The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
The Phantom Menace: Omitted Variable Bias in Econometric Research
Abstract
Quantitative political science is awash in control variables. The justification for these bloated specifications is usually the fear of omitted variable bias. A key underlying assumption is that the danger posed by omitted variable bias can be ameliorated by the inclusion of relevant control variables. Unfortunately, as this article demonstrates, there is nothing in the mathematics of regression analysis that supports this conclusion. The inclusion of additional control variables may increase or decrease the bias, and we cannot know for sure which is the case in any particular situation. A brief discussion of alternative strategies for achieving experimental control follows the main result.
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
“Yet, determining which combination(s) of variables to blind is non-trivial and the risk of omitted variable bias should not be downplayed [71,160].”
From Fairness in machine learning: A survey (2024) · cites this paper as [71]
In the citing paper
“Yet, determining which combination(s) of variables to blind is non-trivial and the risk of omitted variable bias should not be downplayed [71,160].”
From Fairness in machine learning: A survey (2024) · cites this paper as [71]
Citations
Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
(2012) · American Economic Review · doi:10.1257/aer.102.2.i · Cited by 14,047
Sims, C. A. (1980) · Econometrica · doi:10.2307/1912017 · Cited by 12,841
Designing Social Inquiry: Scientific Inference in Qualitative Research
King, G., Keohane, R. O., & Verba, S. (1994) · Princeton University Press · doi:10.1515/9781400821211 · Cited by 7,225
Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
Vuong, Q. H. (1989) · Econometrica · doi:10.2307/1912557 · Cited by 5,929
Minnesota studies in the philosophy of science
Feigl, H., & Scriven, M. S. (1956) · Cited by 4,420
Mallows, C. L. (1995) · Technometrics · doi:10.1080/00401706.1995.10484370 · Cited by 2,622
Statistical Methods for Social Scientists
(1977) · Elsevier eBooks · doi:10.1016/c2009-0-22083-6 · Cited by 1,880
INADMISSIBILITY OF THE USUAL ESTIMATOR FOR THE MEAN OF A MULTIVARIATE NORMAL DISTRIBUTION
Stein, C. M. (1956) · doi:10.1525/9780520313880-018 · Cited by 1,875
Normative and Structural Causes of Democratic Peace, 1946–1986
Maoz, Z., & Russett, B. M. (1993) · American Political Science Review · doi:10.2307/2938740 · Cited by 1,536
Research works printed but not linked: 9
- Achen, C.H., 2005, Conflict Management and Peace Science, 22
- Akaike, H., Second International Symposium of Information Theory
- Cox, D.R., 1961, Proceedings of the Fourth Berkeley Symposium, 105
- Greene, W.H., 2003, Econometric aanalysis
- Johnston, J., 1997, Econometric methods
- Kadera, K.M., 2005, Conflict Management and Peace Science, 22
- Maddala, G.S., 1977, Econometrics
- Mallows, C.L., 1973, Technometrics, 15, 671
- Welch, F., 1975, American Economic Review, 65, 63
Cited by
Sorted by citations
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812
“Omitted variable bias4 occurs when one or more important variables are left out of the model [38, 114, 131].”
BackgroundA Tale of Two Cultures: Contrasting Quantitative and Qualitative Research
Mahoney, J., & Goertz, G. (2006) · Political Analysis · doi:10.1093/pan/mpj017 · Cited by 904
Gartzke, E. A. (2007) · American Journal of Political Science · doi:10.1111/j.1540-5907.2007.00244.x · Cited by 794
Sensitivity Analysis of Empirical Results on Civil War Onset
Hegre, H., & Sambanis, N. (2006) · Journal of Conflict Resolution · doi:10.1177/0022002706289303 · Cited by 1,286
Cambridge Handbook of Experimental Political Science
Druckman, J. N. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511921452 · Cited by 622
Let's put garbage-can regressions and garbage-can probits where they belong
Achen, C. H. (2005) · Conflict Management and Peace Science · doi:10.1080/07388940500339167 · Cited by 576
“(For rigorous and enlightening treatment of this point, see Clarke, 2005.)”
BackgroundA Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences
Goertz, G., & Mahoney, J. (2012) · Cited by 574
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646
“It also ignores that discrimination may not be one variable in isolation, but rather the result of several joint characteristics [217] and as such determining which combination(s) of variables to blind is non-trivial and the phenomenon of omitted variable bias should not be downplayed [68, 150].”
