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.

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.

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.

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 = 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.

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).

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 = 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.

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.

Humans may have an innate need to affiliate with nature; this need has been termed biophilia. Humanising nature may connect to biophilia. An experimental design with 167 participants tested the hypothesis that a humanised description of the functioning of trees that focused on similarities between tree and human functioning would have a greater impact than a description of purely biological functions of trees. Participants randomly assigned to the humanising nature condition had higher mean scores for positive affect and empathy related to the target aspect of nature as well as greater pro-environmental intention. A MANOVA showed that the humanising nature condition had a significantly greater overall impact than the control condition. Positive affect and empathy were significantly different between groups. A serial mediation analysis found that positive affect and empathy connected the intervention with pro-environmental intention. Humanising nature holds promise as an approach to meeting biophilia needs. The findings may be globally relevant to the interaction of humans with nature.

At the beginning of 2020, the entire world was shocked by a global health emergency. According to the literature, fear, high mortality and health restrictions had significant psychological consequences on the population. This study evaluates the French lockdown's impact on the grieving process and how people worked through their grief. Two semi-structured interviews were conducted with 31 participants who had lost a loved one between March 2020, June, and September 2021 (T0) and 6 months later (T1). Subsequently, they were divided into two groups: those who lost someone during the first lockdown (Group 1) and those who lost someone outside the lockdown periods (Group 2). The interviews were analysed using the ALCESTE software, a statistical analysis tool for textual data based on word co-occurrences. This research significantly advances the understanding of bereavement during crises, providing new perspectives and practical insights for policymakers, healthcare professionals and support organisations. Its methodological innovation and detailed analysis contribute to the ongoing discussion on grief and resilience in challenging circumstances. Ultimately, this study lays the foundation for improved support and intervention strategies tailored to the needs of bereaved individuals during crises.

We uncovered latent profiles of intergenerational and digital solidarity between middle-aged parents and their oldest young adult children during the COVID-19 pandemic. Furthermore, we investigated whether solidarity latent profiles were related to middle-aged parents' psychological well-being. We used data from the 2022 survey of the Longitudinal Study of Generations (LSOG), which involved 234 middle-aged parents providing information about their oldest young adult children. Using latent profile analysis, we uncovered five solidarity profiles (Tight-knit traditional, distant-but-digitally connected, obligatory, sociable, and conflictual) in relationships between middle-aged parents and their oldest young adult children during the pandemic. Furthermore, we found that middle-aged parents belonging to the distant-but-digitally connected and tight-knit traditional profiles had enhanced psychological well-being than those in the conflictual profile during the pandemic. These findings indicate that middle-aged parents' use of digital communication with young adult children benefited their psychological well-being during the pandemic. Moreover, using digital communication may be related to strong solidarity between middle-aged parents and young adult children when they live independently.

Moral self-efficacy refers to individuals' beliefs in their capability to effectively mobilise motivation, cognitive resources and strategic actions to achieve moral performance particularly in challenging situations. We adopt the conceptualization of moral self-efficacy that encompasses both self-reflective and behavioural components. The self-reflective dimension pertains to one's perceived capability to reflect on past moral lapses, while the behavioural dimension involves one's perceived capability to regulate future moral conduct. The study aims to explore moral self-efficacy as a "dynamic" process unfolding over time, focusing on the reciprocal influence between its self-reflective and behavioural dimensions in hindering the development of moral disengagement. Utilising a three-wave design with a sample of 1308 employees (50% females) at Time 1 results of a structural equation model support the hypothesized interplay between self-reflective and behavioural moral self-efficacy over time. In addition, our findings partly support our hypothesized relationships between moral self-efficacy dimensions and moral disengagement: self-reflective moral self-efficacy directly and negatively influenced the development of moral disengagement over time, while behavioural moral self-efficacy negative influenced it only indirectly through self-reflective moral self-efficacy.

This tutorial introduces the graded response model (GRM), a tool for testing measurement precision within the item response theory (IRT) paradigm, which is useful for informing researchers about the item and person properties of their measurement. The tutorial aims to guide applied researchers through a unidimensional GRM analysis in the R environment, using the psych, mirt and ggmirt packages. GRM is specifically designed to examine the psychometric properties of psychological scales with polytomous (Likert-style) items. The tutorial illustrates the procedure using data from the Open Psychometrics Database on the right-wing authoritarianism (RWA) scale, outlining the theoretical underpinnings of GRM and steps for data preparation, testing model assumptions, model fitting, plotting item parameters and interpretation of results.

