Multidimensional Graded Response Model Research Articles

Assessment of Item Parameter and Model-Data Fit of 4IR Teachers' Effectiveness Scale in Secondary Schools

In secondary schools in Southwest Nigeria, the Fourth Industrial Revolution Chemistry Teachers Effectiveness Scale (4IRCTES) items were evaluated for discrimination item parameter and model-data fit to the underlying construct. In this study, a descriptive survey research design was employed. The survey encompassed 4,986 Chemistry teachers in the southwestern region of Nigeria. 35 Chemistry teachers made up the study’s sample. 4IRCTES was the instrument utilised to collect the data. Research question two was based on the absolute overall model-data fit, or M2 statistic, whereas research question one used the Multidimensional Graded Response Model (MGRM) of the Item Response Theory (IRT) to analyse the item parameter (item discrimination). Result showed that items of 4IRCTES discriminated well between teachers with low effectiveness and teachers with high effectiveness. Finally, in the study, result showed that the MGRM of the IRT is the substantial model-data fit for the 4IRCTES. The study concluded that the 4IRCTES items effectively distinguished between teachers who were ineffective and those who were effective.

A Metropolis–Hastings Robbins–Monro algorithm via variational inference for estimating the multidimensional graded response model: a calculationally efficient estimation scheme to deal with complex test structures

A Metropolis–Hastings Robbins–Monro algorithm via variational inference for estimating the multidimensional graded response model: a calculationally efficient estimation scheme to deal with complex test structures

Methods for measuring career readiness of high school students: based on multidimensional item response theory and text mining

In contemporary society, career readiness holds paramount significance for individual life, exerting a direct influence on initial employment, job satisfaction, and the sense of career identity. Framed within multidimensional item response theory and text mining, this study embarks on exploring assessment methodologies for high school students’ career readiness by revising the “Career Readiness Questionnaire – Adolescent Version” and employing text mining techniques. Study One collected 1261 valid data points through cluster sampling. With the aid of Bayesian multivariate item response theory parameter estimation procedures and R language, the career readiness measurement tool was revised, yielding a concise scale that aligns with psychometric requirements. The research findings indicated that the concept of “career readiness” is more suitable for the multidimensional graded response model than for the bifactor model. The dataset’s discrimination parameters fell within the range of [1.59, 3.84], the difficulty parameters fell between [−2.91, 2.24], and the peak values of the maximum information functions fell within [0.24, 2.35]. After six items with the lowest peaks were removed (Items 4, 5, 6, 31, 32, and 33), the remaining 30 items composed the Chinese concise version “Career Readiness Questionnaire – Adolescent Version,” with discrimination parameters ranging from [1.45, 3.38], difficulty parameters between [−3.31, 1.76], and maximum information function peaks within [0.50, 2.64]. Building upon the effective participants from Study One, Study Two matched questionnaire data with textual information, resulting in 1012 valid participants. Leveraging text mining, a machine learning model was constructed to predict high school students’ career readiness based on essay texts. The results of Study 2 prove that the revised lexicon was more accurate in feature extraction. Building upon this, the machine learning model for essay text demonstrated excellent performance in predicting career readiness, with random forest outperforming the other algorithms. This study provides a novel approach for schools and parents to comprehend the state of career readiness among high school students, offering a convenient and effective tool for educational activities related to students’ career development.

How well does the Index of Race-Related Stress-brief function in a trauma-exposed Black community sample? An item response theory examination of discrimination and difficulty parameters.

Race-related stress negatively impacts the mental health of Black Americans to a greater degree than other racialized groups. Additionally, trauma exposure is associated with more severe levels of posttraumatic stress disorder for individuals who also experience race-related stress. Therefore, an accurate assessment of race-related stress in a trauma-exposed sample of Black Americans is necessary to ensure the validity and reliability of empirical findings regarding race-related stress and intervention efficacy. The Index of Race-Related Stress (IRRS) is one of the most commonly used measures to assess race-related stress among Black Americans. Due to a lack of psychometric support for the abbreviated version of IRRS-brief (IRRS-B) on a trauma-exposed sample of Black Americans, our study aims to address this gap in literature. We used item response theory (IRT) to assess item difficulty, discrimination, and factor structure in a sample of trauma-exposed Black Americans (n = 226). We employed a multidimensional graded response model with corresponding items loaded on to the three previously established factors of the IRRS. The most discriminating items asked about observing harsh treatment of Black individuals, experiencing less courtesy in establishments, and being stared at as though you do not belong. The item with the lowest difficulty described negative media representation of Black individuals while the item with the highest difficulty described lack of positive media portrayals of Black Americans. These results indicate that items varied considerably in the degree to which they adequately captured race-related stress. Future research should use IRT with newly worded questions to further improve the assessment of race-related stress in Black Americans. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Preliminary Study for Gatekeepers Self-Efficacy Scale among Resident Assistants (GSS- RA)

