The influence of anxiety sensitivity on mindfulness

Although many individuals who engage in mindfulness endorse less anxiety, others experience heightened anxiety when focusing on particular sensations. This study aimed to determine whether specific mindfulness items related to observing internally or externally differ in individuals with panic disorder (PD) or elevated anxiety sensitivity (AS). We examined a clinical sample of 1521 Japanese individuals who completed online self-report questionnaires. Several multiple indicator multiple cause (MIMIC) models investigated differential item functioning among the items in the Observe facet of the Five Facet Mindfulness Questionnaire (FFMQ), based on PD and/or AS. This process was repeated to examine the relationship between the Anxiety Sensitivity Index-3 (ASI-3) subscales and particular items of the Observe facet. Increased AS correlated with increased observing generally, and increased AS was associated with greater scores on observing internal items and lower scores on external items. When PD and AS were analyzed simultaneously, only AS remained significant. The cognitive subscale showed the same pattern of results as the total ASI-3 subscale. We conclude that AS modulates the mindfulness experience.

Associations Between Perceptions of the Work Environment and Job Burnout Based on MIMIC Models Among 679 Knowledge Workers

In the current times, knowledge work and knowledge worker play an important role in organizational development. The purpose of this paper is to examine the associations between perceptions of the work environment and job burnout among 679 knowledge workers with a publicly available data. Based on the exploratory factor analysis, five multiple indicators multiple causes (MIMIC) models are acceptable and confirm, including socioeconomic factors→perceptions of the work environment model, socioeconomic factors→job burnout model, perceptions of the work environment←socioeconomic factors→job burnout model, perceptions of the work environment→job burnout model, and job burnout→perceptions of the work environment model. The results from MIMIC models indicated job burnout has significant associations with perceptions of the work environment. The implications of these results for well-beings among the knowledge workers are discussed.

Exploring the Likelihood of a Country Being a Tax Haven Using MIMIC Models

The multiple indicators multiple causes (MIMIC) framework is used to analyze dimensions related to causation and indicators of tax haven status. Robust results were obtained that identify a country’s tax burden and area as causes of a country adopting policies usually observed in tax havens. The level of social security contributions as a proportion of public revenues and the ratio of indirect to direct taxes were found to be statistically significant indicators of tax havens. Data from 68 countries for more than twenty years were analyzed, enabling the results to contribute to a deepening of the current debate about tax havens and their socio-economic profiles.

Contrast to background influences predation on aposematic but not cryptic artificial caterpillars in a Brazilian coastal shrubland

Aposematism and crypticity are visual defensive strategies against predation; however, the relative effectiveness of these two strategies to reduce the risk of predation is not yet fully understood. We evaluated the risk of predation for caterpillars with cryptic and aposematic colouration as well as the probability of predation relative to the natural variation of contrast with the substrate. We expected that the two models would experience similar predation attempts and that the contrast with the substrate would be negatively related to the predation on aposematic mimic models and positively to the predation of cryptic models. Overall, 224 models were laid out along a transect and exposed to predation for five consecutive days during winter and autumn. Daily predation was 11.0% (winter) and 4.8% (autumn). Significant differences were not observed between predation rates on the two model types (50.6% aposematic). Most of the predated models had arthropod marks (86.4%) and only 13.6% had bird marks. The chance of predation was higher the greater the contrast between the aposematic mimic model and the substrate, although no relationship was observed for the cryptic model. Our results suggest that the two colour patterns do not differ in their defensive effectiveness and that micro-habitat selection might define the predation risk on aposematic mimic caterpillars in environments dominated by arthropod predators.

MIMIC models for uniform and non-uniform DIF as moderated mediation models

In this note we describe how multiple indicators multiple cause (MIMIC) models for studying uniform and non-uniform differential item functioning (DIF) can be conceptualized as mediation and moderated mediation models. Conceptualizing DIF within the context of a moderated mediation model helps us understand DIF as the effect of some variable on our measurements which is not accounted for by the latent variable of interest. In addition, this allows us to apply useful concepts and ideas from the mediation and moderation literature: (1) improving our understanding of uniform and non-uniform DIF as direct effects and interactions, (2) understanding the implication of indirect effects in DIF analysis, (3) clarifying the interpretation of the “uniform DIF parameter” in the presence of non-uniform DIF, and (4) probing interactions and using the concept of “conditional effects” to better understand the patterns of DIF across the range of the latent variable.

MIMIC Models for Uniform and Nonuniform DIF as Moderated Mediation Models

In this article, the authors describe how multiple indicators multiple cause (MIMIC) models for studying uniform and nonuniform differential item functioning (DIF) can be conceptualized as mediation and moderated mediation models. Conceptualizing DIF within the context of a moderated mediation model helps to understand DIF as the effect of some variable on measurements that is not accounted for by the latent variable of interest. In addition, useful concepts and ideas from the mediation and moderation literature can be applied to DIF analysis: (a) improving the understanding of uniform and nonuniform DIF as direct effects and interactions, (b) understanding the implication of indirect effects in DIF analysis, (c) clarifying the interpretation of the “uniform DIF parameter” in the presence of nonuniform DIF, and (d) probing interactions and using the concept of “conditional effects” to better understand the patterns of DIF across the range of the latent variable.

Exploring the Test of Covariate Moderation Effects in Multilevel MIMIC Models

In multilevel multiple-indicator multiple-cause (MIMIC) models, covariates can interact at the within level, at the between level, or across levels. This study examines the performance of multilevel MIMIC models in estimating and detecting the interaction effect of two covariates through a simulation and provides an empirical demonstration of modeling the interaction in multilevel MIMIC models. The design factors include the location of the interaction effect (i.e., between, within, or across levels), cluster number, cluster size, intraclass correlation (ICC) level, magnitude of the interaction effect, and cross-level measurement invariance status. Type I error, power, relative bias, and root mean square of error of the interaction effects are examined. The results showed that multilevel MIMIC models performed well in detecting the interaction effect at the within or across levels. However, when the interaction effect was at the between level, the performance of multilevel MIMIC models depended on the magnitude of the interaction effect, ICC, and sample size, especially cluster number. Overall, cross-level measurement noninvariance did not make a notable impact on the estimation of interaction in the structural part of multilevel MIMIC models when factor loadings were allowed to be different across levels.

Variable Selection in Structural Equation Models with Regularized MIMIC Models

Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real world behavioural data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This poses a difficult modeling scenario that entails large models with a comparably limited number of observations given the number of parameters (also known as the “small n, large p” problem). We here describe a particular strategy to overcoming this challenge, called regularization. Regularization, a method to penalize model complexity during estimation, has proven a viable option for estimating parameters in this small n, large p settings, but has so far mostly been used in linear regression models. Here we show how to integrate regularization within structural equation models, a popular analytic approach in psychology. We first describe the rationale behind regularization in regression contexts, and how it can be extended to regularized structural equation modeling (Jacobucci, Grimm, & McArdle, 2016). Our approach is evaluated through the use of a simulation study, showing that regularized SEM outperforms traditional SEM estimation methods in situations with a large number of predictors, or when sample size is small. We illustrate the power of this approach in a N=627 example from the CAM-CAN study, modeling the neural determinants of visual short term memory. We illustrate the performance of the method and discuss practical aspects of modeling empirical data, and provide a step-by-step online tutorial.