Methods, Data, and Software

Methods, Data, and Software


This page covers my methodological work, my written software packages and guides, as well as my portfolio of datasets and data solutions. 


Publications

Negative Case Selection: Justifications and Consequences for Set-Theoretic MMR


Sociological Methods & Research, 46(4): 739-771. 2017.

https://doi.org/10.1177/0049124115591015


Abstract: The combined usage of qualitative comparative analysis (QCA) and process tracing (PT) in set-theoretic multi-method research (MMR) holds great potential for reaching valid inferences. Established views of case selection after QCA hold that studying negative cases provides lessons about the causes of an outcome in a limited set of circumstances. In particular, recommendations focus on negative cases only if they contradict the analysis or if suitably similar positive match cases exist to leverage comparisons. By contrast, I argue that set-theoretic MMR can gain from studying negative cases even when these conditions do not hold. First, negative cases can give insights into why an outcome fails to occur. Second, they can help guard against theoretical inconsistency between explanations for the outcome and its absence. Third, they can ensure that the mechanisms producing the outcome and its absence are not too similar to be logically capable of resulting in different outcomes. Following these arguments, I recommend that studies of negative cases in set-theoretic MMR focus on failure mechanisms in carefully bounded populations, search for theoretical inconsistency among mechanisms, and focus in part on the mechanism proposed to produce the outcome.

Fuzzy-set Case Studies


Sociological Methods & Research, 46(3): 422-455. 2017.

https://doi.org/10.1177/0049124115578032


Abstract: Contemporary case studies rely on verbal arguments and set theory to build or evaluate theoretical claims. While existing procedures excel in the use of qualitative information (information about kind), they ignore quantitative information (information about degree) at central points of the analysis. Effectively, contemporary case studies rely on crisp sets. In this article, I make the case for fuzzy-set case studies. I argue that the mechanisms that are the focal points of contemporary case study methods can be modeled as set-theoretic causal structures. I show how case study claims translate into sufficiency statements. And I show how these statements can be evaluated using fuzzy-set tools. This procedure permits the use of both qualitative and quantitative information throughout a case study. As a consequence, the analysis can determine whether one or more cases are both qualitatively and quantitatively consistent with its claims. Or whether some or all cases are consistent by kind but not by degree.



Software Guides and Packages


Conjoint Experiments with Multiple Questions in Qualtrics. This guide shows how to randomise the order of questions following conjoint experiments in Qualtrics when a conjoint is reiterated. The guide is available here.


Conjoint experiments offline in Qualtric. This guide shows how to implement conjoint experiments in Qualtrics without access to the internet and hence without reliance on dynamic online resources. The guide is available here.


Introduktion til surveys med formR (in Danish, with Jonas Krogh Madsen and Joakim Schollert Larsen). The guide shows how to implement simple surveys in the flexible, free-to-use online survey platform formR. The guide is available here


Dfid.dev: A Package for Managing Partially Overlapping Three-Dimensional Data Structures. Roskilde: Department of Social Science and Business (Package for the R environment)


Family.resemblance: A Package for Generalised Membership Assignment in Family Resemblance, Mixed, and Mixture Concept Structures. Roskilde: Department of Social Science and Business (Package for the R environment)


Coarsefuz: A Package for Analysing Coarse Fuzzy Sets. Roskilde: Department of Social Science and Business (Package for the R environment)


Amce.diag: A Package for Partially Automated Assumption Tests for Average Marginal Component Effects in Conjoint Analyses. Odense: Department of Political Science and Public Management (Package for the R environment)


Data and code


[In this section, I will upload datasets, replication code, and solutions to data problems when relevant]

Contact: ksass@ruc.dk