As the importance of promoting college student mental health increases and campuses work to prevent suicide, resident assistants (RAs) are called upon to serve as gatekeepers to facilitate professional help as a part of suicide prevention initiatives on campuses. However, assessing the efficacy of suicide prevention training is lacking. This study develops and validates the Gatekeepers Self-Efficacy Scale among resident assistants (RAs) based on the Question, Persuade, Response suicide prevention model. Exploratory Factor Analysis (EFA), Parallel Analysis, and Multidimensional Graded Response model (MGRM) were used with 302 RAs sample. Two factors were found, (1) Communicating about Crisis and (2) Knowledge of Resources, with appropriate item fit and parameter estimates. The response patterns of the two factors and their correlations with objective suicide prevention knowledge were estimated and discussed. The implications of these findings for practical application are discussed, along with suggestions for future studies.

Genetic associations with mixed neuropathologies in the aging brainGenetic associations with mixed neuropathologies in the aging brain

AbstractBackground““Mixed” neuropathologies (NPs) are common in aged brains. Classic Alzheimer’s disease neuropathologic change (ADNC) proteinopathies are amyloid‐β plaques and tau neurofibrillary tangles (NFTs). In addition, α‐synuclein and TDP‐43 are also key proteinaceous disease markers, and cerebrovascular diseases (CVDs) often coexist. Although the scientific problems regarding mixed NPs are increasingly recognized, genetic associations with mixed NPs have not been well‐investigated. In this study, we applied item response theory (IRT) to optimize the clustering and combination of mixed NPs and examined the genetic associations.MethodNP data were drawn from the National Alzheimer’s Coordinating Center (NACC) NP v10‐11 forms through the March 2022 data freeze, including Thal phase ratings (A score); Braak NFT stage (B score); CERAD neuritic plaque scores (C score); Lewy bodies (LBs); TDP‐43; hippocampal sclerosis (HS); cerebral amyloid angiopathy (CAA); infarct and lacunes; microinfarcts; atherosclerosis of the circle of Willis; and brain arteriolosclerosis. We determined dominant pathology patterns in 1,228 participants who had no missing data using a multidimensional graded response model within the IRT framework. The NACC NP data were linked to the U.S. Alzheimer’s Disease Research Centers genetic data provided by the Alzheimer’s Disease genetic Consortium.ResultFrom the leave one out cross validation simulation, the model fitness tests showed that the 4‐factor model had fewer differences from the data than the other models. As shown in Figure, the predominant NPs were TDP‐43 and HS for factor 1 (F1), CVDs other than CAA for factor 2 (F2), A, B, and C scores and CAA for factor 3 (F3), and LBs for factor 4 (F4). The TMEM106B locus was genome‐wide significant with F1 (TDP‐43/HS) and the APOE locus was genome‐wide significant with F3 (ADNC/CAA) after adjusted for age at death, sex, and other factor scores. The SDK1 and FMNL2 loci were not genome‐wide significant, but had noteworthy associations with F2 (CVDs).ConclusionOur new approach to create mixed NP scores could detect the TMEM106B and APOE loci as TDP‐43/HS related and ADNC related regions, respectively. Although previous studies reported SDK1 as an ADNC related gene, we showed that the gene may be associated with CVDs.

