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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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The Problem with Having Two Watches: Assessment of Fit When RMSEA and CFI Disagree
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“Mueller and Hancock (2010) also noted that inconsistent fit indices can occur and advised not to simply dismiss a bad-looking index when inconsistency occurs, but to take into account the various indices when interpreting the results.”
BackgroundCantele, S., & Zardini, A. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.02.016 · Cited by 509
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College English writing affect: Self-efficacy and anxiety
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“Similarly, it has been noted that “depending on these factors, analyses for very simple latent class models may be carried out probably with as few as 30 subjects, whereas other analyses require thousands of subjects” (Lubke, 2010, p. 215).”
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“Although not investigated here, when subpopulation data are not available, it may be possible to use finite mixture models (Lubke, 2010) to investigate subpopulations that may be leading to a spurious clustering effect.”
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“The most common approach is to calculate and interpret the sample’s RMSEA (Hancock & Mueller, 2010; McDonald & Ho, 2002).”
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“We confirmed fit using a combination of entropy, classification error, and, as suggested by Samuelsen and Dayton (2010), the meaningfulness of latent class proportions.”
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Wang, L., Li, S., & Gao, S. S. (2013) · Business Strategy and the Environment · doi:10.1002/bse.1790 · Cited by 209
Empathy, sex and fluid intelligence as predictors of theory of mind
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Fang, J., Wang, X., Wen, Z., & Zhou, J. (2020) · Addictive Behaviors · doi:10.1016/j.addbeh.2020.106430 · Cited by 186
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“Good model fit was present when either the chisquare test was non-significant, or RMSEA and SRMR values were less than .08, and CFI was more than .95 (Bandalos & Finney, 2010; Hu & Bentler, 1999; Phakiti, 2018).”
MethodThe Dimensionality of Language Ability in Young Children
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Mei, B., Brown, G. T. L., & Teo, T. (2018) · Journal of Educational Computing Research · doi:10.1177/0735633117700144 · Cited by 168
“Next, given the adequate number of items for model identification (Bandalos & Finney, 2010), the reverse-coded item in CSE (i.e., I would feel nervous using CALL 2.0.) was excluded from further analysis because it had a much higher mean than the CSE partner items and indicated reverse skew, which…”
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“Given that our data were positively skewed, we used a square-root transformation (Howell, 2007) to normalize Internet use so that skewness was below 2 (i.e., skewness = 1.58; Hancock & Mueller, 2010).”
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“…in more appropriate standard error estimates, and allows researchers to ask and answer nuanced questions about interactions that occur across the multiple levels of analysis (McCoach, 2010; McCoach & Adelson, 2010; Raudenbush & Bryk, 2002; Raudenbush, Bryk, Cheong, Congdon, & Du Toit, 2011).”
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“However, because of the sample sizes required for such models, these fit indices are rarely informative and we do not report them (Bandalos & Finney, 2010).”
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“However, hierarchical data show some degree of interdependence (McCoach 2010), which should be taken into consideration.”
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Written Feedback in Mathematics: Mediated by Students' Perception, Moderated by Goal Orientation
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“Using a model with strong MI (CFI = .980; RMSEA = .031, 90% CI [.023, .038]; SRMR = .029), we were able to confirm higher latent scores for perceived stress in women in Study 2.”
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Singh, S. K., & Aggarwal, Y. (2017) · Journal of Happiness Studies · doi:10.1007/s10902-017-9882-x · Cited by 162
Guidelines for Reporting Quantitative Methods and Results in Primary Research
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“For example, EFA has existed for over 100 years (Cudeck & MacCallum, 2007), and many papers on reporting standards have been published (e.g., Bandalos & Finney, 2010).”
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“The research goal classifies the studies into confirmatory (hypothesis-testing) and exploratory (finding structures) research approaches (cf. Hancock and Mueller, 2010; Lei and Wu, 2007).”
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von Stumm, S. (2016) · Intelligence · doi:10.1016/j.intell.2016.11.006 · Cited by 100
“However, LGC models are notoriously poor fitting, because they are more constrained than other structural equationmodels, which have relatively many free parameters, resulting in an unrealistically good fit (Preacher, 2010).”
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Lee, C. S., Hayes, K. N., Seitz, J. C., DiStefano, R., & O’Connor, D. (2016) · International Journal of Science Education · doi:10.1080/09500693.2015.1136452 · Cited by 117
“…and validity of the engagement subscales was found in this study and past research, by nature, self-report surveys pose risk to the reliability and validity of the data due to context and person-specific characteristics (e.g. social desirability bias, cognitive fatigue) (Stapleton, 2010).”
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Kim, E., Dedrick, R. F., Cao, C., & Ferron, J. M. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1228042 · Cited by 97
“Although guidelines for single-level exploratory and confirmatory factor analysis (Bandalos & Finney, 2010; DiStefano & Hess, 2005; Henson & Roberts, 2006; Jackson, Gillaspy, & Purc-Stephenson, 2009) have been presented along with reporting practices for structural equation modeling (McDonald & Ho,…”
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“The parameter estimation technique used in the threelevel HLM analysis full information maximum likelihood (FIML) (McCoach, 2010; Stephen W. Raudenbush & Bryk, 2002).”
