Full presentations of many of the entries below have already been distributed to BMS subscribers and RC33 members over the BMS-RC33 distribution list
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Laurent Hébert-Dufresne, Joshua A. Grochow and Antoine Allard, “Multi-scale Structure and Topological Anomaly Detection via a New Network Statistic - The Onion Decomposition”, Scientific Reports (2016) 6, article number: 31708. We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting sub-graphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks.
Colin Elman, John Gerring and James Mahoney, “Case Study Research - Putting the Quant Into the Qual”, Sociological Methods & Research, 2016 45(3): 375-391. Case studies are usually considered a qualitative method. However, some aspects of case study research – notably, the selection of cases – may be viewed through a quantitative template. In this symposium, we invite authors to contemplate the ways in which case study research might be conceived, and improved, by applying lessons from large-n cross-case research.
John Gerring and Lee Cojocaru, “Selecting Cases for Intensive Analysis - A Diversity of Goals and Methods”, Sociological Methods & Research, 2016 45(3): 392-423. This study revisits the task of case selection in case study research, proposing a new typology of strategies that is explicit, disaggregated, and relatively comprehensive. A secondary goal is to explore the prospects for case selection by algorithm, aka ex ante, automatic, quantitative, systematic, or model-based case selection. We lay out a suggested protocol and then discuss its viability. Our conclusion is that it is a valuable tool in certain circumstances, but should probably not determine the final choice of cases unless the chosen sample is medium-sized. Our third goal is to discuss the viability of medium-n samples for case study research, an approach closely linked to algorithmic case selection and occasionally practiced by case study researchers. We argue that medium-n samples occupy an unstable methodological position, lacking the advantages of efficiency promised by traditional, small-n case studies but also lacking the advantages of representativeness promised by large-n samples.
Michael C. Herron and Kevin M. Quinn, “A Careful Look at Modern Case Selection Methods”, Sociological Methods & Research, 2016 45(3): 458-92. Case studies appear prominently in political science, sociology, and other social science fields. A scholar employing a case study research design in an effort to estimate causal effects must confront the question, how should cases be selected for analysis? This question is important because the results derived from a case study research program ultimately and unavoidably rely on the criteria used to select the cases. While the matter of case selection is at the forefront of research on case study design, an analytical framework that can address it in a comprehensive way has yet to be produced. We develop such a framework and use it to evaluate nine common case selection methods. Our simulation-based results show that the methods of simple random sampling, influential case selection, and diverse case selection generally outperform other common methods. And, when a research design mandates that only a very small number of cases, say one or two, be selected in the course of a research program, the very simple method of sampling from the largest cell of a 2 × 2 table is competitive with other, more complicated, case selection methods. We show as well that a number of common case selection strategies work well only in idiosyncratic situations, and we argue that these methods should be abandoned in favor of the more powerful and robust case selection methods that our analytical framework identifies.
Thomas Grauenhorst, Michael Blohm and Achim Koch, “Respondent Incentives in a National Face-to-face Survey - Do They Affect Response Quality?”, Field Methods, 2016 28: 266-83. Respondent incentives are a popular instrument to achieve higher response rates in surveys. However, the use of incentives is still a controversial topic in the methodological literature with regard to the possible reduction or increase in response quality. We conducted an experiment in a large-scale German face-to-face study in which the treatment group was promised a modest monetary incentive. We used different indicators of response quality and compared the incentivized group with the control group. Our results indicate that in general there are no systematic differences between the incentivized and the control group concerning response quality. We found some hints that specific subgroups react differently to incentives in terms of response behavior. While response quality usually tends to be lower for older respondents, we found that in the incentivized group the response quality is higher for older respondents as compared to younger ones regarding the level of item nonresponse.
Joshua Kjerulf Dubrow and Irina Tomescu-Dubrow, “The Rise of Cross-national Survey Data Harmonization in the Social Sciences - Emergence of an Interdisciplinary Methodological Field”, Quality & Quantity, 2016 50(4): 1449-67. Cross-national survey data harmonization combines surveys conducted in multiple countries and across many time periods into a single, coherent dataset. Methodologically, ex post survey data harmonization is especially complex because it combines projects that were not specifically designed to be comparable. We examine the institutional and intellectual history of nine large scale ex post survey data harmonization (SDH) projects in the social sciences from the 1980s to the 2010s. An interdisciplinary methodological field of SDH slowly emerges, facilitated in part by a partnership between academia and government and from the coordinated contributions of social scientists, survey methodologists and computer scientists. While there has been a learning process, it is in terms of accumulated practicalities, and not with the coordination or institutional apparatus one would expect from a 30-year effort.
