The DOI matches its Crossref record.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cited by
Sorted by citations
Items 101–200 of 943
Revisiting the Model Size Effect in Structural Equation Modeling
Shi, D., Lee, T., & Terry, R. (2017) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2017.1369088 · Cited by 69
“Many fit indexes are directly based on the chi-square statistic (e.g., CFI; Bentler, 1990) and have been routinely reported in SEM applications as standard tools for evaluating model fit (McDonald & Ho, 2002; Mueller & Hancock, 2010).”
MethodSwedish student perceptions of achievement practices: The role of intelligence
Brown, G., & Eklöf, H. (2018) · Intelligence · doi:10.1016/j.intell.2018.05.006 · Cited by 72
Personality characteristics that are valued in teams: Not always “more is better”?
Curșeu, P. L., Ilieș, R., Vîrgă, D., Maricutoiu, L. P., & Sava, F. A. (2018) · International Journal of Psychology · doi:10.1002/ijop.12511 · Cited by 70
“Following the recommendations provided by McCoach (2010), we built the multilevel models sequentially, and we estimated four multilevel models for each sample.”
MethodBoakye-Dankwa, E., Nathan, A., Barnett, A. H., Busija, L., Lee, R. S. Y., Pachana, N. A., Turrell, G., & Cerin, E. (2018) · Cities · doi:10.1016/j.cities.2018.07.002 · Cited by 70
Cooper, M. R., Loukas, A., Case, K. R., Marti, C. N., & Perry, C. L. (2018) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2017.11.027 · Cited by 68
Yeung, S. S., King, R. B., Nalipay, M. J. N., & Cai, Y. (2022) · British Journal of Educational Psychology · doi:10.1111/bjep.12495 · Cited by 82
Making the Mark: Are Grades and Deep Learning Related?
Campbell, C. M., & Cabrera, A. F. (2014) · Research in Higher Education · doi:10.1007/s11162-013-9323-6 · Cited by 81
Cerveró, G. A., Suárez-Rodríguez, J. M., Díaz‐García, I., & Cifuentes, S. C. (2019) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12413 · Cited by 68
Examining the Influence of the Wine Festival Experience on Tourists’ Quality of Life
Krüger, S., Rootenberg, C., & Ellis, S. M. (2012) · Social Indicators Research · doi:10.1007/s11205-012-0013-0 · Cited by 69
Structural Equation Modeling: Possibilities for Language Learning Researchers1
Hancock, G. R., & Schoonen, R. (2015) · Language Learning · doi:10.1111/lang.12116 · Cited by 67
Self-citationDecent Work and Work Motivation in Knowledge Workers: the Mediating Role of Psychological Capital
Ferraro, T., Pais, L., Moreira, J. M., & dos Santos, N. R. (2017) · Applied Research in Quality of Life · doi:10.1007/s11482-017-9539-2 · Cited by 91
“In general, values close to .95 for the CFI and NFI indicate an excellent fit (Hu and Bentler 1999; Kline 2011; Maroco 2014; Meyers et al. 2013), whereas values of .90 or greater reflect a reasonable fit (Hair et al. 2010; Mueller and Hancock 2010; Lomax 2010).”
BackgroundAreepattamannil, S., & Khine, M. S. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.03.058 · Cited by 70
A methodological review of structural equation modelling in higher education research
Green, T. (2015) · Studies in Higher Education · doi:10.1080/03075079.2015.1021670 · Cited by 69
Structural Equation Modeling in Language Testing and Learning Research: A Review
In’nami, Y., & Koizumi, R. (2011) · Language Assessment Quarterly · doi:10.1080/15434303.2011.582203 · Cited by 69
Correctional Treatment of Sexual and Violent Offenders
Woessner, G., & Schwedler, A. (2014) · Criminal Justice and Behavior · doi:10.1177/0093854813520544 · Cited by 65
Assessing construct structural validity of epidemiological measurement tools: a seven-step roadmap
Reichenheim, M. E., Hökerberg, Y. H. M., & de Moraes, C. L. (2014) · Cadernos de Saúde Pública · doi:10.1590/0102-311x00143613 · Cited by 71
Wang, J., Wang, M., & Lei, L. (2023) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2023.106236 · Cited by 73
Workplace flourishing: Measurement, antecedents and outcomes
Redelinghuys, K., Rothmann, S., & Botha, E. (2018) · SA Journal of Industrial Psychology · doi:10.4102/sajip.v45i0.1549 · Cited by 79
“As a result of the cross-sectional nature of the study, two opposing models (6a and 6b) were tested for comparable fit purposes, as suggested by Hancock and Mueller (2010).”
