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 for normal distribution using skewness ≤2.0 and kurtosis ≤7.0.
“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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Un sistema de clasificación de los diseños de investigación en psicología
Ato, M., López-García, J. J., & Benavente, A. (2013) · Anales de Psicología · doi:10.6018/analesps.29.3.178511 · Cited by 2,254 · see citing works
El análisis factorial exploratorio de los ítems: una guía práctica, revisada y actualizada
Lloret-Segura, S., Traver, A. F., Hernández, A., & Tomás-Marco, I. (2014) · Anales de Psicología · doi:10.6018/analesps.30.3.199361 · Cited by 1,798 · see citing works
Understanding the Model Size Effect on SEM Fit Indices
Shi, D.-X., Lee, T., & Maydeu-Olivares, A. (2018) · Educational and Psychological Measurement · doi:10.1177/0013164418783530 · Cited by 843 · see citing works
The Problem with Having Two Watches: Assessment of Fit When RMSEA and CFI Disagree
Lai, K., & Green, S. B. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2015.1134306 · Cited by 514 · see citing works
“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 472 · see citing works
Differentiated Instruction in Secondary Education: A Systematic Review of Research Evidence
Smale-Jacobse, A. E., Meijer, A., Helms‐Lorenz, M., & Maulana, R. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02366 · Cited by 560 · see citing works
Evaluating the Relationship Between Boredom and Academic Outcomes: A Meta-Analysis
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Valtorta, N. K., Kanaan, M., Gilbody, S., & Hanratty, B. (2018) · European Journal of Preventive Cardiology · doi:10.1177/2047487318792696 · Cited by 323 · see citing works
Multiple Regression in L2 Research: A Methodological Synthesis and Guide to Interpreting R 2 Values
Plonsky, L., & Ghanbar, H. (2018) · Modern Language Journal · doi:10.1111/modl.12509 · Cited by 322 · see citing works
College English writing affect: Self-efficacy and anxiety
Woodrow, L. (2011) · System · doi:10.1016/j.system.2011.10.017 · Cited by 332 · see citing works
Putting the “Person” in the Center
Woo, S. E., Jebb, A. T., Tay, L., & Parrigon, S. (2018) · Organizational Research Methods · doi:10.1177/1094428117752467 · Cited by 314 · see citing works
“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).”
BackgroundGuthrie, J. T., Klauda, S. L., & Ho, A. N. (2013) · Reading Research Quarterly · doi:10.1002/rrq.035 · Cited by 294 · see citing works
Wal, A. L. J. T., Alexy, O. T., Block, J., & Sandner, P. (2016) · Administrative Science Quarterly · doi:10.1177/0001839216637849 · Cited by 261 · see citing works
Construct Meaning in Multilevel Settings
Stapleton, L. M., Yang, J. S., & Hancock, G. R. (2016) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998616646200 · Cited by 237 · see citing works
“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.”
BackgroundSelf-citationTeachers’ information and communication technology competences: A structural approach
Cerveró, G. A., Alonso, N. O., Suárez-Rodríguez, J. M., & Díaz‐García, I. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.05.002 · Cited by 237 · see citing works
Evaluating SEM Model Fit with Small Degrees of Freedom
Shi, D., DiStefano, C., Maydeu-Olivares, A., & Lee, T. (2022) · Multivariate Behavioral Research · doi:10.1080/00273171.2020.1868965 · Cited by 256 · see citing works
“The most common approach is to calculate and interpret the sample’s RMSEA (Hancock & Mueller, 2010; McDonald & Ho, 2002).”
MethodMaggin, D. M., Chafouleas, S. M., Goddard, K. M., & Johnson, A. H. (2011) · Journal of School Psychology · doi:10.1016/j.jsp.2011.05.001 · Cited by 223 · see citing works
A Comprehensive Meta-analysis of Handwriting Instruction
Santangelo, T., & Graham, S. (2015) · Educational Psychology Review · doi:10.1007/s10648-015-9335-1
Safeguard Power as a Protection Against Imprecise Power Estimates
Perugini, M., Gallucci, M., & Costantini, G. (2014) · Perspectives on Psychological Science · doi:10.1177/1745691614528519 · Cited by 203 · see citing works
Gage, N. A., Scott, T. M., Hirn, R. G., & MacSuga‐Gage, A. S. (2017) · Behavioral Disorders · doi:10.1177/0198742917714809 · Cited by 220 · see citing works
“We confirmed fit using a combination of entropy, classification error, and, as suggested by Samuelsen and Dayton (2010), the meaningfulness of latent class proportions.”
