International Journal of Psychology最新文献

Mediation models are often conducted in psychology to understand mechanisms and processes of change. However, current best practices for handling missing data in mediation models are not always used by researchers. Missing data methods, such as full information maximum likelihood (FIML) and multiple imputation (MI), are best practice methods of handling missing data. However, FIML or MI are rarely used to handle missing data when testing mediation models, instead analyses used listwise deletion methods, the default in popular software. Compared to listwise deletion, the implementation of FIML or MI to handle missing data reduces parameter estimate bias, while maintaining the sample collected to maximise power and generalizability of results. In this tutorial, we review how to implement full-information maximum likelihood and MI using best practice methods of testing the indirect effect. We demonstrate how to implement these methods using both R and JASP, which are both free, open-source software programmes and provide online supplemental materials for these demonstrations. These methods are demonstrated using two example analyses, one using a cross-sectional mediation model and one using a longitudinal mediation model examining how student-athletes reported worry about COVID predicts their perceived stress, which in turn predicts satisfaction with life.

The current study aimed to assess whether online suicide risk assessment affects state mood and is the first to examine if suicide-related self-stigma or coping related to suicidal ideation are predictors of mood change. The Australian participants (N = 661, M_age = 34.9, SD = 12.3, 57.1% female), recruited through a crowd-sourcing platform, completed a visual analogue mood measure before and after the Suicidal Ideation Attributes Scale (SIDAS), an assessment tool. Followed by a modified version of the Internalised Stigma Scale, the Brief COPE and DASS-21. State mood did not change from pre- to post-suicide risk assessment in the overall sample, t(662) = −.16, p = .868, d = −.01. Contrary to hypotheses, neither self-stigma nor coping were related to mood change following exposure to the SIDAS. The multiple regression model was not significant, F(9,643) = 1.16, p = .31., nor was any single predictor including gender, current Suicide risk β = −.04, t = −.80 or psychological distress β = −.09, t = −1.76, p = .08. These findings suggest that online exposure to a suicide risk tool is unlikely to be iatrogenic in relation to state mood, even in the context of elevated self-stigma, suicidal ideation and psychological distress.

This paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods for handling complex models and small sample sizes. Basic concepts and fundamental issues relevant to BSEM are introduced, such as prior setting, model convergence, and model fit evaluation and so on. The paper also provides illustrative examples of commonly employed BSEMs, including confirmatory factor analysis (CFA) models, mediation models and multigroup CFA models, accompanied by empirical data and computer codes to facilitate implementation. Our goal is to provide researchers with novel ideas for empirical research and equip them to overcome challenges inherent to traditional methods. As BSEM continues to gain traction in various fields, we anticipate its development will feature improved methods, techniques and reporting standards.

Self-efficacy beliefs have cyclical nature as they enhance performance and performance, in turn, influences subsequent self-efficacy beliefs. Likewise, teacher self-efficacy is proposed to shape teaching quality which, in turn, informs future teacher self-efficacy beliefs. To examine these associations, longitudinal studies are needed but are still sparse. Therefore, the present research employed a three-wave longitudinal design to examine the predictive effects of teacher self-efficacy on teaching quality as well as the predictive effects of teaching quality on future teacher self-efficacy by using data from large samples of secondary school teachers (N = 1030) and their students (N = 17,381). Teachers self-reported their efficacy for student engagement, efficacy for instructional strategies and efficacy for classroom management whereas students rated the teaching quality (i.e., cognitive activation, classroom management, and student support) of their teachers. The results of the multilevel structural equation modelling showed that all three dimensions of teacher self-efficacy predicted teaching quality but teaching quality, in turn, predicted only teacher efficacy for student engagement. These results suggest that efforts in raising teacher self-efficacy may show fruitful in raising overall teaching effectiveness.

The recent advances in technological capabilities have led to a massive production of time-series data and remarkable progress in longitudinal designs and analyses within psychological research. However, implementing time-series analysis can be challenging due to the various characteristics and complexities involved, as well as the need for statistical expertise. This paper introduces a statistical pipeline on time-series analysis for studying the changes in a single process over time at either a population or individual level, both retrospectively and prospectively. This is achieved through systemization and extension of existing modelling and inference techniques. This analytical approach enables practitioners not only to track but also to model and evaluate emerging trends and apparent seasonality. It also allows for the detection of unexpected events, quantifying their deviations from baseline and forecasting future values. Given that other discernible population- and individual-level changes in psychological and behavioural processes have not yet emerged, continued surveillance is warranted. A near real-time monitoring tool of time-series data could guide community psychological responses across multiple ecological levels, making it a valuable resource for field practitioners and psychologists. An empirical study is conducted to illustrate the implementation of the introduced analytical pipeline in practice and to demonstrate its capabilities.

