International Journal of Psychology最新文献

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one profile of adjusted behaviours (56%), one of the elevated attention-seeking behaviours (21%), a profile featuring withdrawn and disengaged behaviours (15%) and a relatively rare profile emphasising aggressive behaviours (8%).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.

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.

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.

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).