Methodological Innovations Online

MI Home > Vol. 6, No 2 (2011) Special Edition on Case-Based Approaches to the Analysis of Quantitative Data

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reviewed papers

Special Edition on the Case-Based Approaches to the Analysis of Quantitative Data

1. Introduction to the Special Issue: Case-Based Approaches to the Analysis of Quantitative Data		PDF
	Barry Cooper and Judith Glaesser

2. Use of Clustering Methods to Understand More about the Case abstract		PDF
	Allison B. Dymnicki and David B. Henry

3. Social Network Analysis and Qualitative Comparative Analysis: Their mutual benefit for the explanation of policy network structures abstract		PDF
	Manuel Fischer

4. Selecting cases for in-depth study from a survey dataset: an application of Ragin’s configurational methods1 abstract		PDF
	Judith Glaesser and Barry Cooper

5. Conditional hypotheses in comparative social science: mixed-method approaches to middle-sized data analysis abstract		PDF
	Johan Hellström

6. Crisp-Set Qualitative Comparative Analysis (csQCA), Contradictions and Consistency Benchmarks for Model Specification abstract		PDF
	Axel Marxa and Adrian Dusa

7. Exploring when maternal interest is sufficient for high attainment in mathematics: A configurational analysis using longitudinal data abstract		PDF
	Stephanie Thomson


Abstracts

2. Use of Clustering Methods to Understand More about the Case

During the past seventy years, the field of cluster analysis has emerged, accompanied by a plethora of methods, algorithms, concepts, and terminology that are used in cluster-related research. We refer to cluster analysis (CA) as a general approach composed of several multivariate methods for delineating natural groups or clusters in data sets. In this paper, we describe the ability of CA to provide rich information about the individual case and highlight potential underlying social processes. First, we discuss the theory behind CA as well as differentiate between more and less familiar clustering approaches. Second, we illustrate the value of less familiar clustering techniques by comparing the results of a four wave growth mixture model of family variables versus clustering the same data with a more familiar two-step approach. The growth mixture modelling approach suggested a one-class cluster solution where all families shared similar growth trajectories in parenting practices and family relationship characteristics. However the two-step clustering approach suggested a four-class solution. Finally, we describe ways that CA allows researchers to model processes whose outcomes are the results of a combination of multiple factors and additional benefits of less familiar clustering methods.

3. Social Network Analysis and Qualitative Comparative Analysis: Their mutual benefit for the explanation of policy network structures

By switching the level of analysis and aggregating data from the micro-level of individual cases to the macro-level, quantitative data can be analysed within a more case-based approach. This paper presents such an approach in two steps: In a first step, it discusses the combination of Social Network Analysis (SNA) and Qualitative Comparative Analysis (QCA) in a sequential mixed-methods research design. In such a design, quantitative social network data on individual cases and their relations at the micro-level are used to describe the structure of the network that these cases constitute at the macro-level. Different network structures can then be compared by QCA. This strategy allows adding an element of potential causal explanation to SNA, while SNA-indicators allow for a systematic description of the cases to be compared by QCA. Because mixing methods can be a promising, but also a risky endeavour, the methodological part also discusses the possibility that underlying assumptions of both methods could clash. In a second step, the research design presented beforehand is applied to an empirical study of policy network structures in Swiss politics. Through a comparison of 11 policy networks, causal paths that lead to a conflictual or consensual policy network structure are identified and discussed. The analysis reveals that different theoretical factors matter and that multiple conjunctural causation is at work. Based on both the methodological discussion and the empirical application, it appears that a combination of SNA and QCA can represent a helpful methodological design for social science research and a possibility of using quantitative data with a more case-based approach.

4. Selecting cases for in-depth study from a survey dataset: an application of Ragin’s configurational methods

While ‘establishing the phenomena’, to use Merton’s phrase, is an important part of the sociological enterprise, in then accounting for such empirical regularities, theoretical models are required to understand causal processes. Both regression analysis and configurational methods applied to large datasets can establish patterns of relationships. Following a realist view, we assume that causal mechanisms have generated such patterns, and sound theoretical models are required to understand them. In-depth case studies can contribute to advancing such causal knowledge. We describe how, in the particular context of the configurational mode of analysis that characterises Ragin’s Qualitative Comparative Analysis (QCA), we have selected individuals for in-depth study with the eventual purpose of advancing causal or explanatory understanding of conjunctural empirical regularities concerning educational careers. While forms of regression analysis seek to establish the net effects of ‘independent’ variables, QCA, employing Boolean algebra, analyses the conjunctions of conditions sufficient and/or necessary for an outcome to occur. QCA aims to preserve, holistically, the features of cases and is therefore well-suited to case selection. We use QCA both to undertake an initial large scale cross-case analysis and to subsequently select cases to develop theoretical understanding via within-case analysis. Using QCA’s measures of consistency with relations of sufficiency and necessity, we can classify cases as typical and deviant, with these two types of cases playing different roles in testing and developing theory. Drawing on analyses of the German SOEP dataset undertaken as part of a larger study which is applying case-based configurational methods to English and German survey datasets while undertaking subsequent in-depth interviews with selected cases, we demonstrate how QCA can be used to select cases for interview in a systematic and theoretically informed manner.

