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Updated: Aug 12, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Compensating for memory losses throughout aging: validation and normalization of the memory compensation
Sophie Martin1, Clémence Mazzocco2, Pascale Maury2
1EPSYLON, Université Montpellier III, France; UGECAM-UEROS, Castelnau Le Lez, France.
Aim:
The MCQ is a seven-factor scale that measures individual differences in the tendency to select particular strategies and to overcome perceived or real memory losses. Our aim was to establish a French version of the MCQ and to evaluate its psychometric properties in a lifespan perspective. We first tested the underlying factor structure of the MCQ in a large sample of 749 adults from aged from 18 to 92 years.
Results:
The results showed that the factor structure of the French version corresponded well with the one obtained in English-, Dutch- and Spanish-speaking samples, supporting the cross-national robustness of the MCQ. We confirmed a seven-factor order model that supports the construct validity of the questionnaire. The reliabilities of the scales were good (α>.70) to acceptable (α=.66 and .62). Criterion validity was verified by means of significant correlations between health composites and MCQ subscales. Gender and Age affected most of the MCQ subscales but not the Level of Education (LE).
Conclusion:
The MCQ revealed to be a heuristic tool for assessing daily compensatory behaviors that are developed in order to achieve successful aging. Thus, regression-based normative data and a user-friendly computer program were provided to facilitate scoring and norming by clinicians and researchers who need to assess daily compensatory behaviors.
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