Conditioning Diagnostics: Collinearity and Weak Data in Regression

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Belsley, D. A., Kuh, E., & Welsch, R. E. (1980) · Wiley Series in Probability and Statistics · doi:10.1002/0471725153 · Cited by 7,297

Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., et al. (2013) · Ecography · doi:10.1111/j.1600-0587.2012.07348.x · Cited by 9,998

Global prevalence of anxiety disorders: a systematic review and meta-regression

Baxter, A. J., Scott, K. M., Vos, T., & Whiteford, H. A. (2013) · Psychological Medicine · doi:10.1017/s003329171200147x · Cited by 1,537

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

P. Vatcheva, K., & Lee, M. (2016) · Epidemiology: Open Access · doi:10.4172/2161-1165.1000227 · Cited by 1,291

Cognitive Function at 3 Years of Age after Fetal Exposure to Antiepileptic Drugs

Meador, K. J., Baker, G. A., Browning, N., Clayton-Smith, J., Combs-Cantrell, D. T., Cohen, M., Kalayjian, L. A., Kanner, A., Liporace, J. D., Pennell, P. B., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0803531 · Cited by 812

The presence and severity of chronic kidney disease predicts all-cause mortality in type 1 diabetes

Groop, P.-H., Thomas, M. C., Moran, J. L., Wadèn, J., Thorn, L. M., Mäkinen, V.-P., Rosengård-Bärlund, M., Saraheimo, M., Hietala, K., Heikkilä, O., et al. (2009) · Diabetes · doi:10.2337/db08-1543 · Cited by 638

Bookworms versus nerds: Exposure to fiction versus non-fiction, divergent associations with social ability, and the simulation of fictional social worlds

Mar, R. A., Oatley, K., Hirsh, J., dela Paz, J., & Peterson, J. B. (2006) · Journal of Research in Personality · doi:10.1016/j.jrp.2005.08.002 · Cited by 614

Optimal Linear Combinations of Neural Networks

Hashem, S. (1997) · Neural Networks · doi:10.1016/s0893-6080(96)00098-6 · Cited by 499

A test of self-determination theory in the exercise domain

Edmunds, J., Ntoumanis, N., & Duda, J. L. (2006) · Journal of Applied Social Psychology · doi:10.1111/j.0021-9029.2006.00102.x · Cited by 482

Generating global brand equity through corporate social responsibility to key stakeholders

Torres, A., Bijmolt, T. H. A., Tribó, J. A., & Verhoef, P. (2012) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2011.10.002 · Cited by 323

Bayesian Inference for Comparative Research

Western, B., & Jackman, S. (1994) · American Political Science Review · doi:10.2307/2944713 · Cited by 257

Multicollinearity in hierarchical linear models

Yu, H., Jiang, S., & Land, K. C. (2015) · Social Science Research · doi:10.1016/j.ssresearch.2015.04.008 · Cited by 222

Multivariable Modeling Strategies

Harrell, F. E. (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7_4 · Cited by 221

Relationship between Mycobacterium tuberculosis phylogenetic lineage and clinical site of tuberculosis

Click, E. S., Moonan, P. K., Winston, C. A., Cowan, L. S., & Oeltmann, J. E. (2012) · Clinical Infectious Diseases · doi:10.1093/cid/cir788 · Cited by 137

What you read matters: The role of fiction genre in predicting interpersonal sensitivity.

Fong, K., Mullin, J. B., & Mar, R. A. (2013) · Psychology of Aesthetics, Creativity, and the Arts · doi:10.1037/a0034084 · Cited by 133

Quality of life in women with lipoedema: a contextual behavioral approach

Dudek, J. E., Białaszek, W., & Ostaszewski, P. (2016) · Quality of Life Research · doi:10.1007/s11136-015-1080-x · Cited by 108

Sociodemographic, health-related, and social predictors of subjective well-being among Chinese oldest-old: A national community-based cohort study

Cheng, G., & Yan, Y. (2021) · BMC Geriatrics · doi:10.1186/s12877-021-02071-7 · Cited by 92

Model reduction and a priori kinetic parameter identifiability analysis using metabolome time series for metabolic reaction networks with linlog kinetics

Nikerel, I. E., van Winden, W. A., Verheijen, P. J. T., & Heijnen, J. J. (2009) · Metabolic Engineering · doi:10.1016/j.ymben.2008.07.004 · Cited by 71

Main Determinants of Lapse in the German Life Insurance Industry

Kiesenbauer, D. (2012) · North American Actuarial Journal · doi:10.1080/10920277.2012.10590632 · Cited by 62

Alexithymia, Negative Emotions, and Sexual Behavior in Heterosexual University Students from Italy

Scimeca, G., Bruno, A., Pandolfo, G., Micò, U., Romeo, V. M., Abenavoli, E., Schimmenti, A., Zoccali, R., & Muscatello, M. R. A. (2013) · Archives of Sexual Behavior · doi:10.1007/s10508-012-0006-8 · Cited by 34

Sleep and high-risk behavior in military service members: a mega-analysis of four diverse U.S. Army units

Mantua, J., Bessey, A. F., Mickelson, C. A., Choynowski, J. J., Noble, J. J., Burke, T. M., McKeon, A. B., & Sowden, W. J. (2021) · SLEEP · doi:10.1093/sleep/zsaa221 · Cited by 33

A test of the role of metaworry in the prediction of worry severity in an elderly sample

Nuevo, R., Montorio, I., & Borkovec, T. D. (2004) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/j.jbtep.2004.03.002 · Cited by 22

LLM feedback for academic writing: Effects on students’ performance and engagement

Glüsing, R., Fleckenstein, J., Schmidt, F. T. C., & Möller, J. (2026) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2026.102463 · Cited by 2