First impressions count: Therapists' impression on patients' motivation and helping alliance predicts psychotherapy dropout.

Objective: With meta-analytically estimated rates of about 25%, dropout in psychotherapies is a major concern for individuals, clinicians, and the healthcare system at large. To be able to counteract dropout in psychotherapy, accurate insights about its predictors are needed.

Method: We compared logistic regression models with two machine learning algorithms (elastic net regressions and gradient boosting machines) in the prediction of therapy dropout in two large inpatient samples ( = 1,691 and  = 12,473) using baseline and initial process variables reported by patients and therapists.

Predicting treatment response using machine learning: A registered report.

Objective: Previous research on psychotherapy treatment response has mainly focused on outpatients or clinical trial data which may have low ecological validity regarding naturalistic inpatient samples. To reduce treatment failures by proactively screening for patients at risk of low treatment response, gain more knowledge about risk factors and to evaluate treatments, accurate insights about predictors of treatment response in naturalistic inpatient samples are needed.

Methods: We compared the performance of different machine learning algorithms in predicting treatment response, operationalized as a substantial reduction in symptom severity as expressed in the Patient Health Questionnaire Anxiety and Depression Scale.

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

Sound scale construction is pivotal to the measurement of psychological constructs. Common item sampling procedures emphasize aspects of reliability to the disadvantage of aspects of validity, which are less tangible. We use a health knowledge test as an example to demonstrate how item sampling strategies that focus on either factor saturation or construct coverage influence scale composition and demonstrate how to find a trade-off between these two opposing needs.

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).

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.