Linear Mixed-Model Analysis Better Captures Subcomponents of Attention in a Small Sample Size of Persons With Aphasia
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".