Latent variable modeling

The study aims to explore the intricate relationships between brain activity, behavioral processes, and decision-making using structural equation modeling (SEM) techniques. While existing research has advanced our understanding of these domains, gaps remain in synthesizing neurocognitive and behavioral factors in a unified framework. The primary objective of the study is to examine the neural mechanisms underlying decision-making and behavior, focusing on how brain function influences both cognitive and emotional decision processes. By integrating insights from neuroimaging, behavioral analysis, and decision theory, the study presents a novel approach to mapping these interconnected systems through SEM. The findings indicate significant associations between brain structures and behavioral outcomes, such as emotion regulation, impulsivity, and decision-making biases. Moreover, the study highlights contrasting evidence regarding the role of brain areas in cognitive flexibility and decision consistency, pointing to unresolved questions in neurocognitive models of decision-making. Key trends identified include the increasing use of neuroimaging and SEM to bridge the gap between neurological and behavioral research, alongside emerging methodologies to assess the dynamic interaction between cognitive processes and decision outcomes. The study's contributions lie in offering a comprehensive SEM-based framework for understanding brain-behavior interactions, providing a holistic model that accommodates both cognitive and emotional components in decision-making. However, the study also faces limitations, such as the reliance on cross-sectional data and potential biases in model selection. Future research should explore longitudinal studies to examine the evolution of brain-behavior dynamics over time and investigate the neurobiological basis of maladaptive decision-making, particularly in clinical populations. This work holds significant implications for improving our understanding of the neurocognitive underpinnings of behavior and decision-making, with applications ranging from mental health interventions to consumer behavior and policy design.

In this paper, we address the question of which potential use of a robot in a health-care environment is imagined by people that are not experts in robotics, and how these people imagine to teach new movements to a robot. We report on the preliminary results of our investigation , in which we conducted 40 interviews with non-experts in robotics and a focus group with professional caregivers.

Traditional methods of test parameterization have been found defective in terms of assuming one score and not providing information on skills mastery profile of the examinees, in addition to non-estimation of the fourth parameter-slipping parameter of test items. Cognitive diagnostic modeling (CDM), specifically deterministicinput-noisy-and-gate (DINA) model is a robust procedure for measuring the skills mastery profile of the examinees, when there is no attribute hierarchy. DINA model also estimates four item parameters: discriminating, difficult, guessing and slipping which hitherto are not implemented as a whole in traditional techniques including the item response theory (IRT). Physics was chosen because it is commingled with civilization. Also, secondary school Physics teachers predominantly use teacher-made tests in their internal examinations in Nigeria. Such instrument cannot be used to precipitate the true ability levels of the students for purposes of promotion. The data collected plus the harmonized Q-matrix were analyzed using CDM package in r software version 3.4.3 via R-Studio version 1.0.153. The results showed that: (i) eighteen items in the test fit the DINA model while thirty-two items had misfit. (ii) nine attributes were mastered by the examinees while three attributes were not mastered. It was recommended that schools' item banks should be created by the governments for uploading psychometrically robust test items for teachers' use.

The current study examines the performance of the extended unconstrained approach (EXUC) and the latent moderated structural equation modeling procedure (LMS) in situations where quadratic and interaction terms are tested simultaneously and investigates their limitations with regard to the employment of parallel and congeneric measures, relatively low indicator reliabilities, and relatively large numbers of indicators. By means of a Monte Carlo study, we found LMS to be the best option for testing multiple nonlinear effects given sufficient sample size (n 500) and normally distributed exogenous variables. Its advantages became more prominent when indicator reliabilities were heterogeneous and small. The EXUC was a viable option for estimating the model when indicators were parallel and exhibited large indicator reliabilities. An empirical example of the results is provided, and the relevance of measurement model characteristics to assess nonlinear relationships is discussed.

A novel variable influence on projection approach for O2PLS models, named VIP O2PLS , is presented in this paper. VIP O2PLS is a model-based method for judging the importance of variables. Its cornerstone is the 2-way formalism of the O2PLS models; i.e. the use of both predictive and orthogonal normalized loadings of the two modelled data matrices, and also a new weighting system based on the sum of squares of both data blocks (X, Y). The VIP O2PLS algorithm has been tested in one synthetic data set and two real cases, and the outcomes have been compared to the PLS-VIP, VIP OPLS , and i-PLS methods. The purpose is to achieve a sharper and enhanced model interpretation of O2PLS models by using the new VIP O2PLS method for assessing the importance of both X-and Y-variables.