Democracy and Human Development
Gerring, J., Thacker, S. C., & Alfaro, R. (2012) · The Journal of Politics · doi:10.1017/s0022381611001113 · Cited by 495
“First, Ross includes a lagged dependent variable in most estimations, introducing the possibility of unit root problems and of confounding by way of colliders (Clarke 2005).”
BackgroundNatural Experiments in the Social Sciences: A Design-Based Approach
Dunning, T. (2012) · Thammasat University Digital Collections · Cited by 466
Rule, N. O., & Ambady, N. (2008) · Psychological Science · doi:10.1111/j.1467-9280.2008.02054.x · Cited by 368
Seven deadly sins of contemporary quantitative political analysis
Schrodt, P. A. (2013) · Journal of Peace Research · doi:10.1177/0022343313499597 · Cited by 273
Diversity, Disparity, and Civil Conflict in Federal States
Bakke, K. M., & Wibbels, E. M. (2006) · World Politics · doi:10.1353/wp.2007.0013 · Cited by 254
Model Uncertainty and Robustness
Young, C., & Holsteen, K. (2015) · Sociological Methods & Research · doi:10.1177/0049124115610347 · Cited by 257
“Two control variables clearly stand out as most influential: marital status and race.”
BackgroundFetal Origins and Parental Responses
Almond, D. V., & Bhashkar Mazumder, B. (2013) · Annual Review of Economics · doi:10.1146/annurev-economics-082912-110145 · Cited by 230
Long-Term Economic Distress, Cultural Backlash, and Support for Brexit
Carreras, M., Carreras, Y. İ., & Bowler, S. (2019) · Comparative Political Studies · doi:10.1177/0010414019830714 · Cited by 228
“The second critical challenge is endogeneity due to omitted variable bias (K. A. Clarke, 2005; Steiner & Kim, 2016).”
BackgroundZahrt, A. F., Athavale, S. V., & Denmark, S. E. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00425 · Cited by 212
The within and cross domain effects of work-family enrichment: A meta-analysis
Zhang, Y. E., Xu, S., Jin, J., & Ford, M. T. (2017) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2017.11.003 · Cited by 222
Divide and Conquer or Divide and Concede: How Do States Respond to Internally Divided Separatists?
Cunningham, K. G. (2011) · American Political Science Review · doi:10.1017/s0003055411000013 · Cited by 201
Robustness Tests for Quantitative Research
Neumayer, E., & Plümper, T. (2017) · Cambridge University Press eBooks · doi:10.1017/9781108233590 · Cited by 198
Challenging Issues in Survey Research: Discussion and Suggestions
Speklé, R. F., & Widener, S. K. (2017) · Journal of Management Accounting Research · doi:10.2308/jmar-51860 · Cited by 188
Determinants of Sanctions Effectiveness: Sensitivity Analysis Using New Data
Bapat, N. A., Heinrich, T., Kobayashi, Y., & Morgan, T. (2012) · International Interactions · doi:10.1080/03050629.2013.751298 · Cited by 187
Valente, M. J., & MacKinnon, D. P. (2017) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2016.1274657 · Cited by 173
“The difference in the b path across models can be thought of as an unmeasured confounder problem by applying results from Clark (2005) and Hanushek and Jackson (1977).”
MethodBayesian Model Averaging: Theoretical Developments and Practical Applications
Montgomery, J., & Nyhan, B. J. (2010) · Political Analysis · doi:10.1093/pan/mpq001 · Cited by 181
“Finally, the perceived necessity to control for large numbers of potential confounds has led to bloated specifications that decrease efficiency without necessarily decreasing omitted variable bias (Clarke 2005).”
BackgroundReputation and Interstate Conflict
Crescenzi, M. J. C. (2007) · American Journal of Political Science · doi:10.1111/j.1540-5907.2007.00257.x · Cited by 175
Nikolaev, B., Boudreaux, C. J., & Palich, L. E. (2018) · Journal of Small Business Management · doi:10.1111/jsbm.12400 · Cited by 185
“The problem is that when the model is not correctly specified, controlling for some variables but not others is as likely to reduce bias as it is to increase it (Clarke 2005, 2009).”
