{'fraction_non_alphanumeric': 0.022727272727272728, 'fraction_numerical': 0.0, 'mean_word_length': 5.951754385964913, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Repeated measures design in crowdsourcing-based experiments for visualization

Crowdsourcing platforms, such as Amazon's Mechanical Turk (MTurk), are providing visualization researchers with a new avenue for conducting empirical studies. While such platforms offer several advantages over lab-based studies, they also feature some "unknown" or "uncontrolled" variables, which could potentially introduce serious confounding effects in the resultant measurement data. In this paper, we present our experience of using repeated measures in three empirical studies using MTurk. Each study presented participants with a set of stimuli, each featuring a condition of an independent variable. Participants were exposed to stimuli repeatedly in a pseudo-random order through four trials and their responses were measured digitally. Only a small portion of the participants were able to perform with absolute consistency for all stimuli throughout each experiment. This suggests that a repeated measures design is highly desirable (if not essential) when designing empirical studies for crowdsourcing platforms. Additionally, the majority of participants performed their tasks with reasonable consistency when all stimuli in an experiment are considered collectively. In other words, to most participants, inconsistency occurred occasionally. This suggests that crowdsourcing remains a valid experimental environment, provided that one can integrate the means to observe and alleviate the potential confounding effects of "unknown" or "uncontrolled" variables in the design of the experiment.