MethodSchool as a loosely coupled organization? An empirical examination using national SASS 2003–04 data
Shen, J., Gao, X., & Xia, J. (2016) · Educational Management Administration & Leadership · doi:10.1177/1741143216628533 · Cited by 61
Freimuth, V. S., Jamison, A., Hancock, G. R., Musa, D., Hilyard, K. M., & Quinn, S. C. (2017) · Risk Analysis · doi:10.1111/risa.12790 · Cited by 59
Self-citationApplied Psychometrics: Writing-Up a Factor Analysis Construct Validation Study with Examples
Kyriazos, T. A. (2018) · Psychology · doi:10.4236/psych.2018.911144 · Cited by 65
Reporting Monte Carlo Studies in Structural Equation Modeling
Boomsma, A. (2013) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2013.797839 · Cited by 60
Management control systems in high-tech start-ups: An empirical investigation
Samagaio, A., Crespo, N. F., & Rodrigues, R. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2017.12.028 · Cited by 66
Variation in English Worldwide
Bohmann, A. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108751339 · Cited by 59
Li, H., Gan, Z., Leung, S. O., & An, Z. (2022) · SAGE Open · doi:10.1177/21582440221086659 · Cited by 58
“Items were accepted as belonging to their intended factor when their loading was >0.40, and factors had to have at least three items which were conceptually aligned (Bandalos & Finney, 2010).”
BackgroundInfluentialAuthentic Leadership, Trust (in the Leader), and Flourishing: Does Precariousness Matter?
Kleynhans, D. J., Heyns, M. M., Stander, M. W., & Beer, L. T. D. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.798759 · Cited by 93
Barry, A. E., Whiteman, S. D., Piazza-Gardner, A. K., & Jensen, A. C. (2013) · Journal of American College Health · doi:10.1080/07448481.2013.823973 · Cited by 58
Wang, X., & Qiao, Y. (2022) · Journal of Youth and Adolescence · doi:10.1007/s10964-022-01655-9 · Cited by 59
“The Skewness and kurtosis of parental phubbing, self-esteem, and suicidal ideation fell within the acceptable range (i.e., skewness < |2.0| and kurtosis < |7.0 | ; Hancock & Mueller, 2010).”
BackgroundInfluentialMixture Modeling: Applications in Educational Psychology
Harring, J. R., & Hodis, F. (2016) · Educational Psychologist · doi:10.1080/00461520.2016.1207176 · Cited by 51
“2For latent growth models, Preacher (2010) suggested using root mean square error of approximation and the comparative fit index among other fit alternative fit indices.”
MethodLee, S., Oh, I., Jeon, H. J., & Roh, S. (2017) · Journal of Epidemiology · doi:10.1016/j.je.2016.06.008 · Cited by 56
Cashing in on Distress: The Expansion of Fringe Financial Institutions During the Great Recession
Faber, J. W. (2016) · Urban Affairs Review · doi:10.1177/1078087416684037 · Cited by 56
Bernardo, A. B. I., Cai, Y., & King, R. B. (2021) · British Journal of Educational Psychology · doi:10.1111/bjep.12411 · Cited by 61
Exploring empathy in cyberbullying with serious games
Ferreira, P. C., Simão, A. M. V., Paiva, A., Martinho, C., Prada, R., Ferreira, A. I., & Santos, F. C. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104155 · Cited by 58
Dramatic Declines in Higher Education Appropriations: State Conditions for Budget Punctuations
Li, A. Y. (2016) · Research in Higher Education · doi:10.1007/s11162-016-9432-0 · Cited by 55
“This is especially true if the omitted variable is strongly correlated with included variables (Allison 2010).”
