# Guiding Principles for Using Mixed Methods Research in Software Engineering

arXiv: https://arxiv.org/abs/2404.06011 · HTML: https://mlsanity.com/paper/2404.06011/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2024.

## Built on by (most cited first, 4 of 4)

- [Toward Agentic Software Engineering Beyond Code: Framing Vision, Values, and Vocabulary](https://mlsanity.com/paper/2510.19692/) (2025)
- [Get on the Train or be Left on the Station: Using LLMs for Software Engineering Research](https://mlsanity.com/paper/2506.12691/) (2025)
- [Consumer Attitudes Towards AI in Digital Health: A Mixed-Methods Survey in Australia](https://mlsanity.com/paper/2604.27744/) (2026)
- [Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers](https://mlsanity.com/paper/2608.27927/) (2026)

## Abstract (arXiv)

Mixed methods research is often used in software engineering, but researchers outside of the social or human sciences often lack experience when using these designs. This paper provides guiding principles and advice on how to design mixed method research, and to encourage the intentional, rigorous, and innovative application of mixed methods in software engineering. It also presents key properties of core mixed method research designs. Through a number of fictitious but recognizable software engineering research scenarios, we showcase how to choose suitable designs and consider the inevitable trade-offs any design choice leads to. We describe several antipatterns that illustrate what to avoid in mixed method research, and when mixed method research should be considered over other approaches.