Statistical Methods
Ndreka, B., Dey, D. K., & Xu, R. (2024). Evaluating methods for estimating influence effects in social networks: a simulation study. Communications in Statistics-Simulation and Computation, 1-17.
Frank, K. A., Lin, Q., Xu, R., Maroulis, S., & Mueller, A. (2023). Quantifying the robustness of causal inferences: Sensitivity analysis for pragmatic social science. Social Science Research, 102815.
Chen, X., Wang, H., Lyu, W., & Xu, R. (2022). The Mann-Kendall-Sneyers test to identify the change points of COVID-19 time series in the United States. BMC Medical Research Methodology, 22(1), 1-9.
Frank, K. A., Lin, Q., Maroulis, S., Mueller, A. S., Xu, R., Rosenberg, J. M., ... & Zhang, L. (2022). Response to" Three Comments on the RIR method". Journal of clinical epidemiology, S0895-4356.
Xu, R. (2021). Estimating Social Influence Using Latent Space Adjusted Approach in R. The R Journal.
Frank, K. A., Lin, Q., Maroulis, S., Mueller, A. S., Xu, R., Rosenberg, J. M., ... & Zhang, L. (2021). Hypothetical case replacement can be used to quantify the robustness of trial results. Journal of Clinical Epidemiology.
Xu, R., & Frank, K. A. (2021). Sensitivity analysis for network observations with applications to inferences of social influence effects. Network Science, 9(1), 73-98.
Xu, R. (2020). Statistical methods for the estimation of contagion effects in human disease and health networks. Computational and Structural Biotechnology Journal.
Xu, R., DeShon, R. P., & Dishop, C. R. (2019). Challenges and Opportunities in the Estimation of Dynamic Models. Organizational Research Methods, 1094428119842638.
Xu, R. (2018). Alternative Estimation Methods for Identifying Contagion Effects using Longitudinal Social Network Data: A Latent-Space Adjusted Approach. Social Networks,54, 101-117.
Computational Social Science
Song, Y., Xu, R., Huang, Y. H. C., Ni, S., & Fan, Y. (2024). Assessing the Interplay Between Public Attention and Government Responsiveness With Digital Trace Data: Navigating Leadership and Followership in China’s COVID-19 Vaccination Campaign. Social Science Computer Review, 08944393241258217.
Lim, T. Y., Xu, R., Ruktanonchai N., Saucedo, O., Childs, L. M., Jalali, M. S., ... & Ghaffarzadegan, N. (2023). Why Similar Policies Resulted In Different COVID-19 Outcomes: How Responsiveness And Culture Influenced Mortality Rates: Study examines why similar policies resulted in different COVID-19 outcomes in using data from more than 100 countries. Health Affairs, 42(12), 1637-1646.
Rahmandad, H., Xu, R., & Ghaffarzadegan, N. (2022). A missing behavioral feedback in COVID-19 models is the key to several puzzles. BMJ Global Health.
Rahmandad, H., Xu, R., & Ghaffarzadegan, N. (2022). Enhancing Long-term Forecasting: Learning from COVID-19 Models. PLOS Computational Biology.
Xu, R.*, Rahmandad, H.*, Gupta, M., DiGennaro, C., Ghaffarzadegan, N., Amini, H., & Jalali, M. S. (2021). Weather, air pollution, and SARS-CoV-2 transmission: a global analysis. The Lancet Planetary Health, 5(10), e671-e680. * Co-equal first authors.
Ghaffarzadegan, N.*, & Xu, R.* (2019). Late retirement, early careers, and the aging of U.S. science and engineering professors. PLOS ONE. * Co-equal first authors.
Frank, K. A.*, & Xu, R.*, Penuel, W. R. (2018). Implementation of Evidence Based Practice in Human Service Organizations: Implications from Agent-Based Models. Journal of Policy Analysis and Management. * Co-equal first authors.
Ghaffarzadegan,N., Rad, A. A., Xu, R., Middlebrooks, S. E., Mostafavi, S.,Shepherd,M., Chambers, L., Boyum, T. (2018). Dell's SupportAssist Customer Adoption Model: Enhancing the Next Generation of Data-Intensive SupportServices. System Dynamics Review.
Xu, R, Frank, K.A. (2016). The Implications for Network Structure of Dynamic Feedback between Influence and Selection. Social Computing, Behavioral-Cultural Modeling, and Prediction. Springer International Publishing.
Network Analysis
Cabral, J., Morzillo, A. T., & Xu, R. (2023). A Stakeholder Network for Managing Multiple Forest Stressors and Roadside Forests. Society & Natural Resources, 1-18.
Xu, R., Divito, J., Bannor, R., Schroeder, M., & Pagoto, S. (2022). Predicting Participant Engagement in a Social Media–Delivered Lifestyle Intervention Using Microlevel Conversational Data: Secondary Analysis of Data From a Pilot Randomized Controlled Trial. JMIR formative research, 6(7), e38068.