Introduction: This study is part of the analysis of the Latin American Integrated Market (MILA) made up of the Peruvian, Chilean and Colombian stock exchanges since 2011, and expanded in 2014 with the incorporation of Mexico. Objective: The main objective is to analyze the behavior of the stock market indices of the MILA member countries and the dynamics of their interrelationship. Methodology: Indices were normalized, stationarity was evaluated using the Augmented Dickey-Fuller test (ADF), the Johansen cointegration test was applied, and a VAR in differences was estimated to analyze the interactions between stock indices. The period of analysis covers from 2015 to 2022. Results: The series were non-stationary at level and integrated in order one. No evidence of cointegration was found between MILA indices, nor in subsets. The VAR model showed significant short-term relationships, especially between the Mexican Index of Prices and Quotations (IPC), the General Index of the Lima Stock Exchange (IGBVL), the Selective Stock Price Index (IPSA) and the Colombian Capitalization Index (COLCAP). Impulse-response analyses confirmed transient interdependencies. Conclusions: There is no long-term stock market integration between the MILA markets. However, significant short-term interactions are detected. This suggests the transmission of shocks and common reactions to external events, with implications for regional diversification and more effective financial integration policies.

The COVID-19 pandemic introduced significant disruptions and challenges to the learning environment for many post-secondary students with many shifting entirely to remote online learning. Barriers to academic success already experienced in traditional face-to-face classes may be compounded in the online environment and exacerbated by stressors related to the pandemic. In 2020–2021, post-secondary institutions were faced with the reality of rolling out fully online instruction with limited access to resources for assisting students in this transition. Instructional interventions that target students’ ability to self-regulate their learning have been shown to improve academic performance and self-regulated learning (SRL) competencies have also been found to mediate the effect of SRL interventions on higher education. However, few studies have examined the efficacy of fully online SRL intervention on mitigating the impact of psychological distress and academic challenges on academic su...

Work or employee engagement might be increased through job re-design interventions such as top-down managerial interventions, bottom-up job crafting or bipolar ideals. However, there is a lack of specific under- standing how public-sector employees react on specific top-down interventions aimed for increasing thei r vigor, dedication and absorption. In order to gain more insight into how a top-down (managerial) job re-design intervention could foster work engagement, we conducted a four-wave longitudinal diary study on the sample of white-collar employees and their respective superviso rs from two public-sector organizations. Specifically, we explored the effect of the m anagerial job redesign intervention on public-sector employee work engage- ment between baseline (T1) and post-intervention (T4). We tested a multiple mediator-single outcome model in which a job design intervention influences work enga gement by changing multiple job characteristics. Fi- nally, we also checked for ...

1. Introduction: The Question of Neural Coherence
Neuroscience frequently treats subthreshold neural activity as a localized, non-functional byproduct of membrane fluctuations. However, this perspective may overlook the possibility that even in the absence of action potentials, a neuron participates structurally in the readiness of the entire organism. Can a silent neuron still belong to an anticipatory field?

Background: We aimed to identify different categorical phenotypes based upon the DSM-V criteria of alcohol use disorders (AUD) among alcohol users who had at least one drink per week in the past year (n = 948). Methods: Data are from the São Paulo Megacity Mental Health Survey collected in 2005-2007, as part of the World Mental Health Survey Initiative. A latent class analysis of the 11 DSM-5-AUD criteria was performed using Mplus, taking into account complex survey design features. Weighted logistic regression models were used to examine demographic correlates of the DSM-5-AUD latent classes. Results: The best latent-class model was a three-class model. We found a "non-symptomatic class" (69.7%), a "use in larger amounts class" (23.2%), defined by high probability (>70%) of the "use in larger amounts" criterion only, and a "high-moderate symptomatic class" (7.1%), defined by high-moderate probability of all the 11 AUD criteria. Compared to those in the non-symptomatic class, individuals in the "highmoderate symptomatic class" were more likely to have been married, have lower educational attainment and to be unemployed or in non-regular/informal employment. Those on the "use in larger amounts class" were more likely to have been married or never married. The two symptomatic classes clearly represented the dimensionality of the new proposed AUD criteria, and could be more specifically targeted by different prevention or treatment strategies. DSM-5-AUD has the advantage of shedding light on risky drinkers included in the "use in larger amounts class", allowing for preventive interventions, which will reach a large number of individuals.