BackgroundInfluentialVogiazides, L., & Mondani, H. (2019) · Journal of Ethnic and Migration Studies · doi:10.1080/1369183x.2019.1588717 · Cited by 55
Here for You: Attachment and the Growth of Empathic Support for Friends in Adolescence
Stern, J. A., Costello, M. A., Kansky, J., Fowler, C., Loeb, E. L., & Allen, J. P. (2021) · Child Development · doi:10.1111/cdev.13630 · Cited by 59
“…in longitudinal analyses or missing data within waves, we used full information maximum likelihood (FIML) methods, which have been shown to yield the least-biased estimates when all available data are used for longitudinal analyses (vs. listwise deletion of missing data; Mueller & Hancock, 2010).”
MethodMeasuring Science Instructional Practice: A Survey Tool for the Age of NGSS
Hayes, K. N., Lee, C. S., DiStefano, R., O’Connor, D., & Seitz, J. C. (2016) · Journal of Science Teacher Education · doi:10.1007/s10972-016-9448-5 · Cited by 63
“Finally, the instrument was considered for areas where items could be reduced to mitigate survey fatigue, a source of measurement error (Stapleton, 2010).”
Fu, R., Lee, J., Chen, X., & Wang, L. (2020) · Child Development · doi:10.1111/cdev.13360 · Cited by 53
Efficacy trial of the Second Step Early Learning (SSEL) curriculum: Preliminary outcomes
Upshur, C. C., Heyman, M., & Wenz-Gross, M. (2017) · Journal of Applied Developmental Psychology · doi:10.1016/j.appdev.2017.03.004 · Cited by 60
Mapping Hispanic-Serving Institutions: A Typology of Institutional Diversity
Núñez, A., Crisp, G., & Elizondo, D. (2016) · The Journal of Higher Education · doi:10.1080/00221546.2016.11777394 · Cited by 52
Measuring Attitudes Towards the Internet: The General Internet Attitude Scale
Joyce, M., & Kirakowski, J. (2015) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2015.1064657 · Cited by 62
Initial Validation of the Physical Education Marginalization and Isolation Survey (PE-MAIS)
Gaudreault, K. L., Richards, K. A. R., & Woods, A. M. (2016) · Measurement in Physical Education and Exercise Science · doi:10.1080/1091367x.2016.1257994 · Cited by 52
“Content validity is supported if individuals knowledgeable in the subject matter agree that the items are a reasonable representation of the underlying construct (Knapp & Mueller, 2010).”
BackgroundMethodProfiles of language learning motivation: Are new and own languages different?
Oga-Baldwin, W. L. Q., & Fryer, L. K. (2020) · Learning and Individual Differences · doi:10.1016/j.lindif.2020.101852 · Cited by 58
Ross, A., & Searle, M. S. (2018) · Environment and Behavior · doi:10.1177/0013916517751582 · Cited by 53
Metropolitan innovation, firm size, and business survival in a high-tech industry
Tsvetkova, A., Thill, J., & Strumsky, D. (2014) · Small Business Economics · doi:10.1007/s11187-014-9550-z · Cited by 50
Taboada, A. (2011) · Instructional Science · doi:10.1007/s11251-011-9196-z · Cited by 55
Roberson‐Nay, R., & Kendler, K. S. (2011) · Psychological Medicine · doi:10.1017/s0033291711000547 · Cited by 50
Ahmed, E., & Ward, R. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.043 · Cited by 60
Suzuki, K., Hiratani, M., Mizukoshi, N., Hayashi, T., & Inagaki, M. (2018) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2018.08.006 · Cited by 63
Schalk, L., Edelsbrunner, P. A., Deiglmayr, A., Schumacher, R., & Stern, E. (2018) · Learning and Instruction · doi:10.1016/j.learninstruc.2018.09.006 · Cited by 50
“This statistical approach prevents the underestimation of standard errors due to the data dependencies within school classes (McCoach, 2010).”
BackgroundCovet Thy Neighbor or “Reverse Policy Diffusion”? State Adoption of Performance Funding 2.0
Li, A. Y. (2017) · Research in Higher Education · doi:10.1007/s11162-016-9444-9 · Cited by 49
“In any case, multicollinearity is a minor concern in survival analysis because each unit change in a predictor has a multiplicative effect on the hazard rate, not a linear effect (Allison 2010).”
