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

Suicide 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. We 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), combining a large set of self- and other-reported variables from different categories. Both machine learning algorithms outperformed logistic regressions and achieved similar balanced accuracies (.76 when using data 3 years before the self-reported lifetime suicide attempts and .85 when using data from the same measurement wave). We identified essential variables that should be considered when screening for suicidal behavior. Finally, we discuss the usefulness of complex machine learning models in suicide prediction.

Public events knowledge in an age-heterogeneous sample: Reminiscence bump or bummer?

The reminiscence bump describes an increased recollection of autobiographic experiences made in adolescence and early adulthood. It is unclear if this phenomenon can also be found in declarative knowledge of past public events. To answer this question, we assessed public events knowledge (PEK) about the past 6 decades with a 120-item knowledge test across six domains in a sample of 1,012 Germans that were sampled uniformly across the ages of 30-80 years. General and domain-specific PEK scores were analyzed as a function of age-at-event. Scores were lower for public events preceding participants' birth and stayed stable from the age-at-event of 5-10 years onward. There was no significant peak in PEK in adolescence or early adulthood, arguing against an extension of the reminiscence effect to factual knowledge. We examined associations between PEK and relevant variables such as crystallized intelligence (Gc), news consumption, and openness to experience with structural equation models. Strong associations between PEK and Gc were established, whereas the associations of PEK with news consumption and openness were mainly driven by their link to declarative knowledge. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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. 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.

Caught in the Act: Predicting Cheating in Unproctored Knowledge Assessment.

Cheating is a serious threat in unproctored ability assessment, irrespective of countermeasures taken, anticipated consequences (high vs. low stakes), and test modality (paper-pencil vs. computer-based). In the present study, we examined the power of (a) self-report-based indicators (i.e., Honesty-Humility and Overclaiming scales), (b) test data (i.e., performance with extremely difficult items), and (c) para data (i.e., reaction times, switching between browser tabs) to predict participants' cheating behavior. To this end, 315 participants worked on a knowledge test in an unproctored online assessment and subsequently in a proctored lab assessment. We used multiple regression analysis and an extended latent change score model to assess the potential of the different indicators to predict cheating. In summary, test data and para data performed best, while traditional self-report-based indicators were not predictive. We discuss the findings with respect to unproctored testing in general and provide practical advice on cheating detection in online ability assessments.

A Reappraisal of the Threshold Hypothesis of Creativity and Intelligence.

Intelligence has been declared as a necessary but not sufficient condition for creativity, which was subsequently (erroneously) translated into the so-called threshold hypothesis. This hypothesis predicts a change in the correlation between creativity and intelligence at around 1.33 standard deviations above the population mean. A closer inspection of previous inconclusive results suggests that the heterogeneity is mostly due to the use of suboptimal data analytical procedures. Herein, we applied and compared three methods that allowed us to handle intelligence as a continuous variable. In more detail, we examined the threshold of the creativity-intelligence relation with (a) scatterplots and heteroscedasticity analysis, (b) segmented regression analysis, and (c) local structural equation models in two multivariate studies (N1 = 456; N2 = 438). We found no evidence for the threshold hypothesis of creativity across different analytical procedures in both studies. Given the problematic history of the threshold hypothesis and its unequivocal rejection with appropriate multivariate methods, we recommend the total abandonment of the threshold.