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Factor analysis in the development and refinement of clinical assessment instruments.
Abstract
The goals of both exploratory and confirmatory factor analysis are described and procedural guidelines for each approach are summarized, emphasizing the use of factor analysis in developing and refining clinical measures. For exploratory factor analysis, a rationale is presented for selecting between principal components analysis and common factor analysis depending on whether the research goal involves either identification of latent constructs or data reduction. Confirmatory factor analysis using structural equation modeling is described for use in validating the dimensional structure of a measure. Additionally, the uses of confirmatory factor analysis for assessing the invariance of measures across samples and for evaluating multitrait-multimethod data are also briefly described. Suggestions are offered for handling common problems with item-level data, and examples illustrating potential difficulties with confirming dimensional structures from initial exploratory analyses are reviewed.
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“Also specifying models cautiously and dropping estimation of extraneous parameters is also an option”
From Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General (2018) · cites this paper as Floyd & Widaman, 1995
In the citing paper
“According to this rule, data collection process was initiated with the aim of reaching at least 150 patients, which is five times of 30 questions.”
From Turkish Translation of the Patterns of Activity Measure-Pain in Patients with Chronic Low Back and Neck Pain: Validity and Reliability. (2022) · cites this paper as Floyd & Widaman, 1995
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“This procedure is routinely adopted, reduces measurement error, creates more accurate SEM models, and allows for greater confidence in the resulting analyses [65–68].”
From A structural equation model of the relationship between insomnia, negative affect, and paranoid thinking (2017) · cites this paper as [67]
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Self-citation
Metadata
- Authors
- Floyd, F. J., & Widaman, K. F.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- ORCID
- Widaman, Keith F.
- Venue
- Psychological Assessment
- Published
- 1995-09
- Volume
- 7
- Issue
- 3
- Pages
- 286-299
- ISSN
- 1040-3590 (ISSN-L)
- 1939-134X
- OpenAlex
- W2039173306
- Cited by
- 3,970
- References
- 68
- Citations by year
- 2026: 97 · 2025: 164 · 2024: 184 · 2023: 168 · 2022: 193 · 2021: 179 · 2020: 173 · 2019: 166 · 2018: 192 · 2017: 208 · 2016: 180 · 2015: 194 · 2014: 203 · 2013: 201 · 2012: 189
- FWCI
- 31.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Assessment (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Psychology › Applied Psychology › Psychological Testing and Assessment
- Health Sciences › Nursing › Research and Theory › Health Education and Validation
- Keywords
- factor analysis, clinical assessment tools, instrument development, exploratory factor analysis, confirmatory factor analysis, principal component analysis, common factor analysis, structural equation modeling, dimensional structure, measurement invariance, latent constructs
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 ›
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