This paper unpacks the foundations of epistemology, exploring its significance in shaping both educational theory and practice. It examines how epistemological beliefs — our assumptions about the nature and certainty of knowledge — profoundly influence learners' approaches to problem-solving, resilience, and academic success. Drawing on research from Schommer-Aikins, Dweck, and Qian & Alvermann, the paper highlights how naive beliefs, such as viewing knowledge as simple and static, can foster learned helplessness, while more sophisticated beliefs cultivate deeper learning strategies and cognitive flexibility. Additionally, the paper explores the role of Generative AI as a tool for fostering learner autonomy, offering personalised feedback, metacognitive scaffolding, and tailored learning pathways that empower students to take ownership of their development. By combining insights from philosophy, psychology, and emerging technologies, this paper argues for a more intentional alignment of epistemological awareness and AI-enhanced pedagogy to develop independent, reflective, and resilient learners.

We investigated the Depression→Distortion hypothesis by examining the effects of maternal depressive symptoms on cross-informant discrepancies in reports of child behavior problems and several measures of parent-child relationship. The sample included ninety-six 6 to 10-year-old children diagnosed with ADHD-Combined Type, and their mothers, who provided baseline data before participating in a randomized clinical trial. Measures incorporated child characteristics, self-reports of maternal depressive symptoms, parenting practices, and laboratory mother-child interactions. Elevations in maternal depressive symptoms were associated with maternal reports of negative parenting style but not with observed laboratory interactions. Mothers' levels of depressive symptoms predicted negative biases in their reports of their child's ADHD symptoms, general behavior problems, and their own negative parenting style. Whereas levels of depressive symptoms did not predict observed parenting behaviors, maternal distortions did predict problematic parent-child interactions. Exploratory analyses showed a marginally significant mediation effect of the relationship between maternal depressive symptomatology and reports of negative parenting by depressive distortions. We discuss implications of linkages between depressive symptoms in mothers, depression-related distortions, and mother-child relationships for research and intervention in developmental psychopathology.

Hepatitis B virus (HBV) and hepatitis C virus (HCV) infect liver cells (hepatocytes) and are responsible for most cases of chronic liver disease. HCV/HBV co-infected patients have a greater risk of severe liver disease, cirrhosis and liver cancer than mono-infected patients. In this paper, we develop a within-host HCV/HBV co-infection model with distributed delays and antibody immunity. The model involves nine delay differential equations (DDEs) that reflect the interactions between uninfected cells, latent HCV-infected cells, active HCV-infected cells, latent HBV-infected cells, active HBV-infected cells, free HCV particles, free HBV particles, HCV-specific antibodies, and HBV-specific antibodies. We test the nonnegativity and boundedness of the solutions. We compute all steady states and affirm the global stability. We confirm the results through numerical simulations. We found that time delays decrease the basic reproduction numbers leading to the stability of uninfected steady state. Also, fewer treatment efficacies will be required when employing a model with time delays. Moreover, increasing the stimulation rate of antibody immunity increases the densities of uninfected cells during co-infection.

On the one hand, the factors Gf and Gc in the Cattell-Horn-Carroll (CHC) model of intelligence are hypothesized to represent individual differences in unique psychological or biological capacities. On the other hand, they are interpreted as representing the theoretical variables fluid and crystallized intelligence in investment theory. With respect to Gc, this leads to a theoretical conflict because in investment theory crystallized intelligence is not a capacity but purely a statistical entity. We contend that if CHC factor Gc represents a capacity, it cannot represent crystallized intelligence, and if Gc represents crystallized intelligence, it does not represent a capacity. In addition, from our discussion of Gc, we conclude that in investment theory the factors Gf and g represent one and the same capacity. In support of our theoretical conclusions, we implemented the CHC model in a confirmatory factor analysis of a HCA (Human Cognitive Abilities project) data set. As expected, Gf and g were statistically indistinguishable. Gc was effectively absent, because it was statistically equivalent to verbal comprehension. Factors Gc and g could be removed from the model without any reduction in model fit. We argue that in the CHC taxonomy the factors Gc and g are redundant as explanatory variables.

