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ObjectiveYouth who experience behavioral and emotional problems are at risk for sleep disturbance, while sleep disturbance also perpetuates behavioral and emotional problems. While the relationship between sleep and psychopathology in clinical mental health samples is well-established, exploration of the underlying mechanisms maintaining this relationship is limited. The purpose of this study is to explore within-person variability in emotion regulation as a mechanism of the relationship between sleep and psychopathology in a clinical youth sample.MethodsUsing a within-person design, 25 children (ages 6–11; 64% male; 44% non-Hispanic White) presenting to outpatient behavioral health treatment with mental health concerns were recruited to participate in a 14-day study. Daily reports of objective sleep duration via actigraphy, self-reported subjective sleepiness, and parent-reported internalizing and externalizing problems and emotion regulation were collected. Multilevel mediation analyses were used to examine the mediating effect of emotion regulation on the daily-level relationship between sleep and behavior problems.ResultsAt the within-person level, emotion dysregulation was a significant mediator of the relationships between objective sleep duration and both externalizing [MCCI (0.0005–0.0063)] and internalizing problems [MCCI (0.0001–0.0025)]. Contrary to hypotheses, when youth slept more than usual, internalizing and externalizing problems were worse through the indirect effect of increased emotion dysregulation.ConclusionsInconsistencies in schedules and routines, even if in a positive direction, may have short-term negative consequences for youth with emotional and behavioral concerns. Future research should look to address sleep variability and how deviations in routine may impact behavior more broadly, through the indirect effects of emotion regulation.

A maximum likelihood estimation routine is presented for a generalized structural equation model that permits a combination of response variables from various distributions (e.g., normal, Poisson, binomial, etc.). The likelihood function does not have a closed-form solution and so must be numerically approximated, which can be computationally demanding for models with several latent variables. However, the dimension of numerical integration can be reduced if one or more of the latent variables do not directly affect any nonnormal endogenous variables. The method is demonstrated using an empirical example, and the full estimation details, including first-order derivatives of the likelihood function, are provided.

Background and Hypothesis Influential models of psychosis indicate that the impact of putative causal factors on positive symptoms might be explained partly through affective disturbances. We aimed to investigate whether pathways from stress and self-esteem to positive symptoms, as well as reversal pathways from symptoms to stress and self-esteem, were mediated through specific affective disturbances across the extended psychosis phenotype. Study Design Using experience sampling methodology, 178 participants (65 high-schizotypy, 74 at-risk mental state, and 39 first-episode psychosis) were assessed on levels of momentary stress, self-esteem, anxiety, sadness, psychotic-like experiences (PLE), and paranoia. Multilevel mediation models were fit to examine indirect effects of each of these pathways. Considering evidence of mediation, each indirect pathway will be combined in a single model to explore their relative contributions. Study Results Anxiety, sadness, and self-esteem mediated the pathways from stress to PLE and paranoia in daily-life. In the pathway to paranoia, sadness, and self-esteem showed larger contributions than anxiety. Pathways from self-esteem to PLE and paranoia were mediated by anxiety and sadness, the later showing a larger contribution. Pathways from symptoms to stress, but not from symptoms to self-esteem, were differently explained by emotional states; sadness lost its mediating effect and anxiety was the most important mediator. Few differences across groups were found. Conclusions This study lends support to psychological models of psychosis that highlight the relevance of affective disturbances in the risk and expression of psychosis. Furthermore, specific influences of different negative emotional states were identified, which could enhance psychological treatments.

BACKGROUND Androgen deprivation therapy (ADT) may affect cognitive function in men with prostate cancer (PCa). This study examined whether insomnia symptoms mediate the relationship between ADT and perceived cognitive function and whether depressive symptoms, fatigue severity, and physical activity moderate the strength of this relationship. METHODS This was a prospective study of ADT recipients (n = 83) who were matched with control patients with PCa who were not on ADT (n = 92) and with controls with no history of cancer (n = 112) over a 2-year follow-up period. Perceived cognitive function and satisfaction were assessed with the Everyday Cognition Scale. Insomnia was assessed with the Insomnia Severity Index. Multilevel mediation analyses were conducted to estimate the indirect effect of ADT on perceived cognitive function through insomnia symptoms. Exploratory moderated mediation analyses assessed whether the indirect effect of ADT on perceived cognitive function through insomnia symptoms was dependent on levels of fatigue, depression, or physical activity. RESULTS Insomnia symptoms significantly mediated the relationship between receipt of ADT and perceived cognitive function (P < .001) and satisfaction with cognition (P < .001) after controlling for comorbidities. Men with greater fatigue had a more pronounced association of ADT with insomnia severity. Men with greater depressive symptoms had a stronger association between insomnia severity and worse perceived cognitive function. Physical activity was not a significant moderator of the relationship between ADT and perceived cognitive function. CONCLUSIONS Insomnia influenced the relationship between ADT and perceived cognitive abilities. Interventions to address insomnia, fatigue, and depression may improve perceived cognitive function.