Baumann, D., & Baumann, K. (2014) · Journal of Cheminformatics · doi:10.1186/s13321-014-0047-1 · Cited by 178
“variable bias) [66].”
Achieving Statistical Significance with Control Variables and Without Transparency
Lenz, G. S., & Sahn, A. (2020) · Political Analysis · doi:10.1017/pan.2020.31 · Cited by 124
“Numerous articles have discussed bias introduced by controls (Clarke 2005; Pearl 2009, 2010, 2011; Cole et al. 2010; Bhattacharya and Vogt 2012; Wyss et al. 2014; Middleton et al. 2016; Wooldridge 2016).”
BackgroundFactions and finance in China: elite conflict and inflation
(2008) · Choice Reviews Online · doi:10.5860/choice.46-1019 · Cited by 167
Zhang, Y., Zheng, Y., Zhang, L., Xu, S., Liu, X., & Chen, W. (2021) · Asia Pacific Journal of Management · doi:10.1007/s10490-018-9639-z · Cited by 167
Omitted variable bias: A threat to estimating causal relationships
Wilms, R., Mäthner, E., Winnen, L., & Lanwehr, R. (2021) · Methods in Psychology · doi:10.1016/j.metip.2021.100075 · Cited by 196
Drydakis, N. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10249-6 · Cited by 180
“If the AI estimates are statistically significant in Models I and II, where critical information is included, then the empirical specification is not sensitive due to unobserved factors related to the use of AI applications (Clarke, 2005).”
BackgroundYackee, J. W. (2008) · Law & Society Review · doi:10.1111/j.1540-5893.2008.00359.x · Cited by 156
Fischer, J. A. V., & Sousa‐Poza, A. (2008) · Health Economics · doi:10.1002/hec.1341 · Cited by 154
Nielsen, B. B., & Raswant, A. (2018) · Journal of World Business · doi:10.1016/j.jwb.2018.05.003 · Cited by 178
The New Regionalism and Policy Interdependence
Baccini, L., & Dür, A. (2011) · British Journal of Political Science · doi:10.1017/s0007123411000238 · Cited by 151
Personal Selling Elasticities: A Meta-Analysis
Albers, S., Mantrala, M. K., & Sridhar, S. (2010) · Journal of Marketing Research · doi:10.1509/jmkr.47.5.840 · Cited by 146
“…in a particular study setting omits a relevant and plausible influencer of sales (e.g., price, advertising, promotions), the resultant personal selling elasticity estimate may be biased, though any predictions with respect to the direction of the bias may be unfounded (e.g., Clarke 2005).”
BackgroundRegression Techniques for Examining Land Use/Cover Change: A Case Study of a Mediterranean Landscape
Millington, J. D. A., Perry, G. L. W., & Calcerrada, R. R. (2007) · Ecosystems · doi:10.1007/s10021-007-9020-4 · Cited by 150
“Unfortunately, as one author has recently highlighted, this problem is logically unavoidable (and cannot be solved simply by adding increasing numbers of predictor variables) but can be ameliorated by ensuring appropriate research design ( Clarke 2005 ).”
BackgroundThe Impact of Institutional Coup-Proofing on Coup Attempts and Coup Outcomes
Böhmelt, T., & Pilster, U. H. (2014) · International Interactions · doi:10.1080/03050629.2014.906411 · Cited by 148
Johndrow, J. E., & Lum, K. (2019) · The Annals of Applied Statistics · doi:10.1214/18-aoas1201 · Cited by 141
“In the case of regression models, this is known as omitted variable bias [Clarke, 2005].”
MethodRenshon, J. (2016) · International Organization · doi:10.1017/s0020818316000163 · Cited by 149
The connection between urbanization and carbon emissions: a panel evidence from West Africa
Musah, M., Kong, Y., Mensah, I. A., Antwi, S. K., & Donkor, M. (2020) · Environment Development and Sustainability · doi:10.1007/s10668-020-01124-y · Cited by 141
Global development and the future of the protected area strategy
McDonald, R. I., & Boucher, T. M. (2010) · Biological Conservation · doi:10.1016/j.biocon.2010.09.016 · Cited by 137
Statistical Critiques of the Democratic Peace: Caveat Emptor
Dafoe, A. (2011) · American Journal of Political Science · doi:10.1111/j.1540-5907.2010.00487.x · Cited by 133
Mattes, M., Leeds, B. A., & Carroll, R. (2014) · International Studies Quarterly · doi:10.1111/isqu.12175 · Cited by 135
Relative rebel strength and the onset and outcome of civil war mediation
Clayton, G. (2013) · Journal of Peace Research · doi:10.1177/0022343313491587 · Cited by 142
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646
“It also ignores that discrimination may not be one variable in isolation, but rather the result of several joint characteristics [217] and as such determining which combination(s) of variables to blind is non-trivial and the phenomenon of omitted variable bias should not be downplayed [68, 150].”
