Linear Mixed-Model Analysis Better Captures Subcomponents of Attention in a Small Sample Size of Persons With Aphasia

Abstract

Purpose: Although there are several reports of attention deficits in aphasia, studies are typically limited to a single component within this complex domain. Furthermore, interpretation of results is affected by small sample size, intraindividual variability, task complexity, or nonparametric statistical models of performance comparison. The purpose of this study is to explore multiple subcomponents of attention in persons with aphasia (PWA) and compare findings and implications from various statistical methods—nonparametric, mixed analysis of variance (ANOVA), and linear mixed-effects model (LMEM)—when applied to a small sample size. Method: Eleven PWA and nine age- and education-matched healthy controls (HCs) completed the computer-based Attention Network Test (ANT). ANT examines the effects of four types of warning cues (no, double, central, spatial) and two flanker conditions (congruent, incongruent) to provide an efficient way to assess the three subcomponents of attention (alerting, orienting, and executive control). Individual response time and accuracy data from each participant are considered for data analysis. Results: Nonparametric analyses showed no significant differences between the groups on the three subcomponents of attention. Both mixed ANOVA and LMEM showed statistical significance on alerting effect in HCs, orienting effect in PWA, and executive control effect in both PWA and HCs. However, LMEM analyses additionally highlighted significant differences between the groups (PWA vs. HCs) for executive control effect, which were not evident on either ANOVA or nonparametric tests. Conclusions: By considering the random effect of participant ID, LMEM was able to show deficits in alerting and executive control ability in PWA when compared to HCs. LMEM accounts for the intraindividual variability based on individual response time performances instead of relying on measures of central tendencies.