A Mokken scale analysis of the Kessler-6 screening measure among Chinese older population: findings from a national survey

Abstract

Background The aging population increases rapidly across the world. Timely and effective screening of their mental-health problems is important to individuals, families, and the whole society. The Kessler-6 screening measure (K6) is a very popular instrument for non-specific psychological distress. However, few studies have focused on the psychometric properties of this instrument in the older population. Methods The present study employed Mokken scale analysis to evaluate its dimensionality and structure. This study also used differential item functioning (DIF) to examine whether the same structure existed across sex in a national representative sample of old Chinese people. Data were drawn from a public data set, the 2010 China Family Panel Studies (CFPS2010), and responses from a total of 6450 participants aged 60 years old and above (3136 males and 3314 females) were included in the final analysis. Results Mokken scale analysis supported the unidimensional structure of the K6. Differential item functioning (DIF) analysis revealed that two of the six items (“Hopeless” and “Everything was an effort”) were marked for DIF based on the Chi-square. However, their impacts were negligible in terms of McFadden’s pseudo R2. Conclusions The K6 demonstrates adequate psychometric properties in the old Chinese population. The sum of all six items can be used as an indicator of non-specific psychological distress. Differences in the indicator across sex should be considered as a real difference in psychological distress between the female and the male.

Factor structure and longitudinal measurement invariance of the K6 among a national representative elder sample of China

“…We conducted a series of confirmatory factor analyses to examine which one fit the data best among the four candidate models identified previously. These models included a one-factor model proposed by Kessler et al with all items loaded on the same factor [ 6 ], a two-factor model proposed by Lee et al with three items (Nervous", "Restless or fidgety", and "Everything was an effort") loaded on the anxiety factor and the rest three items ("Hopeless", "Depressed", and "Worthless") loaded on the depression factor [ 20 ], a two-factor model proposed Bessaha with two items ("Nervous" and "Restless or fidgety") loaded on the anxiety factor, while all the other four items on the depression factor [ 21 ], as well as our two-factor model, with three items("Depressed", "Nervous", and "Restless or fidgety") loaded on the anxiety factor, and the other three items("Hopeless", "Everything was an effort ", and "Worthless ") on the depression factor [ 3 ]. Table 4 shows the model goodness-of-fit indices.…”

“…Zhang and Li (2020) argued that the diverse findings might be due to the differences in samples and statistical methods. For example, some studies collected responses from the general population, and other studies investigated in more specific populations, such as adolescents and emerging adults [ 3 ]. Moreover, most studies explicitly or implicitly assumed the responses on the K6 as continuous.…”

“…The China Health and Retirement Longitudinal Study (CHARLS) reported that the prevalence estimate of depression disorder was up to 33.09% [ 2 ]. As the number of older adults is rapidly growing, prevention, screening, and treatment of mental health problems in this population increasingly become a heavy burden to individuals, families, and even the whole society [ 3 ].…”

“…Nevertheless, other factor solutions were proposed in a few studies, such as a modified single-factor model(with residual correlations among some items) [ 4 ], a two-factor model (with an item ("Everything was an effort") loading on the second factor) [ 5 ], a two-factor solution( with three items ("Nervous", "Restless or fidgety", and "Everything was an effort") on the anxiety factor, and another three items ("Hopeless", "Depressed", and "Worthless") on the depression factor) [ 20 ], a two-factor model (with four items formed the depression factor, and the rest two items ("Nervous" and "Restless or fidgety") formed the anxiety factor) and a second-order two-factor model [ 21 , 22 ]. In a more recent study, we derived a two-factor model with exploratory factor analysis (EFA), with three items("Depressed", "Nervous", and "Restless or fidgety") loaded on the anxiety factor and the other three items("Hopeless", "Everything was an effort ", and "Worthless ") on the depression factor[ 3 ]. We also compared it with previous models using confirmatory factor analysis (CFA) and found that only this model was acceptable regarding model-data fit indexes.…”

Simplified Post-stroke Functioning Assessment Based on ICF via Dichotomous Mokken Scale Analysis and Rasch Modeling

“…The 5-point qualifier system remained unchanged. In recent years, non-parametric IRT models based on Mokken scale analysis (MSA) have begun to attract attention in the medical areas ( 15 – 17 ). In comparison with the necessary Guttman hypothesis held by the Rasch model, which means that highly competent subjects must be bound to score on the easy tasks ( 18 ), MSA holds the probabilistic Guttman hypothesis that high-ability subjects were more likely to complete low-difficulty tasks.…”