MethodStudent Conceptions of Feedback: Impact on Self-Regulation, Self-Efficacy, and Academic Achievement
Brown, G. T. L., Peterson, E. R., & Yao, E. S. (2016) · British Journal of Educational Psychology · doi:10.1111/bjep.12126 · Cited by 198 · see citing works
Wang, L., Li, S., & Gao, S. S. (2013) · Business Strategy and the Environment · doi:10.1002/bse.1790 · Cited by 190 · see citing works
Empathy, sex and fluid intelligence as predictors of theory of mind
Ibáñez, A. M., Huepe, D., Gempp, R., Gutiérrez, V. V., Rivera-Rei, Á. A., & Toledo, M. I. (2012) · Personality and Individual Differences · doi:10.1016/j.paid.2012.11.022 · Cited by 187 · see citing works
Fang, J., Wang, X., Wen, Z., & Zhou, J. (2020) · Addictive Behaviors · doi:10.1016/j.addbeh.2020.106430 · Cited by 186 · see citing works
Shen, J., Leslie, J. M., Spybrook, J. K., et al. (2011) · American Educational Research Journal · doi:10.3102/0002831211419949 · Cited by 185 · see citing works
Women’s empowerment in East Africa: Development of a cross-country comparable measure
Miedema, S. S., Haardörfer, R., Girard, A. W., & Yount, K. M. (2018) · World Development · doi:10.1016/j.worlddev.2018.05.031 · Cited by 186 · see citing works
Jewkes, R. K., Fulu, E., Naved, R. T., Chirwa, E. D., Dunkle, K., Haardörfer, R., Garcia‐Moreno, C., & on behalf of the UN Multi-country Study on Men and Violence Study Team (2017) · PLoS Medicine · doi:10.1371/journal.pmed.1002381 · Cited by 188 · see citing works
Dinçer, A., Yeşilyurt, S., Noels, K. A., & Lascano, D. I. V. (2019) · SAGE Open · doi:10.1177/2158244019853913 · Cited by 200 · see citing works
“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
Language and Reading Research Consortium (2015) · Child Development · doi:10.1111/cdev.12450 · Cited by 172 · see citing works
Mei, B., Brown, G. T. L., & Teo, T. (2018) · Journal of Educational Computing Research · doi:10.1177/0735633117700144 · Cited by 168 · see citing works
“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…”
InfluentialGeneralizability theory: A practical guide to study design, implementation, and interpretation
Briesch, A. M., Swaminathan, H., Welsh, M. E., & Chafouleas, S. M. (2014) · Journal of School Psychology · doi:10.1016/j.jsp.2013.11.008 · Cited by 176 · see citing works
Ravizza, S. M., Uitvlugt, M. G., & Fenn, K. M. (2016) · Psychological Science · doi:10.1177/0956797616677314 · Cited by 165 · see citing works
“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).”
