Supplementary Material of "Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods"

This repository contains the supplementary material for the article Thiel, K. E., Baumeister, M., Krämer, N., Groll, A., Pauly, M., & Wischnewski, M. (2026). Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods (arXiv:2604.02992). arXiv. https://doi.org/10.48550/arXiv.2604.02992. It includes the raw survey export, one preprocessing script, and three R Markdown analysis documents. The material is intended to enable transparent reconstruction of the reported preprocessing and statistical analyses.

Regularization methods, widely advocated to reduce overfitting and stabilise inference, are readily available in modern software, but are not consistently used by data analysts. We investigate this implementation gap in a large-scale empirical study of trust in, and acceptance of, regularization techniques, based on N = 606 data analysts. Drawing on measurement frameworks from technology acceptance research, we survey practitioners and embed a randomized experiment to test whether written methodological recommendations increase trust or intended use. Instead, adoption intentions are strongly associated with analysts' perceptions of ease of implementation and practical benefit, such as improved bias control or interpretability. Perceived social norms also emerge as a central driver. These results indicate that uptake of statistical methodology depends less on formal recommendations than on usability, perceived utility, and community practice.