursing Measurement
DOI 10.1891/jnum.10.2.135.52552
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1891/jnum.10.2.135.52552

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Show all 20 references

3 of the 20 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

  1. Kerlinger, F. (1986). Factor analysis. In F. Kerlinger, Foundations in behavioral research (3rd ed., pp. 569-593). Fort Worth: Holt, Rinehart, and Winston.
  2. 10.1037//1082-989X.4.1.84 · DOI · Paper page
  3. Zeller, R. A. (1980). Measurement in the social sciences. London: Cambridge University Press.

Cited by

Sorted by citations

Items 201–300 of 416

Related

See all

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
DOI
10.1891/jnum.10.2.135.52552
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review