Objective: Although the adverse effects of chronic pain on work productivity and daily life pursuits are clear, the within-person dynamics of pain, goal cognition, and engagement in work-related and lifestyle goals remain uncharted. This study investigated the impact of pain intensity (assessed on 3 occasions each day) and goal-related schematic thinking (ratings of importance, planning, and goal pursuit opportunities, assessed only in the morning) on afternoon and evening work and lifestyle goal pursuit.

Methods: A community sample of working adults with chronic pain (N = 131) were screened and interviewed about their work and lifestyle goals and completed a 21-day telephonic diary. Hierarchical linear modeling was used to estimate within-person and between-person effects.

Results: At the within-person level, morning pain intensity was inversely related to schematic cognition concerning work and lifestyle goals, whereas, at the between-person level, morning pain intensity varied positively with schematic thinking about work goals as well with afternoon lifestyle goal pursuit. At both the between- and within- analytic levels, morning goal schemas were positively associated with the pursuit of each type of goal in the afternoon and again in the evening. Moreover, positive carry-over effects of morning goal schemas on next day afternoon goal pursuit were observed.

Conclusions: Whereas morning pain intensity exhibited inconsistent effects across analytic levels, morning goal-related schematic thinking consistently predicted goal pursuit across analytic levels, type of goal, and time of day. These findings have implications for treatment and prevention of pain’s potentially deleterious effects on workplace and lifestyle goals.

We evaluated the cross-sex and -ethnic (Hispanic/Latino, non-Hispanic White) measurement invariance of anxiety symptoms based on the Spence Children’s Anxiety Scale (SCAS) as well as SCAS anxiety symptoms’ correspondence with scores on the 5-item Screen for Child Anxiety Related Emotional Disorders (SCARED) and teacher ratings of child anxiety. Based on data corresponding to 702 children (M age = 9.65, SD = 0.70; 51.9 % girls; 55 % Hispanic/Latino), findings showed some sex and ethnic variations in SCAS measured anxiety at the item and scale levels. Moreover, SCAS correspondence to the 5-item SCARED was found across ethnicity and sex. SCAS correspondence to teacher ratings was found for non-Hispanic White boys and non-Hispanic White girls, marginally in Hispanic/Latino boys, and poorly in Hispanic/Latino girls.

This study examined the relations between parental socialization of child anxious behaviors (i.e., reinforcement, punishment, modeling, transmission of information) and child anxiety and related problems at varying child sensitivity levels. Data corresponding to 70 clinic-referred children (M age = 9.86 years; 50% girls; 49% Hispanic/Latino, 51% Caucasian) showed that for children with low (but not high) anxiety sensitivity, anxiety-related parental socialization behaviors were associated with more child anxiety and depression symptoms. Findings also indicated that parental socialization of anxious behaviors and anxiety sensitivity functioned similarly in the prediction of anxiety and depression across Caucasian and Hispanic/Latino children. There were no significant mean level variations across child sociodemographic characteristics in general, but anxiety-promoting parenting behaviors were twice as high in Hispanic/Latino compared to Caucasian families.

Methodologists have developed mediation analysis techniques for a broad range of substantive applications, yet methods for estimating mediating mechanisms with missing data have been understudied. This study outlined a general Bayesian missing data handling approach that can accommodate mediation analyses with any number of manifest variables. Computer simulation studies showed that the Bayesian approach produced frequentist coverage rates and power estimates that were comparable to those of maximum likelihood with the bias-corrected bootstrap. We share an SAS macro that implements Bayesian estimation and use 2 data analysis examples to demonstrate its use.