Fibromyalgia: the role of sleep in affect and in negative event reactivity and recovery

OBJECTIVE: Fibromyalgia (FM) syndrome is a chronic pain condition characterized by diffuse muscle pain, increased negative mood, and sleep disturbance. Until recently, sleep disturbance in persons with FM has been modeled as the result of the disease process or its associated pain. The current study examined sleep disturbance (i.e., sleep duration and sleep quality) as a predictor of daily affect, stress reactivity, and stress recovery. DESIGN AND MEASURES: A hybrid of daily diary and ecological momentary assessment methodology was used to evaluate the psychosocial functioning of 89 women with FM. Participants recorded numeric ratings of pain, fatigue, and positive and negative affect 3 times throughout the day for 30 consecutive days. At the end of each day, participants completed daily diary records of positive and negative life events. In addition, participants reported on their sleep duration and sleep quality each morning. RESULTS: After accounting for the effects of positive events, negative events, and pain on daily affect scores, it was found that sleep duration and quality were prospectively related to affect and fatigue. Furthermore, the effects of inadequate sleep on negative affect were cumulative. In addition, an inadequate amount of sleep prevented affective recovery from days with a high number of negative events. CONCLUSIONS: These results lend support to the hypothesis that sleep is a component of allostatic load and has an upstream role in daily functioning.     

The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects

In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem.     

Impact of Sluggish Cognitive Tempo and Attention-Deficit/Hyperactivity Disorder Symptoms on Adults’ Quality of Life

The symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) have been linked to dysfunction in numerous life domains for both children and adults. As such, it is likely that individuals with these and other related symptoms (e.g., sluggish cognitive tempo, SCT) may also experience impaired quality of life. The current study examines the association between ADHD and SCT symptoms and quality of life (QOL) in a community sample of adults. Quality of life data collected from 983 participants (M age = 45.6 years) were analyzed primarily through a series of hierarchical multiple regressions employing SCT and ADHD symptom clusters, demographic, and anxiety and depression scale variables as predictors. Results generally supported the hypothesis that ADHD and SCT symptoms would negatively associate with QOL. Amongst the indicators, inattention and SCT emerged as the strongest predictors of low QOL. These findings underscore the negative impact of ADHD symptoms in adulthood, the independent contribution of SCT, and the importance of considering QOL in prospective research and intervention.     

Low-income African American women's attempts to convince their main partner to use condoms

In this cross-sectional study the authors examine condom use attempts and condom use among 305 high-risk, low-income African American women who reported having a main partner. Women who had recently attempted to convince their main partner to use condoms were almost 10 times more likely to have recently used condoms with their partner than women who had not made an attempt. Among the subsample of 116 women who had recently made a condom use attempt with their main partner, having a history of childhood abuse and having one's main partner raise infidelity questions in response to the condom use attempt were negatively associated with recent condom use with this partner. Findings provide initial insights into the importance of women's condom use attempts, as well as subgroups of women who may encounter special challenges convincing their main partner to use condoms.     

Imitation Is the Sincerest Form of Cheating: The Influence of Direct Knowledge and Attitudes on Academic Dishonesty

What effect does witnessing other students cheat have on one's own cheating behavior? What roles do moral attitudes and neutralizing attitudes (justifications for behavior) play when deciding to cheat? The present research proposes a model of academic dishonesty which takes into account each of these variables. Findings from experimental (vignette) and survey methods determined that seeing others cheat increases cheating behavior by causing students to judge the behavior less morally reprehensible, not by making rationalization easier. Witnessing cheating also has unique effects, controlling for other variables.     

A Computationally Efficient State Space Approach to Estimating Multilevel Regression Models and Multilevel Confirmatory Factor Models

Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated. The utility of the state space approach is demonstrated with either a simulated or real example for each multilevel model. It is concluded that the results from the state space approach are essentially identical to those from specialized multilevel regression modeling and structural equation modeling software. More importantly, the state space approach offers researchers a computationally more efficient alternative to fit multilevel regression models with a large number of Level 1 units within each Level 2 unit or a large number of observations on each subject in a longitudinal study.     

Body Surveillance on Instagram: Examining the Role of Selfie Feedback Investment in Young Adult Women’s Body Image Concerns

Sex Roles - Selfies are self-taken self-portrait photographs captured with mobile phones, and they are among the most common forms of self-expression on the photo-based social network Instagram....     

Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.