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Measurement errors occur very commonly in practice. After fitting the model, influence diagnostics is an important step in statistical data analysis. The most frequently used diagnostic method for measurement error models is the local influence. However, this methodology may fail to detect masked influential observations. To overcome this limitation, we propose the use of the conformal normal curvature with the forward search algorithm. The results are presented through easy to interpret plots considering different perturbation schemes. The proposed methodology is illustrated with three real data sets and one simulated data set, two of which have been previously analyzed in the literature. The third data set deals with the stability of the hygroscopic solid dosage in pharmaceutical processes to ensure the maintenance of product safety quality. In this application, the analytical mass balance is subject to measurement errors, which require attention in the modeling process and diagnostic analysis.

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Considering the context of functional data analysis, we developed and applied a new Bayesian approach via the Gibbs sampler to select basis functions for a finite representation of functional data. The proposed methodology uses Bernoulli latent variables to assign zero to some of the basis function coefficients with a positive probability. This procedure allows for an adaptive basis selection since it can determine the number of bases and which ones should be selected to represent functional data. Moreover, the proposed procedure measures the uncertainty of the selection process and can be applied to multiple curves simultaneously. The methodology developed can deal with observed curves that may differ due to experimental error and random individual differences between subjects, which one can observe in a real dataset application involving daily numbers of COVID-19 cases in Brazil. Simulation studies show the main properties of the proposed method, such as its accuracy in estimating the coefficients and the strength of the procedure to find the true set of basis functions. Despite having been developed in the context of functional data analysis, we also compared the proposed model via simulation with the well-established LASSO and Bayesian LASSO, which are methods developed for non-functional data.

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Introducción. Debido a la poca evidencia sobre el modelamiento de los patrones de alimentación y actividad física (AF), basado en variables latentes, el presente estudio de revisión pretende describir las técnicas estadísticas aplicadas para modelar estos patrones en niños y adolescentes y valorar su calidad metodológica. Materiales y métodos. La búsqueda se realizó en bases de datos electrónicas (Science Direct, PubMed, SCOPUS, Web of science y Cochrane) con las palabras "diet", 'physical activity', children y 'latent variable'. Se incluyeron artículos que utilizaron modelos estadísticos basados en variables latentes para analizar patrones de alimentación y AF en niños y adolescentes sanos, publicados entre 2014­2019, en inglés o español. Resultados. Entre los 27 artículos seleccionados, el Modelo de Ecuaciones Estructurales (MEE) fue el más utilizado (77,78%); seguido del Modelo de Perfil Latente (7,41%), mientras, el restante, 14,81% aplican el Modelo del Factor Común, Modelo Ecológico y el Modelo de Regresión Logística Multinivel. El MEE fue aplicado a 12 de los 16 artículos con enfoque de AF, y en 7 de los 9 artículos con enfoque de Alimentación. El 48,15% de los estudios sí justificaba el uso del modelo, y el 37,04% poseen una calidad "Excelente" (cumplen el 85% o más de los ítems de STROBE). Conclusiones. El MEE fue el más utilizado para abstraer los patrones de AF y alimentación en niños y adolescentes, sin embargo, solo la mitad de los artículos justifica su pertinencia. Las guías de reporte de estudios deberían evaluar la calidad metodológica de los modelos estadísticos aplicados(AU)

Introduction. Due to the limited evidence on the modeling of eating and physical activity (PA) patterns based on latent variables, the present review study aims to describe the statistical techniques applied to model these patterns in children and adolescents and to assess their methodological quality. Materials and methods. The search was performed in electronic databases (Science Direct, PubMed, SCOPUS, Web of science and Cochrane) with the words 'diet', 'physical activity', children and 'latent variable'. We included articles that used statistical models based on latent variables to analyze diet and PA patterns in healthy children and adolescents, published between 2014-2019, in English or Spanish. Results. Among the 27 selected articles, the Structural Equation Model (SEM) was the most used (77.78%); followed by the Latent Profile Model (7.41%), while, the remaining 14.81% applied the Common Factor Model, Ecological Model and Multilevel Logistic Regression Model. The SEM was applied to 12 of the 16 articles with PA approach, and in 7 of the 9 articles with eating approach. The 48.15% of studies did justify the use of the model, and 37.04% were classified as "Excellent" quality (meet 85% or more of the STROBE items). Conclusions. The SEM was the most commonly used to model the PA and eating patterns in children and adolescents, however, only half of the articles justify their relevance. Study reporting guidelines should evaluate the methodological quality of the statistical models applied(AU)

