Linearity vs. Circularity? On Some Common Misconceptions on the Differences in the Research Process in Qualitative and Quantitative Research

Methodological discussions often oversimplify by distinguishing between “the” quantitative and “the” qualitative paradigm and by arguing that quantitative research processes are organized in a linear, deductive way while qualitative research processes are organized in a circular and inductive way. When comparing two selected quantitative traditions (survey research and big data research) with three qualitative research traditions (qualitative content analysis, grounded theory and social-science hermeneutics), a much more complex picture is revealed: The only differentiation that can be upheld is how “objectivity” and “intersubjectivity” are defined. In contrast, all research traditions agree that partiality is endangering intersubjectivity and objectivity. Countermeasures are self-reflexion and transforming partiality into perspectivity by using social theory. Each research tradition suggests further countermeasures such as falsification, triangulation, parallel coding, theoretical sensitivity or interpretation groups. When looking at the overall organization of the research process, the distinction between qualitative and quantitative research cannot be upheld. Neither is there a continuum between quantitative research, content analysis, grounded theory and social-science hermeneutics. Rather, grounded theory starts inductively and with a general research question at the beginning of analysis which is focused during selective coding. The later research process is organized in a circular way, making strong use of theoretical sampling. All other traditions start research deductively and formulate the research question as precisely as possible at the beginning of the analysis and then organize the overall research process in a linear way. In contrast, data analysis is organized in a circular way. One consequence of this paper is that mixing and combining qualitative and quantitative methods becomes both easier (because the distinction is not as grand as it seems at first sight) and more difficult (because some tricky issues of mixing specific to mixing specific types of methods are usually not addressed in mixed methods discourse).DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Process-Oriented Micro-Macro-Analysis: Methodological Reflections on Elias and Bourdieu

Both Norbert Elias and Pierre Bourdieu argue that both the research question and social theory should guide methodology and methods. Both theorists argue that any social theory should keep in mind the importance of macro-level structures; in micro-level interactions and social change. In order to fully grasp patterns of social change, social scientists would require a complex causal processed-oriented micro-macro-analysis. Because in research practice, it is almost never possible to implement the full methodology, both Elias and Bourdieu used a short-cut by using a six-step methodology, consisting of (1) self-reflexivity and detachment; (2) an explaining of the theoretical perspective; (3) a reconstructing of the figuration’s/field’s socio-genesis; (4) a reconstructing of the micro-level; (5) a reconstructing of the macro-level; and (6) a theoretical synopsis. By using an example on local variation of economic practices, the paper illustrates this six-step methodology and concludes with further questions for future research

Taking perspectivity seriously: a suggestion of a conceptual framework for linking theory and methods in longitudinal and comparative research

'Einer der Hauptsstreitpunkte in der Debatte darum, ob sich die Soziologie wissenschaftstheoretisch eher positivistisch oder konstruktivistisch verorten solle, ist die Frage, ob und wie (stark) die Subjektivität des Forschers dessen Forschung(sergebnisse) verformt. In der Geschichtswissenschaft scheint dieses Problem seit langem gelöst bzw. verlagert: Gesteht man sich ein, dass sich die Subjektivität des Forschers niemals völlig ausschalten lässt, erscheint es hilfreicher, die Auswirkung verschiedener Formen der Subjektivität (Verstehen, Perspektivität und Parteilichkeit) auf den Forschungsprozess zu untersuchen. Der Aufsatz widmet sich der Frage des Umgangs mit Perspektivität. Ausgehend von dem Argument, dass verschiedene theoretische Perspektiven auf dasselbe Phänomen einerseits nützlich sind, andererseits aber die Gefahr besteht, dass dadurch einer Disziplin die gemeinsame Kommunikationsbasis verlorengeht und Forschungsergebnisse nicht mehr miteinander vergleichbar sind, wird ein Bezugsrahmen vorgeschlagen, in dem Forscher ihre Forschungsfrage anhand von vier Dimensionen verorten können: 1. Handlungsbereich, 2. Handlungsebene, 3. Raum und 4. Zeit mit den Subdimensionen 4a. Zeitschicht und 4b. Verlaufsform. Hierdurch kann die Forschungsfrage präzisiert, und es können die für eine Theorie und Fragestellungen geeigneten Datenerhebungs- und -auswertungsverfahren gewählt werden.' (Autorenreferat)'Positivism and Constructivism often seem to be inconsolable positions in sociological discourse. The main point of dispute is if subjectivity influences perception of reality and thus social research. Using a distinction made by German historians, the author frames the problem differently: The question is not if subjectivity influences perception (it does!) but how it frames perception. In other words, one can distinguish between 'good' and 'bad' subjectivity. Three forms of subjectivity have to be distinguished: partiality ('Parteilichkeit'); perspectivity ('Perspektivität') and 'Verstehen'. The author addresses the problem of perspectivity: If we allow for multi-perspectivity in a globalizing world, how can we compare results? Is there any common ground for social scientists from different theoretical backgrounds? She argues that social scientists need a common framework which is not theory itself but which helps to compare social theories and link them with both methodology and research practice. Using such a framework, researchers can classify their theoretical and research goals, determine the appropriate data and methodologies for answering their question. The author suggests that such a framework should consist of at least sub-dimensions, which of course have to be filled with content: 1. Action Sphere; 2. Analysis Level; 3. Space; 4. Time with the two sub-dimensions 4a. duration and 4b. pattern.' (author's abstract)

