| Interpretation and identification of within-unit and cross-sectional variation in panel data models J Kropko, R Kubinec PloS one 15 (4), e0231349, 2020 | 177 | 2020 |
| Choosing between multinomial logit and multinomial probit models for analysis of unordered choice data J Kropko The University of North Carolina at Chapel Hill, 2007 | 176 | 2007 |
| On the stationary distribution of iterative imputations J Liu, A Gelman, J Hill, YS Su, J Kropko Biometrika 101 (1), 155-173, 2014 | 141 | 2014 |
| Multiple imputation for continuous and categorical data: comparing joint multivariate normal and conditional approaches. J Kropko, B Goodrich, A Gelman, J Hill Political Analysis 22 (4), 2014 | 118 | 2014 |
| Simulating duration data for the Cox model JJ Harden, J Kropko Political Science Research and Methods 7 (4), 921-928, 2019 | 47 | 2019 |
| Beyond the hazard ratio: generating expected durations from the cox proportional hazards model J Kropko, JJ Harden British Journal of Political Science 50 (1), 303-320, 2020 | 31 | 2020 |
| Why the two-way fixed effects model is difficult to interpret, and what to do about it J Kropko, R Kubinec Kropko J, Kubinec R (2020) Interpretation and identification of within-unit …, 2018 | 31 | 2018 |
| Epidemiology of drug use among biracial/ethnic youth and young adults: Results from a US population-based survey TT Clark, AB Nguyen, J Kropko Journal of psychoactive drugs 45 (2), 99-111, 2013 | 28 | 2013 |
| Issue scales, information cues, and the proximity and directional models of voter choice J Kropko, KK Banda Political Research Quarterly 71 (4), 772-787, 2018 | 25 | 2018 |
| coxed: duration-based quantities of interest for the Cox proportional hazards model J Kropko, JJ Harden R package version 0.3 3, 2020 | 16 | 2020 |
| A comparison of three discrete choice estimators J Kropko Unpublished manuscript, 2010 | 15 | 2010 |
| coxed: Duration-based quantities of interest for the Cox proportional hazards model. R package version 0.3. 3 J Kropko, JJ Harden | 11 | 2020 |
| Who’sa Directional Voter and Who’sa Proximity Voter? An Application of Finite Mixture Modeling to Issue Voting in the 2008 American Presidential Election J Kropko Political Methodology Conference, 2012 | 10 | 2012 |
| New approaches to discrete choice and time-series cross-section methodology for political research J Kropko The University of North Carolina at Chapel Hill, 2011 | 9 | 2011 |
| Choosing between multinomial logit and multinomial probit models for analysis of unordered choice data ((Doctoral dissertation, The University of North Carolina at Chapel Hill) J Kropko The University o f North Carolina at Chapel Hill, 2008 | 9 | 2008 |
| Multiple imputation for continuous and categorical data: Comparing joint and conditional approaches J Kropko, B Goodrich, A Gelman, J Hill Columbia University, Department of Statistics. New York, 2013 | 8 | 2013 |
| coxed: An R Package for Computing Duration-Based Quantities from the Cox Proportional Hazards Model. J Kropko, JH Jeffrey R J. 11 (2), 38, 2019 | 7 | 2019 |
| Choosing Between Multinomial Logit and Multinomial Probit Models for Analysis of Unordered Choice Data, ProQuest J Kropko Ann Arbor, MI, 2007 | 7 | 2007 |
| Measuring the Dynamics of Political Power: a Time-Series IRT Model. Presentation J Kropko Political Methodology Society, 2013 | 6 | 2013 |
| Mathematics for social scientists J Kropko SAGE Publications, 2015 | 5 | 2015 |
