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Gambling disorder (GD) is a behavioral addiction that causes serious harm to individuals' mental health and social functioning. This study aimed to explore whether gamblers' illness representations (IR) for GD are related to disordered and/or controlled patterns of gambling under the framework of commonsense model of self-regulation (CSM). Based on CSM, different facets of IR do not exist in isolation but together form a total illness schema; we hence used a person-centered analysis method, latent profile analysis (LPA), to classify gamblers by their overall IR for GD (including consequences, personal and treatment control, timeline cyclical, emotional representations, and coherence) into groups and then tested whether these groups differed in GD symptoms, controlled gambling behaviors, and help-seeking intention. An anonymous online survey was conducted on a social media platform, and valid responses from 589 past-year adult gamblers were collected. LPA results supported a four-profile model, revealing four profiles of GD representations: the weak-perception profile, average profile, tensed-up profile, and rational profile. Significant differences were found in all outcome variables in these four profile groups. In particular, the rational group exhibited the lowest levels of GD symptoms and impaired behavioral control, the highest levels of responsible and controlled gambling, and the highest levels of help-seeking intention, whereas the weak-perception group reported the lowest levels of responsible and controlled gambling behaviors. The findings provide empirical support for the application of CSM to addictive behaviors, as well as preliminary evidence for the potential use of CSM-based interventions for GD prevention.

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Missing data is a common occurrence in mediation analysis. As a result, the methods used to construct confidence intervals around the indirect effect should consider missing data. Previous research has demonstrated that, for the indirect effect in data with complete cases, the Monte Carlo method performs as well as nonparametric bootstrap confidence intervals (see MacKinnon et al., Multivariate Behavioral Research, 39(1), 99-128, 2004; Preacher & Selig, Communication Methods and Measures, 6(2), 77-98, 2012; Tofighi & MacKinnon, Structural Equation Modeling: A Multidisciplinary Journal, 23(2), 194-205, 2015). In this manuscript, we propose a simple, fast, and accurate two-step approach for generating confidence intervals for the indirect effect, in the presence of missing data, based on the Monte Carlo method. In the first step, an appropriate method, for example, full-information maximum likelihood or multiple imputation, is used to estimate the parameters and their corresponding sampling variance-covariance matrix in a mediation model. In the second step, the sampling distribution of the indirect effect is simulated using estimates from the first step. A confidence interval is constructed from the resulting sampling distribution. A simulation study with various conditions is presented. Implications of the results for applied research are discussed.

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Mediation, moderation, and moderated mediation are common in behavioral research models. Several tools are available for estimating indirect effects, conditional effects, and conditional indirect effects and forming their confidence intervals. However, there are no simple-to-use tools that can appropriately form the bootstrapping confidence interval for standardized conditional indirect effects. Moreover, some tools are restricted to a limited type of models. We developed an R package, manymome, which can be used to estimate and form confidence intervals for indirect effects, conditional effects, and conditional indirect effects, standardized or not, using a two-step approach: model parameters are estimated either by structural equation modeling using lavaan or by a set of linear regression models using lm, and then the coefficients are used to compute the requested effects and form confidence intervals. It can be used when there are missing data if the model is fitted by structural equation modeling. There are only a few limitations on some aspects of a model, and no inherent limitations on the number of predictors, the number of independent variables, or the number of moderators and mediators. The goal is to have a tool that allows researchers to focus on model fitting first and worry about estimating the effects later. The use of the model is illustrated using a few numerical examples, and the limitations of the package are discussed.

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The multivariate delta method was used by Yuan and Chan to estimate standard errors and confidence intervals for standardized regression coefficients. Jones and Waller extended the earlier work to situations where data are nonnormal by utilizing Browne's asymptotic distribution-free (ADF) theory. Furthermore, Dudgeon developed standard errors and confidence intervals, employing heteroskedasticity-consistent (HC) estimators, that are robust to nonnormality with better performance in smaller sample sizes compared to Jones and Waller's ADF technique. Despite these advancements, empirical research has been slow to adopt these methodologies. This can be a result of the dearth of user-friendly software programs to put these techniques to use. We present the betaDelta and the betaSandwich packages in the R statistical software environment in this manuscript. Both the normal-theory approach and the ADF approach put forth by Yuan and Chan and Jones and Waller are implemented by the betaDelta package. The HC approach proposed by Dudgeon is implemented by the betaSandwich package. The use of the packages is demonstrated with an empirical example. We think the packages will enable applied researchers to accurately assess the sampling variability of standardized regression coefficients.

