Latent Constructs Research Articles

A comparative analysis of multivariate approaches for data analysis in management sciences

The researchers use the SEM-based multivariate approach to analyze the data in different fields, including management sciences and economics. Partial least square structural equation modeling (PLS-SEM) and covariance-based structural equation modeling (CB-SEM) are powerful data analysis techniques. This paper aims to compare both models, their efficiencies and deficiencies, methodologies, procedures, and how to employ the models. The outcomes of this paper exhibited that the PLS-SEM is a technique that combines the strengths of structural equation modeling and partial least squares. It is imperative to know that the PLS-SEM is a powerful technique that can handle measurement error at the highest levels, trim and unbalanced datasets, and latent variables. It is beneficial for analyzing relationships among latent constructs that may not be candidly witnessed and might not be applied in situations where traditional SEM would be infeasible. However, the CB-SEM approach is a procedure that pools the strengths of both structural equation modeling and confirmatory factor analysis. The CB-SEM is a dominant multivariate technique that can grip multiple groups and indicators; it is beneficial for analyzing relationships among latent variables and multiple manifest variables, which can be directly observed. The paper concluded that the PLS-SEM is a more suitable technique for analyzing relations among latent constructs, generally for a small dataset, and the measurement error is high. However, the CB-SEM is suitable for analyzing compound latent and manifest constructs, mainly when the goal is to generalize results to specific population subgroups. The PLS-SEM and CB-SEM have specific efficiencies and deficiencies that determine which technique to use depending on resource availability, the research question, the dataset, and the available time.

Keeping promises in cause-related marketing campaigns: development and validation of promised impact evidence scale

PurposeThis study aims to investigate the potential positive effects of cause-related marketing (CRM) campaigns that show evidence of commitment to espoused causes. It examines whether consumers respond positively when a CRM campaign promises to deliver proof of the espoused cause.Design/methodology/approachThis study adopted the grounded theory approach to conceptualize the promised impact evidence construct. A promised impact evidence scale was developed and validated using robust qualitative and quantitative methods, including item response theory estimates.FindingsThe study provides evidence for promised impact evidence as a reflective second-order latent construct. The promised impact evidence scale demonstrates strong internal consistency, reliability and validity. In addition, this study posits that promised impact evidence is an antecedent of advertising trust, purchase intention, advertising credibility and persuasive and selling intent.Originality/valueThis study positioned the promised impact evidence scale against the theoretical underpinnings of the persuasion knowledge model. Specifically, this scale contributes to existing knowledge because it applies the persuasion knowledge model in CRM campaigns by adopting an acceptance focus, as opposed to the rejection focus used in developing persuasion knowledge model scales.

Modeling individuals' prescription medicine borrowing behavior

BackgroundMedicine-sharing behavior can be divided into two groups: borrowing medicine from someone else and lending medicine to someone else without consulting a doctor or a pharmacist. There are many reasons for the behavior that leads individuals to share their prescription medicines. However, there is a lack in the literature to investigate individuals' medicine-sharing behavior and related antecedents and outcomes of this behavior. Objective(s)The main objective of this study is to model individuals’ prescription medicine borrowing (PMB) behavior via the structural equation model based on the Theory of Planned Behavior (TPB). MethodsA measurement tool was developed according to TPB. Exploratory factor analysis was conducted to determine subfactors of PBM behavior. In the second step, confirmatory factor analysis (CFA) was conducted to confirm whether there is a relationship between the observed variables and their underlying latent constructs. Finally, a structural equation model was conducted to specify the relationships between latent variables. IBM Statistical Package for Social Sciences (IBM SPSS Software) version 22 and LISREL 8.80 were used for data analysis. ResultsStudy findings reveal that the participants' attitudes and intentions towards PMB are low, and the rate of exhibiting this behavior is also low. In the proposed prescription medicine borrowing behavior model, the positive effect of attitude towards PMB, perceived behavioral control related PMB, and subjective norm about PMB on intention towards PMB were found statistically significant. Intention towards PMB has been found to have a positive statistically significant effect on PMB behavior. ConclusionsBased on the results of this study, it is possible to say that TPB is an appropriate theory for modeling PMB. Thus, this model will shed light on healthcare providers and policy-makers to avoid individuals from the adverse effects of PMB behavior.