Detecting Multidimensional DIF in Polytomous Items with IRT Methods and Estimation Approaches

AbstractThe purpose of this study was to investigate multidimensional DIF with a simple and nonsimple structure in the context of multidimensional Graded Response Model (MGRM). This study examined and compared the performance of the IRT‐LR and Wald test using MML‐EM and MHRM estimation approaches with different test factors and test structures in simulation studies and applying real data sets. When the test structure included two dimensions, the IRT‐LR (MML‐EM) generally performed better than the Wald test and provided higher power rates. If the test included three dimensions, the methods provided similar performance in DIF detection. In contrast to these results, when the number of dimensions in the test was four, MML‐EM estimation completely lost precision in estimating the nonuniform DIF, even with large sample sizes. The Wald with MHRM estimation approaches outperformed the Wald test (MML‐EM) and IRT‐LR (MML‐EM). The Wald test had higher power rate and acceptable type I error rates for nonuniform DIF with the MHRM estimation approach.The small and/or unbalanced sample sizes, small DIF magnitudes, unequal ability distributions between groups, number of dimensions, estimation methods and test structure were evaluated as important test factors for detecting multidimensional DIF.

A Gibbs-INLA algorithm for multidimensional graded response model analysis.

In this paper, we propose a novel Gibbs-INLA algorithm for the Bayesian inference of graded response models with ordinal response based on multidimensional item response theory. With the combination of the Gibbs sampling and the integrated nested Laplace approximation (INLA), the new framework avoids the cumbersome tuning which is inevitable in classical Markov chain Monte Carlo (MCMC) algorithm, and has low computing memory, high computational efficiency with much fewer iterations, and still achieve higher estimation accuracy. Therefore, it has the ability to handle large amount of multidimensional response data with different item responses. Simulation studies are conducted to compare with the Metroplis-Hastings Robbins-Monro (MH-RM) algorithm and an application to the study of the IPIP-NEO personality inventory data is given to assess the performance of the new algorithm. Extensions of the proposed algorithm for application on more complicated models and different data types are also discussed.

Bayesian inference for multidimensional graded response model using Pólya-Gamma latent variables

In the context of both cognitive and affective tests, items are usually designed to involve more than two responses, for which polytomous models are applicable. The purpose of this paper is to propose a highly effective Pólya-Gamma Gibbs sampling algorithm based on auxiliary variables to estimate the multidimensional graded response model that has been widely used in psychological, educational, and health-related assessment. The strategy is based on the Pólya Gamma family of distributions which provides a closed-form posterior distribution for logistic-based models. With the introduction of the two latent variables, the full conditional distributions are tractable, and consequently the Gibbs sampling is easy to implement. Nice features including empirical performance of the proposed methodology are demonstrated by simulation studies. Finally, two empirical data sets were analysed to demonstrate the efficiency and utility of the proposed method.

Item parameter estimations for multidimensional graded response model under complex structures

Item parameter recovery in the compensatory multidimensional graded response model (MGRM) under simple and complex structures with rating-scale item response data was examined. A simulation study investigated factors that influence the precision of item parameter estimation, including sample size, intercorrelation between the dimensions, and test lengths for the MGRM under balanced and unbalanced complex structures, as well as the simple structure. The item responses for the MGRM were generated and analyzed across conditions using the R package mirt. The bias and root mean square error (RMSE) was used to evaluate item parameter recovery. Results suggested that item parameter estimation was more accurate in balanced complex structure conditions than in unbalanced or simple structures, especially when the test length was 40 items, and the sample size was large. Further, the mean bias and RMSE in the recovery of item threshold estimates along the two dimensions for both balanced and unbalanced complex structures were consistent across all conditions.

LASSO-Based Pattern Recognition for Replenished Items With Graded Responses in Multidimensional Computerized Adaptive Testing.

As a branch of statistical latent variable modeling, multidimensional item response theory (MIRT) plays an important role in psychometrics. Multidimensional graded response model (MGRM) is a key model for the development of multidimensional computerized adaptive testing (MCAT) with graded-response data and multiple traits. This paper explores how to automatically identify the item-trait patterns of replenished items based on the MGRM in MCAT. The problem is solved by developing an exploratory pattern recognition method for graded-response items based on the least absolute shrinkage and selection operator (LASSO), which is named LPRM-GR and facilitates the subsequent parameter estimation of replenished items and helps maintaining the effectiveness of item replenishment in MCAT. In conjunction with the proposed approach, the regular BIC and weighted BIC are applied, respectively, to select the optimal item-trait patterns. Simulation for evaluating the LPRM-GR in pattern recognition accuracy of replenished items and the corresponding item estimation accuracy is conducted under multiple conditions across different numbers with respect to dimensionality, response-category numbers, latent trait correlation, stopping rules, and item selection criteria. Results show that the proposed method with the two types of BIC both have good performance in pattern recognition for item replenishment in the two- to four-dimensional MCAT with the MGRM, for which the weighted BIC is generally superior to the regular BIC. The proposed method has relatively high accuracy and efficiency in identifying the patterns of graded-response items, and has the advantages of easy implementation and practical feasibility.

