Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
Abstract
This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 . Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.
Integrity signals
Registry
Cites
Statistics
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?
The independent variables were centered to avoid potential problems with respect to multicollinearity.
“To study the interaction effects, we centered the independent variables to avoid potential problems with respect to multicollinearity (53).”
Decreases in delay discounting rates can also be caused by factors other than the training itself, such as learning effects, regression to the mean, or expectations.
“since decreases in DD rates can also be caused by other factors than the training itself, such as learning effects, regression to the mean, or expectations (Boot, Simons, Stothart, & Stutts, 2013; Cohen, Cohen, West, & Aiken, 2013).”
From Behavioral trainings and manipulations to reduce delay discounting: A systematic review (2019) · cites this paper as Cohen, Cohen, West, & Aiken, 2013
Cited by
Sorted by citations
Mediation in experimental and nonexperimental studies: New procedures and recommendations.
Shrout, P. E., & Bolger, N. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.4.422 · Cited by 11,335 · see citing works
Justice at the millennium: A meta-analytic review of 25 years of organizational justice research.
Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O. L. H., & Ng, K. Y. (2001) · Journal of Applied Psychology · doi:10.1037/0021-9010.86.3.425 · Cited by 5,942 · see citing works
Impacts of the COVID-19 pandemic on life of higher education students: A global perspective
Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020) · Sustainability · doi:10.3390/su12208438 · Cited by 2,047 · see citing works
Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning
Yarkoni, T., & Westfall, J. (2017) · Perspectives on Psychological Science · doi:10.1177/1745691617693393 · Cited by 1,930 · see citing works
Development and Validation of the Political Skill Inventory
Ferris, G. R., Treadway, D. C., Kolodinsky, R. W., Hochwarter, W. A., Kacmar, C. J., Douglas, C., & Frink, D. D. (2005) · Journal of Management · doi:10.1177/0149206304271386 · Cited by 1,400 · see citing works
Michener, L. A., McClure, P. W., & Sennett, B. J. (2002) · Journal of Shoulder and Elbow Surgery · doi:10.1067/mse.2002.127096 · Cited by 1,137 · see citing works
Grisales, E. A. D., & Aguilera‐Caracuel, J. (2019) · Journal of Business Ethics · doi:10.1007/s10551-019-04177-w · Cited by 1,235 · see citing works
Lichtenstein, D. R., Netemeyer, R. G., & Burton, S. (1990) · Journal of Marketing · doi:10.1177/002224299005400305 · Cited by 1,054 · see citing works
Personality and prosocial behavior: A theoretical framework and meta-analysis.
Thielmann, I., Spadaro, G., & Balliet, D. P. (2019) · Psychological Bulletin · doi:10.1037/bul0000217 · Cited by 689 · see citing works
The dynamics of masculine-agentic and feminine-communal traits: Findings from a prospective study.
Abele, A. E. (2003) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.85.4.768 · Cited by 565 · see citing works
Rubenstein, A. L., Eberly, M., Lee, T. W., & Mitchell, T. R. (2018) · Personnel Psychology · doi:10.1111/peps.12226 · Cited by 473 · see citing works
Remanufacturing for the Circular Economy: An Examination of Consumer Switching Behavior
Hazen, B. T., Mollenkopf, D. A., & Wang, Y. (2016) · Business Strategy and the Environment · doi:10.1002/bse.1929 · Cited by 431 · see citing works
Quality and productivity outcomes relating to continuous integration in GitHub
Vasilescu, B., Yu, Y., Wang, H., Devanbu, P., & Filkov, V. (2015) · Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering · doi:10.1145/2786805.2786850 · Cited by 406 · see citing works
Peterson, S. J., Walumbwa, F. O., Byron, K. L., & Myrowitz, J. (2008) · Journal of Management · doi:10.1177/0149206307312512 · Cited by 381 · see citing works
Namkung, J. M., Peng, P., & Lin, X. (2019) · Review of Educational Research · doi:10.3102/0034654319843494 · Cited by 376 · see citing works
The impact of big data on world-class sustainable manufacturing
Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Παπαδόπουλος, Θ. (2015) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-015-7674-1 · Cited by 366 · see citing works
The “human side” of open innovation: The role of employee diversity in firm-level openness
Bogers, M. L. A. M., Foss, N. J., & Lyngsie, J. (2017) · Research Policy · doi:10.1016/j.respol.2017.10.012 · Cited by 360 · see citing works
Mental health monitoring with multimodal sensing and machine learning: A survey
Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Tørresen, J. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.09.003 · Cited by 356 · see citing works
Liu, F., Wang, Y., Li, M., Wang, W., Li, R., Zhang, Z., Lu, G., & Chen, H. (2016) · Human Brain Mapping · doi:10.1002/hbm.23430 · Cited by 348 · see citing works
Ideology, Motivated Reasoning, and Cognitive Reflection: An Experimental Study
Kahan, D. M. (2012) · doi:10.2139/ssrn.2182588 · Cited by 331 · see citing works
The Influence of Gender on the Performance of Organizational Citizenship Behaviors
Kidder, D. L. (2002) · Journal of Management · doi:10.1177/014920630202800504 · Cited by 325 · see citing works
Hassan, A., Saleem, I., Anwar, I., & Hussain, S. A. (2020) · Education + Training · doi:10.1108/et-02-2020-0033 · Cited by 298 · see citing works
Abou-Foul, M., Ruiz-Alba, J., & Soares, A. (2020) · Production Planning & Control · doi:10.1080/09537287.2020.1780508 · Cited by 283 · see citing works
Iacobucci, D., Grayson, K. A., & Ostrom, A. L. (1994) · Advances in services marketing and management · doi:10.1016/s1067-5671(94)03013-8 · Cited by 282 · see citing works
Malik, S., Chadhar, M., Vatanasakdakul, S., & Chetty, M. (2021) · Sustainability · doi:10.3390/su13169404 · Cited by 319 · see citing works
Similarity encoding for learning with dirty categorical variables
Cerda, P., Varoquaux, G. P., & Kégl, B. (2018) · Machine Learning · doi:10.1007/s10994-018-5724-2 · Cited by 271 · see citing works
Is faster really better? An empirical test of the implications of innovation speed
Kessler, E. H., & Bierly, P. E. (2002) · IEEE Transactions on Engineering Management · doi:10.1109/17.985742 · Cited by 263 · see citing works
Effects of plant diversity on soil carbon in diverse ecosystems: a global meta‐analysis
Chen, X., Chen, H. Y. H., Chen, C., Ma, Z., Searle, E. B., Yu, Z., & Huang, Z. (2019) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12554 · Cited by 256 · see citing works
van Dyk, N., Bahr, R., Whiteley, R. J., Tol, J. L., Kumar, B. D., Hamilton, B. H., Farooq, A., & Witvrouw, E. (2016) · The American Journal of Sports Medicine · doi:10.1177/0363546516632526 · Cited by 247 · see citing works
Long‐term, amplified responses of soil organic carbon to nitrogen addition worldwide
Xu, C., Xu, X., Ju, C., Chen, H. Y. H., Wilsey, B. J., Luo, Y., & Fan, W. (2020) · Global Change Biology · doi:10.1111/gcb.15489 · Cited by 251 · see citing works
Hehman, E., Flake, J. K., & Calanchini, J. (2017) · Social Psychological and Personality Science · doi:10.1177/1948550617711229 · Cited by 243 · see citing works
Cultural Cognition of Scientific ConsensusPreprint
Kahan, D. M., Jenkins‐Smith, H. C., & Braman, D. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1549444 · Cited by 234 · see citing works
Grisales, E. A. D., Aguilera‐Caracuel, J., Guerrero‐Villegas, J., & Sánchez, E. G. (2020) · Business Strategy and the Environment · doi:10.1002/bse.2572 · Cited by 244 · see citing works
Higher education students’ perceptions of ChatGPT: A global study of early reactions
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., et al. (2025) · PLOS ONE · doi:10.1371/journal.pone.0315011 · Cited by 376 · see citing works
Dai, L., Eden, L., & Beamish, P. W. (2016) · Strategic Management Journal · doi:10.1002/smj.2599 · Cited by 238 · see citing works
Martela, F., & Riekki, T. J. J. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01157 · Cited by 252 · see citing works
Ardito, L., Petruzzelli, A. M., Pascucci, F., & Peruffo, E. (2018) · Business Strategy and the Environment · doi:10.1002/bse.2248 · Cited by 228 · see citing works
Between Legitimacy and Efficiency: An Institutional Theory of Corporate Giving
Jeong, Y., & Kim, T. Y. (2018) · Academy of Management Journal · doi:10.5465/amj.2016.0575 · Cited by 226 · see citing works
