Evaluation of the Psychometric Properties of the Five Facet of Mindfulness Questionnaire.

ObjectiveThe Five Facet of Mindfulness Questionnaire (FFMQ) is widely used to assess mindfulness. The present study provides a psychometric evaluation of the FFMQ that includes item response theory (IRT) analyses and evaluation of item characteristic curves.MethodWe administered the FFMQ, the Beck Depression Inventory-II, the Ruminative Response Scale, and the Emotion Regulation Questionnaire to a heterogenous sample of 240 community-based adults. We estimated internal consistency reliability, item-scale correlations, categorical confirmatory factor analysis, and IRT graded response models for the FFMQ. We also estimated correlations among the FFMQ scales and correlations with the other measures included in the study.ResultsInternal consistency reliabilities for the five FFMQ scales were 0.82 or higher. A five-factor categorical model fit the data well. IRT-estimated item characteristic curves indicated that the five response options were monotonically ordered for most of the items. Product-moment correlations between simple-summated scoring and IRT scoring of the scales were 0.97 or higher.ConclusionsThe FFMQ accurately identifies varying levels of trait mindfulness. IRT-derived estimates will inform future adaptations to the FFMQ (e.g., briefer versions) and the development of future mindfulness instruments

Modelling the spring ozone maximum and the interhemispheric asymmetry in the remote marine boundary layer 1. Comparison with surface and ozonesonde measurements

Here we report a modelling study of the spring ozone maximum and its interhemispheric asymmetry in the remote marine boundary layer (MBL). The modelled results are examined at the surface and on a series of time-height cross sections at several locations spread over the Atlantic, the Indian, and the Pacific Oceans. Comparison of model with surface measurements at remote MBL stations indicate a close agreement. The most striking feature of the hemispheric spring ozone maximum in the MBL can be most easily identified at the NH sites of Westman Island, Bermuda, and Mauna Loa, and at the SH site of Samoa. Modelled ozone vertical distributions in the troposphere are compared with ozone profiles. For the Atlantic and the Indian sites, the model generally produces a hemispheric spring ozone maximum close to those of the measurements. The model also produces a spring ozone maximum in the northeastern and tropical north Pacific close to those measurements, and at sites in the NH high latitudes. The good agreement between model and measurements indicate that the model can reproduce the proposed mechanisms responsible for producing the spring ozone maximum in these regions of the MBL, lending confidence in the use of the model to investigate MBL ozone chemistry (see part 2 and part 3). The spring ozone maximum in the tropical central south Pacific and eastern equatorial Pacific are less well reproduced by the model, indicating that both the transport of $O_3$ precursors from biomass burning emissions taking place in southeastern Asia, Australia, Oceania, southern Africa, and South America are not well represented in the model in these regions. Overall, the model produces a better simulation at sites where the stratosphere and biomass burning emissions are the major contributors.Comment: 24 pages, 8 figure

A Logistic Regression and Markov Chain Model for the Prediction of Nation-state Violent Conflicts and Transitions

Using open source data, this research formulates and constructs a suite of statistical models that predict future transitions into and out of violent conflict and forecasts the regional and global incidences of violent conflict over a ten-year time horizon. A total of thirty predictor variables are tested and evaluated for inclusion in twelve conditional logistic regression models, which calculate the probability that a nation will transition from its current conflict state, either In Conflict or Not in Conflict, to a new state in the following year. These probabilities are then used to construct a series of nation-specific Markov chain models that forecast violent conflict, as well as yield insights into regional conflict trends out to year 2024 and beyond. The logistic regression models proposed in this study achieve training dataset accuracies of 88.76%, and validation dataset accuracies of 84.67%. Additionally, the Markov models achieve three year forecast accuracies of 85.16% during model validation. This study predicts that global violent conflict rates remain constant through year 2024, but are projected to increase beyond that timeframe with 95 of the 182 considered nations projected to be in a state of violent conflict from the current 84 nations in conflict