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Background: Recent efforts have been made to collect data on neighborhood-level attributes and link them to longitudinal population-based surveys. These linked data have allowed researchers to assess the influence of neighborhood characteristics on the health of older adults in the US. However, these data exclude Puerto Rico. Because of significantly differing historical and political contexts, and widely ranging structural factors between the island and the mainland, it may not be appropriate to apply current knowledge on neighborhood health effects based on studies conducted in the US to Puerto Rico. Thus, we aim to (1) examine the types of neighborhood environments older Puerto Rican adults reside in and (2) explore the association between neighborhood environments and all-cause mortality. Methods: We linked data from the 2000 US Census to the longitudinal Puerto Rican Elderly Health Conditions Project (PREHCO) with mortality follow-up through 2021 to examine the effects of the baseline neighborhood environment on all-cause mortality among 3,469 participants. Latent profile analysis, a model-based clustering technique, classified Puerto Rican neighborhoods based on 19 census block group indicators related to the neighborhood constructs of socioeconomic status, household composition, minority status, and housing and transportation. The associations between the latent classes and all-cause mortality were assessed using multilevel mixed-effects parametric survival models with a Weibull distribution. Results: A five-class model was fit on 2,477 census block groups in Puerto Rico with varying patterns of social (dis)advantage. Our results show that older adults residing in neighborhoods classified as Urban High Deprivation and Urban High-Moderate Deprivation in Puerto Rico were at higher risk of death over the 19-year study period relative to the Urban Low Deprivation cluster, controlling for individual-level covariates. Conclusions: Considering Puerto Rico's socio-structural reality, we recommend that policymakers, healthcare providers, and leaders across industries to (1) understand how individual health and mortality is embedded within larger social, cultural, structural, and historical contexts, and (2) make concerted efforts to reach out to residents living in disadvantaged community contexts to understand better what they need to successfully age in place in Puerto Rico.

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BACKGROUND: Anthropometric measurements of healthy children differ in different parts of the world due to the diverse ethnicity and cultural backgrounds of families. In longitudinal studies, appropriate modeling of repeated anthropometric measures can improve the understanding of patterns of change, determinants of patterns, and variations in patterns of change over time. The objective of this study was to examine the latent change in physical height of children in Ethiopia, India, Peru, and Vietnam. METHOD: Longitudinal data of 6601 children aged 1 to 15 years were obtained from the Young Lives cohort study. The data were analyzed using a latent basis growth curve model. RESULTS: The findings of the study revealed that the rates of growth did not remain constant across the time intervals, which indicates the nonlinearity of the growth trajectory over time. For instance, children had the highest rate of growth between age 1 and 5 years, then between age 8 and 12 years, and a low rate of growth was observed between age 12 and 15 years. At the first measurement occasion (age 1 year) females were 0.826 cm (p < 0.0001) times shorter than males. The mean height at one year of age ranged from 72.13 cm in Ethiopia to 72.62 cm in India. Children in India and Vietnam had higher mean height at age one year. However, no significant difference in mean height at age one year was found between Ethiopian and Peruvian children, ([Formula: see text]). Peruvian and Vietnamese children grew at a faster rate, while Indian children grew at a slower rate than Ethiopian children. CONCLUSION: We found substantial latent growth variations among children in four low- and middle-income countries. The latent trajectories differed by gender and country. The outcomes of the study could aid in detecting inequalities in children's height growth.

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Subjective well-being is a major psychological construct in the research tradition. Along with literature, authors have distinguished between hedonic and eudaimonic well-being. The aim of this study is to determine the role of some psychosocial variables plays in the perceived well-being is conceived from a hedonic or a eudaimonic perspective. The sample consisted of 1016 people of 55 years and older in a Spanish sample and 277 people of 55 years old or older from a Costa Rican sample. Both samples were part of the Longitudinal Older Learners (LOL) study. A structural model with latent variables was estimated with Mplus. The results point out that, the traditional variables included on successful aging models are relevant for explaining older adults' well-being in both countries with some differences on the hedonic and eudaimonic approach of successful aging on the Latin cultural context studied.

