Psychometric Properties of the Montreal Cognitive Assessment (MoCA): A Comprehensive Investigation
Bibliographic record
Abstract
BackgroundThe Montreal Cognitive Assessment (MoCA) is a common tool for the assessment of global cognition in older adults. Despite its popularity among clinicians and researchers, the test’s psychometric properties are still uncertain.ObjectiveTo examine fundamental psychometrics properties of the MoCA that have not been established so far (i.e., factorial structure, general factor saturation, and measurement invariance).DesignCohort study.SettingPopulation-based invitation-type survey in city and rural areas in Hyogo prefectures and Tokyo Metropolitan, Japan.SubjectsIndividuals (N = 2,408) aged 69 to 91 clustered in three cohorts (69-71-year-olds, 79-81-year-olds, and 89-91-year-olds).MethodsExploratory Factor Analysis and Confirmatory Factor Analysis.ResultsThe MoCA shows an overall stable hierarchical factorial structure and a satisfactory general factor saturation. Also, measurement invariance occurs across participants with different age, educational level, economic status, and gender. ConclusionThis comprehensive investigation upholds the idea that the MoCA is a psychometrically valid tool for the assessment of global cognition in older adults.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".