Analyzing and Comparing Univariate, Multivariate, and Bifactor Generalizability Theory Designs for Hierarchically Structured Personality Traits

We demonstrate how to use structural equation models to represent generalizability theory-based univariate, multivariate, and bifactor model designs. Analyses encompassed multi-occasion data obtained from the recently expanded form of the Big Five Inventory (BFI-2) that measures the broad personality domain constructs Agreeableness, Conscientiousness, Extraversion, Negative Emotionality, and Open-Mindedness along with three nested subdomain facets within each global domain. Results overall highlighted the importance of taking both item and occasion effects into account but underscored additional benefits of the multivariate and bifactor designs in providing more appropriate indices of generalizability for composite scores and effective ways to gauge subscale added value. Bifactor models further extended partitioning of universe score variance to separate general and group factor effects at both composite and subscale levels, expanded score consistency indices to distinguish or combine such effects, and allowed for further evaluation of score dimensionality and subscale viability. We provide guidelines, formulas, and code in R for analyzing all illustrated designs within the article and extended online Supplemental Material.

Evaluating depression anxiety and stress assessment before and during the COVID-19 pandemic using generalisability theory

The current study investigated the assessment of depression, anxiety, and stress during normal and COVID-19 pandemic conditions. Generalisability theory (G-theory) was applied to examine stable and dynamic aspects of psychological distress and the overall reliability of the Depression, Anxiety and Stress Scales (DASS-21), using data from two independent samples collected on three occasions with 2- to 4-week intervals. The US data (n = 115) was collected before the COVID-19 pandemic, and the New Zealand (NZ) data (n = 114) was obtained during the pandemic. The total DASS-21 demonstrated excellent reliability in measuring enduring symptoms of psychological distress (G = .94-.96) across both samples. While all DASS-21 subscales demonstrated good reliability with the pre-pandemic US sample, the subscales' reliability was below an acceptable level for the NZ sample. Findings from this study indicate that the overall psychological distress is enduring and can be reliably measured by the DASS-21 across different conditions and populations, while shifts across depression, anxiety and stress levels are likely during emergency and uncertainty, as seen in the COVID-19 pandemic.

Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review

Stress, depression, and anxiety are a person's physiological states that emerge from various body features such as speech, body language, eye contact, facial expression, etc. Physiological emotion is a part of human life and is associated with psychological activities. Sad emotion is relatable to negative thoughts and recognized in three stages containing stress, anxiety, and depression. These stages of Physiological emotion show various common and distinguished symptoms. The present study explores stress, depression, and anxiety symptoms in student life. The study reviews the psychological features generated through various body parts to identify psychological activities. Environmental factors, including a daily routine, greatly trigger psychological activities. The psychological disorder may affect mental and physical health adversely. The correct recognition of such disorder is expensive and time-consuming as it requires accurate datasets of symptoms. In the present study, an attempt has been made to investigate the effectiveness of computerized automated techniques that include machine learning algorithms for identifying stress, anxiety, and depression mental disorder. The proposed paper reviews the machine learning-based algorithms applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. During the review process, the proposed study found that artificial intelligence and machine learning techniques are well recommended and widely utilized in most of the existing literature for measuring psychological disorders. The various machine learning-based algorithms are applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. There has been continuous monitoring for the body symptoms established in the various existing literature to identify psychological states. The present review reveals the study of excellence and competence of machine learning techniques in detecting psychological disorders' stress, depression, and anxiety parameters. This paper shows a systematic review of some existing computer vision-based models with their merits and demerits.

Depressive Symptoms, Perceived Stress, Self-Compassion and Nonsuicidal Self-Injury Among Emerging Adults: An Examination of the Between and Within-Person Associations Over Time

Many emerging adults report experiencing mental health challenges (e.g., depressive symptoms and perceived stress) during the transition to university. These mental health challenges often coincide with increased engagement in nonsuicidal self-injury (NSSI; e.g., self-cutting or burning without lethal intent), but longitudinal research exploring the nature of the associations among depressive symptoms, perceived stress, and NSSI are lacking. In the present study, it was examined whether depressive symptoms and perceived stress predicted increased risk for NSSI over time (or the reverse), and whether these effects were mediated or moderated by self-compassion. The sample consisted of 1,125 university students (Mage = 17.96 years, 74% female), who completed an online survey three times in first year university. A random intercept cross-lagged panel model revealed that higher depressive symptoms, perceived stress, NSSI, and lower self-compassion often co-occurred, but only NSSI predicted increased perceived stress over time. Theoretical and practical implications are discussed.

Investigating Stable and Dynamic Aspects of the Vietnamese Self-Compassion Scale using Generalisability Theory

Objectives

Evaluating comprehensive reliability of the Vietnamese Self-Compassion Scale (VSCS) and its ability to distinguish between trait (stable) vs state (dynamic) aspects of self-compassion using Generalisability Theory (G-Theory) is necessary. This investigation contributes to both reliability and validity of research that uses the VSCS to measure self-compassion in Vietnamese adults.

Methods

In a sample of 155 Vietnamese adults who completed the VSCS at three occasions that were each 2 weeks apart, a G-study was conducted to measure reliability and trait vs state aspects of each VSCS subscale and the short-form VSCS, and a D-study was conducted to examine the effects of removing subscales on overall scale reliability as well as evaluate trait vs state aspects of each item.

Results

With G-coefficients of 0.93–0.98, both the complete and short-form VSCS (VSCS-SF) demonstrated excellent reliability in measuring trait self-compassion. Three of the six subscales—self-judgement, mindfulness, and kindness—also demonstrated excellent reliability, with G-coefficients of 0.82–0.85. Eighteen of the 26 items measured trait more than state. The remaining eight items reflected a mixture of trait and state, but this did not affect overall reliability.

Conclusions

This study indicated that the VSCS, VSCS-SF, and three VSCS subscales reliably measured trait self-compassion, with scores generalisable across the Vietnamese population and occasions. Thus, overall self-compassion levels remained stable over time, which is useful for evaluating the effectiveness of an intervention because significant changes of self-compassion are likely to be long-lasting.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12671-022-01950-3.