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Resumen: El objetivo de este trabajo es contribuir a la comprensión de la relación entre la memoria de trabajo y la inteligencia fluida. Para esto se realizó una búsqueda de artículos empíricos en las bases de datos PubMed y Google Académico utilizando una combinación de los términos en español y en inglés memoria de trabajo (working memory) e inteligencia fluida (fluid intelligence). La revisión de la literatura indica que el control atencional y la capacidad de recuperar información de la memoria a largo plazo, serían los procesos a través de los cuales la memoria de trabajo y la inteligencia fluida se relacionan. Sin embargo, existen resultados contradictorios respecto del rol que la retención de la información y que la capacidad de procesamiento de la información presentan en esta relación. Se concluye señalando algunos aspectos metodológicos que mejorarían la comprensión de la relación entre la memoria de trabajo y la inteligencia fluida.

Abstract: The aim of this paper is to contribute to the understanding of the relationship between working memory and fluid intelligence. A search was conducted in PubMed and Google Scholar database using a combination of the Spanish and English terms working memory (memoria de trabajo) and fluid intelligence (inteligencia fluida). The review indicates that attention control and the ability to retrieve information from long-term memory would be the processes through which working memory and fluid intelligence are related. However, there are conflicting results regarding the role of retention of information and information processing capacity in this relationship. We concluded by pointing out some methodological aspects that would enhance the understanding of the relationship between working memory and fluid intelligence.

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Los modelos de variables latentes y los modelos cognitivos formales comparten algunos aspectos de su objeto de estudio, varios aspectos filosóficos y parte de su metodología. A pesar de ello, existe poca comunicación entre sus teorías y hallazgos. Con el objetivo de resaltar similitudes y diferencias entre ambos enfoques en este estudio se implementó y evaluó un modelo formal que propone la interferencia entre representaciones como el mecanismo que limita la capacidad de memoria de trabajo (i.e., el modelo de interferencia de Oberauer & Kliegl, 2006). Además, se incorporó una manipulación experimental que tuvo como objetivo evaluar el papel de la inhibición en respuestas inminentes y el desplazamiento de la atención entre tareas en el contexto del modelo de interferencia. A partir de estos hallazgos se expusieron algunas conexiones que podrían facilitar el acercamiento entre los modelos cognitivos formales y los modelos psicométricos basados en la teoría de la variable latente.

Latent variable models and formal cognitive models share some elements of their object of study, various philosophical aspects, and some parts of their methodology. Nevertheless, little communication exists between their theories and findings. In order to highlight similarities and differences, this study implemented and tested a formal model proposing that interference among representations is a mechanism limiting working memory capacity (i.e., the interference model of Oberauer & Kliegl, 2006). Furthermore, the study incorporated an experimental manipulation to evaluate the role of the inhibition in prepotent responses and task switching in the interference model framework. These findings were used to expose some connections that could facilitate a rapprochement between formal cognitive models and psychometric models based on the latent variable theory.

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Objective:To test the four-factor model of the Hare Psychopathy Checklist - Revised (PCL-R) empirical construct of psychopathy in a Chilean prison population by using instruments that supply different types of data.Methods:Two hundred and nine male inmates of the Prison of Los Andes, Chile, were evaluated. Confirmatory factor analysis was carried out with the PCL-R and the Self-Report of Psychopathy - III - Short Form (SRP-III-SF).Results:The distributions of total PCL-R and SRP-III-SF scores were normal (Kolmorogov-Smirnov [K-S] = 1.04, p = 0.230; K-S = 0.812, p = 0.525, respectively), with means of 20.9±6.8 for the former and 61.6±15.2 for the latter. Model fit was good for the PCL-R (Tucker Lewis index [TLI] = 0.96; root mean square error of approximation [RMSEA] = 0.04) and for the SRP-SF (TLI = 0.94, RMSEA = 0.04).Conclusions:The results obtained with the PCL-R and the SRP-SF showed adequate fit to the empirical four-factor model of psychopathy and support this model. As foreseeable, fit was better for the PCL-R, which combines several sources of information.