5. Conditional hypotheses in comparative social science: mixed-method
approaches to middle-sized data analysis

This paper discusses under which circumstances and how configurational comparative methods (i.e. QCA) and statistical methods can be combined to provide tests for the ‗quasi‘-sufficiency of any given set of combination of causal conditions. When combined, QCA provides the ability to explore causal substitutability (i.e. multiple paths to a given outcome) and the ways in which many multiple causes interact with one another to produce effects, while the statistical elements can provide robust indications of the probable validity of postulated hypotheses. The potential utility of the mixed-method approach for analyzing political phenomena is demonstrated by applying it to cross-national data regarding party positions on European integration and party-based Euroscepticism in Western Europe. The findings show that oppositional stances to European integration are partly associated with non-governmental ideological fringe parties on both the left and right. The empirical example presented in this paper demonstrates that configurational methods can be successfully combined with statistical methods and supplement the QCA-framework by providing statistical tests of ‗almost sufficient‘ claims. However, combining QCA with statistical methods can sometimes be problematic in middle-sized data analysis, especially as the latter usually cannot handle limited diversity (i.e. insufficient information) in the data and/or overtly complex relationships (i.e. having a large number of conjunctional conditions or interacting variables).

6. Crisp-Set Qualitative Comparative Analysis (csQCA), Contradictions and Consistency Benchmarks for Model Specification

The purpose of this paper is to address and test two assumptions on which csQCA is based, namely that csQCA will generate contradictions and low consistency scores if models are ill-specified. The first part of the paper introduces csQCA in general and as a stepwise approach. In a second part a real-life example is introduced with the purpose of illustrating how csQCA operates and as an input for a simulation in the subsequent part. The third part introduces contradictions, consistency, their interrelatedness and the assumptions which are made with regard to contradictions and consistency. Subsequently the assumptions are tested via a simulation on the basis of a csQCA analysis of over 5 million random datasets. The paper argues that researchers cannot always assume that csQCA will generate contradictions or low consistency scores when models are ill-specified. Such an assumption is only justified when csQCA applications take limitations with regard to model specification (the number of conditions and the number of cases) into account. Benchmark tables for model specification purposes are developed. Since these tables are based on a probability value of 0.5 the paper also tests the results for contradictions and consistency for the probabilities which were present in a real-life example. This test shows that the 0.5 probability generates an appropriate measure for the occurrence of contradictions and consistency indicating that the benchmark tables can be used for different applications with different distributions of 0's and 1's in the conditions and outcomes. The paper ends with a conclusion.

7. Exploring when maternal interest is sufficient for high attainment in mathematics: A configurational analysis using longitudinal data

Qualitative Comparative Analysis (QCA) is a case-based method, developed by Ragin (1987, 2000), to analyse medium- and large-n datasets. It uses Boolean algebra to show which configurations of factors in a model are either necessary and/or sufficient for a specified outcome. In the social world, we rarely see perfect necessity and sufficiency but we can use QCA to assess the degree of necessity or sufficiency to find configurations which are quasi-necessary or quasi-sufficient. In this paper, I use crisp-set QCA on data from the 1970 Birth Cohort Study (BCS70) to investigate which configurations of sex, maternal interest, social class and, later, ability are quasi-sufficient for various levels of attainment in maths. Firstly, I explain how to conduct QCA, through the use of examples, before using a set-theoretic measure of consistency to explore the relationship between sex, social class, maternal interest and, what I term, above-average attainment in mathematics. To this model, I then introduce an additional factor of general ability (operationalised as several dichotomous factors, each indicating a certain level of ability) leading to instances of configurations having strong subset relations but containing very few cases. These rows, called remainders, cannot be included in a solution without theoretical justification (Ragin, 2008). For the final stage of the analysis, I create, for two different general ability levels, a „most-complex solution‟ (which excludes all remainder rows) and a parsimonious solution (which includes any remainder row contributing to parsimony). These act as boundaries for the „intermediate solution‟ which contains only those remainders which can, theoretically, be thought to obtain the outcome. I then discuss each intermediate solution and note that, in one case, it is the same as the relevant most-complex version.

Methodological Innovations Online. ISSN: 1748-0612