Case Selection and Causal Inference in Qualitative Research

Abstract:     

The validity of causal inferences in qualitative research depends on the selection of cases. We contribute to current debates on qualitative research designs by using Monte Carlo simulations to evaluate the performance of different case selection techniques or algorithms. We show that causal inference from qualitative research becomes more reliable when researchers select cases from a larger sample, maximize the variation in the variable of interest, simultaneously minimize variation of the confounding factors, and ignore all information on the dependent variable. We also demonstrate that causal inferences from qualitative research become much less reliable when the variable of interest is strongly correlated with confounding factors, when the effect of the variable of interest becomes small relative to the effect of the confounding factors, and when researchers analyze dichotomous dependent variables.

Number of Pages in PDF File: 49