The distribution of parent-reported attention-deficit/hyperactivity disorder and subclinical autistic traits in children with and without an ADHD diagnosis.

Autistic traits are often reported to be elevated in children diagnosed with attention-deficit/hyperactivity disorder (ADHD). However, the distribution of subclinical autistic traits in children with ADHD has not yet been established; knowing this may have important implications for diagnostic and intervention processes. The present study proposes a preliminary model of the distribution of parent-reported ADHD and subclinical autistic traits in two independent samples of Australian children with and without an ADHD diagnosis. Factor mixture modelling was applied to Autism Quotient and Conners' Parent Rating Scale - Revised responses from parents of Australian children aged 6-15years who participated in one of two independent studies. A 2-factor, 2-class factor mixture model with class varying factor variances and intercepts demonstrated the best fit to the data in both discovery and replication samples. The factors corresponded to the latent constructs of 'autism' and 'ADHD', respectively. Class 1 was characterised by low levels of both ADHD and autistic traits. Class 2 was characterised by high levels of ADHD traits and low-to-moderate levels of autistic traits. The classes were largely separated along diagnostic boundaries. The largest effect size for differences between classes on the Autism Quotient was on the Social Communication subscale. Our findings support the conceptualisation of ADHD as a continuum, whilst confirming the utility of current categorical diagnostic criteria. Results suggest that subclinical autistic traits, particularly in the social communication domain, are unevenly distributed across children with clinically significant levels of ADHD traits. These traits might be profitably screened for in assessments of children with high ADHD symptoms and may also represent useful targets for intervention.

Measuring Early Relational Health Using PediaTrac TM in a Diverse Sample of Infant-Caregiver Dyads.

Early relational health (ERH) is a key developmental predictor and outcome in infancy and early childhood that reflects social-emotional well-being and promotes resilience throughout childhood. Currently, there is no gold-standard developmental screening tool for ERH in pediatric care settings. This study examined the psychometric properties of items assessing ERH that are part of a web-based, caregiver-report screening tool called PediaTrac TM . It was hypothesized that ERH could be reliably estimated and that second-order factors would be revealed within the latent construct ERH. Participants included 571 caregivers of term (n = 331; ≥37 weeks) and preterm (n = 240; <37 weeks) infants recruited shortly after birth from several academic medical centers and a community health clinic. Caregivers completed PediaTrac modules at birth and 2, 4, 6, 9, 12, 15, and 18 months; data for this study are from the newborn through 12-month periods. Results from Item Response Theory Graded Response Modeling revealed excellent reliability for the PediaTrac ERH domain at all time points, ranging from 0.96 to 0.98. Exploratory factor analyses revealed 4 to 5 second-order factors, representing Parent-Child Relationship, Parent Distress, Parenting Stress, Parenting Efficacy, Sensitivity, and Perceptions of Child, depending on period. The caregiver-report developmental screening tool, PediaTrac, reliably measures ERH during the first year of life. The measure has promising clinical utility in pediatric clinic settings for tracking ERH over time to ensure early social-emotional well-being and to identify concerns as early as possible.

DIF Analysis with Unknown Groups and Anchor Items

Ensuring fairness in instruments like survey questionnaires or educational tests is crucial. One way to address this is by a Differential Item Functioning (DIF) analysis, which examines if different subgroups respond differently to a particular item, controlling for their overall latent construct level. DIF analysis is typically conducted to assess measurement invariance at the item level. Traditional DIF analysis methods require knowing the comparison groups (reference and focal groups) and anchor items (a subset of DIF-free items). Such prior knowledge may not always be available, and psychometric methods have been proposed for DIF analysis when one piece of information is unknown. More specifically, when the comparison groups are unknown while anchor items are known, latent DIF analysis methods have been proposed that estimate the unknown groups by latent classes. When anchor items are unknown while comparison groups are known, methods have also been proposed, typically under a sparsity assumption – the number of DIF items is not too large. However, DIF analysis when both pieces of information are unknown has not received much attention. This paper proposes a general statistical framework under this setting. In the proposed framework, we model the unknown groups by latent classes and introduce item-specific DIF parameters to capture the DIF effects. Assuming the number of DIF items is relatively small, an L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_1$$\\end{document}-regularised estimator is proposed to simultaneously identify the latent classes and the DIF items. A computationally efficient Expectation-Maximisation (EM) algorithm is developed to solve the non-smooth optimisation problem for the regularised estimator. The performance of the proposed method is evaluated by simulation studies and an application to item response data from a real-world educational test.

