The DOI matches its Crossref record; 49 of the 50 references it cites were found in a registry or an index.
Sample size in factor analysis.
Abstract
The factor analysis literature includes a range of recommendations regarding the minimum sample size necessary to obtain factor solutions that are adequately stable and that correspond closely to population factors. A fundamental misconception about this issue is that the minimum sample size, or the minimum ratio of sample size to the number of variables, is invariant across studies. In fact, necessary sample size is dependent on several aspects of any given study, including the level of communality of the variables and the level of overdetermination of the factors. The authors present a theoretical and mathematical framework that provides a basis for understanding and predicting these effects. The hypothesized effects are verified by a sampling study using artificial data. Results demonstrate the lack of validity of common rules of thumb and provide a basis for establishing guidelines for sample size in factor analysis. In the factor analysis literature, much attention has be;;n given to the issue of sample size. It is widely understood that the use of larger samples in applica-tions of factor analysis tends to provide results such that sample factor loadings are more precise estimates of population loadings and are also more stable, or les s variable, across repeated sampling. Despite gen-eral agreement on this matter, there is considerable di'/ergence of opinion and evidence about the ques-tion of how large a sample is necessary to adequately acnieve these objectives. Recommendations and find-ings about this issue are diverse and often contradic-tory. The objectives of this article are to provide a
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“recent simulations show that the sample size required to recover a stable factor structure is a joint function of number of factors, number of items per factor (with the required sample size stabilizing after 6–10 items per factor), and strength of factor loadings (MacCallum et al., 1999; Mundfrom et al., 2005).”
From The structure of adult ADHD. (2017) · cites this paper as MacCallum et al., 1999
In the citing paper
“Additionally, SEM flexibility allowing the examination of complex associations, multiple data types, model and/or group comparisons thus, developing general rules regarding sample size requirements are impractical (MacCallum et al., 1999; Wolf et al., 2013).”
From Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General (2018) · cites this paper as MacCallum et al., 1999
In the citing paper
“While this sample size may not be considered very large, it is much in line with sample sizes in other strategy studies (Phelan, Ferreira, and Salvador, 2002) and exceeds common recommendations for advanced statistical analyses (e.g., MacCallum et al., 1999).”
From On the contingent value of dynamic capabilities for competitive advantage: The nonlinear moderating effect of environmental dynamism (2014) · cites this paper as MacCallum et al., 1999
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 MacCallum et al., 1999
In the citing paper
“We therefore followed the recommendations of MacCallum et al. (1999) and Schönbrodt and Perugini (2013) and aimed to collect data from ~300 participants using each technology.”
From Technology evaluations are associated with psychological need satisfaction across different spheres of experience: an application of the METUX scales (2023) · cites this paper as MacCallum et al., 1999
In the citing paper
“Following the recommendations of MacCallum et al. (1999) regarding sample size for factor analyses, we aimed to collect data from 300 participants (150 participants who use Facebook and 150 participants who use virtual learning platforms).”
From Technology evaluations are associated with psychological need satisfaction across different spheres of experience: an application of the METUX scales (2023) · cites this paper as MacCallum et al., 1999
In the citing paper
“The minimum sample size was estimated to be 360 participants following the established guidelines for survey validation studies, considering 36 items with 10 participants per item [42-44].”
From Assessing Health Students' Attitudes and Usage of ChatGPT in Jordan: Validation Study (2023) · cites this paper as [43]
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“We therefore followed the recommendations of MacCallum et al. (1999) and Schönbrodt and Perugini (2013) and aimed to collect data from ∼ 300 participants using each technology.”
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“This was slightly lower than our target, but exceeds the threshold that MacCallum et al. (1999) described as being generally considered “more than acceptable” (p. 92 ) and likely to enable good recovery of population factors under all but the worst of conditions.”
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“A total of 124 valid samples were involved in the factor analysis, meeting the recommended sample size for factor analysis (Arrindell & Van der Ende, 1985; MacCallum et al., 1999).”
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“…appropriate sample size for factor analysis (e.g., minimum of 10 participants per item; Boateng et al., 2018), larger samples provide more stable factor loadings (MacCallum et al., 1999) and 500 or more observations is considered a good sample size to conduct factor analyses (Boateng et al., 2018).”
Akgun, M., & Toker, S. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790980 · Cited by 2
“Items with low communalities ( .20) or high cross-loadings were considered for removal, following standard psychometric guidelines [45, 60].”
InfluentialMahy, T., & Li, H. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2026.2623216 · Cited by 2
“In factor analysis, a sample size of at least 300 is recommended when communalities are consistently low (i.e., most or all items 0.5), there are only a small number of factors, and only three or four indicators for each factor (Maccallum et al., 1999).”
Influential
Metadata
- Authors
- MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S.
- Affiliations
- The Ohio State University (US)
- University of California, Riverside (US)
- Venue
- Psychological Methods
- Published
- 1999-03
- Volume
- 4
- Issue
- 1
- Pages
- 84-99
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2122376319
- Cited by
- 5,497
- References
- 50
- Citations by year
- 2026: 456 · 2025: 418 · 2024: 359 · 2023: 311 · 2022: 359 · 2021: 396 · 2020: 413 · 2019: 318 · 2018: 286 · 2017: 276 · 2016: 259 · 2015: 226 · 2014: 230 · 2013: 233 · 2012: 177
- FWCI
- 27.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Methods (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- sample size, factor analysis, minimum sample size, communality, rules of thumb
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