Johannes Stark "Taking Control of the Control Condition"

Control conditions in experimental management research are crucial for establishing causal relationships by isolating the effects of independent variables (IVs) on dependent variables (DVs). Despite their importance, control conditions receive little attention. This review examines the current state of control condition selection and design in high-tier management journals from 2021 to 2023. True control conditions, such as no-treatment, all-but-treatment, and treatment-as-usual controls, offer robust baselines for comparison. In contrast, pseudo-control conditions, including opposite-treatment-level and alternative-treatment conditions, can lead to false causal claims due to their inherent limitations. Our review of 958 experiments from 421 study papers shows that 23% of the studies we reviewed presented causal claims that were not backed by their designs, misinterpreting their results and possibly exaggerating effect sizes. The lack of transparency and construct validity further exacerbates these issues. In response we offer guidelines for selecting and designing control conditions and propose best practices for enhancing transparency.

Info: Experiments are widely used in various management disciplines, from organizational behavior (Thau et al., 2014) and operations management (Lonati et al., 2019) to strategy research (Bolinger et al., 2022). Based on our review, we will present best-practices and guidelines for selecting and designing control conditions to simplify the research process and support authors and reviewers in experimental management research.

Johannes Stark is a PhD candidate in organizational behavior at KLU. His research focusses on interpersonal and intergroup dynamics related to social class in organizations.