Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research

Abstract

We describe an emerging research practice that we call Large-N Qualitative Analysis (LNQA), outline its core components and codify best practice. LNQA starts with hypothesized regularities and causal mechanisms. Regularities take two basic forms: $ \mathrm{Y} $ generalizations (if $ \mathrm{Y} $ then $ \mathrm{X} $ ) or $ \mathrm{X} $ generalizations (if $ \mathrm{X} $ then $ \mathrm{Y} $ ), albeit with more complex variants. To establish a causal generalization requires defining its scope. The strength of the regularity is simply the percentage of cases conforming with the causal claim. The causal force of LNQA, however, comes from within-case causal inference, which demonstrates the presence and operation of the postulated mechanisms in all cases in the scope. The method thus partly obviates problems arising from case selection in qualitative and multimethod work. We also identify a multimethod variant (M-LNQA), which combines LNQA with experimental, quasi-experimental, or observational statistical analysis. An appendix introduces over fifty examples of the method.