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The results in a structural equation modeling (SEM) analysis can be influenced by just a few observations, called influential cases. Tools have been developed for users of R to identify them. However, similar tools are not available for AMOS, which is also a popular SEM software package. We introduce the FINDOUT toolset, a group of SPSS extension commands, and an AMOS plugin, to identify influential cases and examine how these cases influence the results. The SPSS commands can be used either as syntax commands or as custom dialogs from pull-down menus, and the AMOS plugin can be run from AMOS pull-down menu. We believe these tools can help researchers to examine the robustness of their findings to influential cases.

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Researchers can generate bootstrap confidence intervals for some statistics in SPSS using the BOOTSTRAP command. However, this command can only be applied to selected procedures, and only to selected statistics in these procedures. We developed an extension command and prepared some sample syntax files based on existing approaches from the Internet to illustrate how researchers can (a) generate a large number of nonparametric bootstrap samples, (b) do desired analysis on all these samples, and (c) form the bootstrap confidence intervals for selected statistics using the OMS commands. We developed these tools to help researchers apply nonparametric bootstrapping to any statistics for which this method is appropriate, including statistics derived from other statistics, such as standardized effect size measures computed from the t test results. We also discussed how researchers can extend the tools for other statistics and scenarios they encounter.

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Moderation effects in multiple regression, tested usually by the inclusion of a product term, are frequently investigated in health psychology. However, several issues in presenting the moderation effects in standardized units and their associated confidence intervals are commonly observed. While an old method had been proposed to standardize variables in moderated regression before fitting a moderated regression model, this method was rarely used due to inconvenience and even when used, the confidence intervals derived were biased. Here, we attempt to solve these two problems by providing a tool to conveniently conduct standardization in moderated regression without the step of standardizing the variables beforehand and to accurately form the nonparametric bootstrapping confidence intervals for this standardized measure of moderation effects. Health psychology researchers are now equipped with a tool that can be used to report and interpret standardized moderation effects correctly. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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OBJECTIVE: Dispositional characteristics like emotional stability and social cynicism have been consistently associated with negative affect, which is a known predictor and outcome of poor sleep quality. This study hypothesized a bidirectional relationship of sleep quality with emotional stability and social cynicism over a five-year period. METHODS: Participants were 7,181 Chinese people, who completed two waves of online surveys from a larger panel study. Questionnaires on Big Five personality traits, social cynicism, and sleep quality were administered twice at a five-year interval. RESULTS: Cross-lagged analysis revealed a significant bidirectional relationship between emotional stability and sleep quality over five years as hypothesized. However, there was no association between sleep quality and social cynicism in either direction. CONCLUSION: Our study provides consistent evidence of a bidirectional relationship between sleep quality and emotional stability in a five-year time-frame among a younger population (mean age = 24.86). Given the critical role of emotional stability in various areas of functioning, the findings highlight the importance of sleep health education in young adults, who are going through a critical period of personality development.

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In this study we investigated the influence of data nonnormality in the primary studies on meta-analysis of the standardized mean difference (SMD) for a two-independent-group design. The bias, mean squared error, and confidence interval coverage probability of the mean effect sizes under different types of population distributions were compared. Also, the performance of the Q test was examined. The results showed that oppositely skewed distributions (i.e., distributions skewed in different directions) showed poor performance for point and interval estimates of mean effect sizes in meta-analysis, especially when the tails were pointing toward each other. The previously found adverse impacts due to nonnormality in primary studies do not disappear when primary studies with nonnormal data are meta-analyzed, even when the average sample size and number of studies are large. The results also showed that, when the tails were pointing toward each other, the Type I error rates of the Q test were inflated. We suggest that the impact of violating the assumption of normality should not be ignored in meta-analysis.

