A daily diary study of relationships between feelings of gratitude and well-being

Much of the research on relationships between gratitude and well-being has concerned between-person level relationships, and this research suggests that increasing people’s feelings of gratitude can increase their well-being. To complement this research, we examined such relationships at the within-person level. Participants (N = 130) in the present study described their well-being and how grateful they felt each day for two weeks. MLM analyses found that at the within-person level, daily feelings of gratitude and hedonic and eudaimonic well-being were positively related. Lagged analyses found that feelings of gratitude on one day were positively related to hedonic (but not eudaimonic) well-being on the next day, and well-being on one day was not related to gratitude on the next day. These results complement existing research and suggest that causal relationships between gratitude and well-being may vary as a function of whether gratitude is measured in more affective or cognitive terms.     

Generalized multilevel structural equation modeling

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.gllamm can be downloaded from http://www.gllamm.org. The paper was written while Sophia Rabe-Hesketh was employed at and Anders Skrondal was visiting the Department of Biostatistics and Computing, Institute of Psychiatry, King's College London.     

School-related stress,social support,and distress: prospective analysis of reciprocal and multilevel relationships

This three-wave prospective study investigated the reciprocal relationships among school-related stress, school-related social support, and distress in a cohort of 767 secondary school students (mean age 13.9 years). Stress, support, and distress were measured at three occasions with six-month lags between. Reciprocal relationships were analyzed with multivariate multilevel modeling (MLwiN). Each of the three factors at baseline predicted change in one or two of the other factors at subsequent measurements, indicating a complex pattern of reciprocal relationships among stress, support, and distress across time. A high level of distress at baseline predicted a lower level of support and a higher level of stress six months later. High levels of stress at baseline predicted a higher level of distress and a lower level of support 12 months later. The results are consistent with a transactional and dynamic model of stress, support, and distress, and indicate the need to view school-related stress, support, and distress as mutually dependent factors.     

Models for the statistics and mechanisms of response speed and accuracy

Van Breukelen offers a promising method for modeling both response speed and response accuracy. However, the underlying conception of both dependent measures is somewhat flawed, leading the author to conclude that the approach possesses limitations that, under revised assumptions, may not hold. The central misconception, and a set of related misconceptions, is addressed, and it is suggested that this approach holds a good deal of promise for application in the perceptual and cognitive sciences.     

Personality and lexical decision times for evaluative words

We studied personality influences on accessibility of pleasant and unpleasant stimuli in a sample of 129 students. Self‐reports and reports by knowledgeable informants on extraversion, neuroticism, approach temperament and avoidance temperament were combined with a go/no‐go lexical decision task that included pleasant, unpleasant and neutral words, and two response modes, manual and vocal. The data were analysed using multilevel modelling. Extraversion and approach temperament predicted faster identification of pleasant words than of neutral and of unpleasant words. Vocal responses took longer than manual responses, but mode of response did not interact with the valence of the words. Copyright © 2009 John Wiley & Sons, Ltd.     

On Standardizing Within-Person Effects: Potential Problems of Global Standardization

Person-mean centering has been recommended for disaggregating between-person and within-person effects when modeling time-varying predictors. Multilevel modeling textbooks recommended global standardization for standardizing fixed effects. An aim of this study is to evaluate whether and when person-mean centering followed by global standardization can accurately estimate fixed-effects within-person relations (the estimand of interest in this study) in multilevel modeling. We analytically derived that global standardization generally yields inconsistent (asymptotically biased) estimates for the estimand when between-person differences in within-person standard deviations exist and the average within-person relation is nonzero. Alternatively, a person-mean-SD standardization (P-S) approach yields consistent estimates. Our simulation results further revealed (1) how misleading the results from global standardization were under various circumstances and (2) the P-S approach had accurate estimates and satisfactory coverage rates of fixed-effects within-person relations when the number of occasions is 30 or more (in many conditions, performance was satisfactory with 10 or 20 occasions). A daily diary data example, focused on emotional complexity, was used to empirically illustrate the approaches. Researchers should choose standardization approaches based on theoretical considerations and should clearly describe the purpose and procedure of standardization in research articles.     

Importation and deprivation factors influencing teacher-targeted aggression among secondary school students in Germany: A multilevel analysis

Relying on an importation and deprivation framework, the study assessed a variety of risk factors associated with self-reported teacher-targeted aggression among ninth grade students (n = 5,673). Using a cross-sectional school survey conducted in one German federal state, two forms of teacher-targeted aggression were assessed: verbal (insulting, threatening, and mocking) and physical (beating and pushing) aggression. Every ninth student reported verbal aggression, while 0.5% of students reported physical aggression against teachers. Multilevel probability models showed that individual importation factors (low self-control, male gender, and exposure to severe parental violence), together with individual deprivation factors (repeated victimization by teachers and low school achievement) play a role in explaining teacher-targeted aggression. The school-level deprivation factor of negative teacher–student relationships was also relevant, whereas low teacher control and attending lower-level schools were unrelated to the perpetration of teacher-targeted aggression. The present study stresses the need to acknowledge the multilevel etiology of teacher-targeted aggression.     

Beyond the aggregate score: Using multilevel modeling to examine trajectories of laboratory-measured aggression

Aggression is often measured in the laboratory as an iterative “tit-for-tat” sequence, in which two aggressors repeatedly inflict retaliatory harm upon each other. Aggression researchers typically quantify aggression by aggregating across participants’ aggressive behavior on such iterative encounters. However, this “aggregate approach” cannot capture trajectories of aggression across the iterative encounters and needlessly eliminates rich information in the form of within-participant variability. As an alternative approach, I used multilevel modeling (MLM) to examine the slope of aggression across the 25-trial Taylor Aggression Paradigm as a function of trait physical aggression and experimental provocation. Across two preregistered studies (combined N = 392), participants exhibited a modest decline in aggression. This decline reflected a reciprocal strategy, in which participants responded to an initially-provocative opponent with greater aggression that then decreased over time to match their opponent's declining levels of aggression. Against predictions, trait physical aggression and experimental provocation did not affect participants’ overall trajectories of aggression. Yet, exploratory analyses suggested that the participants’ tendency to reciprocate their opponent's aggression with more aggression was greater at higher levels of trait physical aggression and attenuated among participants who had already been experimentally provoked by their opponent. These findings (a) illustrate several advantages of an MLM approach as compared with an aggregate approach to iterative laboratory aggression paradigms; (b) demonstrate that the magnifying effects of trait aggression and experimental provocation on laboratory aggression are stable over brief time-frames; and (c) suggest that modeling the opponent's behavior on such tasks reveals important information.