Bennie, J. A., De Cocker, K., Teychenne, M. J., Brown, W. J., & Biddle, S. J. H. (2019) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-019-0797-2 · Cited by 224 · see citing works
Wu, H., Guo, Y., Yang, Y., Zhao, L., & Guo, C. (2021) · Educational Psychology Review · doi:10.1007/s10648-021-09600-1 · Cited by 246 · see citing works
Afsar, B., Badir, Y. F., Saeed, B. B., & Hafeez, S. (2016) · The International Journal of Human Resource Management · doi:10.1080/09585192.2016.1244893 · Cited by 223 · see citing works
The threat of COVID‐19 and its influence on nursing staff burnout
García, G. M., & Calvo, J. C. A. (2020) · Journal of Advanced Nursing · doi:10.1111/jan.14642 · Cited by 224 · see citing works
Laws of concatenated perception: Vision goes for novelty, decisions for perseverance
Pascucci, D., Mancuso, G., Santandrea, E., Libera, C. D., Plomp, G., & Chelazzi, L. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000144 · Cited by 204 · see citing works
A Nasty but Effective Negotiation Strategy: Misrepresentation of a Common-Value Issue
O’Connor, K. M., & Carnevale, P. J. D. (1997) · Personality and Social Psychology Bulletin · doi:10.1177/0146167297235006 · Cited by 202 · see citing works
The determinants of customers’ intention to use smart lockers for last-mile deliveries
Yuen, K. F., Wang, X., Ma, F., & Wong, Y. D. (2019) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2019.03.022 · Cited by 205 · see citing works
How Task and Person Conflict Shape the Role of Positive Interdependence in Management Teams
Janssen, O., de Vliert, E. V., & Veenstra, C. (1999) · Journal of Management · doi:10.1177/014920639902500201 · Cited by 206 · see citing works
Du, H.-B., Liu, D., Sovacool, B. K., Wang, Y., Ma, S.-F., & Li, R. (2018) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2018.05.008 · Cited by 192 · see citing works
Global signal of top-down control of terrestrial plant communities by herbivores
Jia, S., Wang, X., Yuan, Z., Lin, F., Ye, J., Hao, Z., & Luskin, M. S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707984115 · Cited by 194 · see citing works
Plant mixture balances terrestrial ecosystem C:N:P stoichiometry
Chen, X., & Chen, H. Y. H. (2021) · Nature Communications · doi:10.1038/s41467-021-24889-w · Cited by 206 · see citing works
Effect-Size Measures and Meta-Analytic Thinking in Counseling Psychology Research
Henson, R. K. (2006) · The Counseling Psychologist · doi:10.1177/0011000005283558 · Cited by 200 · see citing works
Dynamic cluster-based over-demand prediction in bike sharing systems
Chen, L., Zhang, D., Wang, L., Yang, D., Ma, X., Li, S., Wu, Z., Pan, G., Nguyen, T. M. T., & Jakubowicz, J. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971652 · Cited by 201 · see citing works
Kim, B., & Winstein, C. J. (2016) · Neurorehabilitation and neural repair · doi:10.1177/1545968316662708 · Cited by 192 · see citing works
Danaei, G., Fahimi, S., Lu, Y., Zhou, B., Hajifathalian, K., Cesare, M. D., Lo, W., Reis-Santos, B., Cowan, M., Shaw, J. E., et al. (2015) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00129-1 · Cited by 191 · see citing works
Tams, S., Legoux, R., & Léger, P.-M. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2017.11.026 · Cited by 188 · see citing works
Effects of Global Market Conditions on Brand Image Customization and Brand Performance
Roth, M. S. (1995) · Journal of Advertising · doi:10.1080/00913367.1995.10673489 · Cited by 185 · see citing works
Indicators of firm technological capability: validity and performance implications
Schoenecker, T. S., & Swanson, L. (2002) · IEEE Transactions on Engineering Management · doi:10.1109/17.985746 · Cited by 180 · see citing works
de Bérail, P., Guillon, M., & Bungener, C. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.05.007 · Cited by 191 · see citing works
Hoque, M. R. (2016) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-016-0289-0 · Cited by 181 · see citing works
Pubertal Timing, Overweight, and Psychological Adjustment
Alsaker, F. (1992) · The Journal of Early Adolescence · doi:10.1177/0272431692012004004 · Cited by 176 · see citing works
Hagedoorn, M., Kuijer, R. G., Buunk, B. P., et al. (2000) · Health Psychology · doi:10.1037/0278-6133.19.3.274 · Cited by 175 · see citing works
