Psych

Studies on the dimensionality and factor structure of the Sense of Coherence-13 (SOC-13) scale have produced inconsistent results, and there is a need for comprehensive psychometric testing of the scale in different populations and using diverse methodologies. SOC refers to the individual’s ability to perceive life as comprehensible, manageable, and meaningful. The current study investigated the dimensionality of the SOC-13 through the use of confirmatory factor analysis (CFA), ancillary bifactor indices and item response theory in a sample of young adults in South Africa. Participants were students (n = 322) who completed the SOC-13, the Connor–Davidson Resilience Scale, the PTSD Checklist for DSM-5 and short forms of the Center for Epidemiological Studies Depression Scale, and the trait scale of the Spielberger State-Trait Anxiety Inventory. CFA indicated the best fit for a one-factor model, but the problematic parameter estimates raise concerns about the construct validity of the scale. Non-parametric item response theory (Mokken scale analysis [MSA]) identified limitations in the original 13-item version, suggesting a more dependable seven-item version (SOC-7). This revised scale exhibited strong psychometric characteristics and was consistent with the theoretical foundations that underpin the construct. We verified the unidimensional structure of the SOC with the more stringent parametric item-response theory (Rasch analysis) which confirmed that the seven-item SOC is unidimensional. Rasch analysis confirmed the measurement invariance of the SOC-13 in terms of gender and area of residence. The study suggests that a shorter seven-item version consisting of items from the three components of sense of coherence has comparative properties to the 13-item version but the evidence does not provide support for the use of the SOC-13 as a multidimensional measure. Research in the area of sense of coherence would benefit from further validation studies of both the original SOC-13 and the revised SOC-7, especially across populations and settings. Full article

The focus on mental health during emerging adulthood (EA) is necessary due to the development of mental disorders before the age of 25 and the adverse effects of stress and anxiety on psychological well-being. Mindfulness has been shown to aid in coping with stress and anxiety, while intolerance of uncertainty can lead to higher distress levels. The aim of this research was to explore the prevalence and interrelationships between anxiety, stress, intolerance of uncertainty, and mindfulness, as well as potential gender differences. The study involved 425 emerging adults, with a majority of female participants. Anxiety and stress levels were found to be mild, with anxiety tending towards a moderate level in women. Gender differences were observed solely within the dimensions of intolerance of uncertainty, with women displaying higher levels of intolerance. For women, all of the measured variables correlated as expected; stress and anxiety showed moderate positive relationships with the dimensions of intolerance of uncertainty but negative ones with mindfulness. Furthermore, the dimensions of intolerance of uncertainty were negatively correlated with mindfulness in the female sample. Whereas for men, only anxiety and stress correlated positively, and stress also exhibited a negative relationship with mindfulness. Additionally, the dimensions of intolerance of uncertainty showed low negative correlations with mindfulness. Significant predictors for anxiety were inhibitory anxiety, as one of the dimensions of intolerance of uncertainty and mindfulness. On the other hand, all of the measured predictors were statistically significant for stress, indicating that being intolerant of uncertainty and less mindful were associated with higher stress levels. An identifying factor contributing to anxiety and stress during EA, for both men and women, is essential because it increases our understanding, which may lead to more efficient prevention and treatment strategies. Full article

Exploratory factor analysis (EFA) is a very widely used statistical procedure in the social and behavioral sciences. This technique features in validity studies, as well as investigations of latent structure underlying observed measurements. A primary aspect of using EFA is determining the number of factors to retain. In addition to theoretical considerations, a variety of statistical tools have been developed and recommended for use in assisting researchers with respect to factor retention. Some research has been conducted to investigate the accuracy of these methods in the case of continuous factor indicators. The purpose of the current simulation study was to extend this earlier work to situations in which the indicator variables are dichotomous, as with questionnaire or test items. Results of this study revealed that an approach based on the combined results of the empirical Kaiser criterion, comparative data, and Hull methods, as well as Gorsuch’s CNG scree plot test by itself, all yielded accurate results with respect to the number of factors to retain. Implications for practice are discussed. Full article