Song, Y., Lin, Q., Kwon, K. H., Choy, C. H., & Xu, R. (2021). Contagion of offensive speech online: An interactional analysis of political swearing. Computers in Human Behavior, 107046.
Xu, R., & Cavallo, D. (2021). Social Network Analysis of the Effects of a Social Media–Based Weight Loss Intervention Targeting Adults of Low Socioeconomic Status: Single-Arm Intervention Trial. Journal of Medical Internet Research, 23(4), e24690.
Xu, R., Lakeh, A. B., & Ghaffarzadegan, N. (2020). Examining the characteristics of impactful research topics: A case of three decades of HIV-AIDS research. Journal of Informetrics, 15(1), 101122.
Xu, R., Ghaffarzadegan, N. (2018). Neuroscience bridging scientific disciplines in health: who builds the bridge, who pays for it? Scientometrics.
Song, Y., Xu, R. (2018). Affective Ties that Bind: Investigating the Affordances of Social Networking Sites for Commemoration of Traumatic Events. Social Science Computer Review.
Moore, J. A., Xu, R., Frank, K.A.,Draheim, H., & Scribner, K. T. (2015). Social network analysis of mating patternsin American black bears (Ursus americanus). Molecular Ecology, 24(15), 4010-4022.
Statistical Packages
Narvaiz, S., Lin, Q., Rosenberg, J. M., Frank, K. A., Maroulis, S. J., Wang, W., & Xu, R. (2024). konfound: An R Sensitivity Analysis Package to Quantify the Robustness of Causal Inferences. Journal of Open Source Software, 9(95), 5779.
Xu, R., Frank, K. A., Maroulis, S. J., & Rosenberg, J. M. (2019). konfound: Command to quantify robustness of causal inferences. The Stata Journal, 19(3), 523-550.
Rosenberg, J. M., Xu, R., Frank, K. A. (2018). KONFOUND: R module to quantify robustness of causal inferences. https://jrosen48.github.io/konfound http://konfound-it.com/
Frank, K. A. & Xu, R. (2017). KONFOUND: Stata module to quantify robustness of causal inferences. https://ideas.repec.org/c/boc/bocode/s458298.html
Book Chapters
Busenbark, J., Frank, K. A., Maroulis, S. J., Xu, R., Lin, Q. (2021). Quantifying the Robustness of Empirical Inferences in Strategic Management: The Impact Threshold of a Confounding Variable and Robustness of Inference to Replacement. Research Methodology in Strategy and Management. 16.
Frank, K. A., & Xu, R. (2020). Causal inference for social network analysis. The Oxford Handbook of Social Networks, 288-310.
Food Environment Research
Lyu, W., Chen, X., Miao, C., Lin, Q., Xiang, X., Zhang, G., & Xu, R. (2025). Revisiting the modified Retail Food Environment Index (mRFEI): examining food access inequities over a decade in the United States. Discover Public Health, 22(1), 1-10.
Lin, Q., Chen, X., Xiang, X., Lyu, W., Miao, C., Zhang, G., & Xu, R. (2025). Association of activity-based food environment index with obesity-related cancer mortality in the US. BMC medicine, 23(1), 167.
Burkholder, K., Bennett, B. L., McKee, S. L., Cohen, J. F., Xu, R., & Schwartz, M. B. (2024). Participation in the US Department of Agriculture's Summer Meal Programs: 2019‐2021. Journal of School Health.
Gombi-Vaca, M. F., Xu, R., Schwartz, M. B., & Caspi, C. E. (2023). Construct validity of the Charitable Food Nutrition Index. Preventive Medicine Reports, 36, 102515.
Xu, R., Huang, X., Zhang, K., Lyu, W., Ghosh, D., Li, Z., & Chen, X. (2023). Integrating human activity into food environments can better predict cardiometabolic diseases in the United States. Nature Communications, 14(1), 7326.
Lyu, W., Seok, N., Chen, X., & Xu, R. (2023). Using Crowdsourced Food Image Data for Assessing Restaurant Nutrition Environment: A Validation Study. Nutrients, 15(19), 4287.
Wang, Y., Xu, X., Kuchmaner, C. A., Xu, R. (2023). But it was supposed to be healthy! How expected and actual nutritional value affect the content and linguistic characteristics of online reviews for food products. Journal of Consumer Psychology. 33(4), 743-761.
Jin, A., Chen, X., Huang, X., Li, Z., Caspi, C. E., & Xu, R. (2023). Selective Daily Mobility Bias in the Community Food Environment: Case Study of Greater Hartford, Connecticut. Nutrients, 15(2), 404.
Schwartz, M. B., Schneider, G. E., Xu, R., Choi, Y. Y., Atoloye, A. T., Bennett, B. L., ... & Appel, L. J. (2022). Retail Soda Purchases Decrease and Water Purchases Increase: 6-Year Results From a Community-Based Beverage Campaign. AJPM Focus, 100008.
