Manipulation in Organizational Research: On Executing and Interpreting Designs from Treatments to Primes

Abstract

While other applied sciences systematically distinguish between manipulation designs, organizational research does not. Herein, we disentangle distinct applications that differ in how the manipulation is deployed, analyzed, and interpreted in support of hypotheses. First, we define two archetypes: treatments, experimental designs that expose participants to different levels/types of a manipulation of theoretical interest, and primes, manipulations that are not of theoretical interest but generate variance in a state that is. We position these and creative derivations (e.g., interventions and invariant prompts) as specialized tools in our methodological kit. Second, we review 450 manipulations published in leading organizational journals to identify each type's prevalence and application in our field. From this we derive our guiding thesis that while treatments offer unique advantages (foremost establishing causality), they are not always possible, nor the best fit for a research question; in these cases, a non-causal but accurate test of theory, such as a prime design, may prove superior to a causal but inaccurate test. We conclude by outlining best practices for selection, execution, and evaluation by researchers, reviewers, and readers.