Tobit models in strategy research: Critical issues and applications
Amore, M. D., & Murtinu, S. (2019) · Global Strategy Journal · doi:10.1002/gsj.1363 · Cited by 173 · see citing works
Caniëls, M. C. J., & Veld, M. (2016) · The International Journal of Human Resource Management · doi:10.1080/09585192.2016.1216881 · Cited by 175 · see citing works
Intrinsic motivation, external reward, and their effect on overall motivation and performance
Hendijani, R., Bischak, D. P., Árvai, J., & Dugar, S. (2016) · Human Performance · doi:10.1080/08959285.2016.1157595 · Cited by 170 · see citing works
Bing, M. N., Davison, H. K., Minor, I., Novičević, M. M., & Frink, D. D. (2011) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2011.02.006 · Cited by 163 · see citing works
E-cigarette use and asthma in a multiethnic sample of adolescents
Schweitzer, R. J., Wills, T. A., Tam, E. K., Pagano, I., & Choi, K. (2017) · Preventive Medicine · doi:10.1016/j.ypmed.2017.09.023 · Cited by 163 · see citing works
Firm Environmental Performance under Scrutiny: The Role of Strategic and Organizational Orientations
Ardito, L., & Dangelico, R. M. (2018) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.1470 · Cited by 169 · see citing works
Assessing inter individual differences with task related functional neuroimaging
Lebreton, M., Bavard, S., Daunizeau, J., & Palminteri, S. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0681-8 · Cited by 160 · see citing works
Negative to positive shifts in diversity effects on soil nitrogen over time
Chen, X., Chen, H. Y. H., Searle, E. B., Chen, C., & Reich, P. B. (2020) · Nature Sustainability · doi:10.1038/s41893-020-00641-y · Cited by 160 · see citing works
Bilal, A., Xiao-ping, L., Nanli, Z., Sharma, R. B., & Jahanger, A. (2021) · Sustainability · doi:10.3390/su14010236 · Cited by 178 · see citing works
Partial least squares path modeling using ordinal categorical indicators
Schuberth, F., Henseler, J., & Dijkstra, T. K. (2016) · Quality & Quantity · doi:10.1007/s11135-016-0401-7 · Cited by 163 · see citing works
Coordination Breakdowns and Their Impact on Development Productivity and Software Failures
Cataldo, M., & Herbsleb, J. D. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.32 · Cited by 156 · see citing works
World-class sustainable manufacturing: framework and a performance measurement system
Dubey, R., Gunasekaran, A., & Chakrabarty, A. (2015) · International Journal of Production Research · doi:10.1080/00207543.2015.1012603 · Cited by 157 · see citing works
Temporal changes in soil C‐N‐P stoichiometry over the past 60 years across subtropical China
Yu, Z., Wang, M., Huang, Z., Lin, T., Vadeboncoeur, M. A., Searle, E. B., & Chen, H. Y. H. (2017) · Global Change Biology · doi:10.1111/gcb.13939 · Cited by 154 · see citing works
Wiengarten, F., Fan, D., Lo, C. K. Y., & Pagell, M. D. (2017) · Journal of Operations Management · doi:10.1016/j.jom.2016.12.001 · Cited by 155 · see citing works
Instrumental learning of traits versus rewards: dissociable neural correlates and effects on choice
Hackel, L. M., Doll, B. B., & Amodio, D. M. (2015) · Nature Neuroscience · doi:10.1038/nn.4080 · Cited by 148 · see citing works
Alghamdi, F. S. (2018) · Journal of Innovation and Entrepreneurship · doi:10.1186/s13731-018-0081-8 · Cited by 171 · see citing works
Fine‐root functional trait responses to experimental warming: a global meta‐analysis
Wang, J., Defrenne, C. E., McCormack, M., Yang, L., Tian, D., Luo, Y., Hou, E., Yan, T., Li, Z., Bu, W., et al. (2021) · New Phytologist · doi:10.1111/nph.17279 · Cited by 151 · see citing works
Han, D.-K. M., Wittlin, M., Peters, E., Slovic, P., Ouellette, L., Braman, D., & Mandel, G. N. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1871503 · Cited by 158 · see citing works
Significant sparse polygenic risk scores across 813 traits in UK Biobank
Tanigawa, Y., Qian, J., Venkataraman, G. R., Justesen, J. M., Li, R., Tibshirani, R. J., Hastie, T. J., & Rivas, M. A. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010105 · Cited by 148 · see citing works
Peng, P., Wang, C., & Namkung, J. M. (2018) · Review of Educational Research · doi:10.3102/0034654317753350 · Cited by 147 · see citing works