In a planned missing design, the nonresponses occur according to the researcher’s will, with the goal of increasing data quality and avoiding overly extensive questionnaires. When adjusting a structural equation model to the data, there are different criteria to evaluate how the theoretical model fits the observed data, with the root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI) and Tucker–Lewis index (TLI) being the most common. Here, I explore the effect of the nonresponses due to a specific planned missing design—the three-form design—on the mentioned fit indices when adjusting a structural equation model. A simulation study was conducted with correctly specified model and one model with misspecified correlation between factors. The CFI, TLI and SRMR indices are affected by the nonresponses, particularly with small samples, low factor loadings and numerous observed variables. The existence of nonresponses when considering misspecified models causes unacceptable values for all the four fit indexes under analysis, namely when a strong correlation between factors is considered. The results shown here were performed with the simsem package in R and the full information maximum-likelihood method was used for handling missing data during model fitting. Full article

A crucial challenge in Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation is to diagnose the convergence of the chains so that the draws can be expected to closely approximate the posterior distribution on which inference is based. A close approximation guarantees that the MCMC error exhibits only a negligible impact on model estimates and inferences. However, determining whether convergence has been achieved can often be challenging and cumbersome when relying solely on inspecting the trace plots of the chain(s) or manually checking the stopping criteria. In this article, we present a SAS macro called %automcmc that is based on PROC MCMC and that automatically continues to add draws until a user-specified stopping criterion (i.e., a certain potential scale reduction and/or a certain effective sample size) is reached for the chain(s). Full article

A constituting feature of item response models is that item and person parameters share a latent scale and are therefore comparable. The Person–Item Map is a useful graphical tool to visualize the alignment of the two parameter sets. However, the “classical” variant has some shortcomings, which are overcome by the new RMX package (Rasch models—eXtended). The package provides the RMX::plotPIccc() function, which creates an extended version of the classical PI Map, termed “PIccc”. It juxtaposes the person parameter distribution to various item-related functions, like category and item characteristic curves and category, item, and test information curves. The function supports many item response models and processes the return objects of five major R packages for IRT analysis. It returns the used parameters in a unified form, thus allowing for their further processing. The R package RMX is freely available at osf.io/n9c5r. Full article

The typical state empathy research used perspective-taking instructions and examined the effect of instructions on empathy-related variables. Empathy-arousing processes were generally not measured. The effect of perspective-taking instructions has been questioned recently. Observers could imagine targets’ feelings without such instructions. This study evoked empathy in Japanese undergraduates (N = 157) without instructional procedure, and based on participants’ responses to questionnaires, measured individual differences between antecedent, process, and intrapersonal outcome variables of state empathy, referring to the organizational model and theories of empathy-arousing processes. The purpose of this study was to measure these variables, examine the causal relationship between them using path analysis, and clarify how empathy occurs. In this way, we could suggest through which processes and antecedent factors intrapersonal empathic outcomes are produced. It is probably the first attempt to clarify how empathy occurs using a social psychological study framework and questionnaire method. This research was originally conducted in 2011 based on two similar studies not published internationally, when only some of the variables were used in our analyses. Afterwards, we constructed another analysis method, reanalyzed the data in 2019 and further reanalyzed in 2023 to obtain the final version of the results. Limitations and scientific and practical implications were discussed. Full article

A mantra often repeated in the introductory material to psychometrics and Item Response Theory (IRT) is that a Rasch model is a probabilistic version of a Guttman scale. The idea comes from the observation that a sigmoidal item response function provides a probabilistic version of the characteristic function that models an item response in the Guttman scale. It appears, however, more difficult to reconcile the assumption of local independence, which traditionally accompanies the Rasch model, with the item dependence existing in a Guttman scale. In recent work, an alternative probabilistic version of a Guttman scale was proposed, combining Knowledge Space Theory (KST) with IRT modeling, here referred to as KST-IRT. The present work has, therefore, a two-fold aim. Firstly, the estimation of the parameters involved in KST-IRT models is discussed. More in detail, two estimation methods based on the Expectation Maximization (EM) procedure are suggested, i.e., Marginal Maximum Likelihood (MML) and Gibbs sampling, and are compared on the basis of simulation studies. Secondly, for a Guttman scale, the estimates of the KST-IRT models are compared with those of the traditional combination of the Rasch model plus local independence under the interchange of the data generation processes. Results show that the KST-IRT approach might be more effective in capturing local dependence as it appears to be more robust under misspecification of the data generation process, but it comes with the price of an increased number of parameters. Full article

