Measuring Statistics Anxiety in a MOOC: A Psychometric Evaluation of the STARS’ Anxiety Domain

Serving millions of diverse learners across the world, Massive Open Online Courses (MOOCs) remain a relevant delivery approach in higher education and beyond. Nevertheless, MOOC environments create some challenges for optimizing learners’ self-regulated learning skills, resulting in high attrition rates in these educational contexts. Pre-existing anxieties can be particularly problematic in the context of statistics-related courses, which are known to create higher levels of anxiety among learners. Informed by the self-regulated learning theory, this study (approved by the Institutional Review Board) investigated whether the anxiety dimension of the Statistics Anxiety Rating Scale (STARS) had evidence of validity and reliability when used to assess learners’ statistics anxiety in a Massive Open Online Course focusing on power and sample size analysis. Results from both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) provide evidence of validity and reliability. The results suggest that the anxiety dimension of the STARS instrument could be used as a pre-assessment tool to gauge learners’ initial statistics anxiety. Based on these results, theoretical and practical considerations are discussed.