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A SEM-based approach using likelihood-based confidence interval (LBCI) has been proposed to form confidence intervals for unstandardized and standardized indirect effect in mediation models. However, when used with the maximum likelihood estimation, this approach requires that the variables are multivariate normally distributed. This can affect the LBCIs of unstandardized and standardized effect differently. In the present study, the robustness of this approach when the predictor is not normally distributed but the error terms are conditionally normal, which does not violate the distributional assumption of ordinary least squares (OLS) estimation, is compared to four other approaches: nonparametric bootstrapping, two variants of LBCI, LBCI assuming the predictor is fixed (LBCI-Fixed-X) and LBCI based on ADF estimation (LBCI-ADF), and Monte Carlo. A simulation study was conducted using a simple mediation model and a serial mediation model, manipulating the distribution of the predictor. The Monte Carlo method performed worst among the methods. LBCI and LBCI-Fixed-X had suboptimal performance when the distributions had high kurtosis and the population indirect effects were medium to large. In some conditions, the problem was severe even when the sample size was large. LBCI-ADF and nonparametric bootstrapping had coverage probabilities close to the nominal value in nearly all conditions, although the coverage probabilities were still suboptimal for the serial mediation model when the sample size was small with respect to the model. Implications of these findings in the context of this special case of nonnormal data were discussed.

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Previous procedures for meta-analyzing dependent correlations have been found to overestimate or underestimate the true variation in effect sizes. Samplewise-adjusted procedures have been shown to perform better than simple within-study means when meta-analyzing dependent correlations. However, such procedures cannot be applied when correction for artifacts such as unreliability is desired. In the present study, we extended the procedures to correct for attenuation due to artifacts when meta-analyzing dependent correlations. Monte Carlo simulation was conducted in order to examine conditions with various degrees of dependence, degrees of heterogeneity, sample sizes, and numbers of studies, among other factors. The previous procedures, including the samplewise-adjusted procedures without correction, yielded biased point estimates and confidence intervals with low coverage probabilities of the population mean correlation and degree of heterogeneity. More importantly, the bias and undercoverage of the confidence interval increased with the mean sample size and number of studies in many conditions. The new samplewise-adjusted procedures with correction for attenuation yielded negligible biases when estimating the mean population correlation, even in the presence of dependent correlations. Given that the need for correction for attenuation due to artifacts is becoming more recognized in meta-analysis, our findings highlight the importance of such considerations when meta-analyzing dependent correlations. Conditions under which these procedures can be further improved are also discussed.

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While both sleep and optimism have been found to be predictive of well-being, few studies have examined their relationship with each other. Neither do we know much about the mediators and moderators of the relationship. This study investigated (1) the causal relationship between sleep quality and optimism in a college student sample, (2) the role of symptoms of depression, anxiety, and stress as mediators, and (3) how circadian preference might moderate the relationship. Internet survey data were collected from 1,684 full-time university students (67.6% female, mean age = 20.9 years, SD = 2.66) at three time-points, spanning about 19 months. Measures included the Attributional Style Questionnaire, the Pittsburgh Sleep Quality Index, the Composite Scale of Morningness, and the Depression Anxiety Stress Scale-21. Moderate correlations were found among sleep quality, depressive mood, stress symptoms, anxiety symptoms, and optimism. Cross-lagged analyses showed a bidirectional effect between optimism and sleep quality. Moreover, path analyses demonstrated that anxiety and stress symptoms partially mediated the influence of optimism on sleep quality, while depressive mood partially mediated the influence of sleep quality on optimism. In support of our hypothesis, sleep quality affects mood symptoms and optimism differently for different circadian preferences. Poor sleep results in depressive mood and thus pessimism in non-morning persons only. In contrast, the aggregated (direct and indirect) effects of optimism on sleep quality were invariant of circadian preference. Taken together, people who are pessimistic generally have more anxious mood and stress symptoms, which adversely affect sleep while morningness seems to have a specific protective effect countering the potential damage poor sleep has on optimism. In conclusion, optimism and sleep quality were both cause and effect of each other. Depressive mood partially explained the effect of sleep quality on optimism, whereas anxiety and stress symptoms were mechanisms bridging optimism to sleep quality. This was the first study examining the complex relationships among sleep quality, optimism, and mood symptoms altogether longitudinally in a student sample. Implications on prevention and intervention for sleep problems and mood disorders are discussed.

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Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM. Random-effects models are well known in conventional meta-analysis but are less studied in MASEM. The primary objective of this paper was to address issues related to random-effects models in MASEM. Specifically, we compared two different random-effects models in MASEM-correlation-based MASEM and parameter-based MASEM-and explored their strengths and limitations. Two examples were used to illustrate the similarities and differences between these models. We offered some practical guidelines for choosing between these two models. Future directions for research on random-effects models in MASEM were also discussed. Copyright © 2016 John Wiley & Sons, Ltd.