Re-assessing the Psychometric Properties of Stress Appraisal Measure in Ghana Using Multidimensional Graded Response Model.

Despite the widespread use of the stress appraisal measure questionnaire in sport psychology literature, information on the psychometric properties of this survey instrument across different cultures and samples is still lacking. This study sought to validate the stress appraisal measure among male football players in the Ghana’s Premier League using the multidimensional item response theory. The descriptive cross-sectional survey design was adopted to recruit 424 footballers from the 2020/2021 Ghana Premier League season using the census approach. The 28-item Stress Appraisal Measure was used to assess six (6) appraisal mechanisms under primary and secondary cognitive appraisals. The ordered polytomous item response theory was used for analyzing the data. The study found that although some items were problematic, the majority of them were found to have good item parameters, effective scale option functioning, and provided adequate empirical information in the measurement of stress appraisal. This research concluded that the stress appraisal measure has promising applicability among male footballers who participated in the premier league in Ghana. Future researchers are encouraged to re-validate the stress appraisal measure with a different sample to contribute to the understanding of the applicability of the instrument in non-western populations.

Multidimensional Computerized Adaptive Testing: A Potential Path Toward the Efficient and Precise Assessment of Applied Cognition, Daily Activity, and Mobility for Hospitalized Patients

ObjectiveTo develop and evaluate an efficient and precise variable-length functional assessment of applied cognition, daily activity, and mobility to inform mobility preservation and rehabilitation service delivery among hospitalized patients. DesignA multidimensional item bank tapping into these dimensions was developed, with all items calibrated using a multidimensional graded response model. The items were adaptively selected from the item banks to maximize the test information, and the test ended when a joint stopping rule was satisfied. A simulation study was conducted based on the completed instrument, the Functional Assessment in Acute Care Multidimensional Computerized Adaptive Test (FAMCAT), to compare its measurement precision and efficiency capabilities relative to conventional unidimensional computerized adaptive testing. Precision was measured by the bias and root mean squared error between the estimated and true (ie, simulated) θ estimates, whereas efficiency was measured by average test length. Data were collected by an interviewer reading questions from a tablet computer and entering patients’ responses. SettingA large Midwestern hospital. ParticipantsA total of 4143 patients hospitalized with medical diagnosis and/or surgical complications, with 2060 in the calibration sample and 2083 in the validation cohort. InterventionNot applicable. ResultsAmong the 2083 patients in the validation sample, FAMCAT administration required an average of 6 (SD=3.11) minutes. Ninety-six percent had their tests terminated by the standard error rule after responding to an average of 22.05 (SD=7.98) items, whereas 15 were terminated by the change in θ rule, with an average test length of 45.27 (SD=11.49). The remaining 76 responded until reaching the maximum test length of 60 items. ConclusionsThe FAMCAT has the potential to satisfy the need for structured, frequent, and precise assessment of functional domains among hospitalized patients with medical diagnosis and/or surgical complications. The results are promising and may be informative for others who wish to develop similar instruments when concurrent assessment of correlated domains is required.

Reliability and validity assessment of the Mental Health Continuum – Short Form for Bangladeshi adults

There is a shortage of psychological tools to assess the mental well-being of Bangladeshi people covering the hedonic and the eudaimonic aspects of well-being. This study aimed to assess the reliability and validity of the Mental Health Continuum Short Form as this instrument assesses both the hedonic and the eudaimonic aspects of well-being. This study utilized online survey data (n = 361). Item analysis results showed that this scale had good discrimination indices, internal consistency reliability, and acceptable average inter-item correlations. Confirmatory factor analysis lent support to the three-factor correlated model with good model fits and sufficient factor loadings. Results from the multidimensional graded response model also supported the efficiency of this scale to assess the mental well-being of Bangladeshi adults. This scale had moderate correlations with perceived stress and anxiety. Overall, findings suggested this scale as a reliable and valid tool for assessing mental well-being. This scale would help mental health practitioners to assess mental well-being and formulate relevant interventions.

