Workaholism and overcommitment are often defined as irrational or obsessive attachments to work, characterized by excessive work investment with negative consequences for health and well-being. However, the relationship between these constructs remains underexplored. In this study, we hypothesized that workaholism and overcommitment represent different stages of the same work addiction dynamic, with overcommitment mediating the relationship between workaholism and burnout. Additionally, we proposed that job satisfaction reinforces this addiction dynamic, strengthening the relationship between workaholism and overcommitment over time. Utilizing data from a three-wave longitudinal study (time-lag = 1 month) involving Italian employees, we tested a moderated mediation model. Our findings indicated that overcommitment at T2 fully mediated the relationship between workaholism at T1 and job burnout at T3. Moreover, job satisfaction at T2 significantly moderated this pathway, suggesting that higher job satisfaction leads to a stronger relationship between workaholism and overcommitment over time. In conclusion, our study highlights the exacerbating effect of job satisfaction on the link between workaholism and overcommitment, which can, in turn, increase employees’ burnout. This research represents the first longitudinal examination of workaholism and overcommitment as stages within the same process, rather than as distinct constructs.

Past studies that examined externalizing and internalizing symptoms in separate samples have found different trajectories associated with harsh parenting. The present study uses a complex set of longitudinal modeling to investigate the developmental trajectories of internalizing and externalizing symptoms associated with harsh parenting. We also explore the moderation of socioeconomic status and ethnicity by testing differences between income and racial groups. Using bivariate and multigroup latent change score modeling, we analyzed 12,909 participants from the Millennium Cohort Study. Results of the bivariate latent change score model showed that harsh parenting had a bidirectional association with externalizing symptoms but only a unidirectional association with internalizing symptoms. A further analysis using multi-group modeling showed that the association between harsh parenting and externalizing and internalizing symptoms differed across ethnic backgrounds but much less on socioeconomic status. Specifically, initial levels of harsh parenting predicted an increase in externalizing symptoms among White participants but not in non-White participants, and these ethnic differences cut across socioeconomic status classifications in a test of the interaction of ethnicity and socioeconomic status. Taken together, our findings suggest that the prevailing cultural norms surrounding harsh parenting may affect the degree to which it negatively impacts children’s mental health.

Researchers often aim to assess whether repeated measures of an exposure are associated with repeated measures of an outcome. A question of particular interest is how associations between exposures and outcomes may differ over time. In other words, researchers may seek the best form of a temporal model. While several models are possible, researchers often consider a few key models. For example, researchers may hypothesize that an exposure measured during a sensitive period may be associated with repeated measures of the outcome over time. Alternatively, they may hypothesize that the exposure measured immediately before the current time period may be most strongly associated with the outcome at the current time. Finally, they may hypothesize that all prior exposures are important. Many analytic methods cannot compare and evaluate these alternative temporal models, perhaps because they make the restrictive assumption that the associations between exposures and outcomes remains constant over time. Instead, we provide a tutorial describing four temporal models that allow the associations between repeated measures of exposures and outcomes to vary, and showing how to test which temporal model is best supported by the data. By finding the best temporal model, developmental psychopathology researchers can find optimal windows for intervention.

Conventional longitudinal behavioral genetic models estimate the relative contribution of genetic and environmental factors to stability and change of traits and behaviors. Longitudinal models rarely explain the processes that generate observed differences between genetically and socially related individuals. We propose that exchanges between individuals and their environments (i.e., phenotype–environment effects) can explain the emergence of observed differences over time. Phenotype–environment models, however, would require violation of the independence assumption of standard behavioral genetic models; that is, uncorrelated genetic and environmental factors. We review how specification of phenotype–environment effects contributes to understanding observed changes in genetic variability over time and longitudinal correlations among nonshared environmental factors. We then provide an example using 30 days of positive and negative affect scores from an all-female sample of twins. Results demonstrate that the phenotype–environment effects explain how heritability estimates fluctuate as well as how nonshared environmental factors persist over time. We discuss possible mechanisms underlying change in gene–environment correlation over time, the advantages and challenges of including gene–environment correlation in longitudinal twin models, and recommendations for future research.