Martin Schultze

Open Science practices are rapidly becoming more common, resulting in the need for students to learn these practices as part of their academic training. Despite the growing relevance, realistic implementations in study programs that effectively mimic openness remain scarce. To address this gap, we developed LIFOS—Local Infrastructure for Open Science, a GitLab-based platform specifically built for student training in Open Science practices. Accompanied by open educational material, LIFOS emulates the Open Science Framework, enabling students to preregister their studies, share data, analysis scripts, and final reports, akin to established open repository platforms. This setup allows archiving student work sustainably and operates distinctly from the research activities of academic staff. Importantly, LIFOS is only accessible within the university, fostering a safe learning environment. Here, we will present the concept and integration of LIFOS, allowing others to adopt the platform in their teaching, and further share insights into its impact and usability based on data we collected from students in psychology bachelor courses who worked with LIFOS.

Aims: This pilot study aimed to investigate the feasibility and initial efficacy of a smartphone-based approach-avoidance task (AAT)-training in reducing problematic internet use among adolescents and young adults. The intervention targets automatic action tendencies (approach biases) toward internet-related cues to reduce maladaptive usage patterns. Methodology: In a randomized controlled pilot trial with a waitlist control group, 24 participants with elevated levels of problematic internet use (aged 12–31 years) were included. Participants in the intervention group engaged in a 21-day AAT training via the PROTECTapp, while the control group was placed on a waitlist. The primary outcome was the severity of problematic internet use measured by the Compulsive Internet Use Scale (CIUS). Secondary outcomes included symptoms of specific online behavioral addictions (i.e., problematic social media use and problematic gaming) and psychological well-being. Results: The intervention group showed a significant reduction in problematic internet use compared to the control group, with a large between-group effect size ( d = -0.98). A higher proportion of participants in the intervention group achieved reliable improvement on the CIUS. No significant effects were observed for secondary outcomes, including gaming, social media use, or psychological well-being. Conclusions: The results support the feasibility and preliminary efficacy of a smartphone-based AAT training in reducing problematic internet use. The findings suggest that AAT-based interventions may represent a promising, low-threshold component within blended treatment approaches for problematic internet use. Further research in larger, stratified samples and with extended follow-up periods is warranted.

Network analysis has become increasingly popular among psychologists. Recognizing that psychological research requires substantive theory has prompted a critical development from descriptively mapping network structures towards testing specific hypotheses. While confirmatory approaches are becoming more accessible to applied researchers, the broader set of available statistical tools for this purpose has not yet been fully utilized by network psychometrics. The present work aims to reduce this gap in two steps: First, we review existing approaches for confirmatory network testing, spanning from classical SEM goodness-of-fit indices to covariance- and resampling-based tests. Next, in a simulation study, we evaluate how effective they are in determining whether a theoretical network aligns with empirical data given varying degrees of network (dis)similarity, number of variables (p), sample size (N), and network estimation methods. Performance is assessed regarding Type I error rates and power, thus providing insights into their applicability and robustness across various data scenarios.

Background: Emergency responders are regularly exposed to potentially traumatic events, placing them at risk of developing posttraumatic stress symptoms (PTSS). Within this group, lifeguards remain an understudied population. The present study examined the building block effect in volunteer emergency responders of the German Lifesaving Association (DLRG), hypothesizing that a higher number of stressful events would be associated with greater PTSS. In addition, potential influencing factors on symptom severity and on the relationship between event exposure and PTSS, including gender, age, perceived social support, and peer support measures, were investigated. Method: Data were collected via a web-based self-report survey, resulting in a final analysis sample of 365 lifeguards from the DLRG. Participants were nested within 198 local subgroups. Generalized linear mixed models were constructed, with predictors entered in a stepwise manner. Results: The findings support the building block effect of stressful experiences, indicating that greater exposure is associated with higher PTSS among lifeguards in Germany. Hypothesis testing further revealed protective effects of higher age, male gender, greater perceived social support, and stronger peer support measures in reducing symptom severity. Interaction effects of these protective factors on the relationship between the number of traumatic events and PTSS were non-significant. Exploratory analyses also suggested that higher rank within the organization may be associated with lower symptom severity. Conclusion: The study highlights the importance of addressing mental health challenges among volunteer lifeguards, acknowledging that PTSS do not necessarily stem from a single worst traumatic event but can develop cumulatively. The findings also indicate target groups for additional training efforts, particularly younger women, and underscore the value of fostering support networks.