Measurement and selection bias in longitudinal data: a framework for re-opening the discussion on data quality and generalizability of social bookkeeping data

'Die Autorin vergleicht Massendaten mit Umfragedaten und anderen Formen prozessproduzierter Daten und diskutiert die Relevanz von Massenakten für die historische, sozialhistorische und soziologische Forschung. Nach einer Analyse des aktuellen Stands der Forschung über Massendaten kommt sie zu dem Schluss, dass die methodologische Diskussion über Verwaltungsdaten wieder aufgegriffen werden sollte. Sie schlägt einen Analyserahmen vor, in den sie sowohl die ältere Debatte aus den 1970ern und 1980ern, als auch die neuere Debatte einordnet. Es scheinen vier Punkte zentral: a) Datenkunde und Messfehler; b) Datenselektion und Stichprobenprobleme, c) Archivierung und EDV sowie d) Datenaufbereitung. Abschließend erläutert die Autorin offene Fragen, die von der Methodenforschung geklärt werden sollten.' (Autorenreferat)'The author compares mass data with survey data and other process-generated data and discusses their relevance for historical, historical social science and sociological research. After summarizing the current state of methodological knowledge on public administrational data, she concludes that the discussion on mass data has to be re-opened. She suggests a framework for such a discussion and links the older German discussion from the 1970s and 1980s to the discussion newly arising. She suggests that the major issues are a) data lore and measurement quality; b) data selection and sampling problems; c) archiving and statistical programs and d) data preparation. After summing up the state of the debate, the authors suggests which questions should be answered in future research.' (author's abstract

Problems of linking theory and data in historical sociology and longitudinal research

'Theory and data are closely linked in empirical research: Data are the main source for building and testing theories, and without theoretical focus, it is impossible to select and interpret data. Still, the relationship between theory and data is only rarely discussed and, if so, only on a general level. Focussing on process-oriented and longitudinal research questions, the authors of this special issue contribute to this discussion by elaborating some data types that can be used for analyzing long-term social processes. For each specific data type, it is important to ask about their specific characteristics and how this effects interpretation. The authors address these questions from a broad range of theories and by either re-analyzing research-elicited data or by using process-generated data.' (author's abstract

Comparing societies and cultures: challenges of cross-cultural survey research as an approach to spatial analysis

The paper shows how cross-cultural, cross-societal, cross-national, multi-national and international comparative survey researchers have been handling space since the 1950s and how it can be used for spatial analysis. Using the concepts of the Survey Life Cycle and the Total Survey Error (TSE), the paper discusses two major methodological problems cross-cultural survey methodology faces: The problems of (1) equivalence and (2) demarcation

Digital Data, Administrative Data, and Survey Compared: Updating the Classical Toolbox for Assessing Data Quality of Big Data, Exemplified by the Generation of Corruption Data

In the digital age, new data types have become available that can, potentially, be used in social science research. Besides data that were originally created for scientific purposes (research-elicited data), administrative mass data (traditional-type big data) and data from digital devices (new-type big data) have become more and more relevant for research processes. Both data types can be subsumed under the term “big data.” In this paper, we scrutinize the quality of administrative mass data on corruption in contrast to research-elicited data (e.g., survey data). Since data quality is crucial for the measurement of a social phenomenon such as corruption, we pose the question of how a social phenomenon can be measured by means of data from these different sources. As a first step, we refer to the so-called Bick-Mueller-Model. It was developed in the 1980s for observing the special features and particularities of administrative mass data (traditional-type big data). We contrast this model with the so-called Error-Approach that is typically applied in survey research. In order to account for new trends in data generation and application, we show the progress that has been made since Bick and Mueller introduced their model and discuss new features of digitalism and new technologies. We conclude that the features of the so-called Bick-Mueller are useful for tackling the particularities of administrative data and also – to some degree – new-type big data. The “error” perspective that is inherent both in the classical survey research and in the so-called Bick-Mueller model also applies to new-type big data when it comes to assessing their quality. Moreover, it is possible that the data from these different sources can complement each other. For this, researchers must be aware of the fact that neither data source actually measures corruption directly. For answering specific research questions, it is crucial to consider the advantages and disadvantages of using specific data types

