Networking in France. Is there a French School of Social Network Analysis?

Social Network Analysis (SNA) is a sociology based on interaction that visualizes and models relations between actors. Whereas interaction is approached by classical scholars, we had to wait until the 1970s and the birth of computer science to see social networks analysis develop. This article investigates the influence of SNA in France from the 1980s and wonder if there is a French school of SNA? To do so, we first resume social networks history and highlight its contribution to sociology. Second, we analyze the trajectory and profile of five “disciplinary entrepreneurs,” whose role in the field is important as they master three necessary languages for SNA: English, Mathematics and Computer Science. Third, in order to put back those individuals in their social structures, we cross SNA with the different French sociological tradition(s) (according to topics and methods). Last, we wonder if the institutionalization process succeeded in the creation of institutions from which a French SNA would be able to expand?     

Developing a culturally-specific mixed methods approach to global research

With much of the development of mixed methods occurring through the works of scholars from the US, the UK, Canada, and the Anglos-Saxon/English-speaking countries, the discourse about a global research community must consider how it needs to be sensitive to diverse cultures. Clearly, the discussion must focus on adapting mixed methods to best fit the problems of countries, the cultural norms of conducting research, and the scholarly talents of in-country investigators. Unfortunately, the mixed methods literature is largely silent on diverse cultural adaptations of mixed methods.In this article, we first trace the involvement of worldwide countries in the historic development of mixed methods. Then, to gain a better understanding of the use and adaptation of mixed methods in diverse world academic cultures, we provide a qualitative study of themes mentioned by participants from seven different countries. From both the historical overview and the qualitative study, we end by making suggestions about specific strategies for a global discourse about mixed methods research, and suggest a de-colonizing presence of the Anglo-Saxon/English-speaking countries in the global expansion of mixed methods.     

How to Construct a Mixed Methods Research Design

This article provides researchers with knowledge of how to design a high quality mixed methods research study. To design a mixed study, researchers must understand and carefully consider each of the dimensions of mixed methods design, and always keep an eye on the issue of validity. We explain the seven major design dimensions: purpose, theoretical drive, timing (simultaneity and dependency), point of integration, typological versus interactive design approaches, planned versus emergent design, and design complexity. There also are multiple secondary dimensions that need to be considered during the design process. We explain ten secondary dimensions of design to be considered for each research study. We also provide two case studies showing how the mixed designs were constructed.     

The Sociology of Inequality and the Rise of Neo-Inequality

Sociologists’ conventional approaches to understanding inequality and poverty do not adequately capture the causes, extent, and consequences of new forms of inequality that have emerged in the late 20th and early 21st centuries. Following a critique of conventional sociological theorizing on inequality and stratification, a synthesis of new sociological/social psychology thinking on inequality, and the use of case study data from Detroit, I explore, describe, and explain how neo-liberal ideology, neo-liberal systems of governance, and neo-liberal policies have created the neo-inequalities that now define our age.     

Sequentielles Mixed-Methods-Sampling: Wie quantitative Sekundärdaten qualitative Stichprobenpläne und theoretisches Sampling unterstützen können

Mixed methods sampling has played a minor role in methodological discussions so far. In particular, there is a lack of information about practical implementations and critical reflections of sampling decisions and sampling steps showing the benefits and challenges of different approaches. Therefore, this paper demonstrates by drawing on an empirical example, how quantitative secondary data analysis (German Socio-Economic Panel, Linked Employer-Employee Data, Employment History) can support the empirically informed formation of contrast groups for qualitative sampling plans as well as field access to these contrast groups. Furthermore, the article will show how an additional theoretical sampling can extend the empirically grounded composition of contrast groups and can build upon the quantitative secondary data. The discussion is based on a project with an explanatory sequential research design which combines quantitative secondary analysis with subsequent expert interviews with human resources managers in companies and narrative interviews with employees. The project focusses on the causes, practices, and consequences of employment relationships with an interrupted membership in the same employing organization (“recalls”).     

Quantifizierung

The challenge in data analysis often lies in taking account of the multi-dimensionality and complexity of the data and at the same time discovering patterns, which requires reduction and simplification. Quantitizing qualitative data can serve as an additional step during data analysis to broaden the perspective and collect complementary views. Examples from research illustrate how information gathered during one data collection can generate different types of data or how qualitative data can be transformed to be analysed statistically. Practicalities of integrating qualitative and quantitative analyses are illustrated to offer good-practice examples for transformation designs (QUAL → QUAN). With the methodological reflection of research practice I evaluate consequences for the field of mixed methods research, in which the statistical analysis of codes created during an interpretive phase plays a minor role.