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The Reviewer's Guide to Quantitative Methods in the Social Sciences
Abstract
The Reviewer’s Guide to Quantitative Methods in the Social Sciences is designed for evaluators of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its thirty-one uniquely structured chapters cover both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail. The book updates readers on each technique’s key principles, appropriate usage, underlying assumptions, and limitations. It thereby assists reviewers to offer constructive commentary on works they evaluate, and also serves as an indispensable author’s reference for preparing sound research manuscripts and proposals. Key features include: The chapters cover virtually all of the popular classic and emerging quantitative techniques, thus helping reviewers to evaluate a manuscript’s methodological approach and its data analysis. In addition, the volume serves as an indispensable reference tool for those designing their own research. For ease of use, all chapters follow the same structure: the opening page of each chapter defines and explains the purpose of that statistical method the next one or two pages provide a table listing various criteria that should be considered when evaluating and applying that methodological approach to data analysis the remainder of each chapter contains numbered sections corresponding to the numbered criteria listed in the opening table. Each section explains the role and importance of that particular criterion. Chapters are written by methodological and applied scholars who are expert in the particular quantitative method being reviewed.
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“Each TAQ item was checked to see if it was normally distributed. According to the guideline, if the skewness was ≤2.0 and the kurtosis was ≤7.0, the item can be regarded as a normal distribution [25].”
From Validation of the Tinnitus Acceptance Questionnaire: Japanese Version (2022) · cites this paper as [25]
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Items 901–943 of 943
Saputra, A., Tania, L., Sunyono, S., Ibrahim, N. H., & Surif, J. (2024) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c01332 · Cited by 1
Statewide Community College Promise Programs: An Event History Analysis
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“LTA allows the investigation of initial status and transitions between profiles from earlier to later time points (Hancock & Mueller, 2010 ).”
Commercialism and Inter‐Industry Pay Differentials in the US Nonprofit Sector
Cui, T. S. (2024) · Nonprofit Management and Leadership · doi:10.1002/nml.21638
Synthesizing Research Results Using Meta‐Analysis
Balkin, R. S., & Kleist, D. M. (2025) · doi:10.1002/9781394346899.ch9
van der Westhuizen, C.-M., Jagals, D., & Verster, M. (2025) · NWU self-directed learning series · doi:10.4102/aosis.2024.bk484.10
Gans, R., & Zhan, M. M. (2025) · International Journal of Business Communication · doi:10.1177/23294884251322496
Broadband Network Simulations Towards Cost Assessments: Case of a Namibian Rural Healthcare
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Beyond self-care: Developing a climate survey for school psychology programs
Tanaka, M. L., Kulkarni, T., Zahn, M. R., Muldrew, A. C., Thompson, H., & Young, K. M. (2025) · Journal of School Psychology · doi:10.1016/j.jsp.2025.101457
Un modelo de docencia en las universidades jesuitas
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Xu, Y., Xu, Z., & Gao, B. (2025) · Scientific Reports · doi:10.1038/s41598-025-07175-3 · Cited by 1
Compassion Questionnaire for Others (CQO)
Khoury, B., & Vergara, R. C. (2025) · Springer eBooks · doi:10.1007/978-3-031-47219-0_123
Embodied Mindfulness Questionnaire (EMQ)
Khoury, B., Vergara, R. C., Spinelli, C., & Sadowski, I. (2025) · Springer eBooks · doi:10.1007/978-3-031-47219-0_74
Interpersonal Mindfulness Questionnaire (IMQ)
Khoury, B., Vergara, R. C., Spinelli, C., & Sadowski, I. (2025) · Springer eBooks · doi:10.1007/978-3-031-47219-0_75
CQSO-20: 20-item Compassion QuestionnairesPreprint
Khoury, B., & Vergara, R. C. (2025) · Research Square · doi:10.21203/rs.3.rs-6812992/v1
Dow, C., Wilson, S. G., McMahon, W. R., Manousakis, J. E., Beatty, C., Ogeil, R. P., & Anderson, C. (2025) · Behavioral Sleep Medicine · doi:10.1080/15402002.2025.2508768 · Cited by 1
Bernabé, M., Merhi, R., Lisbona, A., & Descals, F. J. P. (2025) · SAGE Open · doi:10.1177/21582440251350435
Reporting Growth Modeling Results of Aggression: Key Insights for Meta-Analyzing Change
Sun, Q., & Schlomer, G. L. (2025) · Journal of Aggression Maltreatment & Trauma · doi:10.1080/10926771.2025.2541242
Gans, R. (2025) · SAGE Open · doi:10.1177/21582440251375922
“Other than attitude change and change in intent, which had high kurtosis values indicating a robust central tendency reflecting no change, all measures’ skewness and kurtosis statistics were within the absolute value of 2 (Hancock et al., 2010) and original data were used without transformation.”
