Latent Condition Qualitative Comparative Analysis

In recent years a great deal of time and effort has been expended toward the development of qualitative methods which retain the advantages of case study and small-n methods, but which ameliorate the various problems (lack of transparency, excessive subjectivity, etc.) Qualitative Comparative Analysis (QCA) is one of the more commonly used examples. QCA allows qualitative researchers to evaluate whether specific factors (conditions) and/or constellations of conditions are necessary, sufficient, or both for a given outcome. While QCA represents an important advance in political science methodology, its primary contributions are confined to how conclusions are drawn from data. Even with a rigorous application of QCA methodology, considerable subjectivity remains in how conditions and outcomes are coded. This is the gap we hope to address with this paper. Taking our inspiration from mixed-methods research design, we develop a new version of QCA, which we call Latent Condition-QCA (LC-QCA). The method relies on the fact that even in studies with a small number of units of analysis, researchers often have a much larger number of units of observation. That is, qualitative coding at higher levels is often based on the aggregation of lower-level units within cases (documents nested with countries, speeches nested within leaders, etc.) In LC-QCA, subjective qualitative coding is conducted at the lowest level of abstraction possible. It is assumed that the probability of observing distinct patterns of these codings is conditional upon the presence or absence of latent conditions. We further assume that conditions at the unit of observation level are predicted by latent conditions at the unit-of-analysis level. We then use hierarchical latent class analysis (HLCA) to assign conditions to units of analysis and observation. These condition assignments are then used to conduct QCA in the normal fashion. The logic used to develop LC-QCA demonstrates a framework for the advancement of social science methods through a true mixture of quantitative and qualitative methods.