Differential item functioning analysis of Montreal Cognitive Assessment Scale on educational level
Bibliographic record
Abstract
Objective To explore how much impact that the educational level may exert on the Montreal Cognitive Assessment Scale (MoCA) for screening mild cognitive impairment (MCI) among the old people. Methods 3 230 participants aged 60+ years in 6 districts of Xiamen were selected using multi-stage stratified randomized sampling. One-way ANOVA and Scheffe were performed using SPSS 23.0 to compare the MoCA scores among different educational level groups. Binary logistic regression was used to analyze differential item functioning (DIF). If the P<0.000 3 125 (Bonferroni correction), the corresponding item is considered to have DIF. Results The educational level of the elderly in Xiamen is generally lower, with 39.63% (1 133/2 858) illiterate and 28.04% (801/2 858) educated only at primary school, respectively. MoCA scores of elderly with different educational backgrounds were quite different with statistical significance(χ2=413.73, P<0.01), but Scheffe showed that there was no significant difference in MoCA scores between high school educated and undergraduate. Age, gender and MoCA scores of the elderly were significantly correlated (P<0.01) . The items that had DIF included: trail making test, copy cube,numbers of clock, hands of clock, abstraction 1 and 2, and word 1,2,3, and 5 of delayed recall, adding up to 11 items. The items of trail making test and copy cube only have DIF in the people above junior high school education level, while the 2 items of abstraction dimension only show DIF in the people above senior high school education level. Conclusion Most items of MoCA scale are suitable for screening people with all educational levels except when applying trail making test, copy cube and the 2 items of abstract dimension. Key words: Aged; Differential item functioning analysis; Montreal Cognitive Assessment Scale; Educational
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".