Monetary wisdom: Can yoking religiosity (God) and the love of money (mammon) in performance and humane contexts inspire honesty? The Matthew Effect in Religion

AbstractReligion inspires honesty. The love of money incites dishonesty. Religious and monetary values apply to all religions. We develop a formative theoretical model of monetary wisdom, treat religiosity (God) and the love of money (mammon), as two yoked antecedents—competing moral issues (Time 1), and frame the latent construct in good barrels (performance or humane contexts, Time 2), which leads to (dis)honesty (Time 3). We explore the direct and indirect paths and the model across genders. Our three‐wave panel data (411 participants) show that religious and monetary values are negatively correlated. Directly, religiosity consistently curbs dishonesty; surprisingly, the love of money has no impact on dishonesty. In the performance context, the two mediation effects reduce dishonesty. Across genders, this mediation effect is nonsignificant for males but significantly excites females' honesty. In the humane context, the two mediation effects are nonsignificant. Across genders, for the love of money, males passively curb dishonesty by omission, and females actively engage in honesty by commission. Decision‐makers must challenge people's moral issues, frame them in good barrels, and help people become good apples, choice architects, and moral and ethical decision‐makers, promoting the Matthew effect in religion. We offer practical implications to individuals and organizations.

A Mixture Fluency model using responses and response times with cognitive diagnosis model framework.

Random guessing behaviors are frequently observed in low-stakes assessments, often attributed to factors such as test-takers lacking motivation or experiencing time constraints and fatigue. Existing research suggests that responses stemming from random guessing behaviors introduce biases into the constructs and relationships of interest. This is particularly problematic when estimating the relationship between speed and ability. This study introduces a Mixture Fluency model designed to account for random guessing behaviors while utilizing valid response accuracy and response time to uncover students' latent attribute profiles. The model directly addresses a limitation present in the Fluency cognitive diagnostic model (Wang & Chen, Psychometrika, 85, 600-629, (2020), which assumes that test-takers consistently employ solution behaviors when answering questions. To investigate the effectiveness of the proposed Mixture Fluency model, we conducted a simulation study encompassing various simulation conditions. Results from this study not only confirm the model's ability to detect potential random guessing behaviors but also demonstrate its capacity to enhance the inference of targeted latent constructs within the assessment. Additionally, we showcase the practical utility of the proposed model through an application to real data.

More than entertaining: a typology of family portrayals and inherent persuasive potential

ABSTRACT Family portrayals in entertainment media represent a significant concern for both media and family communication scholars. These fictional families can model both effective and ineffective family behaviors. Research within this realm often focuses on either the content or the effects of portrayals rather than providing a comprehensive picture. The family portrayal typology offers a tool to bridge this content-effects gap by providing explanatory and predictive power regarding family portrayals’ persuasive potentials. Stemming from social cognitive and entertainment theories, this paper identifies six family portrayal types and proposes their varying levels of persuasion based on the latent constructs of a family’s wishful identification arousal, eudaimonic quality, and general family outcomes. Suggestions for future research utilizing the typology are proposed.

Ongoing tracking of engagement in motor learning

Teaching motor skills such as playing music, handwriting, and driving, can greatly benefit from recently developed technologies such as wearable gloves for haptic feedback or robotic sensorimotor exoskeletons for the mediation of effective human-human and robot-human physical interactions. At the heart of such teacher-learner interactions still stands the critical role of the ongoing feedback a teacher can get about the student’s engagement state during the learning and practice sessions. Particularly for motor learning, such feedback is an essential functionality in a system that is developed to guide a teacher on how to control the intensity of the physical interaction, and to best adapt it to the gradually evolving performance of the learner. In this paper, our focus is on the development of a near real-time machine-learning model that can acquire its input from a set of readily available, noninvasive, privacy-preserving, body-worn sensors, for the benefit of tracking the engagement of the learner in the motor task. We used the specific case of violin playing as a target domain in which data were empirically acquired, the latent construct of engagement in motor learning was carefully developed for data labeling, and a machine-learning model was rigorously trained and validated.

