Conference Programme 2015
Tuesday 14th July Wednesday 15th July Thursday 16th July Friday 17th July
Wednesday 15th July, 11:00 - 12:30 Room: O-106
Social science data harmonization and replication: challenges and solutions for the 21st century
|Convenor||Dr Kristi Winters (GESIS - Leibniz Institute for the Social Sciences )|
Session DetailsStatistical analyses often require extensive data preparation and variable harmonization before the research can begin, yet there are no set documentation standards to facilitate transparent and precise replication. Second, increasing numbers of academic journals are adopting data policies to facilitate transparency and replication. This move to best practices increases the burden on researchers to make their data preparation, harmonization and variable transformation work transparent. Third, researchers have crossed the threshold into a digital world where, every day, humans create 2.5 quintillion bytes of data. This has led to a proliferation in datasets and the digital age has made accessing and combining data from multiple sources easier than ever. While this is a tremendous boom to social research, it further increases the need for transparency in variable transformations and harmonizations. All of this comes at a time when research journals space or stylistic constraints result in the omission of methodological details. This panel invites papers that address the challenges - and opportunities - the age of 21st century digital data present to social scientists. Paper topics may include, but are not limited to, issues of replication and replicability, documenting data preparation and variable harmonizations, trends in journal data policies, and resources for documenting data preparation for publication purposes and journal data policies.
Paper Details1. The Inter-Disciplinary Importance of Data Harmonization
Dr Peter Granda (University of Michigan)
This paper will focus on the importance of "output" or "retrospective" data harmonization across both the social and physical sciences with an emphasis on the development and implementation of comprehensive guidelines to create harmonized datasets. Descriptions of these guidelines will be presented and the effect that they have had in studying important health issues. The author will discuss the importance and applicability of a set of general standards for harmonizing data.
2. Harmonizing Large International Projects on Public Opinion: Challenges in Preparing Data for Quantitative Analysis.
Mrs Olena Oleksiyenko (Institute of Philosophy and Sociology Polish Academy of Sciences)
Professor Kazimierz Slomczynski (The Ohio State University; Institute of Philosophy and Sociology, Polish Academy of Sciences)
Dr Irina Tomescu-dubrow (Institute of Philosophy and Sociology, Polish Academy of Sciences)
The presentation discusses the challenges in preparing data for harmonization of 22 international surveys containing questions on protest behavior covering 142 countries and territories in time span from 1966 to 2013. The project database contains 1720 national samples (project*wave*country) for which interviews were conducted. The endeavor is a part of the international project Democratic Values and Protest Behavior: Data Harmonization, Measurement Comparability, and Multi-Level Modeling, coordinated by the Institute of Philosophy and Sociology, Polish Academy of Sciences, and the Mershon Center for International Security Studies, The Ohio State University.
3. Fractal approach to cross-disciplinary research synthesis in social science and harmonization of research projects information space: methodology issues
Mrs Ludmila Kolesnikova (Dr. in Econ.Sc., Professor, IIPAM RANEPA, R&D Co-ordinator, NPO “SOCINCO”)
Mrs Ludmila Vasilenko (Dr. in Soc.Sc., Professor, IIPAM RANEPA, Director, NPO “SOCINCO”)
Mrs Ekaterina Mityasova (MA, Consultant, NPO “SOCINCO”)
The paper suggests new fractal approach to methodology of cross-disciplinary research synthesis in social science and liberal arts on the basis of fractal evolutionary theory of natural ordering. In comparison with theories of selection and nomogenesis it has universal explanatory and prognostic potential. It includes not only synthesis of methods but also synthesis of principles. Key issues for more realistic task of forming ‘United Information Space' (UIS) standards are discussed rather than UIS itself to harmonize common research space. Key implementation risk managerial applications for decision makers are discussed in regards to support of such projects.
4. Increasing transparency and data replicability in the social sciences: a case study using CharmStats
Mr Sebastian Netscher (GESIS - Leibniz Institute for the Social Sciences)
Statistical analyses often require extensive data preparation and variable harmonization, yet there are no agreed documentation standards in the social sciences to facilitate a transparent replication process. Further, researchers who make contributions to their discipline with original data harmonization work are not credited through citations. CharmStats Pro (CS Pro) addresses these problems. CS Pro is free and open-source software for documenting, organizing and publishing data harmonization, aiming to standardize variable harmonization documentation in the social sciences. This article reviews some of the scientific demands driving the need for accurate replication in social research from the perspective of international relations.
5. Harmonizing longitudinal data using CharmStats: the Australian Election Study
Dr Kristi Winters (GESIS - Leibniz Institute for the Social Sciences)
Dr Steven Mceachern (Australian Data Archive)
The harmonization of longitudinal data presents interesting challenges to the scientific norms of replication, reliability and transparency. Many researchers turn to spreadsheet programs in order to organize and document conceptually similar variables over time. The paper reviews the challenges, strategies and solutions applied to longitudinal Australian Election Study variables using the harmonization software CharmStats. Individual variable harmonizations are reviewed and the organization of the codebook is detailed.