MethodInfluentialIndexing the approximate number system
Inglis, M., & Gilmore, C. K. (2013) · Acta Psychologica · doi:10.1016/j.actpsy.2013.11.009 · Cited by 159 · see citing works
Vernon, L., Modecki, K. L., & Barber, B. L. (2018) · Child Development · doi:10.1111/cdev.12836 · Cited by 152 · see citing works
A Tutorial on Interpreting Bifactor Model Scores
DeMars, C. E. (2013) · International Journal of Testing · doi:10.1080/15305058.2013.799067 · Cited by 150 · see citing works
Cooper-Thomas, H. D., Paterson, N., Stadler, M. J., & Saks, A. M. (2014) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2014.02.007 · Cited by 154 · see citing works
Price, M. S., & Weiss, M. R. (2012) · Journal of Applied Sport Psychology · doi:10.1080/10413200.2012.725703 · Cited by 146 · see citing works
Renshaw, T. L., & Cohen, A. S. (2013) · Social Indicators Research · doi:10.1007/s11205-013-0342-7 · Cited by 147 · see citing works
Finbråten, H. S., Wilde‐Larsson, B., Nordström, G. M., Pettersen, K. S., Trollvik, A., & Guttersrud, Ø. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-3275-7 · Cited by 156 · see citing works
Fu, P.-W., Wu, C.-C., & Cho, Y.-J. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2016.10.010 · Cited by 129 · see citing works
Signaling in Secret: Pay for Performance and the Incentive and Sorting Effects of Pay Secrecy
Belogolovsky, E., & Bamberger, P. A. (2014) · Academy of Management Journal · doi:10.5465/amj.2012.0937 · Cited by 140 · see citing works
Brown, G., Hui, S. K. F., Yu, W. M., & Kennedy, K. J. (2011) · International Journal of Educational Research · doi:10.1016/j.ijer.2011.10.003 · Cited by 145 · see citing works
Hamilton, R., McCoach, D. B., Tutwiler, M. S., Siegle, D., Gubbins, E. J., Callahan, C. M., Brodersen, A. V., & Mun, R. U. (2017) · Gifted Child Quarterly · doi:10.1177/0016986217738053 · Cited by 139 · see citing works
“…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).”
BackgroundChen, J., & Guo, W. (2020) · Educational Management Administration & Leadership · doi:10.1177/1741143218781066 · Cited by 138 · see citing works
Harris, L. R., Brown, G. T. L., & Harnett, J. A. (2014) · Educational Assessment, Evaluation and Accountability · doi:10.1007/s11092-013-9187-5 · Cited by 134 · see citing works
Women's empowerment and child nutrition: The role of intrinsic agency
Jones, R., Haardörfer, R., Ramakrishnan, U., Yount, K. M., Miedema, S. S., & Girard, A. W. (2019) · SSM - Population Health · doi:10.1016/j.ssmph.2019.100475 · Cited by 134 · see citing works
“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).”
BackgroundRevisiting ‘A critical review of simulation‐based medical education research: 2003–2009’
McGaghie, W. C., Issenberg, S. B., PETRUSA, E. R., & Scalese, R. J. (2016) · Medical Education · doi:10.1111/medu.12795 · Cited by 133 · see citing works
The impact of social capital on self-efficacy and study success among first-year university students
Brouwer, J., Jansen, E. P. W. A., Flache, A., & Hofman, R. H. (2016) · Learning and Individual Differences · doi:10.1016/j.lindif.2016.09.016 · Cited by 133 · see citing works
The contribution of science parks: a literature review and future research agenda
Lecluyse, L., Knockaert, M., & Spithoven, A. (2018) · The Journal of Technology Transfer · doi:10.1007/s10961-018-09712-x · Cited by 131 · see citing works
“However, hierarchical data show some degree of interdependence (McCoach 2010), which should be taken into consideration.”
BackgroundMethodThe Language Growth of Spanish-Speaking English Language Learners
Rojas, R., & Iglesias, A. (2012) · Child Development · doi:10.1111/j.1467-8624.2012.01871.x · Cited by 128 · see citing works
Written Feedback in Mathematics: Mediated by Students' Perception, Moderated by Goal Orientation
Rakoczy, K., Harks, B., Klieme, E., Blum, W., & Hochweber, J. (2013) · Learning and Instruction · doi:10.1016/j.learninstruc.2013.03.002 · Cited by 125 · see citing works
Reis, D., Lehr, D., Heber, E., & Ebert, D. D. (2019) · Assessment · doi:10.1177/1073191117715731 · Cited by 127 · see citing works
“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.”
MethodInfluentialExclusive e-cigarette use predicts cigarette initiation among college students.