From Context to Self: Longitudinal Overgeneralization of Negative Interpretive Biases in Adolescents
Wang, Y., Zhang, Y., Li, Z., Zhao, J., Wang, Y., & Su, Y. (2025) · Cognitive Therapy and Research · doi:10.1007/s10608-025-10645-7
Ali, M. O. A., Kebelo, K. K., & Woldab, Z. E. (2025) · International Journal of Educational Reform · doi:10.1177/10567879251389932
Common Measurement Issues in a Multilevel Framework
French, B. F., Finch, W. H., & Vo, T. T. (2022) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-64802-873-120251018
Bains, J. S., & Fite, P. J. (2025) · Child & Youth Care Forum · doi:10.1007/s10566-025-09911-2 · Cited by 1
“Effect sizes were interpreted according to common benchmarks (Hancock et al., 2010; Kline, 2011): Pearson’s r correlations were categorized as negligible/weak (± 0.2), moderate (± 0.5), or strong (± 0.8); standardized path coefficient effects were classified as weak (≤ 0.1), moderate (± 0.3), or…”
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Wessels, W., & Botha, K. (2026) · South African Theatre Journal · doi:10.1080/10137548.2025.2594562
Sun, X., & Xin, Z. (2026) · Current Psychology · doi:10.1007/s12144-025-08983-6
“The skewness and kurtosis of these six indicators were all within the acceptable range (i.e., skewness <|2.0| and kurtosis <|7.0|; Hancock et al., 2010) (for more details see Table 2 ).”
InfluentialZhou, J., Wu, J., Zhang, C., & ZHANG, M. (2026) · Current Psychology · doi:10.1007/s12144-026-09207-1
“This transformation resulted in acceptable levels of skewness (less than 2.0) and kurtosis (less than 7.0) in the sample (Hancock et al., 2010).”
InfluentialLu, F., Zhang, L., & Lyu, R. (2026) · Behavioral Psychology/Psicología Conductual · doi:10.31083/bp51211
“The skewness and kurtosis values of all variables—school victimization, depression, self-esteem, and academic performance—fell within acceptable ranges (i.e., skewness | 2.0 | and kurtosis | 7 .0 | ; Hancock et al., 2010).”
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Gurieva, S. D., Mararitsa, L. V., Витальевич, С. А., Kazantseva, T. V., Gundelakh, O. Y., Udavikhina, U. A., Александровна, К. Т., Yumkina, Y. A., Сергеевна, Я. А., Postanina, A. V., et al. (2025) · doi:10.61726/8941.2026.77.11.001
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Amazaki, M., & Kemuriyama, C. (2026) · Journal of Health Psychology Research · doi:10.11560/jhpr.250704163
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Wang, M., Zhang, Z., Gu, J., Wang, J., Zhang, K., & Zhang, E. (2026) · Journal of Youth and Adolescence · doi:10.1007/s10964-026-02346-5
“…expressive distinctive accuracy, and peer acceptance were all exhibited normal distributions (skewness values ranged from − 0.28 to 0.81; kurtosis values ranged from − 0.96 to 0.38), in line with the criterion that absolute values of skewness and kurtosis are less than 2 (Hancock et al., 2010).”
InfluentialShi, X., Wu, T., & Xu, W. (2026) · Archives of Sexual Behavior · doi:10.1007/s10508-026-03454-w
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Egere, O. M., Jones, P. K., & Maas, G. J. P. (2026) · Journal of Small Business and Enterprise Development · doi:10.1108/jsbed-08-2025-0526 · Cited by 1
Time-Dependent Modelling in Psychological Data
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Zou, Y., Chen, Z., Peng, Y., Song, Y., Wang, Y., & Xiao, Y. (2026) · International Conference on Intelligent Engineering and Next-Gen Healthcare Systems (IEHNS) · doi:10.1109/iehns68708.2026.11606697
“It offers valuable insights for future empirical studies and suggests a more rigorous approach to psychological data modeling in the next phase of Structural Equation Modeling (SEM) [11].”
Three methodological approaches, one framework: combining SEM, QCA and ANN in marketing analytics
Abubakar, A. M., Sukhov, A. B., & Hair, J. F. (2026) · Journal of Marketing Analytics · doi:10.1057/s41270-026-00523-w · Cited by 2
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Metadata
- Authors
- Hancock, G. R., Mueller, R. O., Stapleton, L. M., & Mueller, R. O.
- Venue
- Routledge eBooks
- Publisher
- Routledge
- Published
- 2010-04-26
- OpenAlex
- W4214816511
- Cited by
- 942
- References
- 0
- Citations by year
- 2026: 20 · 2025: 56 · 2024: 68 · 2023: 74 · 2022: 75 · 2021: 71 · 2020: 70 · 2019: 96 · 2018: 74 · 2017: 74 · 2016: 86 · 2015: 57 · 2014: 44 · 2013: 39 · 2012: 18
- FWCI
- 28.10
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Routledge eBooks (publishedVersion)
- Topics
- Social Sciences › Education › Diverse Academic Research Areas
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Keywords
- quantitative methods, peer review, manuscript review, research proposals, statistical analysis, methodological assumptions, methodological limitations, research design, social sciences, behavioral science
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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.
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