This paper presents a geometric approach to the skewed log-normal distribution, by making derivations
of the key results on its mean, variance, and skewness through geometric transformations. Theorem 1 and
proposition 1 offer new insights into the distribution's properties and skewness, enhancing its
interpretability by adding clarity and the results be informative.

Le présent article est une exemplification méthodologique de la méthode LMS (Latent Moderated Structural Equations) disponible dans le logiciel Mplus. Des données recueillies pour étudier la motivation d’adolescentes (n = 434) en éducation physique serviront à présenter la méthodologie à suivre pour évaluer l’interaction de variables latentes dans des modèles d’équations structurelles. Le texte focalise sur la compréhension générale du lecteur quant à l’application de cette méthode et un accent est mis sur la présentation et l’interprétation des résultats. En terminant, les avantages de la méthode LMS sont mis de l’avant et des pistes d’exemplifications méthodologiques sont proposées.

In this study, different factors affecting students’ differential equations (DEs) solving abilities were explored at pre university level. To explore main factors affecting students’ differential equations problem solving ability, articles for a 19-year period, from 1996 to 2015, were critically reviewed and analyzed. It was revealed that combination of four factors; “epistemological math problem solving beliefs, belief about usefulness of mathematics, self-regulated learning (SRL) strategies and goal orientations” have great potential to enhance differential equation problem ability. Based on these findings, a conceptual model was developed and presented in this paper to enhance differential equation problem solving ability. This study has provided several important implications for the curriculum designers and teachers to enhance conceptual understanding in differential equation problem solving, particularly in the developing countries..

An extensive comment on the paper by Jonson and Taylor on the postwar experience with inflation in Australia is provided by Jerome Stein. This discussion concentrates OII the papers by Korteweg and Meltzer and by Dutton. The Korteweg and Meltzer paper analyzes the evidence for a Phillips curve in an international context as well as proposing an alternative hypothesis of inflation.

The aim of this study was to understand the variation in response to alcohol use by identifying classes of alcohol users based on alcohol-dependence symptoms and to compare these classes across demographic characteristics, abuse symptoms, and treatment usage. Method: Data from combined 2002-2005 National Survey on Drug Use and Health identifi ed 110,742 past-year alcohol users, age 18 years or older. Latent class analysis defi ned classes based on observed clustering of alcohol-dependence symptoms based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Chi-square statistics were used to test differences in sociodemographic and alcohol-abuse characteristics across classes. Multivariable latent class regressions were used to compare treatment usage across classes. Results: The four-class model had the best overall fi t and identifi ed classes that differed quantitatively and qualitatively, with 2.3% of the users in the most-severe class and 83.8% in the least-severe/ not-affected class. These classes differed in a number of demographic characteristics and alcohol-abuse symptoms. All individuals in the mostsevere class met DSM-IV criteria for alcohol dependence; 80% of this class had alcohol-abuse symptoms. Twenty-six percent of the moderate and 50% of the moderate-high class met dependence criteria. Approximately 19% of the most-severe class and less than 5% of the moderate and moderate-high class received treatment for alcohol in the past year. Conclusions: This study demonstrates that meeting dependence criteria only partially captures variations in responses to severity of alcohol problems. Although individuals in the most-severe class were more likely to perceive need and receive treatment, the percentage of individuals receiving treatment was low. (J.

Motivation for treatment among people with substance use problems is an important aspect of treatment success. Models for treatment motivation are widely debated. Latent Class Analysis can help to demonstrate the appropriateness of available models. The current study utilizes Latent Class Analysis to analyze treatment readiness statements as they relate to the reduction or cessation of marijuana, cocaine, and opioid use among 539 cocaine and opioid users recruited from the community of Baltimore, MD, USA. Participants completed an in-person structured interview including demographic questions, a treatment readiness questionnaire with items on Intention to Stop Use (ISU) and Problem Recognition (PR), current substance abuse treatment utilization, and urinalysis testing for marijuana, cocaine, and heroin. Latent class models were fit to the treatment readiness questionnaire. A four-class model provided the best fit with one class low on both ISU and PR ("Pre-contemplative"), a second class low on ISU, but high on PR ("Contemplative"), a third class high on both ("Preparation/Action"), and a final class high on ISU, but low on PR ("Post-Action"). Compared to the "Contemplative" class, the "Precontemplative" class was significantly more likely to be positive for marijuana, and the "Post-Action" class was significantly less likely to be positive for opioids. The "Preparation/Action" class was significantly more likely to be in treatment. With the exception of the "Post-Action" class, the analysis appears similar to the "Stages of Change" model and suggests that problem recognition and intention to stop use are important domains in the model. However, further longitudinal research is needed to assess predictive validity of model.