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“The use of a principal components analysis with Promax rotation is appropriate because this factor analysis extracts possible factors and examines construct validity when the constructs are assumed to interact with one another (Bandalos and Finney 2010; DeVellis 2012).”
MethodKomperda, R., Pentecost, T. C., & Barbera, J. (2018) · Journal of Chemical Education · doi:10.1021/acs.jchemed.8b00220 · Cited by 100
Blums, A., Belsky, J., Grimm, K. J., & Chen, Z. (2016) · Journal of Cognition and Development · doi:10.1080/15248372.2016.1228652 · Cited by 100
“To determine good model–data fit, recommended cutoffs are > .90 for the CFI and TLI and < .05 for the RMSEA (Kline, 2011; Mueller & Hancock, 2010).”
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Chiu, Y.-L., Liang, J.-C., & Tsai, C.-C. (2013) · Metacognition and Learning · doi:10.1007/s11409-013-9103-x · Cited by 92
Ingersoll, K., Farrell-Carnahan, L. V., Cohen-Filipic, J., Heckman, C., Ceperich, S., Hettema, J., & Marzani-Nissen, G. R. (2011) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2010.12.016 · Cited by 99
Do Examinees Have Similar Test-Taking Effort? A High-Stakes Question for Low-Stakes Testing
Barry, C. L., Horst, S. J., Finney, S. J., Brown, A. R., & Kopp, J. P. (2010) · International Journal of Testing · doi:10.1080/15305058.2010.508569 · Cited by 89
Crocetti, E., Moscatelli, S., der Graaff, J. V., Rubini, M., Meeus, W., & Branje, S. (2016) · European Journal of Personality · doi:10.1002/per.2084 · Cited by 101
“To determine which growth curve best captured observed changes, we compared three models: an interceptonly model (i.e. null model; Preacher, 2010); a model with intercept and linear slope (i.e. a model in which change over time is captured by a linear trend); and a free-change model (i.e. a model…”
MethodCaruso, B. A., Cooper, H. L. F., Haardörfer, R., Yount, K. M., Routray, P., Torondel, B., & Clasen, T. (2018) · SSM - Population Health · doi:10.1016/j.ssmph.2018.06.005 · Cited by 87
More Than Poverty: The Effect of Child Abuse and Neglect on Teen Pregnancy Risk
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Age-Friendly Environments and Self-Rated Health:
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Quin, D., Heerde, J. A., & Toumbourou, J. W. (2018) · Journal of School Psychology · doi:10.1016/j.jsp.2018.04.003 · Cited by 90
Using Single‐Case Research Designs to Demonstrate Evidence for Counseling Practices
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Early Numeracy and Literacy: Untangling the Relation Between Specific Components
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Meta-Analysis of the English Version of the Beck Depression Inventory–Second Edition
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“Multiple methods should be used to detect publication bias (Beretvas, 2010).”
MethodFang, J., Wang, X., Yuan, K., Wen, Z., Yu, X., & Zhang, G. (2020) · Personality and Individual Differences · doi:10.1016/j.paid.2020.109829 · Cited by 80
Women's dietary diversity in rural Bangladesh: Pathways through women's empowerment
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“This rotation produces oblique factors, which allow correlation between dimensions of the construct under study (Bandalos & Finney, 2010).”
BackgroundMethodMoore, T. C., Wehby, J. H., Oliver, R. M., et al. (2016) · Remedial and Special Education · doi:10.1177/0741932516683631 · Cited by 77
“Factor analysis is a procedure for modeling the covariation between a set of observed variables as a function of one or more latent constructs (Bandalos & Finney, 2010).”
MethodImpact of Online Training on Behavioral Transfer and Job Performance in a Large Organization
Martins, L. B., Zerbini, T., & Medina, F. J. (2019) · Journal of Work and Organizational Psychology · doi:10.5093/jwop2019a4 · Cited by 120
“…of skewness (range from .08 to 1.60) and kurtosis (range from .002 to 1.94), with only a few exceptions (6 out of 69 items: 2.34 to 3.94), which is still within the recommended bounds by Hancock and Mueller (2010), did not exceed the reference interval, indicating the normal distribution of data.”
InfluentialRenshaw, T. L., & Steeves, R. M. O. (2016) · Psychology in the Schools · doi:10.1002/pits.21903 · Cited by 76
Niu, G., He, J., Lin, S., Sun, X., & Longobardi, C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17124368 · Cited by 83
Peterson, E. R., Brown, G., & Jun, M. C. (2015) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2015.05.002 · Cited by 93
Evans, D., Butterworth, R., & Law, G. U. (2019) · Teaching and Teacher Education · doi:10.1016/j.tate.2019.03.008 · Cited by 92
Konold, T. R., & Cornell, D. G. (2015) · Journal of School Psychology · doi:10.1016/j.jsp.2015.09.001 · Cited by 75
Perfectionism and performance among STEM students
Rice, K. G., Lopez, F. G., & Richardson, C. M. E. (2012) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2012.12.002 · Cited by 84
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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