Loukas, A., Marti, C. N., Cooper, M., Pasch, K. E., & Perry, C. L. (2018) · Addictive Behaviors · doi:10.1016/j.addbeh.2017.08.023 · Cited by 125 · see citing works
Singh, S., & Aggarwal, Y. (2018) · Journal of Happiness Studies · doi:10.1007/s10902-017-9882-x · Cited by 104 · see citing works
Guidelines for Reporting Quantitative Methods and Results in Primary Research
Norris, J. M., Plonsky, L., Ross, S. J., & Schoonen, R. (2015) · Language Learning · doi:10.1111/lang.12104 · Cited by 121 · see citing works
Brown, G., Harris, L. R., & Harnett, J. A. (2012) · Teaching and Teacher Education · doi:10.1016/j.tate.2012.05.003 · Cited by 124 · see citing works
Price, M. S., & Weiss, M. R. (2011) · Journal of Applied Sport Psychology · doi:10.1080/10413200.2010.520300 · Cited by 120 · see citing works
Multilevel and Single-Level Models for Measured and Latent Variables When Data Are Clustered
Stapleton, L. M., McNeish, D., & Yang, J. S. (2016) · Educational Psychologist · doi:10.1080/00461520.2016.1207178 · Cited by 114 · see citing works
“Each of these assumptions and modeling decisions is crucial (McCoach, 2010), so the applied researcher, prior to settling on a multilevel analysis, should consider the goal of the analysis and, if a multilevel model is warranted, address the many modeling decisions appropriately.”
MethodSelf-citationJia, J., Li, D., Li, X., Zhou, Y., Wang, Y., Sun, W., & Zhao, L. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.03.042 · Cited by 117 · see citing works
Measurement of Women’s Agency in Egypt: A National Validation Study
Yount, K. M., VanderEnde, K. E., Dodell, S., & Cheong, Y. F. (2015) · Social Indicators Research · doi:10.1007/s11205-015-1074-7 · Cited by 113 · see citing works
Applications of Diagnostic Classification Models: A Literature Review and Critical Commentary
Sessoms, J. C., & Henson, R. A. (2018) · Measurement Interdisciplinary Research and Perspectives · doi:10.1080/15366367.2018.1435104 · Cited by 111 · see citing works
“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).”
BackgroundA meta-analysis of empirical e-government research and its future research implications
Wirtz, B. W., & Daiser, P. (2018) · International Review of Administrative Sciences · doi:10.1177/0020852315599047 · Cited by 112 · see citing works
“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).”
BackgroundSeider, S., Clark, S., & Graves, D. (2020) · Child Development · doi:10.1111/cdev.13262 · Cited by 115 · see citing works
Baumann, S., Groß, S., Voigt, L., Ullrich, A., Weymar, F., Schwaneberg, T., Dörr, M., Meyer, C., John, U., & Ulbricht, S. (2017) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.12977 · Cited by 111 · see citing works
Wu, J.-Y. (2017) · Computers & Education · doi:10.1016/j.compedu.2016.10.010 · Cited by 104 · see citing works
von Stumm, S. (2016) · Intelligence · doi:10.1016/j.intell.2016.11.006 · Cited by 100 · see citing works
“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).”
BackgroundMcPhie, M. L., & Rawana, J. S. (2015) · Journal of Adolescence · doi:10.1016/j.adolescence.2015.01.008 · Cited by 99 · see citing works
A Field Study of the Antecedents and Performance Consequences of Perceived Accountability
Mero, N. P., Guidice, R. M., & Werner, S. (2012) · Journal of Management · doi:10.1177/0149206312441208 · Cited by 101 · see citing works
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 102 · see citing works
“…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).”
BackgroundMultilevel Factor Analysis: Reporting Guidelines and a Review of Reporting Practices
Kim, E., Dedrick, R. F., Cao, C., & Ferron, J. M. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1228042 · Cited by 97 · see citing works
“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,…”
MethodCairns, D. (2019) · International Journal of Science Education · doi:10.1080/09500693.2019.1660927 · Cited by 98 · see citing works
“The parameter estimation technique used in the threelevel HLM analysis full information maximum likelihood (FIML) (McCoach, 2010; Stephen W. Raudenbush & Bryk, 2002).”
