Cross-national Survey Harmonization and Analysis: Weights, Data Quality and Multi-level Modeling
This event comprises two workshops. The first workshop is running from May 11-14, and the second, from May 15-16, 2015.
Both workshops benefit from grants by the Polish National Science Centre. Workshop I is funded under the NCN grant 2012/05/N/HS6/03886, Principal Investigator Marta Kolczynska. Workshop II is funded under the international cooperation grant 2012/06/M/HS6/00322, Principal Investigator Kazimierz M. Slomczynski.
The training event Cross-national Survey Harmonization and Analysis: Weights, Data Quality and Multi-level Modeling is co-organized by Cross-national Studies, Interdisciplinary Research and Training program CONSIRT, OSU – Polish Academy of Sciences, with the OSU Mershon Center for International Security Studies, and with the financial support of the OSU Departments of Political Science and Sociology, and the Polish Studies Initiative at OSU.
The goal of this event is to train graduate students to employ survey weights, information on the quality of survey data, and multilevel modeling to address methodological and substantive problems in quantitative social science research. The event features a dataset that consists of the U.S. and of European countries, constructed via ex-post harmonization of various international survey projects, including the World Values Survey, International Social Survey Program, and the European Social Survey.
Day 1 Session I Introduction Slomczynski Tomescu-Dubrow
Day 1 Session II Intro to weights Slomczynski
Day 1 Session II Weighting 1 Zielinski
Day 2 Session I Weighting 2 Zielinski
Day 2 Session II Weighting in cross-national projects Zielinski
Day 3 Session I Assessment of survey quality – overview Tomescu Slomczynski
Day 3 Session II Documentation quality Kolczynska Schoene
Day 4 Session I Data documentation discrepancies Wysmulek Oleksiyenko
Day 4 Session II Errors in Survey Data Powalko
Day 5 Session I Introduction to Multilevel Linear Modeling Kunovich
Day 5 Session II Logistic regression Kunovich