School of Psychology, University of Waikato, Hamilton, New Zealand.
Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, New South Wales, Australia.
Psychogeriatrics. 2023 May;23(3):411-421. doi: 10.1111/psyg.12946. Epub 2023 Feb 13.
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.
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).
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.
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.
16 项认知衰退老年知情者问卷（IQCODE-16）是一种经过充分验证且广泛使用的测量老年人认知变化（CCs）的方法。本研究旨在使用 Rasch 方法来建立 IQCODE-16 的心理测量特性，并验证现有的有序到区间转换算法在多个大样本中的有效性。
使用部分信用 Rasch 模型，使用来自两个年龄在 57-99 岁的参与者的纵向研究的数据（n=918）：老年澳大利亚双胞胎研究（n=450）和堪培拉纵向研究（n=468），并重新使用悉尼记忆与衰老研究（MAS）样本（n=400）来检查 IQCODE-16 的心理测量特性。
初步分析表明 IQCODE-16 的可靠性良好（个人分离指数范围：0.82-0.90）。然而，项目之间存在局部依赖性，几个项目与模型不匹配。复制现有的 Rasch 解决方案无法为所有样本重现最佳 Rasch 模型拟合。将局部依赖性项目组合成三个测试模块可以解决所有的不匹配和局部依赖性问题，并为所有样本提供最佳的 Rasch 模型拟合，证明了其具有维度性、强大的可靠性和在个体因素方面的不变性。因此，产生了新的有序到区间的转换算法，将 IQCODE-16 的有序分数转换为区间数据，以提高其分数的准确性。
本研究的结果支持 IQCODE-16 在测量老年人认知变化方面的可靠性和有效性。使用来自多个独立数据集的样本生成的新有序到区间转换表更具普遍性，可用于提高 IQCODE-16 的精度，而无需改变其原始格式。已经为临床和研究用途制作了一个易于使用的转换器。