How different domains of quality of life are associated with latent dimensions of mental health measured by GHQ-12

Background and objectivesA short form of the General Health Questionnaire (GHQ-12) is a useful screening instrument for assessing mental health. Furthermore, Quality of life (QoL) is a critical treatment outcome in many clinical and health care research settings. This study aimed to reassess the dimensionality of GHQ-12 using Multidimensional Graded Response Model (MGRM) and evaluate how its extracted dimensions are associated with the QoL's domains.MethodsIsfahan Cohort Study 2 (ICS2) is a population-based, ongoing prospective cohort study among adults aged 35 years and older who were free of cardiovascular diseases (CVDs) at the beginning of the study in 2013. A total of 1316 participants, all living in urban and rural areas of Isfahan and Najafabad, Iran was completed the GHQ-12 and WHO QoL-brief version at baseline. Five competing MGRMs with different latent structures were specified for GHQ-12. Factor scores derived from the best fitted model were used to associate with various domains of QoL. Results: The Three-Dimensional model for GHQ-12 was the best-fitted model explaining the Social Function (SF), Self Confidence (SC), and Anxiety/Depression (A/D) as three correlated yet different latent dimensions of mental health. Our findings in full adjusted multivariate regression models showed that a one-SD increase in dimensions of SC and SF was associated with a 38- to 48%-SD and 27- to 38%-SD increase in the domains scores of QoL, respectively. Moreover, for each one‐SD increase in score of A/D dimension, the domains scores of QoL decreased by 29- to 40%-SD. The highest to the lowest standardized coefficients for all latent dimensions of mental health were respectively related to the psychological, physical health, social relationships, and environmental condition domains of QoL. Furthermore, SC, A/D, and SF dimensions of GHQ-12 showed the highest to the lowest degree of association with all domains of QoL.ConclusionsOur findings confirm that the GHQ-12 as a multidimensional rather than unitary instrument measures distinct dimensions of mental health. Furthermore, all aspects of QoL changed when the intensity of latent dimensions of mental health increased. Moreover, the psychological domain of QoL is the most affected by all latent dimensions of mental health, followed by physical health, social relationships, and environmental condition domains. It seems that in an attempt to full recovery as assessed by improved QoL outcomes, treatment of clinical symptoms may not be sufficient. Identifying and differentiating the structures of mental health in each community as well as implementing intervention programs aimed at focusing on specific dimensions may help in the prevention of further deterioration of mental health and improved QoL in the community.

Bayesian estimation of multidimensional polytomous item response theory models with Q-matrices using Stan

The Q-matrix is commonly used in diagnostic classification models and has recently been incorporated into the multidimensional item response theory (MIRT) models to add information about the relationship between items and dimensions of the latent trait. The reformulation of the MIRT models with Q-matrix (MIRT-Q) has presented to improve the precision of the parameters of these models and to provide a simple and intuitive method for users to define the item-trait relationship. This paper aims to explore the incorporation of the Q-matrix in the formulation of MIRT models for polytomous item responses. Specifically, we introduce the incorporation of the Q-matrix into two of the polytomous MIRT models most known and used: the multidimensional graded response (MGR) model, hereinafter called MGR-Q, and the multidimensional generalized partial credit (MGPC) model, hereinafter called MGPC-Q. We provide readers the code of the MGR-Q and MGPC-Q models in Stan, a Bayesian estimation software, and we conduct a simulation study in order to evaluate the parameter recovery of the estimation method. To illustrate the use of both models in practice, we fit them to an operational dataset from 2400 individuals on 13 items and demonstrate the estimation of MGR-Q and MGPC-Q using the Stan program.

Psychometric properties of the Polish version of the Multiple Sclerosis Self-Management Scale – Revised