Factors Affecting the Adoption of an Electronic Word of Mouth Message: A Meta-Analysis
Qahri‐Saremi, H., & Montazemi, A. R. (2019) · Journal of Management Information Systems · doi:10.1080/07421222.2019.1628936 · Cited by 144
“Relevant factors are those that would be significantly correlated with other independent factors included in a model [5, 32].”
BackgroundKoubi, V., Spilker, G., Schaffer, L., & Böhmelt, T. (2016) · Population and Environment · doi:10.1007/s11111-016-0258-7 · Cited by 125
“Furthermore, Clarke (2005, 2009) shows that the inclusion of control variables can actually increase the bias instead of decreasing it.”
BackgroundDoes Crime Pay? Issue Ownership, Political Opportunity, and the Populist Right in Western Europe
Smith, J. M. (2010) · Comparative Political Studies · doi:10.1177/0010414010372593 · Cited by 122
Musah, M., Mensah, I. A., Alfred, M., Mahmood, H., Murshed, M., Omari-Sasu, A. Y., Boateng, F., Nyeadi, J. D., & Coffie, C. P. K. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-17508-0 · Cited by 122
“As indicated by Clarke (2005), the omission of a relevant variable may lead to erroneous estimates.”
BackgroundModelling Local Attitudes to Protected Areas in Developing Countries
Bragagnolo, C., Malhado, A. C. M., Jepson, P. R., & Ladle, R. J. (2016) · Conservation and Society · doi:10.4103/0972-4923.191161 · Cited by 113
Song, S. (2012) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2012.0320 · Cited by 111
(Mis)Using Dyadic Data to Analyze Multilateral Events
Poast, P. (2010) · Political Analysis · doi:10.1093/pan/mpq024 · Cited by 109
Rule of Three, Let It Be? When More Really Is Better
Oneal, J. R., & Russett, B. M. (2005) · Conflict Management and Peace Science · doi:10.1080/07388940500339209 · Cited by 108
“Robert Barro (2001), for example, estimates the effects of fourteen variables on the growth rate in per capita gross domestic product, and Barro was the second most frequently cited economist between 1990 and 2000 (Coupé, 2005).”
BackgroundAuxiliary Force Structure: Paramilitary Forces and Progovernment Militias
Böhmelt, T., & Clayton, G. (2017) · Comparative Political Studies · doi:10.1177/0010414017699204 · Cited by 113
Floridi, L., Holweg, M., Taddeo, M., Amaya, J., Mökander, J., & Wen, Y. (2022) · doi:10.2139/ssrn.4064091 · Cited by 157
Böhmelt, T., & Betzold, C. (2012) · International Environmental Agreements Politics Law and Economics · doi:10.1007/s10784-012-9180-3 · Cited by 108
“First, Clarke (2005) shows that the inclusion of control variables may actually increase the bias instead of decreasing it.”
BackgroundThe Combined Effects of Measurement Error and Omitting Confounders in the Single-Mediator Model
Fritz, M. S., Kenny, D. A., & MacKinnon, D. P. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1224154 · Cited by 109
“As Clarke (2005) points out, when multiple variables are omitted from a model, the combined bias can increase, decrease, or remain the same compared to the bias caused by the first omitted variable, depending”
BackgroundMethodContinuation of Politics by Two Means: Direct and Indirect Violence in Civil War
Balcells, L. (2011) · Journal of Conflict Resolution · doi:10.1177/0022002711400865 · Cited by 107
“In order to deal with this, absent an adequate instrument for competition, I have run all the regressions above with county dummies (Chacón, Robinson, and Torvik 2006; Clarke 2005), and the results (not included here, but available upon request) remain consistent.”