Gombi-Vaca, M. F., Xu, R., Schwartz, M., Battista Hesse, M., Martin, K., & Caspi, C. E. (2022). Validating a Nutrition Ranking System for Food Pantries Using the Healthy Eating Index-2015. Nutrients, 14(19), 3899.
McKee, S., Xu, R., Schwartz, M. (2022) Assessing the Effects of a Statewide Training Initiative on Local School Wellness Policies. Health Promotion Practice.
Chen, X., Johnson, E., Kulkarni, A., Ding, C., Ranelli, N., Chen, Y., & Xu, R.+(2021). An Exploratory Approach to Deriving Nutrition Information of Restaurant Food from Crowdsourced Food Images: Case of Hartford. Nutrients, 13(11), 4132.+Corresponding Author.
McKee, S. L., Gurganus, E. A., Atoloye, A. T., Xu, R., Martin, K., & Schwartz, M. B. (2021). Pilot testing an intervention to educate and promote nutritious choices at food pantries. Journal of Public Health, 1-8.
Wang, Y., Chen, X., Yang, Y., Cui, Y., & Xu, R. (2021). Risk perception and resource scarcity in food procurement during the early outbreak of COVID-19. Public Health.
Martin, K., Xu, R., & Schwartz, M. B. (2020). Food pantries select healthier foods after nutrition information is available on their food bank’s ordering platform. Public Health Nutrition, 1-8.
Cooksey-Stowers, K., Martin K. S., Read, M., Xu, R., Schwartz, M. (2020) Supporting Wellness at Pantries (SWAP): changes to inventory in six food pantries over one year. Journal of Public Health, 1-9.
Wang, Y., Xu, R., Schwartz, M., Ghosh, D., & Chen, X. (2020). COVID-19 and Retail Grocery Management: Insights from a Broad-based Consumer Survey. IEEE Engineering Management Review.
Xu, R., Blanchard, B. E., McCaffrey, J. M., Woolley, S., Corso, L. M., & Duffy, V. B. (2020). Food Liking-Based Diet Quality Indexes (DQI) Generated by Conceptual and Machine Learning Explained Variability in Cardiometabolic Risk Factors in Young Adults. Nutrients, 12(4), 882.
Behavioral Interventions
Pagoto, S., Xu, R., Bannor, R., Idiong, C., Goetz, J., & Fernandes, D. (2024). Comparing Synchronous and Asynchronous Remotely Delivered Lifestyle Interventions: Protocol for a Randomized Noninferiority Trial. JMIR Research Protocols, 13(1), e65323.
Pagoto, S., Lueders, N., Palmer, L., Idiong, C., Bannor, R., Xu, R., & Ingels, S. (2024). Best Practices for Designing and Testing Behavioral and Health Communication Interventions for Delivery in Private Facebook Groups: Tutorial. JMIR Formative Research, 8, e58627.
Pagoto, S. L., Goetz, J. M., Xu, R., Wang, M. L., Palmer, L., & Lemon, S. C. (2024). Randomized non-inferiority trial comparing an asynchronous remotely-delivered versus clinic-delivered lifestyle intervention. International Journal of Obesity, 1-8.
Pagoto, S., Xu, R., Bullard, T., Foster, G. D., Bannor, R., Arcangel, K., Divito, J., Schroeder, M., Cardel, M. I. (2023). An Evaluation of a Personalized Multi-Component Commercial Digital Weight Management Program. Journal of Medical Internet Research. 25, e44955.
Xu, R., Bannor, R., Cardel, M. I., Foster, G. D., & Pagoto, S. (2023). How much food tracking during a digital weight‐management program is enough to produce clinically significant weight loss?. Obesity, 31(7), 1779-1786.
Pagoto, S. L., Schroeder, M. W., Xu, R., Waring, M. E., Groshon, L., Goetz, J. M., ... & Bannor, R. (2022). A Facebook-Delivered Weight Loss Intervention Using Open Enrollment: Randomized Pilot Feasibility Trial. JMIR Formative Research, 6(5), e33663.
Pagoto, S., Waring, M. E., & Xu, R. (2019). A Call for a Public Health Agenda for Social Media Research. Journal of Medical Internet Research, 21(12), e16661.
Other Empirical Research
Wang, H., Xu. R., Qu, S., Schwartz, M. B., Adams, A., Chen, X. (2021). Health inequities in COVID-19 vaccination among the elderly: Case of Connecticut. Journal of Infection and Public Health.
Ghaffarzadegan, N., Xu, R., Larson, R. C., Hawley, J. D. (2018). Symptoms vs. Root Causes: A Needed Structural Shift in Academia to Help Early Careers. BioScience.
Kim, S., Wallsworth, G., Xu, R., Schneider, B., Frank, K., Jacob, B., & Dynarski, S. (2019). The Impact of the Michigan Merit Curriculum on High School Math Course-Taking. Educational Evaluation and Policy Analysis, 41(2), 164-188.