Investigating the factor structure of the Montreal Cognitive Assessment: a qualitative review
Bibliographic record
Abstract
Introduction: The Montreal Cognitive Assessment (MoCA) is one of the most widely used screening instruments for Mild Cognitive Impairment (MCI) and dementia. Despite its popularity, uncertainty remains regarding its factorial structure and psychometric functioning across populations and cultures. This review aims to critically evaluate the factorial validity and dimensionality of the MoCA through Classical Test Theory (CTT) and Item Response Theory (IRT) models. Method: Following the PICO framework, a qualitative review was conducted using PubMed, Web of Science, PsycINFO, and Google Scholar. Inclusion criteria consisted of peer-reviewed empirical studies employing exploratory or confirmatory factor analyses, as well as IRT in samples of older adults. Results: Across CTT studies, findings ranged from two-factor to hierarchical multi-factor models, with a general cognitive factor frequently emerging. IRT analyses generally supported a unidimensional latent structure, identifying Executive Function, Visuospatial, and Language items as the most discriminative, while Orientation and Memory showed low discriminative power. Conclusion: Our results showed that the MoCA primarily measures a general cognitive dimension, reflecting variable contributions from different cognitive domains. Standardizing scoring metrics and ensuring cross-cultural factorial equivalence are essential to enhance the tool's accuracy and interpretation of its score.
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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 | 0.053 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".