Reliable and valid measurement of the various components of prosociality calls for tools that capture its diverse behavioral expressions. Here, we evaluate how economic game measures derived from the Dictator Game, Public Goods Game, and Ultimatum Game as well as a novel Third-Party Intervention Paradigm correspond with their self-reported counterparts within a design informed by a multitrait–multimethod approach. The self-report scales described Help Giving, Peer Punishment, and Moral Courage as behavioral traits in real life. Each game decision was regressed on all three of these scales using data from 22 studies. Convergent validity emerged, with the strongest associations for help giving and the weakest for peer punishment. Discriminant validity was evidenced by the lack of significant cross-correlations, with one minor exception regarding the Peer Punishment scale and the Dictator Game. Overall, the findings support the distinctiveness of the facets, particularly for help giving and moral courage, while highlighting challenges in capturing punishment-related altruism. While several procedures for improving correspondence are discussed, the descriptively higher correlations within methods than across traits suggest a persistent gap in criterion validation.

The handling of missing values in cross-sectional network analysis has been primarily studied in conjunction with regularization techniques. However, nonregularized alternatives, such as neighborhood selection via the Bayesian information criterion (BIC) based on node-wise multiple regression, have been shown to be viable alternatives for psychological networks. Moreover, its localized approach in model selection renders neighborhood selection particularly suitable for situations in which variables show very uneven rates of missing values. In this study, we present two approaches based on multiple imputation (MI), namely stacked and grouped MI, alongside direct and two-step expectation-maximization procedures, to handle missing values. Furthermore, various approaches to calculating sample size, used for computing log-likelihood and BIC, are discussed and evaluated. A simulation study was conducted to assess the performance of these missing data handling methods and sample size definitions. Evaluation criteria included edge recovery, as well as bias in partial correlations and network statistics. The findings indicate that stacked MI performs best overall. The two-step expectation-maximization approach is the fastest and offers adequate performance when the number of observations is very large relative to the proportion of missingness and the size of the network. For sample size calculation, particularly under high levels of missingness, using the local number of observations per node yielded the least bias. The most effective methods for handling missing values in neighborhood selection via BIC are implemented in the new R package mantar. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Research on work values is affected by construct proliferation, weak theoretical integration, and sociocultural blind spots. We address these issues by grounding work values in a motivational continuum via the Circular Work Value Theory. We introduce the modular Circular Work Value Scale (CWVSmod), assessing work values at three levels of abstraction. Across four studies (N = 7,516), we develop, validate, and norm the scales.Study 1 used algorithm-based item selection to construct three scales. Confirmatory factor analyses indicated acceptable fit and measurement invariance (MI) held across age and gender. Study 2 examined cross-language validity: strict MI across countries was supported, correlations with higher-order basic values aligned with theory. Study 3 mapped the nomological network, yielding largely expected convergent/discriminant patterns with personality traits and work behaviors consistent with the circumplex structure. Study 4 provided representative German norms for one module by age, gender, and education; strict MI across demographics and time, and retest reliability, were supported. Across all four studies, the circumplex structure of conflict and compatibility was largely corroborated, with observable deviations of Hedonism-Compatibility. The CWVSmod offers theory-consistent, psychometrically robust, and comparable assessments while its modular design enables efficient screening and fine-grained diagnostics.

The Dark Triad of personality are often assessed using two short instruments, which have been criticized several times in the past. This study aims to optimize one of the most popular short instruments (Short Dark Triad) using a modern selection algorithm. Existing items were revised, new items were added, and items were selected using an ant colony optimization algorithm based on defined psychometric criteria. The final questionnaire was validated against the traits of the Five Factor Model of Personality. The revised questionnaire, the Dark Triad Snapshot, offers a better differentiation of the Dark Triad facets, higher reliability and partial scalar measurement invariance between male and female participants as the Short Dark Triad. Expert judgment and the inclusion of low agreeableness also ensure content validity.

Altruism may not be a unitary concept but may include behaviorally dissociable subfactors. Here, we examined the effects of social distance within and across group boundaries on three facets of altruism: help giving, peer punishment, and moral courage. Using real-life scenarios presented as vignettes, participants were asked to indicate the likelihood that they would engage in the described behaviors across three different social settings: a familiar low-distance in-group, an unfamiliar high-distance in-group, and a hostile out-group. We used the Inclusion of Others in Self (IOS) scale to measure perceived closeness to members of the described social group. We hypothesized that help giving would be most and moral courage least sensitive to variations in social distance. In both studies, results revealed no significant differences in help giving across variations in social distance but a higher self-reported likeliness to show morally courageous acts in the familiar and close in-group compared to the other groups. The results for peer punishment were only partially consistent, following a similar pattern to moral courage. IOS scores discriminated between high and low-distance in-groups, as expected, but did not discriminate well between a high-distance in-group and hostile out-group. On the other hand, facet-specific trait scores correlated significantly with vignette responses. When the three facets were considered together across all vignettes, in-group favoritism became apparent. The studies contribute to our understanding of the social context conditions of altruistic behaviors and call for the refinement of experimental and self-report measures in the study of altruistic behavior.