An Association Between Montessori Education in Childhood and Adult Wellbeing.

Wellbeing, or how people think and feel about their lives, predicts important life outcomes from happiness to health to longevity. Montessori pedagogy has features that enhance wellbeing contemporaneously and predictively, including self-determination, meaningful activities, and social stability. Here, 1905 adults, ages 18-81 (M = 36), filled out a large set of wellbeing scales followed by demographic information including type of school attended each year from 2 to 17. About half the sample had only attended conventional schools and the rest had attended Montessori for between 2 and 16 years (M = 8 years). To reduce the variable set, we first developed a measurement model of wellbeing using the survey data with exploratory then confirmatory factor analyses, arriving at four factors: general wellbeing, engagement, social trust, and self-confidence. A structural equation model that accounted for age, gender, race, childhood SES, and years in private school revealed that attending Montessori for at least two childhood years was associated with significantly higher adult wellbeing on all four factors. A second analysis found that the difference in wellbeing between Montessori and conventional schools existed even among the subsample that had exclusively attended private schools. A third analysis found that the more years one attended Montessori, the higher one's wellbeing as an adult. Unmeasured selection effects could explain the results, in which case research should determine what third variable associated with Montessori schooling causes adult wellbeing. Several other limitations to the study are also discussed. Although some of these limitations need to be addressed, coupled with other research, including studies in which children were randomly assigned to Montessori schools, this study suggests that attending Montessori as a child might plausibly cause higher adult wellbeing.

Multi-session online interpretation bias training for anxiety in a community sample.

The present study assessed target engagement, preliminary efficacy, and feasibility as primary outcomes of a free multi-session online cognitive bias modification of interpretation (CBM-I) intervention for anxiety in a large community sample. High trait anxious participants (N = 807) were randomly assigned to a CBM-I condition: 1) Positive training (90% positive-10% negative); 2) 50% positive-50% negative training; or 3) no-training control. Further, half of each CBM-I condition was randomized to either an anxious imagery prime or a neutral imagery prime. Due to attrition, results from six out of eight sessions were analyzed using structural equation modeling of latent growth curves. Results for the intent-to-treat sample indicate that for target engagement, consistent with predictions, decreases in negative interpretations over time were significantly greater among those receiving positive CBM-I training compared to no-training or 50-50 training, and vice-versa for increases in positive interpretations. For intervention efficacy, the decrease in anxiety symptoms over time was significantly greater among those receiving positive CBM-I training compared to no-training. Interaction effects with imagery prime were more variable with a general pattern of stronger results for those completing the anxious imagery prime. Findings indicate that online CBM-I positive training is feasible and shows some promising results, although attrition rates were very high for later training sessions.

Corrigendum: A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R.

[This corrects the article DOI: 10.3389/fpsyg.2020.00169.].

Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology.

OBJECTIVE: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual's emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. METHODS: To evaluate this, we crowdsourced the analysis of one individual patient's ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. RESULTS: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0-16) and nature of selected targets varied widely. CONCLUSION: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation.

A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R.

One practical challenge in observational studies and quasi-experimental designs is selection bias. The issue of selection bias becomes more concerning when data are non-normal and contain missing values. Recently, a Bayesian robust two-stage causal modeling with instrumental variables was developed and has the advantages of addressing selection bias and handle non-normal data and missing data simultaneously in one model. The method provides reliable parameter and standard error estimates when missing data and outliers exist. The modeling technique can be widely applied to empirical studies particularly in social, psychological and behavioral areas where any of the three issues (e.g., selection bias, data with outliers and missing data) is commonly seen. To implement this method, we developed an R package named ALMOND (Analysis of LATE (Local Average Treatment Effect) for Missing Or/and Nonnormal Data). Package users have the flexibility to directly apply the Bayesian robust two-stage causal models or write their own Bayesian models from scratch within the package. To facilitate the application of the Bayesian robust two-stage causal modeling technique, we provide a tutorial for the ALMOND package in this article, and illustrate the application with two examples from empirical research.

SUAG/on across the adult lifespan.

One of the central concepts within the literature on cognitive aging is the notion of dedifferentiation-the idea that increasing age is associated with an increase in the interrelatedness of different cognitive abilities. Despite the centrality of this dedifferentiation hypothesis, there is a great deal of evidence that both supports and does not support dedifferentiation. We hypothesized that these inconsistent findings were due to (a) the use of different cognitive abilities (i.e., memory vs. speed of processing) that were correlated; and (b) the differing age groups that were used across studies. By using data from 11 well-validated cognitive test batteries (K = 2,355, range of the mean ages of correlations 18-85+), we found evidence for linear dedifferentiation when a test assessing speed of processing was included in the correlation with test of other cognitive abilities. We speculate that previous findings of nonlinear dedifferentiation are likely a result of undiagnosed or unrecognized pathology in a subsample of participants. (PsycINFO Database Record