Levente Littvay is Associate Professor of Political Science at Central European University, Budapest Hungary, where he teaches grad courses in applied statistics, electoral politics, voting behavior, political psychology, American politics. Was honored with CEU’s Distinguished Teaching Award in 2015. Received his MA and PhD in Political Science and an MS in Survey Research and Methodology from the University of Nebraska-Lincoln. Consults regularly, taught numerous research methods workshops and is one of the Academic Convenors of the European Consortium for Political Research Methods Schools. Secured over a half million EUR in grants to conduct research on survey and quantitative methodology, twin and family studies (as the co-director of the Hungarian Twin Registry), and the psychology of radicalism and populism. Has publications in The Journal of Politics, Political Psychology, Politics & Gender, PS, Swiss Political Science Review, BMC Medical Research Methodology, Behavior Genetics, and along with other medical journals, in Twin Research and Human Genetics where he is Associate Editor for Social Sciences.
Methodology: Reaching across disciplines to improve the use or use cases of existing research designs and analytical tools in applied quantitative research.
Twin & Family Studies: Study twins’ families to understand socialization mechanisms, behaviors, attitudes and physiology with an emphasis on gene by environment interactions, gene expression, and comparison of contexts and populations.
Methodology: Foster a friendly learning environment for this scary topic, offering a diverse and broadly applicable interdisciplinary methods education and guiding scholars in becoming better critics, writers, presenters, analysts, and researchers.
Social Science: Guide through complex research applications, encouraging a critical view independent of authors’ rank/authority, leading to thoughtful, creative, and original research that meets the highest scientific, ethical and personal standards.
Design effective studies and apply the appropriate methodologies to real life problems in the world of policy, science and beyond. Produce studies that allow for causal inference to maximize effective resources use.
Tools used include randomized experiments, counterfactual program evaluation and other applications of quasi-experimental designs for causal inference, survey design and analysis, nonresponse correction, structural equation and multilevel models, and general foundations in the philosophy of science.
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