As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

The Interpersonal Regulation Questionnaire (IRQ) is a scale developed to measure the tendency and efficacy of intrinsic interpersonal emotion regulation across positive and negative affective states. As the psychometric properties of the IRQ across cultures and different ages have not been well established, the current study was conducted to examine the applicability of the translated IRQ in a sample of Chinese young adolescents (initial n = 487; 50.20% are males; M = 14.52 years old, SD = .75). The original four-factor structure of the IRQ (i.e., negative-tendency, negative-efficacy, positive-tendency, and positive-efficacy) and other parsimonious models were examined and compared using confirmatory factor analysis. The results demonstrated that only the correlated-four-factor model had acceptable model fit indices. The internal consistencies of the four sub-scales were all above .70. Strict measurement invariance (i.e., configural, metric, and scalar) was achieved between males and females. In addition, latent mean comparison showed that females reported higher negative-efficacy and positive-tendency than males, while no gender variations were found for the remaining two factors. The validity of the IRQ was further supported by its convergent-discriminant associations with emotional well-being and distress, emotional expressivity, social competence, empathic responding, cognitive reappraisal, and delinquent behavior. Taken together, the IRQ is a reliable and valid measure for Chinese young adolescents' intrinsic interpersonal emotion regulation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Findings on the motivational and emotional effects of computerized adaptive testing (CAT) compared to nonadaptive testing (NCAT) have been heterogeneous and inconclusive so far. This meta-analysis aims to determine the specific effects of CAT better by analyzing several moderator variables related to the test, the examinee, and the test setting. To be included in the meta-analysis, studies needed to be original, correlational, quasi-experimental, or experimental studies in English, German, or Chinese, and they needed to compare CAT to NCAT regarding motivational and/or emotional states. A three-level random-effects model with motivation, negative emotion, and positive emotion as dependent variables was estimated. The risk of bias and publication bias were examined. Twenty-seven studies with a total of 190 effect sizes were analyzed. The overall effect of CAT was not significant for motivation and negative emotion and significant for positive emotion after publication bias correction. The moderator analysis showed significant moderator effects for “test speededness” on motivation and positive emotion, “instantaneous feedback on given responses” and “possibility of response revision” on positive emotion, and “average difficulty of presented items” on negative emotion. For CAT configurations, it is recommended to avoid speededness, present easy items, allow for response revision, and provide feedback.

A large number of previous studies have examined the effects of thin-ideal avatars on body image in virtual reality (VR), reporting mixed results. A possible explanation is that individuals' sense of embodiment (SoE) can influence the effects of avatars. Using the body discontinuity paradigm, a paradigm rarely used in prior studies, this study explores how SoE affects users' body image when using thin-ideal avatars in VR. A group of 77 female participants were randomly assigned to one of the SoE conditions to complete a VR task using a thin-ideal avatar. The body image was assessed not only using explicit but also with implicit measures. The result shows that participants in a high SoE condition were more likely to have a more positive actual body image than others in a low SoE condition, regardless of whether implicit or explicit measures were used. Furthermore, the ownership subcomponent of the SoE significantly mediated the relationship between thin-ideal avatars exposure and the actual body image using implicit measures. However, no substantial difference in ideal body image was found. These findings extend our understanding of avatar effects in VR, thereby providing additional evidence for using avatars to explore body image-related phenomena and to use them in psychotherapeutic applications such as treatments for eating disorders.

Understanding the differential strength of effects in the presence of a third variable, known as a moderation effect, is a common research goal in many psychological and behavioural science fields. If structural equation modelling is applied to test effects of interest, the investigation of differential strength of effects will typically ask how parameters of a latent variable model are influenced by categorical or continuous moderators, such as age, socio‐economic status, personality traits, etc. Traditional approaches to continuous moderators in SEMs predominantly address linear moderation effects, risking the oversight of nonlinear effects. Moreover, some approaches have methodological limitations, for example, the need to categorise moderators or to pre‐specify parametric forms of moderation. This tutorial introduces local structural equation modelling (LSEM) in a non‐technical way. LSEM is a nonparametric approach that allows the analysis of nonlinear moderation effects without the above‐mentioned limitations. Using an empirical dataset, we demonstrate the implementation of LSEM through the R‐sirt package, emphasising its versatility in both exploratory analysis of nonlinear moderation without prior knowledge and confirmatory testing of hypothesised moderation functions. The tutorial also addresses common modelling issues and extends the discussion to different application scenarios, demonstrating its flexibility.