BackgroundMethodInfluentialResearch on the Academic Benefits of the Advanced Placement Program
Warne, R. T. (2017) · SAGE Open · doi:10.1177/2158244016682996 · Cited by 49
“…do not handle an additional problem of traditional statistics, which is that they do not permit the accurate estimates of cluster-level variable relationships, such as the impact of cluster variables (e.g., school quality) on individual outcomes (e.g., AP test scores; Hox, 2002; McCoach, 2010).”
BackgroundMcCutcheon, V. V., Arpana Agrawal, A., Kuo, S. I., Su, J., Dick, D. M., Meyers, J. L., Edenberg, H. J., Nürnberger, J. I., Kramer, J., Samuel Kuperman, C., et al. (2017) · Addiction · doi:10.1111/add.14003 · Cited by 50
Flow Activities as a Route to Living Well With Less
Isham, A., Gatersleben, B., & Jackson, T. P. (2018) · Environment and Behavior · doi:10.1177/0013916518799826 · Cited by 49
Brown, G., Chaudhry, H., & Dhamija, R. (2015) · International Journal of Educational Research · doi:10.1016/j.ijer.2015.03.001 · Cited by 49
Culture and capital structure: What else to the puzzle?
Mogha, V., & Williams, B. (2020) · International Review of Financial Analysis · doi:10.1016/j.irfa.2020.101614 · Cited by 52
“We only keep countries with at least 15 firms in an industry sector accounting for sample design with clustered data constraints (Hancock et al. , 2010).”
BackgroundYang, Y., Maeda, Y., & Gentry, M. (2024) · Large-scale Assessments in Education · doi:10.1186/s40536-024-00204-z · Cited by 108
Statistical Methods Used in Gifted Education Journals, 2006-2010
Warne, R. T., Lazo, M., Ramos, T., & Ritter, N. L. (2012) · Gifted Child Quarterly · doi:10.1177/0016986212444122 · Cited by 49
Deneen, C. C., Brown, G., Bond, T. G., & Shroff, R. H. (2013) · Asia-Pacific Journal of Teacher Education · doi:10.1080/1359866x.2013.787392 · Cited by 50
Loeb, E. L., Stern, J. A., Costello, M. A., & Allen, J. P. (2020) · Attachment & Human Development · doi:10.1080/14616734.2020.1821722 · Cited by 48
“Because these procedures have been found to yield less biased estimates than approaches that use listwise deletion of cases with missing data (e.g. simple regression; Mueller & Hancock, 2010), the entire original sample of 184 for the larger study was utilized for these analyses.”
MethodGot political Islam? Are politically moderate Muslims really different from radicals?
Achilov, D., & Şen, S. (2016) · International Political Science Review · doi:10.1177/0192512116641940 · Cited by 47
Single-case synthesis tools II: Comparing quantitative outcome measures
Zimmerman, K. N., Pustejovsky, J. E., Ledford, J. R., Barton, E. E., Severini, K. E., & Lloyd, B. P. (2018) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2018.02.001 · Cited by 49
“Meta-analytic techniques for synthesizing group design research are now well established and used extensively throughout the social and behavioral sciences (Beretvas, 2010; Borenstein et al., 2009; Kavale, 2007).”
MethodEmbodied Mindfulness Questionnaire: Scale Development and Validation
Khoury, B., Vergara, R. C., Sadowski, I., & Spinelli, C. (2021) · Assessment · doi:10.1177/10731911211059856 · Cited by 55
Interpersonal Mindfulness Questionnaire: Scale Development and Validation
Khoury, B., Vergara, R. C., & Spinelli, C. (2022) · Mindfulness · doi:10.1007/s12671-022-01855-1 · Cited by 53
“The rationale was to evaluate the theoretically proposed structure using CFA, but also checking if the same structure would emerge from EFA, allowing us therefore to increase the stability of the IMQ structure (Bandalos & Finney, 2010; Gerbing & Hamilton, 1996; Orcan, 2018; Schmitt, 2011).”