O artigo busca identificar e analisar os principais fatores que impactam a confiança na polícia em Minas Gerais. Utilizou-se a pesquisa "Vitimização e Percepção de Medo em Belo Horizonte e Minas Gerais" de 2009, aplicada em 29 municípios. Além de Belo Horizonte, abrangeu 5 cidades da Região Metropolitana, 7 cidades consideradas polos regionais e 16 municípios com população inferior a 10 mil habitantes. Os resultados mostram que indivíduos confundem funções do trabalho policial com as do sistema de justiça criminal. O contato com a polícia reduz a confiança, especialmente quando ele é iniciado por policiais. Nas cidades menores, onde a polícia é mais próxima dos cidadãos e a criminalidade é menor, a polícia recebe mais confiança do que nas maiores. A percepção de eficiência em solucionar problemas relacionados à violência é a variável que produz maior efeito positivo no nível de confiança que a instituição recebe.

Motivation for treatment among people with substance use problems is an important aspect of treatment success. Models for treatment motivation are widely debated. Latent Class Analysis can help to demonstrate the appropriateness of available models. The current study utilizes Latent Class Analysis to analyze treatment readiness statements as they relate to the reduction or cessation of marijuana, cocaine, and opioid use among 539 cocaine and opioid users recruited from the community of Baltimore, MD, USA. Participants completed an in-person structured interview including demographic questions, a treatment readiness questionnaire with items on Intention to Stop Use (ISU) and Problem Recognition (PR), current substance abuse treatment utilization, and urinalysis testing for marijuana, cocaine, and heroin. Latent class models were fit to the treatment readiness questionnaire. A four-class model provided the best fit with one class low on both ISU and PR ("Pre-contemplative"), a second class low on ISU, but high on PR ("Contemplative"), a third class high on both ("Preparation/Action"), and a final class high on ISU, but low on PR ("Post-Action"). Compared to the "Contemplative" class, the "Precontemplative" class was significantly more likely to be positive for marijuana, and the "Post-Action" class was significantly less likely to be positive for opioids. The "Preparation/Action" class was significantly more likely to be in treatment. With the exception of the "Post-Action" class, the analysis appears similar to the "Stages of Change" model and suggests that problem recognition and intention to stop use are important domains in the model. However, further longitudinal research is needed to assess predictive validity of model.

The common causes (CC) approach is popular in psychopathology research, but nowadays, some experts consider this approach unfit to explain mental disorders. On the other hand, as a new approach, the network approach (NA) claims can provide a better explanation for understanding mental disorders. This study aims to determine the differences between NA and CC approach in psychopathology research. This research is a scoping review study using twelve articles. We searched for articles September-December 2020 in the Scopus and Science Direct databases. The results of the analysis of these articles show a fundamental difference between the two perspectives. The difference lies in the perspective of mental disorders, how to measure symptoms of mental disorders, treatment of mental disorders, and views on comorbidities. Each approach has advantages, although some articles support that NA is the better approach to studying mental disorders. To decide which approach is appropriate for psychopathology research, researchers should adapt it to the research objectives. Perspectives on psychopathology will determine how to answer the research questions and analyze the data.

Individuals form their self-efficacy beliefs by interpreting information from four sources: mastery experience, vicarious experience, social persuasions and physiological or affective states (Bandura, 1986, 1997). Efficacy beliefs influence whether people think erratically or strategically, optimistically or pessimistically. Predictive power of academic self-efficacy is attested by ample researches (Locke, Frederick, Lee & Bobko, 1984; Schunk, 1984) but few efforts have been made to investigate sources underlying these beliefs (Pajares & Urdan, 2006). This study examined the heuristics students use as they form their mathematics self-efficacy from these and other sources. The study also made attempt to
look into more about the factors relating to self-efficacy. Attempts were made to study the influence of the experiences students get in developing their self-efficacy beliefs. The questionnaire was administered to 100 students of 8th grade of both Sarvodaya Schools and Public Schools in Delhi. Results were analyzed quantitatively using t-test and correlation
coefficient. Results suggested mastery experiences, to be the most powerful source in affecting self-efficacy beliefs whereas the physiological factors showed a negative correlation. The present study can be further extended to give implications for student’s higher education and career choices in the future.