MethodArslan, G. (2017) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2017.03.023 · Cited by 98 · see citing works
Hong, A. J., & Kim, H. J. (2018) · The Asia-Pacific Education Researcher · doi:10.1007/s40299-018-0387-0 · Cited by 104 · see citing works
“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 99 · see citing works
Blums, A., Belsky, J., Grimm, K. J., & Chen, Z. (2016) · Journal of Cognition and Development · doi:10.1080/15248372.2016.1228652 · Cited by 100 · see citing works
“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).”
MethodWang, X., Qiao, Y., & Wang, S. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.09.163 · Cited by 99 · see citing works
Allan, N. P., Raines, A. M., Capron, D. W., Norr, A. M., Zvolensky, M. J., & Schmidt, N. B. (2014) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2014.07.006 · Cited by 95 · see citing works
Time to Tango: Expertise and contextual anticipation during action observation
Amoruso, L., Sedeño, L., Huepe, D., Tomio, A., Kamienkowski, J. E., Hurtado, E., Cardona, J. F., González, M. Á. Á., Rieznik, A. A., Sigman, M., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.005 · Cited by 93 · see citing works
Law, K. H., Dimmock, J. A., Guelfi, K. J., Nguyen, T., Gucciardi, D. F., & Jackson, B. (2018) · Applied Psychology Health and Well-Being · doi:10.1111/aphw.12147 · Cited by 95 · see citing works
Chiu, Y.-L., Liang, J.-C., & Tsai, C.-C. (2013) · Metacognition and Learning · doi:10.1007/s11409-013-9103-x · Cited by 92 · see citing works
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 · see citing works
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 · see citing works
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 91 · see citing works
“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 · see citing works
More Than Poverty: The Effect of Child Abuse and Neglect on Teen Pregnancy Risk
Garwood, S. K., Gerassi, L. B., Jonson‐Reid, M., Plax, K. L., & Drake, B. (2015) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2015.05.004 · Cited by 88 · see citing works
Age-Friendly Environments and Self-Rated Health:
Lehning, A. J., Smith, R., & Dunkle, R. E. (2012) · Research on Aging · doi:10.1177/0164027512469214 · Cited by 88 · see citing works
Quin, D., Heerde, J. A., & Toumbourou, J. W. (2018) · Journal of School Psychology · doi:10.1016/j.jsp.2018.04.003 · Cited by 90 · see citing works
Using Single‐Case Research Designs to Demonstrate Evidence for Counseling Practices
Lenz, A. S. (2015) · Journal of Counseling & Development · doi:10.1002/jcad.12036 · Cited by 83 · see citing works
Early Numeracy and Literacy: Untangling the Relation Between Specific Components
Purpura, D. J., & Napoli, A. R. (2015) · Mathematical Thinking and Learning · doi:10.1080/10986065.2015.1016817 · Cited by 91 · see citing works
Meta-Analysis of the English Version of the Beck Depression Inventory–Second Edition
Erford, B. T., Johnson, E., & Bardoshi, G. (2016) · Measurement and Evaluation in Counseling and Development · doi:10.1177/0748175615596783 · Cited by 85 · see citing works
“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 · see citing works
Women's dietary diversity in rural Bangladesh: Pathways through women's empowerment
Sinharoy, S. S., Waid, J. L., Haardörfer, R., Wendt, A. S., Gabrysch, S., & Yount, K. M. (2017) · Maternal and Child Nutrition · doi:10.1111/mcn.12489 · Cited by 78 · see citing works
“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 · see citing works
“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 83 · see citing works
“…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 · see citing works
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 82 · see citing works
Peterson, E. R., Brown, G., & Jun, M. C. (2015) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2015.05.002 · Cited by 87 · see citing works
Evans, D., Butterworth, R., & Law, G. U. (2019) · Teaching and Teacher Education · doi:10.1016/j.tate.2019.03.008 · Cited by 74 · see citing works
Konold, T. R., & Cornell, D. G. (2015) · Journal of School Psychology · doi:10.1016/j.jsp.2015.09.001 · Cited by 75 · see citing works
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 76 · see citing works
- 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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