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Boredom has shown to associate with undesirable effects, but little is known about how international students differ from Canadian students in boredom coping. The purposes of this study are to validate Boredom Coping Scale and evaluate students' differences in boredom coping styles based on 408 Canadian and 104 international students. The confirmatory factor analysis showed that the factor structure, loadings, and covariances were invariant across the two samples. In the latent profile analysis, the Canadian students were classified into three groups who used different combination of coping strategies: Reappraisers, Thinkers, and Criticizers, whereas the international students were grouped into two new categories: Persistent Students and Behaviourists. While Reappraisers and Criticizers are consistent with previous findings, Thinkers, who preferred coping with boredom cognitively through either approach or avoidance, is the new profile. Our results partly supported previous findings and showed that international students were different from Canadian students in boredom coping., Author(s): Chun Ho But [sup.1] , Johnson Ching-Hong Li [sup.1] , Virginia M. C. Tze [sup.2] Author Affiliations: (1) grid.21613.37, 0000 0004 1936 9609, Department of Psychology, University of Manitoba, [...]

Instead of relying on null-hypothesis significance testing (NHST), researchers are consistently advised to use effect sizes (ESs) and confidence intervals (CIs) to convey research findings. However, typical ES measures for most linear models (e.g., multiple regression) assume data normality, a condition that is often violated in behavioral research. This may lead to inaccurate interpretation of ES. In multiple regression models, Cohen's f2, R2 and R adj 2 are employed by researchers, but no study has systematically evaluated their robustness in practice. Thus, this Monte Carlo simulation study evaluates the robustness of f2, R2 and R adj 2 and the associated CIs based on manipulated levels of sample sizes, magnitudes of ESs, numbers of predictors, and data violations (i.e., heavy-tailed, skewed, contaminated, lognormal, and heteroscedastic distributions of errors). This study offers guidelines regarding how robust these ESs are so that researchers can report the most appropriate ES in their research studies. [ABSTRACT FROM AUTHOR]