Consistent with reporting standards for structural equation modelling (SEM), model fit should be evaluated at two different levels, global and local. Global fit concerns the overall or average correspondence between the entire data matrix and the model, given the parameter estimates for the model. Local fit is evaluated at the level of the residuals, or differences between observed and predicted associations for every pair of measured variables in the model. It can happen that models with apparently satisfactory global fit can nevertheless have problematic local fit. This may be especially true for relatively large models with many variables, where serious misspecification is indicated by some larger residuals, but their contribution to global fit is diluted when averaged together with all the other smaller residuals. It can be challenging to evaluate local fit in large models with dozens or even hundreds of variables and corresponding residuals. Thus, the main goal of this tutorial is to offer suggestions about how to efficiently evaluate and describe local fit for large structural equation models. An empirical example is described where all data, syntax and output files are freely available to readers.

Ordinal data such as Likert items, ratings or generic ordered variables are widespread in psychology. These variables are usually analysed using metric models (e.g., standard linear regression) with important drawbacks in terms of statistical inference (reduced power and increased type-1 error) and prediction. One possible reason for not using ordinal regression models could be difficulty in understanding parameters or conducting a power analysis. The tutorial aims to present ordinal regression models using a simulation-based approach. Firstly, we introduced the general model highlighting crucial components and assumptions. Then, we explained how to interpret parameters for a logit and probit model. Then we proposed two ways for simulating data as a function of predictors showing a 2 × 2 interaction with categorical predictors and the interaction between a numeric and categorical predictor. Finally, we showed an example of power analysis using simulations that can be easily extended to complex models with multiple predictors. The tutorial is supported by a collection of custom R functions developed to simulate and understand ordinal regression models. The code to reproduce the proposed simulation, the custom R functions and additional examples of ordinal regression models can be found on the online Open Science Framework repository (https://osf.io/93h5j).

Moral disengagement, a term coined by Bandura, explains how people can hurt others and behave immorally, contradicting their beliefs and values and yet live peaceably with themselves. He developed a conceptual model of moral disengagement based on the Social Cognitive Theory of moral agency. By activating moral disengagement mechanisms, people can disregard their moral standards and behave immorally without any self-censure. This paper synthesises the extensive research on moral disengagement in a youth bullying context to demonstrate the conceptual strength of moral disengagement theory for research and interventions on bullying. It advances the understanding of the contextual basis of moral disengagement by examining its diverse role in bullying episodes that includes bullying perpetration, bullying victimisation and bullying bystanders. The analysis of the different components of moral disengagement representing the loci of the disengagement mechanisms sheds light on how the disengagement processes function in the youth bullying context. Its role as both a moderator and mediator further advances its divergent functioning in the youth bullying context. Finally, implications of the review for advancing knowledge on the moral basis of youth bullying are discussed with suggestions for the development of interventions to attenuate the use of moral disengagement.

We assessed, with validated instruments, whether family atmosphere, religiosity or lifestyle were significant correlates of self-esteem and/or self-control among adolescents (Grades 7–12, n = 2067) in Saudi Arabia. Participants' mean age was 15.5 years; 64% were boys. Higher scores in family atmosphere and religiosity and having fewer lifestyle risk factors were significantly related to higher self-esteem and self-control scores (p < .05; adjusted linear regression models). The odds of scoring low (below median) in both self-esteem and self-control decreased incrementally across the increasing quartiles of family atmosphere and religiosity; the odds decreased incrementally across decreasing number of lifestyle risk factors (p < .05; adjusted multinomial regression). Programmes supporting healthy lifestyles, positive family environments and religiosity may boost self-esteem and self-control among adolescents.

Non-suicidal self-injury (NSSI) is an emerging concern in the field of public health. The objective of this study is to develop a conditional process model to investigate the relationship between social anxiety and NSSI, and the role of impulsiveness and self-esteem in this relationship. A convenience sample of 2717 university students (M_age = 19.81, 22.49% male) from Southern China was recruited. The age range of the participants is between 18 and 25 years. The data were analysed using Spearman correlation analysis, mediation analysis and moderation analysis. The study revealed a positive correlation between social anxiety and NSSI, with impulsiveness serving as a mediating factor in this association. The relationship between social anxiety and NSSI, impulsiveness and social anxiety, impulsiveness and NSSI were all found to be moderated by self-esteem. The prevalence of NSSI among college students exhibited a strong association with social anxiety and impulsiveness. The present investigation additionally demonstrated that there was no significant association between social anxiety, impulsivity and NSSI when high self-esteem was included. This finding implies that self-esteem plays a crucial role in safeguarding against NSSI.