MethodDafoe, A., & Caughey, D. (2016) · World Politics · doi:10.1017/s0043887115000416 · Cited by 108
A political economy of human rights: Oil, natural gas, and state incentives to repress 1
DeMeritt, J. H. R., & Young, J. K. (2013) · Conflict Management and Peace Science · doi:10.1177/0738894212473915 · Cited by 97
“If treaties are correlated with both repression and natural resources, omitting them from our analysis may lead to biased results (Achen, 2002; Clarke, 2005; Ray, 2005).”
BackgroundThe Determinants of Environmental Migrants' Conflict Perception
Koubi, V., Böhmelt, T., Spilker, G., & Schaffer, L. (2018) · International Organization · doi:10.1017/s0020818318000231 · Cited by 96
Morris, T., Manley, D., & Sabel, C. E. (2016) · Progress in Human Geography · doi:10.1177/0309132516649454 · Cited by 106
“Such bias occurs when the effects of an omitted variable (for example divorce) is ‘picked up’ by an intermediate variable (residential mobility), causing the effect of the intermediate variable to be inflated beyond that of its own independent effect (Clarke, 2005; Elwert and Winship, 2014).”
BackgroundMusah, M., Kong, Y., Mensah, I. A., Antwi, S. K., & Donkor, M. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-08488-8 · Cited by 96
“When there is an OVB, the model assigns the influence of the overlooked variables to the estimated effects of the included variables (Barreto 2006; Clarke 2005; Wooldridge 2009).”
BackgroundAuditor Size and Going Concern Reporting
Berglund, N. R., Eshleman, J. D., & Guo, P. W. (2015) · Auditing A Journal of Practice & Theory · doi:10.2139/ssrn.2699810 · Cited by 95
Li, K., Hu, E., Xu, C., Musah, M., Kong, Y., Mensah, I. A., Zu, J., Jiang, W. K., & Su, Y. (2020) · Energy Exploration & Exploitation · doi:10.1177/0144598720980198 · Cited by 89
“This is disadvantageous because according to Clarke (2005), omitted variable bias could lead to unfair and inconsistent estimates.”
BackgroundResultInfluentialRetractedThe Mechanics of Omitted Variable Bias: Bias Amplification and Cancellation of Offsetting Biases
Steiner, P. M., & Kim, Y. (2016) · Journal of Causal Inference · doi:10.1515/jci-2016-0009 · Cited by 88
“, [8–10], see [7], for a brief discussion of this ill-advised rationale for including more rather than less covariates).”
BackgroundMethodCompete or Coordinate? Aid Fragmentation and Lead Donorship
Steinwand, M. C. (2015) · International Organization · doi:10.1017/s0020818314000381 · Cited by 84
“Clarke 2003.”
Politics or the Economy? Domestic Correlates of Dispute Involvement in Developed Democracies
Arena, P., & Palmer, G. (2009) · International Studies Quarterly · doi:10.1111/j.1468-2478.2009.00564.x · Cited by 84
MacDonald, J. A., & O'Brien, E. E. (2010) · Political Research Quarterly · doi:10.1177/1065912909354703 · Cited by 86
Causal Empiricism in Quantitative Research
Samii, C. (2016) · The Journal of Politics · doi:10.1086/686690 · Cited by 127
Peyton, K. (2020) · American Political Science Review · doi:10.1017/s0003055420000076 · Cited by 87
“3; Clarke 2005); and (3) the correct specification is used (see Aronow and Samii 2016; Ho et al. 2007).”
BackgroundReligiosity and Life Satisfaction: A Multi-dimensional Approach
Yeniaras, V., & Akarsu, T. N. (2016) · Journal of Happiness Studies · doi:10.1007/s10902-016-9803-4 · Cited by 86
“To eliminate any possible standard omitted variable bias (Clarke 2005); the SWLS was controlled for variables that we previously categorized under Lea et al.”
The impact of preferential trade agreements on governmental repression revisited
Spilker, G., & Böhmelt, T. (2012) · The Review of International Organizations · doi:10.1007/s11558-012-9155-8 · Cited by 77
Clarke, K. A. (2009) · Conflict Management and Peace Science · doi:10.1177/0738894208097666 · Cited by 81
“The argument in this paper is a generalization and extension of the results of Clarke (2005).”