We investigated the relationship between the prefrontal cortex (PFC) and executive function during a drawing task. Thirty-three participants using pen tablets provided the data for this task. PFC activity was recorded using functional near-infrared spectroscopy (fNIRS) during a simple zig-zag task and a complex periodic line (PL) pattern task. For each task, there was a trace condition and a prediction condition. The Executive Function Questionnaire (EFQ) was used to examine the association between brain-function measurements and executive function during the task. PFC activity was analyzed in the right, middle, and left regions. Oxygenated hemoglobin values measured with fNIRS were converted to z-values and analyzed as a measure of brain activity. Drawing fluency was measured using the line length. In the PL pattern task, the line length was significantly shorter under the prediction condition than under the trace condition. Activity in the right PFC under the prediction condition was significantly higher than that under the trace condition in the PL pattern task, and the score of the EFQ planning subscale was associated with activity in the right PFC. Activity in the right PFC is important for fluent drawing, suggesting that it is also important during drawing activities involving symbols such as letters. Full article

The R packages Dire and EdSurvey allow analysts to make a conditioning model with new variables and then draw new plausible values. This is important because results for a variable not in the conditioning model are biased. For regression-type analyses, users can also use direct estimation to estimate parameters without generating new plausible values. Dire is distinct from other available software in R in that it requires fixed item parameters and simplifies calculation of high-dimensional integrals necessary to calculate composite or subscales. When used with EdSurvey, it is very easy to use published item parameters to estimate a new conditioning model. We show the theory behind the methods in Dire and a coding example where we perform an analysis that includes simple process data variables. Because the process data is not used in the conditioning model, the estimator is biased if a new conditioning model is not added with Dire. Full article

The aim of the present study is to postulate the existence of psychological phenotypes associated with obesity, based on individual history. While metabolic phenotypes have been acknowledged in the field of medicine, the same cannot be affirmed in the realm of psychology. A longstanding tradition in obesity research has sought to identify shared characteristics among individuals affected by obesity, including personality traits. However, research found no adequate empirical evidence to support the existence of a specific psychological and psychopathological profile among individuals with obesity. Recent efforts in the literature have attempted to correlate these findings and ascertain which metabolic phenotype correlates with a diminished quality of life. We propose a novel differentiation between two categories: (1) individuals who affected by obesity since childhood, and (2) individuals who developed obesity following a life event. Further investigations are imperative to amass experimental data that substantiate this hypothesis. Proactively identifying psychological phenotypes is presumed to impact therapeutic outcomes. Full article

Currently, two-level Mokken scale analysis for clustered test data is being developed. This paper contributes to this development by providing model-fit procedures for two-level Mokken scale analysis. New theoretical insights suggested that the existing model-fit procedure from traditional (one-level) Mokken scale analyses can be used for investigating model fit at both level 1 (respondent level) and level 2 (cluster level) of two-level Mokken scale analysis. However, the traditional model-fit procedure requires some modifications before it can be used at level 2. In this paper, we made these modifications and investigated the resulting model-fit procedure. For two model assumptions, monotonicity and invariant item ordering, we investigated the false-positive count and the sensitivity count of the level 2 model-fit procedure, with respect to the number of model violations detected, and the number of detected model violations deemed statistically significant. For monotonicity, the detection of model violations was satisfactory, but the significance test lacked power. For invariant item ordering, both aspects were satisfactory. Full article

Police officers are exposed to several operational and organizational stressors that significantly impact on their mental health. The COVID-19 pandemic was a new stressor that further exacerbated existing stressors, highlighting the need for a better understanding of its impact on the mental health of police officers. This study tested the hypothesis that occupational stressors and PTSD are serial mediators of the relationship between fear of COVID-19 and burnout. Two hundred Portuguese police officers completed an online survey that assessed their fear of COVID-19, exposure to operational and organizational stressors, PTSD, and burnout. The results of the serial mediation analysis indicated that not only do occupational stressors act as mediators between fear of COVID-19 and burnout but also that PTSD is a mediator. The findings of this study underscore the need for interventions to reduce the negative impact of operational and organizational stressors on the mental health of police officers. Furthermore, this study highlights the power of police institutions for prevention and intervention with these professionals. By recognizing the specific stressors that contribute to the development of PTSD and burnout, our study provides a foundation for the development of direct interventions that can help to minimize the adverse effects of these stressors Full article