Grasping processes of innovation empirically: a call for expanding the methodological toolkit; an introduction

"During the past decades, innovation research has yielded countless empirical studies in a variety of disciplines. For all this quantity, we still lack an adequate understanding of basic qualities and mechanisms of its central subject. Which processes and conditions bring innovation about? How does it spread? And what is its genuine nature? Critics argue that these shortcomings have their roots in the conceptual limitations of established perspectives on innovation and in the fact that researchers confine themselves to studying technical and scientific novelties or marketable products. This self-restriction stands in marked contrast to the observation that innovation plays an important role in contemporary societies. The term is at least ubiquitous and its usage common in all societal fields. In the introduction to this HSR Special Issue, we subscribe to this critique and argue that the conceptual reductionism comes along with severe methodical and methodological limitations. These become manifest in a joint dominance of quantitative indicator-based research and ethnographic single case studies. Thus, researchers of innovation disregard a variety of possible data types and forms of analysis and rarely apply complex designs. It is also not common to consider the combination of multiple types of data and analysis in mixed methods approaches. The most serious issue, however, is that mainstream innovation research remains ignorant of a multitude of potential research questions and thereby loses sight of whole areas of interest. An overview of the empirical studies in this HSR Special Issue shows that the range of methods used is wider at the edges of the field of research. In order to relate these methods to each other and to the theoretical foundations of innovation research, we suggest a middle-range debate on methodology." (author's abstract

The Refiguration of Spaces and Methodological Challenges of Cross-Cultural Comparison

In most reflections on cross-cultural comparison, scholars assume that "cultures" can be relatively clearly demarcated spatially and that "space" itself is a given entity. However, theories such as the theory of refiguration of spaces have stressed both that it is important to deconstruct the category "space" itself and that social processes have been characterized by major spatial transformations since the mid-twentieth century. Based on this idea, in two FQS thematic issues scholars from various disciplines will ask what consequences the refiguration of spaces has for cross-cultural comparison and what one can methodologically learn from research on cross-cultural comparison about the analysis of refiguration of spaces. In the first issue, authors from sociology and historical sciences are focusing mostly on the methodological issues. In this article, we provide a frame for this debate by ordering the earlier discussion on cross-cultural comparison along four questions: Why do we compare? Who or what are we comparing where and when? How can we compare? What methodological conclusions can be drawn from the debate on cross-cultural comparison concerning the analysis of social processes across different spatial scales and time layers in order to assess causality?In Debatten zum Kulturvergleich gehen Forschende oft davon aus, dass sich "Kulturen" räumlich relativ klar abgrenzen lassen und dass "Raum" selbst eine gegebene Entität sei. Vertreter*innen von raumsoziologischen Ansätzen wie etwa der Theorie der Refiguration von Räumen unterstreichen jedoch, dass die die Kategorie "Raum" selbst dekonstruiert werden muss und dass empirisch seit der Mitte des zwanzigsten Jahrhunderts große räumliche Transformationen beobachtet werden können. Ausgehend von dieser Beobachtung befassen sich in zwei FQS-Themenschwerpunkten Autor*innen aus verschiedenen Disziplinen damit, was aus der Refiguration von Räumen für den Kulturvergleich folgt und welche methodologischen Lehren man umgekehrt aus der kulturvergleichenden Forschung für die Analyse der Refiguration von Räumen ziehen kann. Im ersten Schwerpunkt fokussieren Autor*innen aus der Soziologie und den Geschichtswissenschaft vorwiegend auf methodologische Fragen. In diesem Beitrag rahmen wir diese Diskussion, indem wir die bisherige Forschung zum Kulturvergleich anhand von vier Fragen ordnen: Warum vergleichen wir? Wen oder was vergleichen wir wo und wann? Wie können wir vergleichen? Welche methodischen Schlussfolgerungen lassen sich aus der Debatte um den kulturübergreifenden Vergleich für die Analyse sozialer Prozesse über verschiedene räumliche Skalierungsebenen und Zeitschichten hinweg ziehen, um Kausalbeziehungen zu analysieren