BackgroundResultSelf-citationAre patents signals for the IPO market? An EU–US comparison for the software industry
Useche, D. (2014) · Research Policy · doi:10.1016/j.respol.2014.04.004 · Cited by 79
Retesting Selectorate Theory: Separating the Effects ofWfrom Other Elements of Democracy
Morrow, J. D., de Mesquita, B. B., Siverson, R. M., & Smith, A. M. (2008) · American Political Science Review · doi:10.1017/s0003055408080295 · Cited by 78
“Adding a measure of democracy to a regression analysis with the measure of the size of the winning coalition, as Clarke and Stone (2008) do, misspecifies the model in the fashion Clarke (2005) has decried elsewhere.”
MethodSovereign Credit and the Fate of Leaders: Reassessing the “Democratic Advantage”
DiGiuseppe, M. R., & Shea, P. E. (2015) · International Studies Quarterly · doi:10.1111/isqu.12181 · Cited by 74
Relative Contributions of a Set of Health Factors to Selected Health Outcomes
Park, H., Roubal, A. M., Jovaag, A., Gennuso, K. P., & Catlin, B. B. (2015) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2015.07.016 · Cited by 70
Fieberg, J., Forester, J. D., Street, G. M., Johnson, D. H., ArchMiller, A. A., & Matthiopoulos, J. (2017) · Ecography · doi:10.1111/ecog.03123 · Cited by 69
“This type of bias, referred to as omitted-variable bias, is well-known and is a function of cor(x1,x2) and cor(y,x2|x1) (Clarke 2005).”
BackgroundNationalism and Conflict: Lessons from International Sports
Bertoli, A. (2017) · International Studies Quarterly · doi:10.1093/isq/sqx029 · Cited by 69
Manna, P., & Ryan, L. L. (2011) · Publius The Journal of Federalism · doi:10.1093/publius/pjr021 · Cited by 70
Issue linkage and international cooperation: An empirical investigation
Poast, P. (2013) · Conflict Management and Peace Science · doi:10.1177/0738894213484030 · Cited by 72
Fordham, B. O., & Asal, V. H. (2007) · International Studies Quarterly · doi:10.1111/j.1468-2478.2007.00438.x · Cited by 70
Xu, Y.-H., Pennington-Gray, L. A., & Kim, J. (2018) · Journal of Travel Research · doi:10.1177/0047287518797197 · Cited by 64
“By introducing control variables into the model, inconsistencies can be ameliorated (Clarke 2005).”
MethodMusah, M., Kong, Y., Mensah, I. A., Li, K., Vo, X. V., Bawuah, J., Agyemang, J. K., Antwi, S. K., & Donkor, M. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-13816-7 · Cited by 65
“E According to Clarke (2005), omitting relevant variables from a regression model could lead to wrong coefficient estimates that could result in erroneous inferences or tests of hypothesis.”
RetractedYamashita, A. F. (2013) · Empirical Software Engineering · doi:10.1007/s10664-013-9250-3 · Cited by 62
“Code smells are closely related to Object-Oriented design principles, heuristics and design patterns, see for example principles and heuristic in Riel (1996) and Coad and Yourdon (1991), and design patterns in Gamma et al. (1994), Brown et al. (1998) and Larman (2004).”
BackgroundBöhmelt, T., & Freyburg, T. (2012) · European Union Politics · doi:10.1177/1465116512458164 · Cited by 64
“This seems important, first, because Clarke (2005) shows that the inclusion of control variables may actually increase the bias, instead of decreasing it.”
