Enhancing Precision of the Telephone Interview for Cognitive Status-Modified (TICS-M) Using the Rasch Model

The Telephone Interview for Cognitive Status-modified (TICS-M) is a well-established and widely used screening instrument for dementia and assessment of global cognitive function in older people. This study aimed to evaluate the psychometric properties of the TICS-M and to enhance the accuracy of the instrument using Rasch methodology. Partial Credit Rasch model was applied to the TICS-M scores. The sample selected for Rasch analysis consisted of 432 participants aged 70-90 years (M = 78.85, SD = 4.73) including 195 males (237 females), and 132 (30.56%) of whom were diagnosed with dementia after the baseline assessment. Initial analysis indicated good reliability of the TICS-M assessment scores, but there were three misfitting items and local dependency issues. Combining locally dependent and misfitting items into super-items achieved the best Rasch model fit for the TICS-M. This modification improved reliability of the assessment scores and resulted in no misfitting items, no local dependency, strict unidimensionality, and invariance across individual factors such as participants age, sex, diagnosis, and in-person neuropsychological assessment scores. Satisfying Rasch model expectations allowed for creation of a transformation table to convert raw TICS-M scores into interval-level data, which improves precision of the instrument. In summary, the TICS-M assessment scores demonstrated excellent reliability as reflected by Person Separation Index (PSI = 0.86) and met expectations of the unidimensional Rasch model after minor adjustments. The ordinal-to-interval transformation table can be used to increase accuracy of the TICS-M without altering its current format. These findings contribute to more accurate assessments of cognitive decline in older people and screening for conditions such as dementia.

Automated summary

The study evaluated the psychometric properties of the TICS-M and used the Rasch methodology to improve its accuracy. The results showed good reliability of the TICS-M assessment scores, but there were some misfitting items and local dependency issues. By combining these problematic items into super-items, the Rasch model fit for the TICS-M improved, resulting in improved reliability and no misfitting or local dependency. The creation of a transformation table allows for converting raw TICS-M scores into interval-level data, increasing the precision of the instrument.