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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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“These two analyses provided information on univariate normality by verifying that the scores were within ±1.5 standard deviations (Hancock et al., 2010).”
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“The skewness and kurtosis of all variables in the study regarding student – student relationships, bullying victimization, perceived discrimination, and parental care fell within the acceptable range (i.e., skewness < |2.0| and kurtosis < |7.0|; Hancock et al. 2010).”
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“34, which are within recommended boundaries to assumed a normal distribution (skewness: [2, 2], kurtosis: [7, 7]) [40].”
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“To allow for the use of teacher age as a proxy for teaching experience, we calculated the Spearman ’ s rank correlation coef fi cient ( r s ) that reports the monotonic (rather than linear) relationship be-tween two (ordinal) ranked variables (Hancock et al., 2010).”
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“We deleted items with low factor loadings (< .40) and double-loaded items (>.30) (Hancock & Mueller, 2010; Watson, 2017).”
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“The skewness and kurtosis for need for uniqueness, anxiety, and hostile attribution bias were within the acceptable range (i.e., skewness <|2.0| and kurtosis <|7.0|; Hancock et al., 2010).”
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“A factor was accepted if item loadings on the expected factor exceeded 0.30 and cross-loadings were less than 0.30 (Bandalos and Finney 2010).”
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“None of the items had an absolute value greater than 2, suggesting little or no distortion should occur (Bandalos & Finney, 2010).”
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“As recommended by Mueller and Hancock (2010), all coefficients were considered collectively.”
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“LGC are useful when some participants do not provide data at all times of assessment and LGC are useful when there are unequal intervals between assessments (Preacher, 2010).”
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“The skewness and kurtosis 3 values were below two indicating that the data were normally distributed (Hancock & Mueller, 2010; Rashid et al., 2020).”
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“Thus, all model fit indices were considered for their consistency, although the statistical significance of chisquare statistics is known to be somewhat overly strict (Mueller & Hancock, 2010).”
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“This method enables researchers to identify latent profiles at multiple time points (e.g., prior to and during COVID-19) and predict initial latent profile membership and the incidence of transitions to different profiles over time (Hancock & Mueller, 2010).”
Longitudinal Models for Repeated Measures Data
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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
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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