Mastering Variation in Human Studies

Abstract

The human factor is prevalent in empirical software engineering research. However, human studies often do not use the full potential of analysis methods by combining analysis of individual tasks and participants with an analysis that aggregates results over tasks and/or participants. This may hide interesting insights of tasks and participants and may lead to false conclusions by overrating or underrating single-task or participant performance. We show that studying multiple levels of aggregation of individual tasks and participants allows researchers to have both insights from individual variations as well as generalized, reliable conclusions based on aggregated data. Our literature survey revealed that most human studies perform either a fully aggregated analysis or an analysis of individual tasks. To show that there is important, non-trivial variation when including human participants, we reanalyze 12 published empirical studies, thereby changing the conclusions or making them more nuanced. Moreover, we demonstrate the effects of different aggregation levels by answering a novel research question on published sets of fMRI data. We show that when more data are aggregated, the results become more accurate. This proposed technique can help researchers to find a sweet spot in the tradeoff between cost of a study and reliability of conclusions.