Longitudinal studies typically suffer from incompleteness of data. Attrition is a major problem in studies of older persons since participants may die during the study or are too frail to participate in follow-up examinations. Attrition is typically related to an individual's health; therefore, ignoring it may lead to too optimistic inferences, for example, about cognitive decline or changes in polypharmacy. The objective of this study is to compare the estimates of level and slope of change in 1) cognitive function and 2) number of drugs in use between the assumptions of ignorable and non-ignorable missingness. This study demonstrates the usefulness of latent variable modeling framework. The results suggest that when the missing data mechanism is not known, it is preferable to conduct analyses both under ignorable and non-ignorable missing data assumptions.

A mixed-effects location scale model allows researchers to study within- and between-person variation in repeated measures. Key components of the model include separate variance models to study predictors of the within-person variance, as well as predictors of the between-person variance of a random effect, such as a random intercept. In this paper, a latent variable mixed-effects location scale model is developed that combines a longitudinal common factor model and a mixed-effects location scale model to characterize within- and between-person variation in a common factor. The model is illustrated using daily reports of positive affect and daily stressors for a large sample of adult women.

In recent years, the concern for durable electrical and electronic products as well as the recovery and recycling of electrical and electronic waste has increased, simultaneously with new initiatives aimed not only at protecting the environment, but ensuring consumer's health, while consolidating their fundamental rights. The research aims to identify the factors that influence the sustainable behaviour of the Romanian consumer of electrical and electronic products. In this sense, the ecological attitude, the purchasing behaviour of these goods, and also the recovery/recycling attitude of electrical and electronic waste were taken into account. Data were collected through an online questionnaire from 421 Romanian respondents and processed with the lavaan software package version 0.6-12 of R for structural equation modelling. The novelty of the article lies in how variables such as propensity to recover/recycle, tendency of purchasing durable goods, ecological behaviour, are put in a new context, combining environmental and consumers' protection. The research makes theoretical contributions by proposing and testing specific consumer protection constructs, which do not appear in the literature. From the environmental public policies point of view, the work highlights the factors that can contribute to the development of a responsible behaviour of both the population and public authorities, to stimulate the purchase of sustainable goods, and to ensure an increase in the collection rate of electrical and electronic products in Romania.

Adolescent pregnancy is associated with poor foetal growth and development which increase the risk of childhood wasting and underweight. However, evidence on how young maternal age affects childhood anthropometry beyond the neonatal period is limited. This systematic review and meta-analysis examined associations between adolescent pregnancy and child wasting and underweight and explored potential underlying social and biological factors. Peer-reviewed literature published in English since 1990 was systematically searched. Eligible studies presented data on wasting and/or underweight in children (≤59 months) born to adolescent mothers (10-19, or ≤24 years where applicable) from low- and middle-income countries. Data extraction used a predefined extraction sheet. Both meta-analysis and qualitative synthesis were performed. Of 92 identified studies, 57 were included in the meta- analysis. The meta-analysis showed that children born to adolescent versus adult mothers were at a higher r...

Recently disordered gambling is reclassified as an addictive disorder which inter alia affects a little but significant proportion of adolescents. The aim of this study is to identify and assess different levels of gambling severity among adolescent gamblers (N=1157) from middle and high schools of Korca region, utilizing a cross-sectional design and self-report questionnaire. Using Exploratory, Parallel, Reliability and Confirmatory Analysis, the PGSI measurement instrument, designed for these purposes, resulted to be an appropriate unidimensional screening tool of adolescent disordered gambling in terms of psychometric properties. A traditional Latent Class Analysis using the nine PGSI-items as indicators is performed to identify and predict subtypes of adolescent gamblers, classifying them into latent classes based on their problem gambling severity levels. The inclusion of three covariates related to adolescent gambling problems into Latent Class Model improved the model, helpin...