Purpose The main aim of this study was to analyse the psychometric properties of the Polish version of the Multiple Sclerosis Self-Management-Revised Scale. Methods A total of 663 patients with multiple sclerosis were included in this study. The Polish version of the Multiple Sclerosis Self-Management-Revised Scale, The General Self-Efficacy Scale, Multiple Sclerosis Impact Scale-29, and Expanded Disability Status Scale were used. Cronbach’s alpha and McDonald’s omega coefficients were calculated to assess the Polish version of the scale’s reliability. The reliability of the scale’s test items was assessed using an information function with the multidimensional graded response model. Confirmatory factor and principal component analyses were conducted. Results Cronbach’s α and McDonald’s ω were obtained (α = 0.896, ω = 0.909). Correlations between the scores of the Polish version of the Multiple Sclerosis Self-Management-Revised Scale and the General Self-Efficacy Scale (r = 0.34; p < 0.001) and the neurological assessment of patients by a physician (r = −0.23; p < 0.001) were significant. The original factor structure of the scale was not confirmed (Chi2(242) = 1480; p < .001; CFI = 0.829; TLI = .805; RMSEA = 0.088, 90% CI = [0.084-0.092]; SRMR = 0.079). Conclusions Using only the total score of the Multiple Sclerosis Self-Management-Revised Scale is recommended for Polish patients. The scale’s factor structure requires further study. Implications for rehabilitation The Polish version of the Multiple Sclerosis Self-Management-Revised Scale was reliable and valid when used in adult multiple sclerosis patients. At present, we recommend using only the total score of the Multiple Sclerosis Self-Management-Revised Scale. Further research is needed to distinguish the detailed subscales of the Polish version of the tool. The Polish version of the Multiple Sclerosis Self-Management-Revised Scale can be used in clinical practice and for research purposes to assess the general level of self-management in Polish-speaking multiple sclerosis patients in Poland.

Performance of the S 2 Statistic for the Multidimensional Graded Response Model.

is a popular item fit index that is available in commercial software packages such as flexMIRT. However, no research has systematically examined the performance of for detecting item misfit within the context of the multidimensional graded response model (MGRM). The primary goal of this study was to evaluate the performance of under two practical misfit scenarios: first, all items are misfitting due to model misspecification, and second, a small subset of items violate the underlying assumptions of the MGRM. Simulation studies showed that caution should be exercised when reporting item fit results of polytomous items using within the context of the MGRM, because of its inflated false positive rates (FPRs), especially with a small sample size and a long test. performed well when detecting overall model misfit as well as item misfit for a small subset of items when the ordinality assumption was violated. However, under a number of conditions of model misspecification or items violating the homogeneous discrimination assumption, even though true positive rates (TPRs) of were high when a small sample size was coupled with a long test, the inflated FPRs were generally directly related to increasing TPRs. There was also a suggestion that performance of was affected by the magnitude of misfit within an item. There was no evidence that FPRs for fitting items were exacerbated by the presence of a small percentage of misfitting items among them.

Modeling of the UCLA Loneliness Scale According to the Multidimensional Item Response Theory

The authors of the University of California Los Angeles Loneliness Scale (UCLA-LS) have considered its structure to be unidimensional. Nevertheless, it has been repeatedly found that response direction can generate groupings of items. In this study, the multidimensional item response theory was applied to examine the psychometric properties of the Argentine version of the UCLA-LS. The participants were 556 residents of the Metropolitan Area of Buenos Aires, Argentina (55% female) with an average age of 43.8. The model fits of three models were compared: the unidimensional Graded Response Model (GRM), the multidimensional GRM, and the bifactor GRM. The results indicated that the bifactor GRM, which considered a general factor and two specific factors related to response direction, was the most adequate one. Loneliness, which was the general factor, explained 70.38% of the common variance. Direct response direction toward loneliness explained 20.09% of the common variance; in other words, it clearly biases item responses, whereas response direction toward companionship has a much lower impact (9.52%).

The structure of prosocial behavior. Its approach through the Multidimensional Item Response Theory’s two-factor model

Problem and objectives: To find out if the structure of the prosocial behavior measured by the Prosocial Behavior Scale (PBS) can be represented by a general factor, i.e. prosociality, combined with specific factors: the subscales of the PBS. Method: The sample consisted of 692 subjects (65% females) recruited by a convenience sampling method and living in Greater Buenos Aires and inland areas of Argentina. The assumptions of unidimensionality and local dependence were checked. Then the relative fit was compared between three models of the Item Response Theory: Graded Response Model (GRM), Multidimensional Graded Response Model (MGRM) and Two-Factor Graded Response Model (TGRM). Results: The TGRM showed the best relative fit. This model was implemented by calculating the conditional and marginal parameters. Likewise, the ECV index was calculated. Conclusions: The PBS items were primarily influenced by prosociality. The Help subscale explained a significant proportion of the common variance. In contrast, the Comfort subscale, considered as a specific factor, explained a small part of said variance.