Must-have, or maybe not? A sensitivity-based extension to necessary condition analysis

The necessary condition analysis (NCA) has become a prominent method for identifying must-have factors required for an outcome. With increasing sample sizes, identifying such must-have factors becomes difficult as extreme responses are more likely to occur. Addressing this concern, we introduce a novel method, the NCA with an effect size sensitivity extension (NCA-ESSE), which allows researchers to better understand the sensitivity of the NCA results to extreme response patterns. We offer guidelines for the NCA-ESSE method’s use and illustrate its efficacy using a well-known job satisfaction model. By extending NCA’s capabilities to assess the sensitivity of necessary conditions, our research enhances the method’s practical utility and helps ensure the robustness and replicability of its outcomes and conclusions.

DDC Class

300: Social Sciences

519: Applied Mathematics, Probabilities

001.4: Research

Lizenz

https://creativecommons.org/licenses/by/4.0/

Publication version

publishedVersion

Name

1-s2.0-S014829632500743X-main-1.pdf

Size

1.26 MB

Format

Adobe PDF

Options

Must-have, or maybe not? A sensitivity-based extension to necessary condition analysis