Measuring Psychological Resilience in Aging: Findings From the Health and Retirement Study and National Longitudinal Study of Adolescent to Adult Health.

This study aimed to evaluate the measurement properties of 2 emerging psychological resilience (PR) measures constructed for use in large national data sources and to test their reliability across social axes including race/ethnicity, gender, and socioeconomic status. Using 2006/2008 data, the Simplified Resilience Score and the Add Health Resilience Scale were tested using overall and multigroup measurement models in a structural equation modeling framework. Both PR measures perform well as reliable, 1-factor latent constructs capturing adaptive capacity at various life stages. Both measures showed measurement consistency across social axes, with specific differences in item measurement across some racial/ethnic groups. The results indicate these measures represent high quality, consistent measures of PR in nationally representative aging and health data. The availability of reliable, valid measures of PR enables consistent evaluation of resilience in health and aging processes.

Success Factors in Management of IT Service Projects: Regression, Confirmatory Factor Analysis, and Structural Equation Models

Although there have been some studies on the success factors for IT software projects, there is still a lack of coherent research on the success factors for IT service projects. Therefore, this study aimed to identify and understand the factors and their relationships that contribute to the success of IT service projects. For this purpose, multivariate regressions and structural equation models (SEMs) were developed and analyzed. The regression models included six project management success criteria used as dependent variables (quality of the delivered product, scope realization and requirements, timeliness of delivery, delivery within budget, customer satisfaction, and provider satisfaction) and four independent variables (agile techniques and change management, organization and people, stakeholders and risk analysis, work environment), which had been identified through exploratory factor analysis. The results showed that not all success factors were relevant to all success criteria, and there were differences in their importance. An additional series of exploratory and confirmatory factor analyses along with appropriate statistical measures were employed to evaluate the quality of these four factors. The SEM approach was based on five latent constructs with a total of twenty components. The study suggests that investing in improving people’s knowledge and skills, using agile methodologies, creating a supportive work environment, and involving stakeholders in regular risk analysis are important for project management success. The results also suggest that the success factors for IT service projects depend on both traditional and agile approaches. The study extensively compared its findings with similar research and discussed common issues and differences in both the model structures and methodologies applied. The investigation utilized mathematical methods and techniques that are not commonly applied in the field of project management success modeling. The comprehensive methodology that was applied may be helpful to other researchers who are interested in this topic.

Doing business in Russia: normative organizational resilience, organizational identity and exit decisions

PurposeOur study examines multinational enterpris (MNE) decisions to withdraw from the Russian market on moral grounds in reaction to the Russo–Ukrainian war. We investigate to what extent these decisions reflect the normative organizational resilience of MNEs under institutional pressures in Russia. We test the impact of various macro- (home democracy, institutional quality, stakeholder pressure) and micro-variables (ESG criteria) that define the organizational identities of MNEs in relation to their withdrawal decisions. Our sample comprises 1,648 companies from 55 countries doing business in Russia before the start of the conflict.Design/methodology/approachTo test our hypotheses, we perform a nuanced analysis using both latent constructs and regression analysis on data for 1,648 MNEs.FindingsOur results are in line with the foreign divestment literature, suggesting that MNEs are likely to exit normatively distant countries.Originality/valueIn this study, we explore the impact of organizational values on normative responses of MNEs to a geopolitical crisis. We introduce a normative organizational resilience construct to demonstrate how MNEs respond to institutional pressures in a host country, in this case Russia. Making exit decisions on moral grounds, MNEs have acted as social actors endowed with moral sense and intentionality, in conformity with their organizational values.

Faculty competences development components: an integrated model for the development of educational leaders in technical education institutes

PurposeThis study primarily aims at assessing Faculty Competences Development Components (FCDCs) that help in the development of educational leaders. The study further formulates a model using seven latent constructs that explain the development of the mechanism of development of educational leaders and extend the benefits of their development to different stakeholders including faculty, educational institutions and society at large.Design/methodology/approachA systematic review of the literature was conducted to identify various FCDCs to construct a conceptual framework. Later, this conceptual framework was tested through empirical research using the structural equation modeling (SEM) technique carried out with respect to technical institutions of Punjab (India).FindingsThe study identified that organizational roles are of the highest importance for the competences development of educational leaders followed by teachers’ attributes and teachers’ roles. The study’s findings also revealed that FCDCs significantly impact beneficiaries by developing competent educational leaders as mediators between the FCDCs and beneficiaries link. The biggest beneficiaries of development are faculty members of these institutes in terms of their performance enhancements.Originality/valueThe study is unique in terms of developing a model for the competences development of educational leaders and helpful in understanding various benefits of the educational leaders to various educational stakeholders.

