Useful Statistical Methods for Human Factors Research in Software Engineering: A Discussion on Validation with Quantitative Data

TL;DR

This paper discusses the importance of applying statistical validation techniques like Test-Retest, Cronbach's alpha, and Exploratory Factor Analysis to enhance the reliability and validity of human factors survey research in software engineering.

Contribution

It advocates for the broader adoption of established statistical validation methods in human factors research within software engineering, illustrating their application.

Findings

Statistical validation improves survey reliability.

Methods like Cronbach's alpha support construct validity.

Applying these techniques enhances research rigor.

Abstract

In this paper we describe the usefulness of statistical validation techniques for human factors survey research. We need to investigate a diversity of validity aspects when creating metrics in human factors research, and we argue that the statistical tests used in other fields to get support for reliability and construct validity in surveys, should also be applied to human factors research in software engineering more often. We also show briefly how such methods can be applied (Test-Retest, Cronbach's {\alpha}, and Exploratory Factor Analysis).