The HEX-ACO-18:Developing an age-invariant HEXACO short scale using ant colony optimization

In this study, we developed an age-invariant 18-item short form of the HEXACO Personality Inventory for use in developmental personality research. We combined the item selection procedure ant colony optimization (ACO) and the model estimation approach local structural equation modeling (LSEM). ACO is a metaheuristic algorithm that evaluates items based on the quality of the resulting short scale, thus directly optimizing criteria that can only be estimated with combinations of items, such as model fit and measurement invariance. LSEM allows for model estimation and measurement invariance testing across a continuous age variable by weighting participants, rather than splitting the sample into artificial age groups. Using a HEXACO-100 dataset of N = 6,419 participants ranging from 16 to 90 years of age, we selected a short form optimized for model fit, measurement invariance, facet coverage, and balance of item keying. To achieve scalar measurement invariance and brevity, but maintain construct coverage, we selected 18 items to represent three out of four facets from each HEXACO trait domain. The resulting HEX-ACO-18 short scale showed adequate model fit and scalar measurement invariance across age. Furthermore, the usefulness and versatility of the item and person sampling procedures ACO and LSEM is demonstrated

Validation and generalizability of machine learning prediction models on attrition in longitudinal studies

Gefördert im Rahmen eines Open-Access-Transformationsvertrags mit dem Verla

Predicting Lifetime Suicide Attempts in a Community Sample of Adolescents Using Machine Learning Algorithms

Gefördert im Rahmen eines Open-Access-Transformationsvertrags mit dem Verla

Compiling Measurement Invariant Short Scales in Cross‐Cultural Personality Assessment Using Ant Colony Optimization

This article earned Open Materials badge through Open Practices Disclosure from the Center for Open Science: https://osf.io/tvyxz/wiki. The materials are permanently and openly accessible at: https://osf.io/ds7j5/. Author's disclosure form may also be found at the Supporting Information in the online version.Gefördert im Rahmen des Projekts DEA

The Road to Hell Is Paved With Good Intentions: How Common Practices in Scale Construction Hurt Validity

Gefördert im Rahmen des Projekts DEA

A Demonstration With the Need for Cognition Scale

Gefördert im Rahmen eines Open-Access-Transformationsvertrags mit dem Verla

Predicting treatment response using machine learning: A registered report

Gefördert im Rahmen des Projekts DEA

Mapping established psychopathology scales onto the Hierarchical Taxonomy of Psychopathology (HiTOP)

The Hierarchical Taxonomy of Psychopathology (HiTOP) organizes phenotypes of mental disorder based on empirical covariation, offering a comprehensive organizational framework from narrow symptoms to broader patterns of psychopathology. We argue that established self-report measures of psychopathology from the pre-HiTOP era should be systematically integrated into HiTOP to foster cumulative research and further the understanding of psychopathology structure. Hence, in this study, we mapped 92 established psychopathology (sub)scales onto the current HiTOP working model using data from an extensive battery of self-report assessments that was completed by community participants and outpatients (N = 909). Content validity ratings of the item pool were used to select indicators for a bifactor-(S-1) model of the p factor and five HiTOP spectra (i.e., internalizing, thought disorder, detachment, disinhibited externalizing, and antagonistic externalizing). The content-based HiTOP scales were validated against personality disorder diagnoses as assessed by standardized interviews. We then located established scales within the taxonomy by estimating the extent to which scales reflected higher-level HiTOP dimensions. The analyses shed light on the location of established psychopathology scales in HiTOP, identifying pure markers and blends of HiTOP spectra, as well as pure markers of the p factor (i.e., scales assessing mentalizing impairment and suspiciousness/epistemic mistrust)

Mapping established psychopathology scales onto the Hierarchical Taxonomy of Psychopathology (HiTOP)

Gefördert im Rahmen des Projekts DEA