Contrasting Variable-Analytic and Case-Based Approaches to the Analysis of Survey Datasets: Exploring How Achievement Varies by Ability across Configurations of Social Class and Sex

Abstract

(2010) 'Contrasting variable-analytic and case-based approaches to the analysis of survey datasets : exploring how achievement varies by ability across congurations of social class and sex.', Methodological innovations online., 5 (1). pp. 4-23. Further information on publisher's website:http://dx.doi.org/10.4256/mio.2010.0007Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThe context for this paper is the ongoing debate concerning the relative merits, for the analysis of quantitative data, of, on the one hand, variable-analytic correlational methods, and, on the other, the case-based set theoretic methods developed by Charles Ragin. While correlational approaches, based in linear algebra, typically use regression to establish the net effects of several "independent" variables on an outcome, the set theoretic approach analyses, more holistically, the conjunctions of factors sufficient and/or necessary for an outcome to occur. Here, in order to bring out key differences between the approaches, we focus our attention on the basic building blocks of the two approaches: respectively, the concept of linear correlation and the concept of a sufficient and/or necessary condition. We initially use invented data (for ability, educational achievement, and social class) to simulate what is at stake in this methodological debate and we then employ data taken from the British National Child Development Study to explore the structuring of the relationship between respondents" early measured ability and later educational achievement across various configurations of parental and grandparental class origin and sex. The substantive idea informing the analysis, derived from Boudon"s work, is that, for respondents from higher class origins, ability will tend to be sufficient but not necessary for later educational achievement while, for lower class respondents, ability will tend to be necessary but not sufficient. We compare correlational analyses, controlling for class and gender, with fuzzy set analyses to show that set theoretic indices can better capture these varying relationships than correlational measures. In conclusion, we briefly consider how our demonstration of some of the advantages of the set theoretic approach for modelling empirical relationships might be related to the debate concerning the relation between observed regularities and causal mechanisms.

Selecting Cases for in-Depth Study from a Survey Dataset: An Application of Ragin's Configurational Methods

“…This account has a clear affinity with a description of the form, 'early achievement is necessary but not sufficient for later achievement'. Furthermore, introducing causal heterogeneity, we might hypothesise that, for some classes, early achievement will tend to be necessary but not sufficient, but others, sufficient but not necessary (see Cooper & Glaesser, 2010b, for some relevant evidence).…”

Rethinking policy-related research: charting a path using qualitative comparative analysis and complexity theory

“…This article argues that the challenge for policy-related research is therefore one of understanding complex causality when causes are to be found in the conditions that combine together in 'cases', including the emergence of 'outcomes' as a result of these combinations (Ragin, 2000). Complex causality has been demonstrated for many social phenomena and is the context in which much policy intervention occurs (Blackman, 2006;Head, 2008;Cooper and Glaesser, 2010). But we need to go beyond the term as a general description of 'wicked issues' and understand what it really means for how our research methods can help inform what policy practitioners should do.…”

“…Quantitative analyses are still often regarded as 'hard' evidence, despite the difficulty of interpreting what a correlation coefficient or odds ratio actually implies for practical intervention and the refusal of many aspects of the social world to conform to the assumptions that underpin common quantitative techniques (Cooper and Glaesser, 2010). In particular, great care is needed in characterising the world with means and variances when 'non-averageness' is typical and a mean, even qualified by a measure of the spread of values around it, has no embodiment in any actual cases (Pawson and Tilley, 1997;Liebovitch and Scheurle, 2000;Chapman, 2004).…”

“…However, to regard the crisp-set technique as simple is very misleading since considerable thought and work has to go into data collection, data reduction, definitions and threshold judgements. This includes judgements about thresholds at which causal combinations are regarded as sufficient when some contradictory cases are evident (Cooper and Glaesser, 2010;Rihoux et al, 2011).…”

Troubling Theory in Case Study Research

“…One alternative to the search for causal relations is to view the general principles we draw on in explaining people's behaviour as rational models, in other words as patterns of intelligible behaviour that we would expect to occur if people are pursuing a particular goal or if 8 A classic illustration of one version of the problem is Skocpol's (1979) study of revolutions, where the number of cases in history that fit her definition of this phenomenon is very small. See, for example, Cooper and Glaesser's application of Ragin's qualitative comparative analysis to large data sets (Cooper 2005;Cooper and Glaesser 2010).…”