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Researchers often combine longitudinal panel data analysis with tests of interactions (i.e., moderation). A popular example is the cross-lagged panel model (CLPM). However, interaction tests in CLPMs and related models require caution because stable (i.e., between-level, B) and dynamic (i.e., within-level, W) sources of variation are present in longitudinal data, which can conflate estimates of interaction effects. We address this by integrating literature on CLPMs, multilevel moderation, and latent interactions. Distinguishing stable B and dynamic W parts, we describe three types of interactions that are of interest to researchers: 1) purely dynamic or WxW; 2) cross-level or BxW; and 3) purely stable or BxB. We demonstrate estimating latent interaction effects in a CLPM using a Bayesian SEM in Mplus to apply relationships among work-family conflict and job satisfaction, using gender as a stable B variable. We support our approach via simulations, demonstrating that our proposed CLPM approach is superior to a traditional CLPMs that conflate B and W sources of variation. We describe higher-order nonlinearities as a possible extension, and we discuss limitations and future research directions.

Research that helps people change their behavior has the potential to improve the quality of lives, but it is too often approached in a way that divorces behavior from the people who need to enact it. In this paper, we propose a personality-informed approach to classifying behavior-change problems and designing interventions to address them. In particular, we argue that interventions will be most effective when they target the appropriate psychological process given the disposition of the participant and the desired duration of change. Considering these dimensions can help to reveal the differences among common types of behavior-change problems, and it can guide decisions about what kinds of intervention solutions will most effectively solve them. We review key concepts and findings from the personality literature that can help us understand the dynamic nature of dispositions and to identify the psychological processes that best explain both short-term variance in behavior and long-term development of personality. Drawing on this literature, we argue that different types of behavior-change problems require different forms of “trait regulation,” and we offer a series of propositions to be evaluated as potential guides for the design of intervention strategies to address them.

People routinely regulate their emotions in order to function more effectively at work, to behave more appropriately in social situations, or simply to feel better. Recently, researchers have begun to examine how people shape their affective states using digital technologies, such as smartphones. In this article, we discuss the emergence of digital emotion regulation, both as a widespread behavioral phenomenon and a new cross-disciplinary field of research. This field bridges two largely distinct areas of enquiry: (a) psychological research into how and why people regulate their emotions, which has yet to systematically explore the role of digital technology, and (b) computing research into how digital technologies impact users’ emotions, which has yet to integrate psychological theories of emotion regulation. We argue that bringing these two areas into better contact will benefit both and will facilitate a deeper understanding of the nature and significance of digital emotion regulation.

Sensory processing sensitivity is an individual difference that captures the extent to which people show heightened emotional reactivity to, and increased cognitive processing of, their environment. Although central to its definition, there has been no research examining whether highly sensitive individuals display stronger reactivity to naturally occurring negative and positive events in everyday life. We addressed this gap by carrying out a 21-day online diary study with 239 participants, varying in sensory processing sensitivity, who reported their daily life-satisfaction, affective experiences, and self-esteem along with appraisals of the most negative and positive events of the day. Multilevel analyses demonstrated that individuals higher in sensory processing sensitivity showed greater reactivity to more subjectively intense negative events, but no difference in their reactivity to positive events. These findings provide initial insights into how sensory processing sensitivity manifests in daily emotional reactivity with greater reactivity to negative events in our study.

This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time. Australian Research Council https://doi.org/10.13039/501100000923

Introduction: Interactive audience response during continuing education (CE) in pharmacy practice increases audience involvement. However, remote-site participants may not have access to interactive technology. This study explores the perceptions of a mobile application audience response system (ARS) by remote pharmacy CE participants. Secondarily, we evaluatedinterest in continued use of ARS, as well as willingness to use as an assessment tool for CE effectiveness. Methods: Pharmacists participating in CE sessions remotely within a health system were provided a unique ARS session code to enter into a free mobile application. Participants then responded to ARS presentation questions. An online survey link was e-mailed to all potential remote participants inquiring about perceptions of ARS use. Results: Of the 52 potential remote users, 28 (53.8%) responded to the survey. The top 3 positive responses included the availability of free software (71.4%), anonymity (57.1%), and ease of use (53.6%). Top 2 barriers included slowing the process down (14.3%) and requiring the use of application software (14.3%). Discussion: Interactive software during pharmacy CE lectures for participants at remote locations within a health system was well accepted. ARS should be considered and further studied for CE lectures at institutions with remote participants.

We discuss three varieties of affective dynamics (affective instability, emotional inertia, and emotional differentiation). In each case, we suggest how these affective dynamics should be operationalized and measured in daily life using time-intensive methods, like ecological momentary assessment or ambulatory assessment, and recommend time-sensitive analyses that take into account not only the variability but also the temporal dependency of reports. Studies that explore how these affective dynamics are associated with psychological disorders and symptoms are reviewed, and we emphasize that these affective processes are within a nexus of other components of emotion regulation. ispartof: Emotion Review vol:7 issue:4 pages:355-361 ispartof: location:England status: published

Four studies investigated whether people tend to see ingroup flaws as part of human nature (HN) to a greater degree than outgroup flaws. In Study 1, people preferentially ascribed high HN flaws to their ingroup relative to two outgroups. Study 2 demonstrated that flaws were rated higher on HN when attributed to the ingroup than when attributed to an outgroup, and no such difference occurred for positive traits. Study 3 replicated this humanizing ingroup flaws (HIF) effect and showed that it was (a) independent of desirability and (b) specific to the HN sense of humanness. Study 4 replicated the results of Study 3 and demonstrated that the HIF effect is amplified under ingroup identity threat. Together, these findings show that people humanize ingroup flaws and preferentially ascribe high HN flaws to the ingroup. These ingroup humanizing biases may serve a group-protective function by mitigating ingroup flaws as “only human.”

The citation impact of a comprehensive sample of articles published in social and personality psychology journals in 1998 was evaluated. Potential predictors of the 10-yr. citation impact of 1,580 articles from 37 journals were investigated, including number of authors, number of references, journal impact factor, author nationality, and article length, using linear regression. The impact factor of the journal in which articles appeared was the primary predictor of the citations that they accrued, accounting for 30% of the total variance. Articles with greater length, more references, and more authors were cited relatively often, although the citation advantage of longer articles was not proportionate to their length. A citation advantage was also enjoyed by authors from the United States of America, Canada, and the United Kingdom. 37% of the variance in the total number of citations was accounted for by the study variables.