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Building on the long common history of board games and intelligence research, we have developed a new deductive reasoning test based on the popular game Mastermind. Research questions of this registered report are: (a) Is a psychometrically sound measurement of the ability to solve Mastermind items possible (i.e., a reliable, one-dimensional measurement with a good coverage of difficulties)? (b) Is the ability to solve Mastermind items substantially related to other measures of cognitive ability (i.e., convergent and divergent validity)? (c) Can item difficulty be predicted by the number of colors, positions, premises and a newly proposed entropy-based index?

Attrition in longitudinal studies is a major threat to the representativeness of the data and the generalizability of the findings. Typical approaches to address systematic nonresponse are either expensive and unsatisfactory (e.g., oversampling) or rely on the unrealistic assumption of data missing at random (e.g., multiple imputation). Thus, models that effectively predict who most likely drops out in subsequent occasions might offer the opportunity to take countermeasures (e.g., incentives). With the current study, we introduce a longitudinal model validation approach and examine whether attrition in two nationally representative longitudinal panel studies can be predicted accurately. We compare the performance of a basic logistic regression model with a more flexible, data-driven machine learning algorithm—gradient boosting machines. Our results show almost no difference in accuracies for both modeling approaches, which contradicts claims of similar studies on survey attrition. Prediction models could not be generalized across surveys and were less accurate when tested at a later survey wave. We discuss the implications of these findings for survey retention, the use of complex machine learning algorithms, and give some recommendations to deal with study attrition.

By violating social norms, deviant behavior is an important issue that affects society as a whole and also has serious consequences for its individuals. Different scientific disciplines have proposed theories on deviant behavior, which often fall short of predicting actual behavior. In this registered report, we will use data of the longitudinal National Study of Adolescent to Adult Health (Add Health) to examine the predictability of juvenile delinquency and criminal behavior in adults, differentiating between drug-related, property, and violent offenses. We propose to compare the predictive accuracy of traditional regression models to different machine learning algorithms (elastic net regressions and gradient boosting machines). Furthermore, we will explore the differences in the selection of the most important predictors of juvenile delinquency vs. adult criminal behavior to consider developmental changes and potentially identify risk factors at an early stage.

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 DEAL

ObjectiveSuicide is a major global health concern and a prominent cause of death in adolescents. Previous research on suicide prediction has mainly focused on clinical or adult samples. To prevent suicides at an early stage, however, it is important to screen for risk factors in a community sample of adolescents. MethodWe compared the accuracy of logistic regressions, elastic net regressions and gradient boosting machines in predicting suicide attempts by 17-year-olds in the Millennium Cohort Study (N = 7,347 of which 48.6% were male), combining a large set of self- and other-reported variables from different categories (e.g., demographics, health, drug use, personality, sexuality). ResultsBoth machine learning algorithms outperformed logistic regressions and achieved quite similar predictive accuracies: Balanced accuracy was .76 for predicting suicide attempts three years in advance and even higher when using data from the same measurement wave (.85). The most important predictor of future suicide attempts was previous self-harm, followed by variables of mental health, emotion and motivation, drug use, sexuality, demography, and victimization. ConclusionSuicide attempts in adolescence can be accurately predicted using panel data of community samples. However, suicide prevention should be tailored to specific phases in the development of adolescents. Our results additionally show that more complex models that allow for nonlinear and interaction effects do not lead to better performance in comparison to linear regularized models.

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.

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. More specifically, we compile three 75-item health knowledge scales using Ant Colony Optimization, a metaheuristic algorithm that is inspired by the foraging behavior of ants, to optimize factor saturation, construct coverage, or a compromise of both. We demonstrate that our approach is well suited to balance out construct coverage and factor saturation when constructing a health knowledge test. Finally, we discuss conceptual problems with the modeling of declarative knowledge and provide recommendations for the assessment of health knowledge.

The concept of differentiation describes increasing or decreasing similarities between inter-individual differences on psychological constructs, reflecting processes of specialization or adaptation. In this study, we studied age-differentiation in personality traits in (1) the trait domain and facet loadings, (2) the correlations between trait domains, and (3) trait domain, facet, and item (residual) variances. We used three large cross-sectional samples (Ns > 3000) covering 16–90 years of age with broad measures of the Big Five, Five-Factor, and HEXACO models. We examined age effects on the model parameters using local structural equation modeling. We found a high stability of the trait domain loadings, suggesting relatively stable trait domain compositions across age. Extraversion-Openness correlations increased across age for all three measures, whereas an increase in the Extraversion-Agreeableness and decrease in the absolute Neuroticism-Extraversion correlations only replicated across the five-dimensional models. Inter-individual differences in personality were similar across age in the trait domains and facets but differed substantially for item residuals. In summary, the structure and individual differences in broad personality traits is relatively stable across the adult lifespan, with most age-differences only affecting the item level.

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, 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).

Contains fulltext : 228242.pdf (Publisher’s version ) (Open Access) Drawing on the concept of a gale of creative destruction in a capitalistic economy, we argue that initiatives to assess the robustness of findings in the organizational literature should aim to simultaneously test competing ideas operating in the same theoretical space. In other words, replication efforts should seek not just to support or question the original findings, but also to replace them with revised, stronger theories with greater explanatory power. Achieving this will typically require adding new measures, conditions, and subject populations to research designs, in order to carry out conceptual tests of multiple theories in addition to directly replicating the original findings. To illustrate the value of the creative destruction approach for theory pruning in organizational scholarship, we describe recent replication initiatives re-examining culture and work morality, working parents’ reasoning about day care options, and gender discrimination in hiring decisions. Significance statement It is becoming increasingly clear that many, if not most, published research findings across scientific fields are not readily replicable when the same method is repeated. Although extremely valuable, failed replications risk leaving a theoretical void - reducing confidence the original theoretical prediction is true, but not replacing it with positive evidence in favor of an alternative theory. We introduce the creative destruction approach to replication, which combines theory pruning methods from the field of management with emerging best practices from the open science movement, with the aim of making replications as generative as possible. In effect, we advocate for a Replication 2.0 movement in which the goal shifts from checking on the reliability of past findings to actively engaging in competitive theory testing and theory building. Scientific transparency statement The materials, code, and data for this article are posted publicly on the Open Science Framework, with links provided in the article. 19 p.