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OBJECTIVE: Sleep and optimism are important psycho-biological and personality constructs, respectively. However, very little work has examined the causal relationship between them, and none has examined the potential mechanisms operating in the relationship. This study aimed to understand whether sleep quality was a cause or an effect of optimism, and whether depressive mood could explain the relationship. METHOD: Internet survey data were collected from 987 Chinese working adults (63.4% female, 92.4% full-time workers, 27.0% married, 90.2% Hong Kong residents, mean age=32.59 at three time-points, spanning about 19 months). Measures included a Chinese attributional style questionnaire, the Pittsburgh Sleep Quality Index, and the Depression Anxiety Stress Scale. RESULTS: Cross-sectional analyses revealed moderate correlations among sleep quality, depressive mood, and optimism. Cross-lagged analyses showed a bidirectional causality between optimism and sleep. Path analysis demonstrated that depressive mood fully mediated the influence of optimism on sleep quality, and it partially mediated the influence of sleep quality on optimism. CONCLUSION: Optimism improves sleep. Poor sleep makes a pessimist. The effects of sleep quality on optimism could not be fully explained by depressive mood, highlighting the unique role of sleep on optimism. Understanding the mechanisms of the feedback loop of sleep quality, mood, and optimism may provide insights for clinical interventions for individuals presented with mood-related problems.

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Values are guiding principles in our life. While some studies found spiritual values to be "healthier," Sagiv and Schwartz (Eur J Soc Psychol 30:177-198, 2000) showed that people holding non-spiritual values were higher on affective well-being. We examined the predictive power of these two types of values with a longitudinal data set collected from Chinese students mainly in Hong Kong. Structural equation modeling revealed that spiritual values (as well as family income) positively predicted quality of life a year later. Non-spiritual, self-enhancement values, did not show any association. Results suggest that developing spiritual values may promote well-being through enabling individuals to find meaning and purpose in life.

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The presence of dependent correlation is a common problem in meta-analysis. Cheung and Chan (2004, 2008) have shown that samplewise-adjusted procedures perform better than the more commonly adopted simple within-sample mean procedures. However, samplewise-adjusted procedures have rarely been applied in meta-analytic reviews, probably due to the lack of suitable ready-to-use programs. In this article, we compare the samplewise-adjusted procedures with existing procedures to handle dependent effect sizes, and present the samplewise-adjusted procedures in a way that will make them more accessible to researchers conducting meta-analysis. We also introduce two tools, an SPSS macro and an R script, that researchers can apply to their meta-analyses; these tools are compatible with existing meta-analysis software packages.

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Although on-campus residence allows easier access to campus facilities, existing studies showed mixed results regarding the relationship between college residence and students' well-being indicators, such as sleep behaviors and mood. There was also a lack of studies investigating the role of chronotype in the relationship between on-campus residence and well-being. In particular, the temporal relationships among these factors were unclear. Hence, this longitudinal study aims to fill in these gaps by first reporting the well-being (measured in terms of mood, sleep, and quality of life) among students living on and off campus across two academic semesters. We explored factors predicting students' dropout in university residences. Although students living on campus differ in their chronotypes, activities in campus residence (if any) are mostly scheduled in the nighttime. We therefore tested if individual differences in chronotype interact with campus residence in affecting well-being. Our final sample consisted of 215 campus residents and 924 off-campus-living students from 10 different universities or colleges in Hong Kong or Macau. Their mean age was 20.2 years (SD=2.3); 6.5% of the participants are female. Participants completed self-reported questionnaires online on their sleep duration, sleep quality, chronotype, mood, and physical and psychological quality of life. Across two academic semesters, we assessed if students living on and off campus differed in our well-being measures after we partialed out the effects of demographic information (including age, sex, family income, and parents' education) and the well-being measures at baseline (T1). The results showed that, campus residents exhibited longer sleep duration, greater sleep efficiency, better sleep quality, and less feeling of stress than off-campus-living students. From one semester to the next, around 10% of campus residents did not continue to live on campus. Logistic regression showed that a morning type was the strongest factor predicting dropout from campus residence. Chronotype significantly moderated the effects of campus residence on participants' physical and psychological quality of life. Although morning-type off-campus-living students have better well-being than their evening-type peers living off campus, morning-type campus residents had worse well-being than other campus residents and they were more likely to discontinue living on campus after one semester. Our findings bear practical significance to college management that morning-type campus residents are shown to be experiencing deteriorating well-being. The authorities may need to review and revise the room-allocation policy in campus residence in improving the well-being among campus residents.

