The Montreal Cognitive Assessment: Norms and Reliable Change Indices for Standard and MoCA-22 Administrations
Bibliographic record
Abstract
OBJECTIVE: The Montreal Cognitive Assessment (MoCA) is among the most frequently administered cognitive screening tests, yet demographically diverse normative data are needed for repeated administrations. METHOD: Data were obtained from 18,410 participants using the National Alzheimer's Coordinating Center Uniform Data Set. We developed regression-based norms using Tobit regression to account for ceiling effects, explored test-retest reliability of total scores and by domain stratified by age and diagnosis with Cronbach's alpha, and reported the cumulative change frequencies for individuals with serial MoCA administrations to gage expected change. RESULTS: Strong ceiling effects and negative skew were observed at the total score, domain, and item levels for the cognitively normal group, and performances became more normally distributed as the degree of cognitive impairment increased. In regression models, years of education was associated with higher MoCA scores, whereas older age, male sex, Black and American Indian or Alaska Native race, and Hispanic ethnicity were associated with lower predicted scores. Temporal stability was adequate and good at the total score level for the cognitively normal and cognitive disorders groups, respectively, but fell short of reliability standards at the domain level. CONCLUSIONS: MoCA total scores are adequately reproducible among those with cognitive diagnoses, but domain scores are unstable. Robust regression-based norms should be used to adjust for demographic performance differences, and the limited reliability, along with the ceiling effects and negative skew, should be considered when interpreting MoCA scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".