The objective of this study was to evaluate the feasibility of Virtual Motivational Interviewing (VIMINT) for improving physical activity among community-dwelling older adults. A feasibility study using a mixed-method single-group pre- and post-design. Each participant received five sessions of motivational interviewing (MI) through the Zoom platform. Feasibility and acceptability were assessed through recruitment, attrition and retention rates; adherence; satisfaction; counsellors' competency; and interviews with participants and counsellors. Other outcomes including physical activity were assessed at baseline, post- and 2-month follow-up. Eight participants were recruited; the mean age was 68.9 ± 3.9 years. The retention rate was 88%, 92.5% of the sessions were attended, and the participants' satisfaction score was 24.14 ± 7.3/32. The counsellors were rated as "good" and "fair" in relational and technical components, respectively. The categories derived from qualitative analysis were session composition, acceptability of outcome measures, positive impact of the VIMINT study and suggestions to improve future studies. The findings showed that VIMINT intervention should be feasible and acceptable for older adults. Evidence from this study provides relevant information that will guide the planning of future studies investigating the effectiveness of virtual MI on physical activity among community-dwelling older adults.

This tutorial delves into dimensionality assessment within the context of psychological measurement instruments, particularly focusing on bifactor models. It underscores the imperative to move beyond traditional fit indices when evaluating factor structures while highlighting the significance of ancillary bifactor indices such as explained common variance, OmegaH and percentage of uncontaminated correlations in gaining a more comprehensive understanding of the interplay between general and specific group factors. The tutorial offers a step-by-step guide to leveraging the power of R software for confirmatory factor analysis and the acquisition of ancillary bifactor indices. Through practical case studies, it elucidates the potential pitfalls of exclusively relying on fit indices and advocates for a balanced, multifaceted approach to dimensionality assessment. By integrating fit measures and ancillary indices, researchers can draw more informed and nuanced conclusions about measurement instrument dimensionality, ultimately enhancing the precision of psychological assessment.

Sense of community coherence refers to how much a person perceives their communal life as comprehensible, manageable and meaningful. The study's purpose was to validate the Sense of Community Coherence Scale using two distinct groups of university students from Germany and Turkey. The scale was adapted to German and Turkish using the translation-back translation method and examined in terms of several indicators of reliability and validity, including cross-cultural measurement invariance with the data collected from 489 individuals from Germany and 533 individuals from Turkey. The findings exhibited satisfactory psychometric properties; specifically, high reliability, good construct and criterion validity, and partial measurement invariance across cultures were observed. As a result, Sense of Community Coherence Scale was concluded to be reliable and partially valid in the samples of Turkish and German university students. Validation of the German and Turkish forms of the scale is expected to contribute to mental health research and practice for allowing the measurement of how individuals feel about their community in the context of manageability, meaningfulness and comprehensibility. This study can help researchers and practitioners design enhancement, prevention and intervention programmes aiming to increase the community sense of coherence.

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.

The present study examined the longitudinal relations among work self-efficacy beliefs, job performance and career success, defined as objective career advancements. We argued that job performance would mediate both the influence of worker's self-efficacy beliefs on career success and the influence of career success on subsequent self-efficacy beliefs. The participants were 976 employees of one of the largest companies in Italy, assessed at three time points (i.e., Waves 1, 2 and 3), spaced apart by 3 years. Job performance significantly mediated the relationship between self-efficacy beliefs and subsequent career success as well as the reverse influence of career success on subsequent self-efficacy beliefs. The posited conceptual model explained a significant portion of the variance in all endogenous variables and has implications for interventions intended to promote the development of individuals within organisations.

Materialism is fundamental to the human value or goal system; therefore, an understanding of its level among Chinese college students and its changes over time is of great value. In the present study, a cross-temporal meta-analysis was performed by reviewing studies that conducted Material Values Scale-based assessment of the materialism level among Chinese university students from 2007 to 2020. Moreover, a time lag analysis was performed to clarify whether variations in materialism level are interpretable with macro-social indicators. Finally, 82 articles on studies enrolling a total of 45,966 Chinese university students were reviewed. The materialism score significantly increased on a yearly basis. Furthermore, macro-social changes in diverse areas, including economic condition (gross domestic product per capita, consumption level of all residents and national disposable income per capita), social connectedness (urbanisation degree and divorce ratio) and overall threat (rate of university enrollment), were the major factors influencing the degree of materialism among the students. By identifying the inclining trend of materialism among these college students across time and using relevant macro-social indicators, a theoretical three-dimensional framework was established to elucidate the degree of materialism among Chinese college students as a group.

Caregivers who interact with children at home can provide a critical, complementary perspective on a child's behaviour functioning. This research used a parent-administered measure of problem behaviours to study perceptions of child behaviours across home situations. We applied latent profile analysis to identify subgroups of children with common behavioural tendencies in a nationally representative sample (N = 709) of 4- to 13-year-old children in Trinidad and Tobago. This study (a) identified latent profiles of children's over- and underactive behaviour problems in varied home settings and (b) examined how profile membership predicted academic skills and teacher-observed problem behaviours. The best-fitting four-profile model included one profile of adjusted behaviours (56%), one of the elevated attention-seeking behaviours (21%), a profile featuring withdrawn and disengaged behaviours (15%) and a relatively rare profile emphasising aggressive behaviours (8%). Children classified in the last profile displayed the poorest academic outcomes and the highest levels of teacher-observed behaviour problems.

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.