Previous studies have shown that meditation-based interventions can have a significant impact on stress and well-being in various populations. To further extend these findings, an 11-min Yoga Nidra meditation that may especially be integrated in a busy daily schedule by people who can only afford short time for breaks was adapted and analyzed in an experimental online study design. The effects of this short meditation on stress, sleep, well-being and mindfulness were examined for the first time. The meditation was provided as audio file and carried out during a period of 30 days by the participants of the meditation group. A Structural Equation Model (SEM) was used to analyze the data with Full Information Maximum Likelihood (FIML) in order to cope with missing data. As expected, the meditation group (N = 341) showed lower stress, higher well-being and improved sleep quality after the intervention (very small to small effect sizes) compared with a waitlist control group (N = 430). It turned out that the meditation had a stronger impact on the reduction of negative affect than on the increase of positive affect and also a stronger effect on affective components of well-being. Mindfulness, as a core element of the meditation, increased during the study within the meditation group. All effects remained stable at follow-up six weeks later. Overall, a large, heterogeneous sample showed that already a very short dose of meditation can positively influence stress, sleep, and well-being. Future research should consider biological markers as well as active control groups.

Properties of psychological variables at the mean or variance level can differ between persons and within persons across multiple time points. For example, cross-sectional findings between persons of different ages do not necessarily reflect the development of a single person over time. Recently, there has been an increased interest in the difference between covariance structures, expressed by covariance matrices, that evolve between persons and within a single person over multiple time points. If these structures are identical at the population level, the structure is called ergodic. However, recent data confirms that ergodicity is not generally given, particularly not for cognitive variables. For example, the g factor that is dominant for cognitive abilities between persons seems to explain far less variance when concentrating on a single person’s data. However, other subdimensions of cognitive abilities seem to appear both between and within persons; that is, there seems to be a lower-dimensional subspace of cognitive abilities in which cognitive abilities are in fact ergodic. In this article, we present ergodic subspace analysis (ESA), a mathematical method to identify, for a given set of variables, which subspace is most important within persons, which is most important between person, and which is ergodic. Similar to the common spatial patterns method, the ESA method first whitens a joint distribution from both the between and the within variance structure and then performs a principle component analysis (PCA) on the between distribution, which then automatically acts as an inverse PCA on the within distribution. The difference of the eigenvalues allows a separation of the rotated dimensions into the three subspaces corresponding to within, between, and ergodic substructures. We apply the method to simulated data and to data from the COGITO study to exemplify its usage.

Assessing the effect mentors have on their mentees is methodologically challenging: most programs merely provide relatively short mentoring durations (typically in the range of 1 year), age ranges are usually rather small, and examining dyads with anything other than questionnaires has proven to be challenging in the past. Thus, although some excellent causal studies do exist, in general causal research is limited in the field and studies are opened up to social desirability. Using a controlled laboratory setting, the current study investigates the causal effect of a mentor's presence on the mentee's empathic accuracy, cognitive functioning, and prosocial behavior. The sample is characterized by a wide age range for mentees and long mentoring durations. Results support the hypothesis that mentees' performance is improved in all three domains when their mentor is present as compared to when their mentor is absent. Furthermore, mentoring duration was positively associated with the mentee's cognitive functioning when controlling for the mentee's age. The current findings extend our knowledge of the benefits of youth mentoring programs and demonstrate the necessity to include laboratory research when investigating mentoring dyads.

Bayesian clustering algorithms, in particular those utilizing Dirichlet Processes (DP), return a sample of the posterior distribution of partitions of a set. However, in many applied cases a single clustering solution is desired, requiring a ’best’ partition to be created from the posterior sample. It is an open research question which solution should be recommended in which situation. However, one such candidate is the sample mean, defined as the clustering with minimal squared distance to all partitions in the posterior sample, weighted by their probability. In this article, we review an algorithm that approximates this sample mean by using the Hungarian Method to compute the distance between partitions. This algorithm leaves room for further processing acceleration. We highlight a faster variant of the partition distance reduction that leads to a runtime complexity that is up to two orders of magnitude lower than the standard variant. We suggest two further improvements: The first is deterministic and based on an adapted dynamical version of the Hungarian Algorithm, which achieves another runtime decrease of at least one order of magnitude. The second improvement is theoretical and uses Monte Carlo techniques and the dynamic matrix inverse. Thereby we further reduce the runtime complexity by nearly the square root of one order of magnitude. Overall this results in a new mean partition algorithm with an acceleration factor reaching beyond that of the present algorithm by the size of the partitions. The new algorithm is implemented in Java and available on GitHub (Glassen, Mean Partition, 2018).

Connections between interindividual differences and people’s behavior has been widely researched in various contexts, often by using top-down group comparisons to explain interindividual differences. In contrast, in this study, we apply a bottom-up approach in which we identify meaningful clusters in people’s concerns about various areas of life (e.g., their own health, their financial situation, the environment). We apply a novel method, Dirichlet clustering, to large-scale longitudinal data from the German Socioeconomic Panel Study (SOEP) to investigate whether concerns of people living in Germany evaluated in 2010 (t0) cluster participants into robust and separable groups, and whether these groups vary regarding their party identification in 2017 (t0 + 7). Clustering results suggest a range of different groups with specific concern patterns. Some of these notably specific patterns of concerns indicate links to party identification. In particular, some patterns show an increased identification with smaller parties as the ‘Bündnis 90/Die Grünen’ (‘Greens’), the left wing party ‘Die Linke’ (‘The Left’) or the right-wing party ‘Alternative für Deutschland’ (‘Alternative for Germany’, AfD). Considering that we identify as many as 37 clusters in total, among them at least six with clearly different party identification, it can also be concluded that the complexity of political concerns may be larger than has been assumed before.