BackgroundBoyd, C. L., & Hoffman, D. A. (2012) · The Journal of Law Economics and Organization · doi:10.1093/jleo/ews021 · Cited by 62
Bias Amplification and Bias Unmasking
Middleton, J. A., Scott, M. A., Diakow, R., & Hill, J. (2016) · Political Analysis · doi:10.1093/pan/mpw015 · Cited by 61
“There is even some controversy within the controversy: in political science, Clarke (2005) contends that researchers are encouraged to include as many covariates as possible, without regard to the potential for increased bias; relatedly, in most intermediate econometrics texts, the choice between…”
BackgroundMethodResultInfluentialControl variables and causal inference: a question of balance
York, R. (2018) · International Journal of Social Research Methodology · doi:10.1080/13645579.2018.1468730 · Cited by 62
“With this consideration in mind, I do not claim that my contribution here is based on a new insight, since Lieberson (1985) had it well before I did. Rather, my contribution is intended to be one of highlighting this still widely unrecognized and misunderstood issue and explaining it in an accessible way. Other than L…”
BackgroundInfluentialA statistical framework for fair predictive algorithmsPreprint
Lum, K., & Johndrow, J. E. (2016) · arXiv.org · doi:10.48550/arxiv.1610.08077 · Cited by 124
Corruption, gender inequality and logistics performance
Larson, P. D. (2020) · The International Journal of Logistics Management · doi:10.1108/ijlm-02-2019-0062 · Cited by 58
Sharma, A., Kumar, V., Borah, S. B., & Adhikary, A. (2022) · Journal of International Business Studies · doi:10.1057/s41267-021-00497-0 · Cited by 66
“, required variables are not included), it may over (under) estimate the strength of an effect, change the sign of an effect, or mask an effect that actually exists, making significant changes in error variance of the variables (Lee, 1982; Green, 1993; Wooldridge, 2009; Clarke, 2005).”
BackgroundModeling the spatial patterns of human wildfire ignition in Yunnan province, China
Ye, J., Wu, M., Deng, Z., Xu, S., Zhou, R., & Clarke, K. (2017) · Applied Geography · doi:10.1016/j.apgeog.2017.09.012 · Cited by 53
“…bias, but it is impossible to include all the relevant variables in a regression equation and omitted variable bias is therefore unavoidable (Clarke, 2005), to fully study wildfire ignited by humans, it would be necessary to include vegetation-fuel, meteorological, and topographical factors.”
BackgroundResultSelf-citationWang, F., Liu, Z., Xue, P., & Dang, A. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.104172 · Cited by 53
The role of secondary education in explaining competitiveness
Baumann, C., & Winzar, H. F. (2014) · Asia Pacific Journal of Education · doi:10.1080/02188791.2014.924387 · Cited by 52
“Even though it is never clear how much or in what direction omitted variables may cause bias (Clarke, 2005), it is usually preferable to include them with time-series and relatively small-sample data (Clarke, 2012; Vance & Ritter, 2012).”
BackgroundPérez-Suay, A., Laparra, V., Mateo‐García, G., Muñoz-Marı́, J., Gómez‐Chova, L., & Camps‐Valls, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71249-9_21 · Cited by 66
“1 age c [1,5] 2 workclass d [6,13] 3 final weight c [14,18] 4 education d [19,34] 5 ed num c [35,39] 6 marital status d [40,46] 7 occupation d [47,60] 8 relationship d [61,66] 9 race d [67,71] 10 sex d [72,73] 11 capital gain c [74,75] 12 capital loss c [76,77] 13 hours× week c [78,82] 14 country d [83,123]”
BackgroundBorder settlement and the movement toward and from negative peace
Owsiak, A. P., Diehl, P. F., & Goertz, G. (2016) · Conflict Management and Peace Science · doi:10.1177/0738894216650420 · Cited by 52
“We purposely limit our control variables because of: (a) the recent call for simpler models (Clarke, 2005), and (b) the lack of sufficient theorizing in this area to date.”
Method
Metadata
- Authors
- Clarke, K. A.
- Affiliations
- University of Rochester (US)
- Corresponding author
- Clarke, Kevin A.
- ORCID
- Clarke, Kevin A.
- Venue
- Conflict Management and Peace Science
- Publisher
- SAGE Publications
- Published
- 2005-09
- Volume
- 22
- Issue
- 4
- Pages
- 341-352
- ISSN
- 0738-8942 (ISSN-L)
- 1549-9219
- OpenAlex
- W2146807455
- Cited by
- 484
- References
- 42
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 23 · 2023: 24 · 2022: 33 · 2021: 28 · 2020: 58 · 2019: 23 · 2018: 30 · 2017: 26 · 2016: 32 · 2015: 25 · 2014: 26 · 2013: 24 · 2012: 32
- FWCI
- 11.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Conflict Management and Peace Science (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Political Science and International Relations › Political Science Research and Education
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- omitted variable bias, control variables, regression analysis, specification bias, experimental control, econometric methodology, political science research
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
