Establishing conversion of the 16‐item Informant Questionnaire on Cognitive Decline in the Elderly scores into interval‐level data across multiple samples using Rasch methodology. Issue 3 (13th February 2023)

• Record Type: Journal Article
• Authors: Truong, Quoc Cuong; Numbers, Katya; Choo, Carol C.; Bentvelzen, Adam C.; Catts, Vibeke S.; Cervin, Matti; Jorm, Anthony F.; Kochan, Nicole A.; Brodaty, Henry; Sachdev, Perminder S.; Medvedev, Oleg N.
• Is Part Of: Psychogeriatrics. Volume 23:Issue 3(2023)
• Page Start: 411
• Page End: 421
• Publication Date: 2023-02-13
• Subjects: cognitive changes -- Informant Questionnaire on Cognitive Decline in the Elderly -- measurement -- Rasch analysis -- reliability; Geriatric psychiatry -- Periodicals; 618.9768905
• DOI: 10.1111/psyg.12946
• Languages: English
• ISSNs: 1346-3500

Abstract

Abstract : Background: The 16‐item Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE‐16) is a well‐validated and widely‐used measure of cognitive changes (CCs) among older adults. This study aimed to use Rasch methodology to establish psychometric properties of the IQCODE‐16 and validate the existing ordinal‐to‐interval transformation algorithms across multiple large samples. Methods: A Partial Credit Rasch model was employed to examine psychometric properties of the IQCODE‐16 using data ( n = 918) from two longitudinal studies of participants aged 57–99 years: the Older Australian Twins Study ( n = 450) and the Canberra Longitudinal Study ( n = 468), and reusing the Sydney Memory and Ageing Study (MAS) sample ( n = 400). Results: Initial analyses indicated good reliability for the IQCODE‐16 (Person Separation Index range: 0.82–0.90). However, local dependency was identified between items, with several items showing misfit to the model. Replicating the existing Rasch solution could not reproduce the best Rasch model fit for all samples. Combining locally dependent items into three testlets resolved all misfit and local dependency issues and resulted in the best Rasch model fit for all samples with evidence of unidimensionality, strong reliability, and invariance across person factors. Accordingly, new ordinal‐to‐interval transformation algorithms were produced to convert the IQCODE‐16 ordinal scores into interval data to improve the accuracy of its scores. Conclusions: The findings of this study support the reliability and validity of the IQCODE‐16 in measuring CCs among older adults. New ordinal‐to‐interval conversion tables generated using samples from multiple independent datasets are more generalizable and can be used to enhance the precision of the IQCODE‐16 without changing its original format. An easy‐to‐use converter has been made available for clinical and research use.