DANS-subsidie ICPSR-Summer Program toegekend
DANS feliciteert Nina Conkova en Wouter Quite met de subsidie voor deelname aan het Summer Program van ICPSR.
Het Inter-university Consortium for Political and Social Research (ICPSR) organiseert ieder jaar een ‘Summer Program in Quantitative Methods of Social Research’. Deelname aan dit programma betekent dat de kandidaten de opgedane kennis kunnen inzetten bij hun huidige en toekomstige onderzoeksactiviteiten. DANS vertegenwoordigt en bekostigt het Nederlandse lidmaatschap van het ICPSR. Hierdoor kunnen onderzoekers van de aangesloten instellingen kosteloos data uit dit omvangrijke data-archief raadplegen.
De subsidieaanvragen zijn beoordeeld door de leden van de Wetenschappelijke Adviesraad van DANS. Zij zijn van mening dat Nina Conkova en Wouter Quite ruimschoots voldoen aan alle gestelde voorwaarden om voor deze subsidie in aanmerking te komen. Met het toekennen van de subsidie hoopt DANS sociaal-wetenschappelijk onderzoek van hoge kwaliteit te stimuleren en het belang van het delen van data te benadrukken.
Nina Conkova obtained her Master’s degree in Population Studies from the Graduate School of Spatial Sciences at the University of Groningen, the Netherlands. Subsequently, Nina was a trainee at the Netherlands Interdisciplinary Demographic Institute and also worked as a freelancer at Population Europe / Max Plank Institute for Demographic Research. In September 2013, Nina became a PhD candidate on Pearl Dykstra’s ERC funded project ‘Families in Context’. Within this project, Nina is researching the circumstances under which non-kin ties serve as a source of support in Europe. Non-kin has largely been neglected in the support literature, leaving open questions such as ‘when do help from friends, neighbors and colleagues complement or substitute family support’ and ‘how is the link between kin and non-kin support influenced by different socio-demographic, economic, and cultural contexts across Europe’. Departing from previous exercises of studying non-kin support in single countries primarily by means of ethnographic methods, in this project Nina seeks to conduct cross-national comparative research. Nina intends to apply advanced quantitative techniques such as multilevel regression analysis, which will in some instances be complemented by in-depth interviews. By attending the ICPSR Summer Program, Nina aims to extend the scope and depth of her statistical and computing skills. During her participation in the full, eight weeks Summer Program, Nina will follow four workshops and several evening courses devoted to computing, missing data and mathematics for social scientists.
Wouter Quite is a PhD candidate in the Department of Sociology at Erasmus University Rotterdam. In 2013 he completed the research master Sociology and Social Research (SaSR) at Utrecht University (cum laude). His research interests are family sociology, social policies, (survey) methodology and statistics, the analyses of large scale (cross national & longitudinal) data, social capital, (online) social networks, social mobility, game theory, rational choice, and experimental sociology/economy. In his current research he addresses cross national differences in support behavior and the motives for family members to provide support. A cross-national comparative perspective is applied to provide a more comprehensive understanding of the influence of national policies, and the economic and cultural context on family support. It is in the light of this research that he will go to the ICPSR summer school in Ann Arbor to learn Bayesian statistics. In cross-national comparative research often multi-level models are applied even with an insufficient amount of countries to analyze (less than 18) this can lead to biased results. Applying Bayesian statistics which can yield reliable results even with fewer countries to analyze helps him to statistically test country differences even when there are data available for less than 18 countries. Next to Bayesian statistics he will follow other courses, such as advanced regression modeling and longitudinal data analysis and learning these different techniques will help him in is future research.