A Rasch model based psychometric evaluation of different versions of the Minimal Insomnia Symptom Scale (MISS) among adults

Abstract Background Insomnia is a common health complaint among adults and is associated with poor health. Sleeping patterns differ due to age, with younger adults having greater problems with sleep deprivation causing problems during daytime, and older adults having greater problems with nocturnal awakenings during nights. Screening tools are needed to initially capture insomnia symptoms in need for further assessment. The study aim was to psychometrically test and compare the original three item Minimal Insomnia Symptom Scale (MISS), revised version of MISS (MISS-R), and a four-item scale (MISS-4) on an adult population, by using the Rasch measurement model. Methods A cross-sectional design was used, and the sample consisted of participants in an online survey of a Citizen Panel. Out of the total sample ( N = 1517), 784 (52%) persons with answers on four sleep items, age, and gender were included in this study. The majority of the sample were men ( n = 536), 248 were women, and 52% were younger (16–59 years). Rasch model analyses focused on targeting, reliability, response category functioning, model fit, differential item functioning (DIF) and score transformation. Results The MISS and the MISS-R had good measurement properties. When adding the item “daytime disturbance” to the MISS, and creating MISS-4, the measurement properties deteriorated. When replacing the original MISS item “not rested by sleep” with the item “daytime disturbance” in the MISS-R, the measurement properties maintained like those of the MISS. There were, as expected, DIF between the younger (below 60 years) and older samples (60 years or older) in insomnia symptoms. The reliability for the MISS was 0.58 and the MISS-R 0.57. Conclusions The MISS and the MISS-R demonstrate good measurement properties as insomnia screening questionnaires. When using either the MISS or the MISS-R to identify potential insomnia, a ≥ 6 cut-off seems to be most appropriate. While psychometrically supported, determination of the optimal cut-score for clinical insomnia identification should consider gold standard diagnostic criteria in future studies.