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.     

Implicit attitude measures: consistency, stability, and convergent validity

In recent years, several techniques have been developed to measure implicit social cognition. Despite their increased use, little attention has been devoted to their reliability and validity. This article undertakes a direct assessment of the interitem consistency, stability, and convergent validity of some implicit attitude measures. Attitudes toward blacks and whites were measured on four separate occasions, each 2 weeks apart, using three relatively implicit measures (response-window evaluative priming, the Implicit Association Test, and the response-window Implicit Association Test) and one explicit measure (Modern Racism Scale). After correcting for interitem inconsistency with latent variable analyses, we found that (a) stability indices improved and (b) implicit measures were substantially correlated with each other, forming a single latent factor. The psychometric properties of response-latency implicit measures have greater integrity than recently suggested.     

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.     

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.     

On the practice of dichotomization of quantitative variables

The authors examine the practice of dichotomization of quantitative measures, wherein relationships among variables are examined after 1 or more variables have been converted to dichotomous variables by splitting the sample at some point on the scale(s) of measurement. A common form of dichotomization is the median split, where the independent variable is split at the median to form high and low groups, which are then compared with respect to their means on the dependent variable. The consequences of dichotomization for measurement and statistical analyses are illustrated and discussed. The use of dichotomization in practice is described, and justifications that are offered for such usage are examined. The authors present the case that dichotomization is rarely defensible and often will yield misleading results.     

The sounds of dominance.

A study was conducted to determine the effects of vocal cues on judgements of dominance in an interpersonal influence context. Physical measures of human vocal cues and participant ratings of dominance were obtained from videotapes of actors delivering short influence messages. After controlling for linguistic and visual content of messages, results indicated that mean amplitude and amplitude standard deviation were positively associated with dominance judgments, whereas speech rate was negatively associated with dominance judgments. An unexpected interaction revealed that mean fundamental frequency (F₀) was positively associated with dominance judgments for male speakers but not significantly associated with dominance judgments for female speakers. F₀ standard deviation was not significantly associated with dominance judgments. Results support the conclusion that dominance judgments are inferred from multiple sources of information and that some vocal markers of dominance are more influential than others.     

Statistical mediation analysis with a multicategorical independent variable

Virtually all discussions and applications of statistical mediation analysis have been based on the condition that the independent variable is dichotomous or continuous, even though investigators frequently are interested in testing mediation hypotheses involving a multicategorical independent variable (such as two or more experimental conditions relative to a control group). We provide a tutorial illustrating an approach to estimation of and inference about direct, indirect, and total effects in statistical mediation analysis with a multicategorical independent variable. The approach is mathematically equivalent to analysis of (co)variance and reproduces the observed and adjusted group means while also generating effects having simple interpretations. Supplementary material available online includes extensions to this approach and Mplus, SPSS, and SAS code that implements it.     

Multilevel SEM Strategies for Evaluating Mediation in Three-Level Data

Strategies for modeling mediation effects in multilevel data have proliferated over the past decade, keeping pace with the demands of applied research. Approaches for testing mediation hypotheses with 2-level clustered data were first proposed using multilevel modeling (MLM) and subsequently using multilevel structural equation modeling (MSEM) to overcome several limitations of MLM. Because 3-level clustered data are becoming increasingly common, it is necessary to develop methods to assess mediation in such data. Whereas MLM easily accommodates 3-level data, MSEM does not. However, it is possible to specify and estimate some 3-level mediation models using both single- and multilevel SEM. Three new alternative approaches are proposed for fitting 3-level mediation models using single- and multilevel SEM, and each method is demonstrated with simulated data. Discussion focuses on the advantages and disadvantages of these approaches as well as directions for future research.