BackgroundNoguera, J. J. M., Vilar, M. M., & Cerveró, G. A. (2014) · Revista Latinoamericana de Psicología · doi:10.1016/s0120-0534(14)70019-6 · Cited by 47
Student perceptions of assessment and student self-efficacy in competence-based education
van Dinther, M., Dochy, F. J. R. C., Segers, M. S. R., & Braeken, J. (2014) · Educational Studies · doi:10.1080/03055698.2014.898577 · Cited by 58
The Importance of Quality Product Design Aspect Based on Computer Aided Design (CAD)
Saleh, B., Rasul, M. S., Affandi, H. M., et al. (2020) · Environment-Behaviour Proceedings Journal · doi:10.21834/ebpj.v5isi3.2545 · Cited by 45
“This method chose because it will able to increase the probability of students in the population to be selected as a sample of the study (Gray, 2013) and improved the accuracy of the results of the survey (Hancock & Mueller, 2010).”
MethodStructural Equation Modeling in Educational Research
In’nami, Y., & Koizumi, R. (2013) · SensePublishers eBooks · doi:10.1007/978-94-6209-332-4_2 · Cited by 48
McIntosh, K., Mercer, S. H., Nese, R. N. T., & Ghemraoui, A. (2016) · Prevention Science · doi:10.1007/s11121-016-0700-1 · Cited by 45
Dar, I. A., & Iqbal, N. (2020) · Stress and Health · doi:10.1002/smi.2932 · Cited by 55
The Impact of Specific Job Stressors on Psychological Contract Breach and Violation
Xavier, I. M., & Jepsen, D. M. (2014) · Human Factors and Ergonomics in Manufacturing & Service Industries · doi:10.1002/hfm.20567 · Cited by 48
McCabe, P. C., Rubinson, F., Dragowski, E. A., & Elizalde-Utnick, G. (2013) · Psychology in the Schools · doi:10.1002/pits.21702 · Cited by 55
Fostering adolescent curiosity through a question brainstorming intervention
Clark, S., Harbaugh, A. G., & Seider, S. C. (2019) · Journal of Adolescence · doi:10.1016/j.adolescence.2019.07.007 · Cited by 44
Gautun, H., & Bratt, C. (2016) · European Journal of Ageing · doi:10.1007/s10433-016-0403-2 · Cited by 43
“We used a cut-off of 0.95 for the comparative fit index, CFI (Mueller and Hancock 2010).”
MethodInfluentialMartínez, B. A., Alonso, M. Á. V., Navas, P., & Gómez, L. E. (2013) · International Journal of Clinical and Health Psychology · doi:10.1016/s1697-2600(13)70019-x · Cited by 43
Williams, M., DeBarger, A. H., Montgomery, B. L., Zhou, X., & Tate, E. D. (2011) · Science Education · doi:10.1002/sce.20465 · Cited by 42
First-Year Teachers’ Uphill Struggle to Implement Inquiry Instruction
Chichekian, T., Shore, B. M., & Tabatabai, D. (2016) · SAGE Open · doi:10.1177/2158244016649011 · Cited by 50
“Assuming extracted factors would have nonzero correlations (Bandalos & Finney, 2010), we used Promax oblique rotation.”
MethodBarstead, M. G., Smith, K. A., Laursen, B. P., Booth‐LaForce, C., King, S., & Rubin, K. H. (2017) · Journal of Research on Adolescence · doi:10.1111/jora.12350 · Cited by 49
Harmsen, R., Helms‐Lorenz, M., Maulana, R., van Veen, K., & van Veldhoven, M. J. P. M. (2018) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2018.1462313 · Cited by 60
Gaisie, E., Han, S. S., & Kim, H. M. (2021) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2021.102292 · Cited by 43
Martínez, R. S., Floyd, R. G., & Erichsen, L. W. (2011) · Journal of School Psychology · doi:10.1016/j.jsp.2011.10.003 · Cited by 44
Tau, B. A., du Plessis, E., Koen, D., et al. (2018) · Curationis · doi:10.4102/curationis.v41i1.1775 · Cited by 42
de la Cruz, M., Zárate, Á., Zamarripa, J., Castillo, I., Castro, N. A. B., Duarte, H., & Valenzuela, K. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.732325 · Cited by 45
Zhoc, K., Cai, Y.-Y., Yeung, S., & Shan, J. (2022) · British Journal of Educational Psychology · doi:10.1111/bjep.12513 · Cited by 40
“CFI and TLI values no smaller than .95 and RMSEA and SRMR no larger than .05 were considered excellent fit criteria (Mueller & Hancock, 2010).”