Beach, S. R. H., & O’Leary, K. D. (1993) · Journal of Social and Personal Relationships · doi:10.1177/0265407593103007 · Cited by 146 · see citing works
van Dyk, N., Bahr, R., Burnett, A., et al. (2017) · British Journal of Sports Medicine · doi:10.1136/bjsports-2017-097754 · Cited by 144 · see citing works
Henry, L. A., Buyl, T., & Jansen, R. J. G. (2018) · Business Strategy and the Environment · doi:10.1002/bse.2247 · Cited by 144 · see citing works
Factorial and Criterion Validity of Scores of a Measure of Belonging in Youth Development Programs
Anderson‐Butcher, D., & Conroy, D. E. (2002) · Educational and Psychological Measurement · doi:10.1177/001316402236882 · Cited by 143 · see citing works
Huang, W., Chen, S., & Nguyen, L. T. (2020) · Sustainability · doi:10.3390/su12218970 · Cited by 148 · see citing works
Xu, J., Shang, Y., Yu, W., & Liu, F. (2019) · Sustainability · doi:10.3390/su11195328 · Cited by 155 · see citing works
Technology acceptance research: Meta-analysis
Marikyan, D., Papagiannidis, S., & Stewart, G. (2026) · Journal of Information Science · doi:10.1177/01655515231191177 · Cited by 140 · see citing works
McFarland, I., Dawes, J. J., Elder, C. L., & Lockie, R. G. (2016) · Sports · doi:10.3390/sports4010011 · Cited by 139 · see citing works
GPS use negatively affects environmental learning through spatial transformation abilities
Ruginski, I. T., Creem-Regehr, S. H., Stefanucci, J. K., & Cashdan, E. (2019) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2019.05.001 · Cited by 137 · see citing works
Hua, A. K., & Ping, O. W. (2018) · European Journal of Remote Sensing · doi:10.1080/22797254.2018.1542976 · Cited by 137 · see citing works
Behavioral trainings and manipulations to reduce delay discounting: A systematic review
Scholten, H., Scheres, A., de Water, E., Graf, U., Granic, I., & Luijten, M. (2019) · Psychonomic Bulletin & Review · doi:10.3758/s13423-019-01629-2 · Cited by 135 · see citing works
Miermeister, C. P., Petersenn, S., Buchfelder, M., et al. (2015) · Acta Neuropathologica Communications · doi:10.1186/s40478-015-0229-8 · Cited by 132 · see citing works
Ebrahimigharehbaghi, S., Qian, Q. K., Meijer, F. M., & Visscher, H. J. (2019) · Energy Policy · doi:10.1016/j.enpol.2019.02.046 · Cited by 129 · see citing works
Lee, N., McLeod, D. M., & Shah, D. V. (2008) · Communication Research · doi:10.1177/0093650208321792 · Cited by 129 · see citing works
Changes in energy consumption according to building use type under COVID-19 pandemic in South Korea
Kang, H., An, J., Kim, H., Ji, C., Hong, T., & Lee, S. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111294 · Cited by 124 · see citing works
Pitafi, A. H., Liu, H., & Cai, Z. (2018) · Telematics and Informatics · doi:10.1016/j.tele.2018.08.001 · Cited by 132 · see citing works
Alfawaire, F., & Atan, T. (2021) · Sustainability · doi:10.3390/su13158445 · Cited by 138 · see citing works
Can I show my skills? Affective responses to artificial intelligence in the recruitment process
Köchling, A., Wehner, M., & Warkocz, J. (2022) · Review of Managerial Science · doi:10.1007/s11846-021-00514-4 · Cited by 140 · see citing works
Liu, F., Xie, B., Wang, Y., Guo, W., Fouché, J., Long, Z., Wang, W., Chen, H., Li, M., Duan, X., et al. (2014) · Brain Topography · doi:10.1007/s10548-014-0386-2 · Cited by 123 · see citing works
Kobarg, S., Stumpf-Wollersheim, J., & Welpe, I. M. (2017) · The Journal of Technology Transfer · doi:10.1007/s10961-017-9583-y · Cited by 123 · see citing works
- Authors
- Cohen
- Corresponding author
- Cohen
- Venue
- Routledge eBooks
- Publisher
- Routledge
- Published
- 2013-06-17
- OpenAlex
- W4239677523
- Cited by
- 3,228
- References
- 0
- Citations by year
- 2026: 413 · 2025: 551 · 2024: 379 · 2023: 262 · 2022: 247 · 2021: 294 · 2020: 171 · 2019: 170 · 2018: 212 · 2017: 189 · 2016: 142 · 2015: 70 · 2014: 16 · 2013: 3 · 2012: 10
- FWCI
- 17.60
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Routledge eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Multidisciplinary Science and Engineering Research
- Keywords
- multiple linear regression, bivariate correlation, regression model specification, confidence intervals, effect size measures, behavioral science, SPSS, SAS, SYSTAT
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
