Dataset for: How happy is happy enough? A cross-cultural comparison of optimal cut points for the Positive Mental Health Scale.

DOI

Objective: As positive mental health (PMH) has a significant impact on general and mental health, it is an important target for interventions. Cut points are a useful basis for identifying participants with a greater need for such interventions. Method: Representative (n=15,370) and student (n=22,833) samples from Germany, Russia, the US, and China were re-analyzed. Three different anchors were used as reference to determine optimal cut points: (1) the Satisfaction with Life Scale, (2) a combined measure of PMH-related questionnaires, and (3) the General Assessment of Functioning Scale. A kernel-based method to determine optimal cut points and bootstrapping to identify potential cross-cultural differences were used. Results: Acceptable to excellent levels of classification accuracy were found in relation to life satisfaction and the combined measure (AUCs between 0.74 and 0.89) across all samples. Using the General Assessment of Functioning Scale resulted in poor discriminatory power (AUC=.69). Cut points identified as optimal differed systematically between countries and samples. The lowest cut points were found in Germany and the highest in the US with Russia and China in between. Cut points for students were lower than for the general population. Conclusions: Country and sample specific cut points for the PMH-scale should be used to identify individuals with high versus low levels of positive mental health. Specifically, we suggest using cut points of 19, 22, and 24 in Germany, Russia, and the US, respectively. For student samples, we recommend cut points of 18, 19 and 20 in Germany, Russia, and China, respectively.

Identifier
DOI https://doi.org/10.23668/psycharchives.12978
Metadata Access https://api.datacite.org/dois/10.23668/psycharchives.12978
Provenance
Creator Bonnin, Gabriel; Hirschfeld, Gerrit; Von Brachel, Ruth; Margraf, Jürgen
Publisher PsychArchives
Contributor Leibniz Institut Für Psychologie (ZPID)
Publication Year 2023
OpenAccess true
Representation
Language English
Resource Type Dataset
Discipline Social Sciences