The Work Design Questionnaire: Spanish version and validation
Bayona, J. A., Caballer, A., & Silla, J. M. P. (2015) · Journal of Work and Organizational Psychology · doi:10.1016/j.rpto.2015.06.001 · Cited by 42
Chen, J., & Brown, G. (2013) · Journal of Education for Teaching International Research and Pedagogy · doi:10.1080/02607476.2013.836338 · Cited by 42
Vogiazides, L. (2018) · Population Space and Place · doi:10.1002/psp.2169 · Cited by 42
A three-level multilevel analysis of Singaporean eighth-graders science achievement
Mohammadpour, E. (2012) · Learning and Individual Differences · doi:10.1016/j.lindif.2012.12.005 · Cited by 42
Testing the Intervention Effect in Single-Case Experiments: A Monte Carlo Simulation Study
Heyvaert, M., Moeyaert, M., Verkempynck, P., Van den Noortgate, W., Vervloet, M., Ugille, M., & Onghena, P. (2017) · The Journal of Experimental Education · doi:10.1080/00220973.2015.1123667 · Cited by 42
“Testing the intervention effect in randomized sequential replication designs A first step in analyzing SCE data is visual analysis (Egel & Barthold, 2010; Kratochwill et al., 2010).”
MethodAn international study on public confidence in police
Morris, C. S. (2014) · Police Practice and Research · doi:10.1080/15614263.2014.951935 · Cited by 41
Socially responsible consumption: an application in Colombia
Castaño, L. E. V., Perdomo‐Ortiz, J., Ocampo, S. D., & Durán, W. F. (2016) · Business Ethics A European Review · doi:10.1111/beer.12128 · Cited by 41
An Examination of Differences in Moral Disengagement and Empathy Among Bullying Participant Groups
Haddock, A. D., & Jimerson, S. R. (2017) · Journal of Relationships Research · doi:10.1017/jrr.2017.15 · Cited by 42
“These outcome measures embody the theoretical dependent variable (Hancock & Mueller, 2010).”
BackgroundKrolak‐Schwerdt, S., Hörstermann, T., Glock, S., & Böhmer, I. (2017) · The Journal of Experimental Education · doi:10.1080/00220973.2017.1370686 · Cited by 39
“Because the design effect could be considered as small (McCoach, 2010), the research question is more dependent on the Type-II error risk and the number of clusters is rather low for a hierarchical regression model, we decided to estimate a nonhierarchical regression model and to report design…”
BackgroundConsumers' opinions and use of food labels: Results from an urban−rural hybrid area in South Africa
van der Merwe, D., Bosman, M., & Ellis, S. M. (2014) · Food Research International · doi:10.1016/j.foodres.2014.03.032 · Cited by 41
Design Issues and Inference in Experimental L2 Research
Hudson, T. D., & Llosa, L. (2015) · Language Learning · doi:10.1111/lang.12113 · Cited by 38
Ely, E., Kennedy, M. J., Pullen, P. C., Williams, M. C., & Hirsch, S. E. (2014) · Teaching and Teacher Education · doi:10.1016/j.tate.2014.07.012 · Cited by 41
Shim, E., Noh, H. L., Yoon, J.-M., Mun, H. S., & Hahm, B. (2018) · Journal of American College Health · doi:10.1080/07448481.2018.1462819 · Cited by 39
Herbaceous responses to herbivory, fire and rainfall variability differ between grasses and forbs
van Coller, H., Siebert, F., Scogings, P. F., et al. (2018) · South African Journal of Botany · doi:10.1016/j.sajb.2018.08.024 · Cited by 40
Keatlholetswe, L., & Malete, L. (2019) · Research Quarterly for Exercise and Sport · doi:10.1080/02701367.2018.1563277 · Cited by 40
“Running multiple regression models is justified and a common feature of exploratory studies like this one (Kelly & Maxwell, 2010).”
MethodXiao, X. (2019) · Atlantic Journal of Communication · doi:10.1080/15456870.2019.1708743 · Cited by 40
“Exploratory Factor Analysis (EFA) was performed to determine the factor structure due to the lack of established scales for affective and cognitive attitude (Bandalos & Finney, 2010).”
Method
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 ›
Are you an author, or do you see a problem with this page? Request a review
