Modeling Skill Acquisition Using Time Series Analysis

Most skill acquisition studies examine pre-post performance test differences using t test or analysis of variance methods. These methods assume independence of variance across trials. That is, the error associated with Trial 1 is uncorrelated with Trial 2 error. This statistical approach is founded on the assumption that information from early trials does not affect later performance. In the case of within-subject performance change, this assumption may be challenged. The purpose of this review is to explore the utility of Autoregressive Integrated Moving Average (ARIMA; McCleary & Hay, 1980) modeling to describe the process of skill acquisition. ARIMA modeling assumes correlation of variances between prior and current performance. Older and younger participants who performed 300 trials of a modified Stroop task produced the data used in this assessment. Group models suggested similar performance trends; however, these group models fail to recognize significant individual variation in skill acquisition pa...