Esta pesquisa tem como objetivo mensuar o valor percebido em serviços de alimentação e visou quantificar o impacto do valor percebido em diferentes constructos posteriores ao consumo de serviços alimentícios no Brasil, sendo testados a satisfação, a intenção de recompra e a recomendação. Para tal, um modelo denominado SERV-PERVAL-PP (Service Perceived Value - Post Purchase) foi desenvolvido por meio de modelagem de equações estruturais com 400 consumidores de fast food. O modelo proposto apresentou boa adequabilidade estatística e videnciou em seus resultados que as alterações nas dimensões que compõem o valor percebido causam impactos nos constructos posteriores ao consumo, tal achado possibilita aos gestores identificarem quais são os elementos mercadológicos ligadas à percepção de valor dos clientes que possuem impacto posterior ao consumo de serviços alimentícios. Essa compreensão possibilita aos gestores operacionalizarem os componentes que compõem o valor percebido visando ampliar o nível dos constructos mercadológicos que são considerados abstratos.

Some robustness questions in structural equation modeling (SEM) are introduced. Factors that affect the occurrence of nonconvergence and improper solutions are reviewed in detail. Recent research on the behaviour of estimators for parameters, standard errors and model fit, under conditions of (non)normality, is summarized. It is emphasized that both model and sample data characteristics affect the statistical behaviour of these estimators. This knowledge may be used to set guidelines for a combined choice of sample size and estimation method. It is concluded that for large models, under a variety of nonnormal conditions, (robust) maximum likelihood estimators have relatively good statistical properties compared to other estimators (GLS, ERLS, ADF or WLS). The cumulative theoretical knowledge about robust (asymptotic) estimators and corrective statistics and the availability of practical guidelines from robustness research together, may enhance statistical practice in SEM and hence lead to more sensible and solid applied research.

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

In this work we derive the exact joint distribution of linear combinations of order statistics and linear combinations of their concomitants and some auxiliary variables in multivariate normal distribution. By extending the results of Sheikhi and Jamalizadeh we investigate some regression equations. Our results generalize those obtained in previous research by Viana, Lee and Loperfido.

Despite an increasingly large body of research that focuses on the potential demand for autonomous vehicles (AVs), risk preference is an understudied factor. Given that AV technology and how it will interact with the evolving mobility system are highly risky, this lack of research on risk preference is a critical gap in current understanding. By using a stated preference survey of 1,142 individuals from Singapore, this study achieves three objectives. First, it develops one measure of psychometric risk preference and operationalizes prospect theory to create two economic risk preference parameters. Second, it examines how these psychometric and economic risk preferences are associated with socioeconomic variables. Third, it analyzes how risk preference influences the mode choice of AVs. The study finds that risk preference parameters are significantly associated with socioeconomic variables: the elderly, poor, females, and unemployed Singaporeans appear more risk-averse and tend to overestimate small probabilities of losses. Furthermore, all three risk preference parameters contribute to the prediction of AV adoption. These modeling results have policy implications at both the aggregate and disaggregate levels. At the aggregate level, people misperceive probabilities, are overall risk-averse, and hence under-consume AVs relative to the social optimum. At the disaggregate level, the elderly, poor, female, and unemployed are more risk-averse and thus are less likely to adopt AVs. These results suggest that it might be valuable for governments to implement policies to encourage technology adoption, particularly for disadvantaged social groups, although caution remains due to uncertainty in the long-term effects of AVs. Individualized risk preference parameters could also inform how to design regulations, safety standards, and liability allocations of AVs since many regulations are essentially mechanisms for risk allocation. One limitation of the paper is that risk preference is measured and modeled only as individual-specific but not alternative-specific variables. Future studies should examine the relationship between the multiple components of risk preference and the multiple risky aspects of AVs.

A small archive of texts from ancient Iraq is used to demonstrate an approach to network analysis in which traditional close reading and computational text analysis go hand-in-hand. The computational methods produce tables and graphs that link back to online editions of the primary material, enabling the user to check the results.

Prevalence of extramedical opioid analgesic use in the US is rising, yet little is known about the nature and extent of problems of dependence related to the use of these drugs. This study uses Latent Class Analysis to empirically define classes of past‐year extramedical opioid analgesic users based on observed clustering of DSM‐IV defined clinical dependence features; multinomial logistic regression is used to describe differences across these groups. The 2002–2003 public data‐files of the National Survey on Drug Use and Health were used to identify 7810 extramedical opioid analgesic users in the past‐year. The best‐fitting four‐class model identified classes that differed quantitatively and qualitatively, with 2% of the users in Class 4 (most severe) and 84% in Class 1 (least severe). Classes 2 and 3 had parallel symptom profiles, but those in Class 3 reported additional problems. Adolescents (12–17 year olds) were at higher odds of being in Class 3 versus older age groups; female...

