Regression discontinuity design: a guide for strengthening causal inference in HRD

Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. This paper aims to educate human resource development (HRD) researchers and practitioners on the implementation of RD design as an ethical alternative for making causal claims about training interventions.

To demonstrate the key features of RD designs, a simulated data set was generated from actual pre-test and post-test diversity training scores of 276 participants from three organizations in the USA. Parametric and non-parametric analyses were conducted, and graphical presentations were produced.

This study found that RD design can be used for evaluating training interventions. The results of the simulated data set yielded statistically significant results for the treatment effects, showing a positive causal effect of the training intervention. The analyses found support for the use of RD models with retrospective training intervention data, eliminating ethical concerns from random group assignment. The results of the non-parametric model provided evidence of the plausibility of finding the right balance between precision of estimates and generalizable results, making it an alternative to experimental designs.

This study contributes to the HRD field by explicating the implementation of a sophisticated, statistical tool to strengthen causal claims, contributing to an evidence-based HRD approach to practice and providing the R syntax for replicating the analyses contained herein.

Despite the growing number of scholarly articles being published in HRD journals, very few have used experimental or quasi-experimental design approaches. Therefore, a very limited amount of research has been devoted to uncovering causal relationships.