An experiment on accuracy, efficiency, productivity and researchers’ satisfaction in digital humanities data analysis: dataset appendix

Data analysis represents the most important group of tasks carried out in research contexts. Due to the current lack of empirical studies about data analysis performance in digital humanities research contexts, we conducted an empirical experiment comparing data analysis performance employed traditional software versus data analysis performance employed software-assistance tools which incorporate cognitive processes in their design. The experiment is designed in terms of accuracy, efficiency, productivity and user satisfaction during the data analysis made by researchers in digital humanities. It allowed us to find some clear benefits of the cognitive inclusion in the software designed for research contexts, with statistically significant differences in terms of accuracy, productivity and researcher’s satisfaction in support of this explicit inclusion, although some efficiency weaknesses are detected. This dataset presents the raw data obtained during the first round of our experiment, with a total of 16 subjects and 32 responses randomly collected as sample. The dataset also contains the results of the statistical analysis performed using a repeated measures experimental design in a mixed lineal model.