St~mmay.-The present study aimed to assess the effect of psychological screening of medical inpatients using the Symptom Check List, SCL-90-R. A sample of 630 medical inpatients who were referred to a psychosomatic consultation-liaison service was assessed using the SCL-90-R. A clinical interview was conducted with all assessed pauents. Patients reEerred to the consultation-liaison service showed high psychological distress. 73% of the subjects were classed as cases using the International Classifcation of Diseases (10th Revision) criteria and psychological impairment. The screening characteristics of the SCL-9&R were not as good as those reported for ocher outpatient samples. The SCL-90-R appears to have been valuable for the consultation-liaison service since subjects with mental disorders scored significantly higher on almost all the scales than subjects wichout mental disorders. The questionnaire can serve as a useful tool for understanding patients' current status r c g r d~n g mental health problems.

Ridder and Woutersen (2003) have shown that under a weak condition on the baseline hazard there exist root-N consistent estimators of the parameters in a semiparametric Mixed Proportional Hazard model with a parametric baseline hazard and unspecified distribution of the unobserved heterogeneity. We extend the Linear Rank Estimator (LRE) of Tsiatis (1990) and Robins and Tsiatis (1991) to this class of models. The optimal LRE is a two-step estimator. We propose a simple first-step estimator that is close to optimal if there is no unobserved heterogeneity. The efficiency gain associated with the optimal LRE increases with the degree of unobserved heterogeneity.

Objective: Loneliness is associated with negative mental health outcomes and is particularly common among adolescents. Yet, little is known about the dynamics of adolescent loneliness in non-Western, low-income nations. Accordingly, we estimated the severity of loneliness in a sample of Kenyan adolescents and used mixed-effects regression modelling to determine the socio-cultural factors associated with loneliness. We also used network analysis to examine the associations between loneliness, depression, and anxiety at the symptom-level. Method: We analyzed data from a large sample (N = 2,192) of school-attending Kenyan adolescents aged 12-19 (58.3% Female, 41.7% Male). Standardized measures of loneliness (ULS-8), depression (PHQ-8), and anxiety (GAD-7) were used. Results: Our mixed-effects model revealed that female and lower-income adolescents felt lonelier. The perception of feeling alone emerged as the aspect of loneliness most strongly linked to depression and anxiety symptoms. Conclusions: Our findings establish an estimate of loneliness levels in Kenyan adolescents, and reveal sociocultural factors associated with loneliness. We found that perceptions of isolation more strongly linked loneliness to psychopathology than did objective measures of isolation or preferences for social contact. Finally, we identify specific aspects of loneliness that could prove to be treatment targets for youth psychopathology; however, further research is needed. Limitations, future directions, and clinical implications are discussed.

Objective: Loneliness is associated with negative mental health outcomes and is particularly common among adolescents. Yet, little is known about the dynamics of adolescent loneliness in non-Western, low-income nations. Accordingly, we estimated the severity of loneliness in a sample of Kenyan adolescents and used mixed-effects regression modelling to determine the socio-cultural factors associated with loneliness. We also used network analysis to examine the associations between loneliness, depression, and anxiety at the symptom-level. Method: We analyzed data from a large sample (N = 2,192) of school-attending Kenyan adolescents aged 1219 (58.3% Female, 41.7% Male). Standardized measures of loneliness (ULS-8), depression (PHQ-8), and anxiety (GAD-7) were used. Results: Our mixed-effects model revealed that female and lower-income adolescents felt lonelier. The perception of feeling alone emerged as the aspect of loneliness most strongly linked to depression and anxiety symptoms. C...

The guiding idea in this paper was to study how scientific reasoning correlates with epistemological beliefs and academic achievement. Based on the study at hand, we argue that the nature of epistemological beliefs is still more the product of the level of scientific reasoning than the other way round. As a practical implication we propose that teacher training programmes should consider epistemological beliefs not only as an important aspect that all learners and teachers should be aware of, but it should be recognised as a central factor that affects our formal and informal learning and teaching in all content areas of knowledge.