Psychometric Properties and Factor Structure of the Hamilton Anxiety Rating Scale among Korean University Students during COVID-19

Background The Hamilton Anxiety Rating Scale (HAM-A) is a 14-item instrument that is extensively used in clinical and epidemiological studies to determine the presence of anxiety symptoms. Despite the widespread application of the HAM-A in research, it remains unclear whether the instrument’s construct is best represented as uni- or multidimensional. This study aimed to assess the reliability and construct validity of the Korean version of the HAM-A through its factor structure. Methods Accordingly, a cross-sectional design was employed to conduct an online survey with 252 university students enrolled in an undergraduate degree program for 4 years in South Korea during the recent coronavirus (COVID-19) pandemic. Confirmatory factor analyses (CFAs) were employed to identify the two-factorial structure of the instrument, i.e., psychic/psychological and somatic. Results The results revealed that the reliability and item characteristics were favorable. Confirmatory factor analyses identified a two-factorial structure of psychic/psychological and somatic with a moderate correlation between the two latent constructs, thus suggesting a single overarching construct of anxiety. Conclusion Our findings suggest that the Korean version of HAM-A was found to be a valid and reliable instrument that can be employed to screen Korean university students for anxiety.

Measures of executive function may not be indicators of latent constructs.

Executive function (EF) is represented by a multidimensional set of measures. The central EFs considered are inhibitory control, working memory and cognitive flexibility (task shifting). Unlike other ability constructs, it has proven difficult to identify latent factors that underlie EF. Research has often taken a factor analytic approach for grouping executive functioning tasks. However, this approach has often proven unsuccessful. We aimed to compare factor analysis to a network analytic approach, as network analysis can summarize the pattern of relationships among elements without creating latent constructs. One hundred and thirteen undergraduate students completed a series of nine executive functioning tasks. In comparing exploratory factor analysis to network analysis, we found neither approach provided a compelling higher order grouping of EF measures.

Psychometric components of the social acceptance toward end-of-life vehicles policy: A case study of Indonesia

In order to support governmental implementation policy and associated interventions related to environmental policy and legislation, understanding the public's acceptability towards these policies is crucial. One significant environmental issue is the proper disposal of end-of-life vehicles (ELVs), which can cause harm to the environment. To assess social acceptability towards ELV policy, a scale was developed consisting of 49 measurement items with five latent constructs: psychological and social cognitive constructs, environmental constructs, and social support. Content validation was performed by three experts who produced an item-content validity index greater than 0.79. To test the validity and reliability of the construct items, a case study was conducted in Jakarta, Bogor, Depok, Tangerang, and Bekasi (JABODETABEK), Indonesia, with 250 respondents. Exploratory factor analysis showed that 48 items matched the validity and reliability requirements, and all constructs showed very good or excellent reliability based on Cronbach's alpha coefficient rule. The developed scale fulfills all the requirements of validity and reliability and can be used as a new instrument to measure a person's desire to implement ELV policies, especially in developing countries like Indonesia. This study can contribute to developing and implementing effective environmental policies and legislation related to ELV.

Analysis of the Effectiveness of Zakat Collection, Management, and Distribution in Improving Social Welfare in Indonesia

This research explores the effectiveness of Zakat collection, management, and distribution in improving social welfare in Indonesia, employing a sample of 250 participants and utilizing Structural Equation Modeling with Partial Least Squares (SEM-PLS). The measurement model analysis demonstrates robust loading factors, reliability, and convergent validity, while the discriminant validity analysis confirms the distinctiveness of the latent constructs. In the structural model, positive and statistically significant relationships are found between the effectiveness of Zakat distribution, collection, management, and the improvement of social welfare. The R-square and adjusted R-square values of 0.602 and 0.592, respectively, indicate a strong explanatory power of the model. These findings underscore the transformative potential of Zakat in addressing socio-economic disparities and contribute valuable insights for policymakers and practitioners in the field of social development.