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OBJECTIVES: Existing studies on sleep and behavioral outcomes are mostly correlational. Longitudinal data is limited. The current longitudinal study assessed how sleep duration and sleep quality may be causally linked to daytime functions, including physical health (physical well-being and daytime sleepiness), psychological health (mood and self-esteem) and academic functioning (school grades and study effort). The mediation role of mood in the relationship between sleep quality, sleep duration and these daytime functions is also assessed. METHODS: A sample of 930 Chinese students (aged 18-25) from Hong Kong/Macau completed self-reported questionnaires online across three academic semesters. Sleep behaviors are assessed by the sleep timing questionnaire (for sleep duration and weekday/weekend sleep discrepancy) and the Pittsburgh sleep quality index (sleep quality); physical health by the World Health Organization quality of life scale-brief version (physical well-being) and Epworth Sleepiness Scale (daytime sleepiness); psychological health by the depression anxiety stress scale (mood) and Rosenberg Self-esteem Scale (self-esteem) and academic functioning by grade-point-average and the college student expectation questionnaire (study effort). RESULTS: Structural equation modeling with a bootstrap resample of 5000 showed that after controlling for demographics and participants' daytime functions at baseline, academic functions, physical and psychological health were predicted by the duration and quality of sleep. While some sleep behaviors directly predicted daytime functions, others had an indirect effect on daytime functions through negative mood, such as anxiety. CONCLUSION: Sleep duration and quality have direct and indirect (via mood) effects on college students' academic function, physical and psychological health. Our findings underscore the importance of healthy sleep patterns for better adjustment in college years.

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Integrating more than 40 years of studies on locus of control (LOC), this meta-analysis investigated whether (a) the magnitude of the relationship between LOC and psychological symptoms differed among cultures with distinct individualist orientations and (b) depression and anxiety symptoms yielded different patterns of cultural findings with LOC. We included studies that examined global self-ratings of LOC and at least 1 of the criterion variables in nonclinical samples (age range: 18-80 years). Data were analyzed on the basis of 152 independent samples, representing the testing of 33,224 adults across 18 cultural regions. Results revealed moderately strong relationships for external LOC with depression symptoms (k = 123, N = 28,490, r = .30, 95% confidence interval [CI] [.27, .32]) and anxiety symptoms (k = 65, N = 13,208, r = .30, 95% CI [.27, .33]). Individualism explained 20% of unique variance only in the external LOC-anxiety relationship: The link between external LOC and anxiety symptoms was weaker for collectivist societies (k = 8, N = 2,297, r = .20, 95% CI [.13, .28]) compared with individualist societies (k = 54, N = 9,887, r = .32, 95% CI [.29, .34]). Such cultural differences were attributed to the reduced emphasis on agentic goals in more collectivist societies. It is noteworthy that external LOC does not carry the same negative connotations across cultures, and members of collectivist societies may be more ready to endorse such items. Culture has been examined at the country level, and the findings may not be applicable to any particular person in a cultural region. Implications for integrating cultural meaning of perceived control into formulation of theories, research design, and intervention programs are discussed.

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Prior research with children generally supports the two-dimensional structure of Attention-Deficit/Hyperactivity Disorder (ADHD; inattentive and hyperactive/impulsive factors) of the DSM-IV-TR as well as invariance of the two-factor structure across nations and cultures. Research with adults supports either a two-factor or three-factor structure depending on reporting source and breadth of symptoms assessed. However, research with adults is limited and there are few studies addressing cross-national invariance in adults. The purposes of this study were to (1) assess relative fit of two- versus three-factor solutions for self-report of childhood and recent ADHD symptoms in adults; and (2) further establish cross-national invariance of factors. Participants included 271 U.S. and 712 Japanese university students who completed a rating scale assessing the 18 DSM-IV-TR ADHD symptoms. Confirmatory factor analysis using Mplus (Version 6) and the mean and variance-adjusted weighted least squares (WLSMV) procedure showed invariance of two- and three-factor models across U.S. and Japanese samples. The two- and three-factor models showed similar fit indices. Neither a two-factor or three-factor model was clearly superior. The two-factor model was favored, however, because it is more parsimonious and consistent with current theory, and because of high correlations between hyperactive and impulsive factors in the three-factor models. Invariance across nations is consistent with previous studies and supports ADHD as a universally valid syndrome rather than a cultural construct. These results add to the limited knowledge of assessment of ADHD symptoms in Japan.

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