An important challenge in statistical modeling is to balance how well our model explains the phenomenon under investigation with the parsimony of this explanation. In structural equation modeling (SEM), penalization approaches that add a penalty term to the estimation procedure have been proposed to achieve this balance. An alternative to the classical penalization approach is Bayesian regularized SEM in which the prior distribution serves as the penalty function. Many different shrinkage priors exist, enabling great flexibility in terms of shrinkage behavior. As a result, different types of shrinkage priors have been proposed for use in a wide variety of SEMs. However, the lack of a general framework and the technical details of these shrinkage methods can make it difficult for researchers outside the field of (Bayesian) regularized SEM to understand and apply these methods in their own work. Therefore, the aim of this paper is to provide an overview of Bayesian regularized SEM, with a focus on the types of SEMs in which Bayesian regularization has been applied as well as available software implementations. Through an empirical example, various open-source software packages for (Bayesian) regularized SEM are illustrated and all code is made available online to aid researchers in applying these methods. Finally, reviewing the current capabilities and constraints of Bayesian regularized SEM identifies several directions for future research. Full article

Previous studies have demonstrated an association between low personal protective equipment (PPE) availability and high stress and anxiety among frontline healthcare workers during the COVID-19 pandemic. It is unclear how other factors, such as infection prevention and control (IPC) training and IPC policy support, correlate with workers’ distress. The current study explores these relationships. We conducted a secondary analysis of a public survey dataset from Statistics Canada. Acute care workers’ survey responses (n = 7379) were analyzed using structural equation modeling to examine relationships between features of the IPC work environment and acute care workers’ ratings of their stress and mental health. We found that PPE availability (β = −0.16), workplace supports (i.e., training, IPC policy compliance, and enforcement) (β = −0.16), and support for staying home when sick (β = −0.19) were all negatively correlated with distress. Together, these features explained 18.4% of the overall variability in workers’ distress. Among surveyed acute care workers, PPE availability was related to their distress; however, having workplace support and an emphasis on staying home when sick was also relevant. Overall, the results highlight that, in addition to PPE availability, workplace supports and emphasis on staying home are important. IPC professionals and healthcare leaders should consider these multiple features as they support acute care workers during future infectious disease outbreaks. Full article

The detection of pain in persons with advanced dementia is challenging due to their inability to verbally articulate the pain they are experiencing. Pain Assessment in Advanced Dementia (PAINAD) is an observer-rated pain assessment tool developed based on non-verbal expressions of pain for persons with severe dementia. This study aimed to perform construct validation of PAINAD for pain assessment in persons with severe dementia in Malaysia. This was a prospective cross-sectional study conducted from 27 April 2022 to 28 October 2022 in eight public hospitals in Malaysia. The PAINAD scale was the index test, and the Discomfort Scale—Dementia of the Alzheimer Type (DS-DAT) and Nurse-Reported Pain Scale (NRPS) were the reference tests for construct and concurrent validity assessment. Pain assessment for the study subjects was performed by two raters concurrently at rest and during activity. The PAINAD score was determined by the first rater, whereas the DS-DAT and NRPS were assessed by the second rater, and they were blinded to each other’s findings to prevent bias. PAINAD showed good positive correlations ranging from 0.325 to 0.715 with DS-DAT and NRPS at rest and during activity, with a p-value of <0.05. It also demonstrated statistically significant differences when comparing pain scores at rest and during activity, pre- and post-intervention. In conclusion, the PAINAD scale is a reliable observer-rated pain assessment tool for persons with severe dementia in Malaysia. It is also sensitive to changes in the pain level during activity and at rest, pre- and post-intervention. Full article

The present Special Issue of Psych, which has been now fully released, aimed to highlight the importance of the Mobile Mental Health Units (MMHUs) in delivering services in rural and remote areas in Greece, and to stress their role as an easily accessible setting that provides a wide range of community-based psychosocial interventions, well beyond usual psychiatric care [...] Full article