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Trauma is an increasingly prominent concept in psychology and society at large. According to the theory of concept creep, it is one of several harm-related concepts that have undergone semantic inflation in recent decades, expanding to encompass new kinds of phenomena (horizontal expansion) and less severe phenomena (vertical expansion). Previous research has demonstrated that “trauma” has come to be used in a widening range of semantic contexts, implying horizontal expansion, but has not investigated vertical expansion. The present study developed a methodology for evaluating vertical expansion and implemented it using an English-language corpus of 825,628 scientific psychology article abstracts from 1970 to 2017. Findings indicate that “trauma” has come to be used in less severe contexts, and this trend may be linked to its rising frequency of use. These findings support the predictions of the concept creep theory and provide a new method for investigating the language dynamics of harm-related concepts.

Introduction: Existing research shows positive associations between humility and well-being, and between civic engagement and well-being. Rarely have humility, civic engagement, and well-being been examined together. We build off of previous cross-sectional findings and a prior longitudinal study that used three waves of data and found significant positive bivariate correlations between humility and the presence of life purpose across time points. Methods: Extending these previous findings, we used six waves of data obtained from graduate students at 18 seminaries across North America (N = 574; Mage = 31.54; 46.7% female; 65.3% White) to explore the dynamic associations among humility and life purpose, along with horizontal transcendence (an indicator of the attitudinal dimension of civic engagement) and social justice activism (an indicator for the behavioral dimension). We explored reciprocal short-run processes and dynamic long-run effects using a general cross-lagged panel model. Results and discussion: We found robust evidence for a reciprocal influence between the presence of life purpose and horizontal transcendence, and longrun effects for initial levels of life purpose to influence later levels of horizontal transcendence. We also found long-run effects for the influence of initial levels of life purpose on later levels of humility, and initial levels of social justice activism on later levels of horizontal transcendence. Implications center on the use of the findings for planning future one-time life purpose and social justice interventions to affect changes in humility and horizontal transcendence. [ABSTRACT FROM AUTHOR]

Multilevel modeling (MLM) is a popular way of assessing mediation effects with clustered data. Two important limitations of this approach have been identified in prior research and a theoretical rationale has been provided for why multilevel structural equation modeling (MSEM) should be preferred. However, to date, no empirical evidence of MSEM's advantages relative to MLM approaches for multilevel mediation analysis has been provided. Nor has it been demonstrated that MSEM performs adequately for mediation analysis in an absolute sense. This study addresses these gaps and finds that the MSEM method outperforms 2 MLM-based techniques in 2-level models in terms of bias and confidence interval coverage while displaying adequate efficiency, convergence rates, and power under a variety of conditions. Simulation results support prior theoretical work regarding the advantages of MSEM over MLM for mediation in clustered data. (Contains 1 figure, 6 tables, and 8 footnotes.)

Several methods for testing mediation hypotheses with 2-level nested data have been proposed by researchers using a multilevel modeling (MLM) paradigm. However, these MLM approaches do not accommodate mediation pathways with Level-2 outcomes and may produce conflated estimates of between- and within-level components of indirect effects. Moreover, these methods have each appeared in isolation, so a unified framework that integrates the existing methods, as well as new multilevel mediation models, is lacking. Here we show that a multilevel structural equation modeling (MSEM) paradigm can overcome these 2 limitations of mediation analysis with MLM. We present an integrative 2-level MSEM mathematical framework that subsumes new and existing multilevel mediation approaches as special cases. We use several applied examples and accompanying software code to illustrate the flexibility of this framework and to show that different substantive conclusions can be drawn using MSEM versus MLM. (Contains 3 figures, 11 footnotes, and 2 tables.)

A significant body of education literature has begun using multilevel statistical models to examine data that reside at multiple levels of analysis. In order to provide a primer for medical education researchers, the current work gives a brief overview of some issues associated with multilevel statistical modeling. To provide an example of this technique, we then present a multilevel analysis examining the relationship between two individual-level variables and the "cross-level" interaction between this relationship and a school-level variable. In offering this discussion and example of multilevel modeling, we hope to provide medical educators with a basic introduction to multilevel statistics, including the advantages of utilizing these techniques.

Researchers often combine longitudinal panel data analysis with tests of interactions (i.e., moderation). A popular example is the cross-lagged panel model (CLPM). However, interaction tests in CLPMs and related models require caution because stable (i.e., between-level, B) and dynamic (i.e., within-level, W) sources of variation are present in longitudinal data, which can conflate estimates of interaction effects. We address this by integrating literature on CLPMs, multilevel moderation, and latent interactions. Distinguishing stable B and dynamic W parts, we describe three types of interactions that are of interest to researchers: 1) purely dynamic or WxW ; 2) cross-level or BxW ; and 3) purely stable or BxB. We demonstrate estimating latent interaction effects in a CLPM using a Bayesian SEM in Mplus to apply relationships among work-family conflict and job satisfaction, using gender as a stable B variable. We support our approach via simulations, demonstrating that our proposed CLPM approach is superior to a traditional CLPMs that conflate B and W sources of variation. We describe higher-order nonlinearities as a possible extension, and we discuss limitations and future research directions. [ABSTRACT FROM AUTHOR]

Cross-lagged panel models (CLPMs) are common, but their applications often focus on "short-run" effects among temporally proximal observations. This addresses questions about how dynamic systems may immediately respond to interventions, but fails to show how systems evolve over longer timeframes. We explore three types of "long-run" effects in dynamic systems that extend recent work on "impulse responses," which reflect potential long-run effects of one-time interventions. Going beyond these, we first treat evaluations of system (in)stability by testing for "permanent effects," which are important because in unstable systems even a one-time intervention may have enduring effects. Second, we explore classic econometric long-run effects that show how dynamic systems may respond to interventions that are sustained over time. Third, we treat "accumulated responses" to model how systems may respond to repeated interventions over time. We illustrate tests of each long-run effect in a simulated dataset and we provide all materials online including user-friendly R code that automates estimating, testing, reporting, and plotting all effects (see https://doi.org/10.26188/13506861). We conclude by emphasizing the value of aligning specific longitudinal hypotheses with quantitative methods. [ABSTRACT FROM AUTHOR]