Acceptance of ChatGPT by undergraduates in Sri Lanka: a hybrid approach of SEM-ANN

PurposeThis study aims to investigate Sri Lankan Government university students’ acceptance of Chat Generative Pretrained Transformer (ChatGPT) for educational purposes. Using the unified theory of acceptance and use of technology 2 (UTAUT2) model as the primary theoretical lens, this study incorporated personal innovativeness as both a dependent and moderating variable to understand students’ ChatGPT use behaviour.Design/methodology/approachThis quantitative study used a questionnaire survey to collect data. A total of 500 legitimate undergraduates from 17 government universities in Sri Lanka were selected for this study. Items for the variables were adopted from previously validated instruments. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS 4 was used to investigate latent constructs’ relationships. Furthermore, the variables’ relative relevance was ranked using a two-stage artificial neural network analysis with the SPSS 27 application.FindingsThe results of the analysis revealed that eight of the nine proposed hypotheses were confirmed. The most significant determinants of behavioural intention were habit and performance expectancy, closely followed by hedonic motivation and perceived ease of use. Use behaviour was highly influenced by both behavioural intention and personal inventiveness. Though personal innovativeness (PI) was suggested as a moderator, the relationship was not significant.Research limitations/implicationsThe research highlights the impact of habit, performance expectancy and perceived ease of use on students’ acceptance of AI applications such as ChatGPT, emphasising the need for efficient implementation techniques, individual variations in technology adoption and continuous support and training to improve students’ proficiency.Originality/valueThis study enhances the comprehension of how undergraduate students adopt ChatGPT in an educational setting. The study emphasises the significance of certain variables in the UTAUT2 model and the importance of PI in influencing the adoption of ChatGPT in educational environments.

Confirmatory factor analysis of the Hyperacusis Impact Questionnaire, Sound Sensitivity Symptoms Questionnaire, and Screening for Anxiety and Depression in Tinnitus, including preliminary analyses of the parent versions for use with children.

We previously reported the results of exploratory factor analysis (EFA) of the Hyperacusis Impact Questionnaire (HIQ), the Sound Sensitivity Symptoms Questionnaire (SSSD) and the Screening for Anxiety and Depression in Tinnitus (SAD-T). Confirmatory Factor Analysis (CFA) is necessary to confirm the latent constructs determined using EFA. CFA should use different samples but with similar characteristics to those used for EFA. The aim was to use CFA to confirm latent constructs derived using EFA of the HIQ, SSSQ and SAD-T. We further evaluated the psychometric properties of parent versions of these questionnaires (indicated by -P), which are intended for use with children. This was a retrospective cross-sectional study. Data for 323 consecutive adults and 49 children who attended a Tinnitus and Hyperacusis Therapy Clinic in the UK within a six-month period were included. Data were collected retrospectively from the records of patients held at the audiology department. CFA with the weighted least-squares mean and variance-adjusted estimator was applied to assess the previously proposed factor structures of the HIQ, SSSQ and SAD-T. The internal consistency of the scales was assessed via Cronbach's alpha (α). The items of the HIQ, SSSQ and SAD-T were tested for measurement invariance regarding age and gender using the multiple indicator multiple cause (MIMIC) model. All questionnaires showed good to excellent internal consistency, with α = 0.93 for the HIQ, 0.87 for the SSSQ, and 0.91 for the SAD-T. The parent versions showed acceptable to good internal consistency, with α = 0.88 for the HIQ-P, 0.71 for the SSSQ-P, and 0.86 for the SAD-T-P. CFA showed that the HIQ, SSSQ, and SAD-T were all one-factor questionnaires and the factors generally were similar to those obtained for the EFA. The MIMIC model showed that all three questionnaires can be considered as measurement invariant, with scores similar across genders and ages. The HIQ, SSSQ and SAD-T are internally consistent one-factor questionnaires that can be used in clinical and research settings to assess the impact of hyperacusis, the severity of sound sensitivity symptoms, and to screen for anxiety and depression symptoms. Future studies should further explore the psychometric properties of the parent versions of the HIQ and SSSQ and SAD-T.