Behavioral scientists use mediation analysis to understand the mechanism(s) by which an effect operates and moderation analysis to understand the contingencies or boundary conditions of effects. Yet how effects operate (i.e., the mechanism at work) and their boundary conditions (when they occur) are not necessarily independent, though they are often treated as such. Conditional process analysis is an analytical strategy that integrates mediation and moderation analysis with the goal of examining and testing hypotheses about how mechanisms vary as a function of context or individual differences. In this article, we provide a conceptual primer on conditional process analysis for those not familiar with the integration of moderation and mediation analysis, while also describing some recent advances and innovations for the more experienced conditional process analyst. After overviewing fundamental modeling principles using ordinary least squares regression, we discuss the extension of these fundamentals to models with more than one mediator and more than one moderator. We describe a differential dominance conditional process model and overview the concepts of partial, conditional, and moderated moderated mediation. We also discuss multilevel conditional process analysis and comment on implementation of conditional process analysis in statistical computing software.

Measurement models, such as factor analysis and item response theory models, are commonly implemented within educational, psychological, and behavioral science research to mitigate the negative effects of measurement error. These models can be formulated as an extension of generalized linear mixed models within a unifying framework that encompasses various kinds of multilevel models and longitudinal models, such as partially nonlinear latent growth models. We introduce the R package PLmixed, which implements profile maximum likelihood estimation to estimate complex measurement and growth models that can be formulated within the general modeling framework using the existing R package lme4 and function optim. We demonstrate the use of PLmixed through two examples before concluding with a brief overview of other possible models.

Item Response Theory (IRT) is a modeling framework that can be applied to a large variety of research questions spanning several disciplines. To make IRT models more accessible for the general researcher, a free tool has been created that can easily conduct one-parameter logistic IRT (1PL) analyses using the convenient point-and-click interface in SPSS without any required downloads or add-ons. This tool, the SPIRIT macro, can fit 1PL models with person and item covariates, DIF analyses, multidimensional models, multigroup models, rating scale models, and several other variations. Example explanatory models are presented with an applied dataset containing responses to an ADHD rating scale. Illustrations of how to fit basic 1PL models as well as two more complicated analyses using SPIRIT are given.

Marketing, consumer, and organizational behavior researchers interested in studying the mechanisms by which effects operate and the conditions that enhance or inhibit such effects often rely on statistical mediation and conditional process analysis (also known as the analysis of “moderated mediation”). Model estimation is typically undertaken with ordinary least squares regression-based path analysis, such as implemented in the popular PROCESS macro for SPSS and SAS ( Hayes, 2013 ), or using a structural equation modeling program. In this paper we answer a few frequently-asked questions about the difference between PROCESS and structural equation modeling and show by way of example that, for observed variable models, the choice of which to use is inconsequential, as the results are largely identical. We end by discussing considerations to ponder when making the choice between PROCESS and structural equation modeling.

A maximum likelihood estimation routine for two-level structural equation models with random slopes for latent covariates is presented. Because the likelihood function does not typically have a closed-form solution, numerical integration over the random effects is required. The routine relies upon a method proposed by du Toit and Cudeck (Psychometrika 74(1):65–82, 2009) for reformulating the likelihood function so that an often large subset of the random effects can be integrated analytically, reducing the computational burden of high-dimensional numerical integration. The method is demonstrated and assessed using a small-scale simulation study and an empirical example.

Analyses have suggested math anxiety is a multidimensional construct. However, previous behavioral genetic work examining math anxiety was unidimensional. Thus, the purpose of the present study was to examine different approaches for specifying behavioral genetic models of math anxiety as a multidimensional construct. Three models were compared: a unidimensional model, a three dimension multidimensional model, and a bi-factor model, which partitioned variance into one common factor shared across three dimensions of math anxiety and examined residual variance in each dimension. The best fitting model was a bi-factor AE model, which suggested moderate heritability of general math anxiety and that each dimension of math anxiety had unique etiological influences not accounted for by shared variance with the general math anxiety factor. Thus, while there was evidence of shared etiology, there was also evidence of some etiological distinction across dimensions of math anxiety. The results demonstrate the importance of taking into account the dimensionality of the scale when interpreting similarity across twins.

An attachment-based, psychotherapeutic parent education course was created for incarcerated mothers and fathers to improve their ability to provide positive parenting and a more stable home environment for their children. The current study assessed the effects of this parenting curriculum on parents’ tendencies to be abusive, their sense of efficacy and satisfaction as a parent, their psychological distress, and their knowledge of child development and positive child guidance strategies. Results of pre-post assessments showed a significant improvement in parents’ sense of efficacy and satisfaction in the parenting role; their knowledge, skills, and behavior as a parent; their understanding of child development; their knowledge of alternatives to using corporal punishment; establishing appropriate parent-child boundaries; and they were less likely to view their child’s independence as a threat. Females showed a significant decrease in distress symptoms. Results are discussed in terms of the critical need for effective, high-quality parent education to break the intergenerational cycle of poor parenting for this at-risk population.

There have been numerous treatments in the clinical research literature about various design, analysis, and interpretation considerations when testing hypotheses about mechanisms and contingencies of effects, popularly known as mediation and moderation analysis. In this paper we address the practice of mediation and moderation analysis using linear regression in the pages of Behaviour Research and Therapy and offer some observations and recommendations, debunk some popular myths, describe some new advances, and provide an example of mediation, moderation, and their integration as conditional process analysis using the PROCESS macro for SPSS and SAS. Our goal is to nudge clinical researchers away from historically significant but increasingly old school approaches toward modifications, revisions, and extensions that characterize more modern thinking about the analysis of the mechanisms and contingencies of effects.