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Apesar de relativo consenso acerca da existência de três funções executivas (FE) básicas, inibição, memória de trabalho e flexibilidade cognitiva, há menor conhecimento sobre sua organização e contribuição à solução de tarefas. O estudo testou diferentes modelos teóricos acerca da estrutura e composição das FE. Utilizou-se a matriz de correlação de Miyake et al. (2000) que avaliou universitários em 15 tarefas de FE. O modelo 1 demonstra um fator geral e componentes específicos. No modelo 2, componentes básicos agrupam-se sob um componente geral FE, e, no modelo 3, os componentes memória de trabalho e inibição são hierarquicamente mais básicos do que a flexibilidade. O modelo 2 apresentou melhor ajuste aos dados. Uma quarta variá vel latente foi relevante e contribuiu para duas tarefas. Sustenta-se um modelo hierárquico, isto é, um fator FE geral e componentes específicos. A comparação de modelos é uma estratégia relevante para a compreensão destes em neuropsicologia.

Despite relative consensus on the existence of three basic executive functions (EF) (inhibition, working memory and cognitive flexibility) there is narrower knowledge on its organization and contribution to task solution. The study tested different theoretical models about the structure and composition of EF. The correlation matrix of Miyake et al. (2000), which evaluated university students in a set of 15 EF tasks, was adopted. Model 1 displays a general factor and specific components. In model 2, the basic components are grouped under a general EF component and, in model 3, the working memory and inhibition components are hierarchically more basic than flexibility. Model 2 showed a better fit to the data. A fourth latent variable was significant and contributed to two tasks. Our findings support a hierarchical model, i.e., a general EF factor and specific components. The comparison of models is a relevant strategy for their understanding in neuropsychology.

A pesar de relativo consenso sobre la existencia de tres funciones ejecutivas (FE) básicas, inhibición, memoria de trabajo y la flexibilidad cognitiva, hay menos conocimiento sobre su organización y cómo contribuyen a la solución de tareas. El estudio probó diferentes modelos teóricos sobre la estructura y composición de las FE. Se utilizó la matriz de correlación de Miyake et al. (2000), que evaluó universitarios en 15 tareas de FE. El modelo 1 demostró un factor general y constituyentes específicos. En el modelo 2, los componentes básicos se agrupan bajo un componente general de FE, y, en el modelo 3, los componentes de memoria de trabajo y de inhibición son jerárquicamente más básicos que la flexibilidad. El Modelo 2 mostró un mejor ajuste a los datos. Una cuarta variable latente fue relevante y contribuyó a dos tareas. El estudio sostiene un modelo jerárquico, es decir, un factor general de FE y componentes específicos. La comparación de los modelos es una estrategia relevante para la comprensión de estos en neuropsicología.

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The analysis of the effects that mobile phone use produces while driving is a topic of great interest for the scientific community. There is consensus that using a mobile phone while driving increases the risk of exposure to traffic accidents. The purpose of this research is to evaluate the drivers' behavior when they decide whether or not to use a mobile phone while driving. For that, a hybrid modeling approach that integrates a choice model with the latent variable "risk perception" was used. It was found that workers and individuals with the highest education level are more prone to use a mobile phone while driving than others. Also, "risk perception" is higher among individuals who have been previously fined and people who have been in an accident or almost been in an accident. It was also found that the tendency to use mobile phones while driving increases when the traffic speed reduces, but it decreases when the fine increases. Even though the urgency of the phone call is the most important explanatory variable in the choice model, the cost of the fine is an important attribute in order to control mobile phone use while driving.

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Many existing cohort studies designed to investigate health effects of environmental exposures also collect data on genetic markers. The Early Life Exposures in Mexico to Environmental Toxicants project, for instance, has been genotyping single nucleotide polymorphisms on candidate genes involved in mental and nutrient metabolism and also in potentially shared metabolic pathways with the environmental exposures. Given the longitudinal nature of these cohort studies, rich exposure and outcome data are available to address novel questions regarding gene-environment interaction (G × E). Latent variable (LV) models have been effectively used for dimension reduction, helping with multiple testing and multicollinearity issues in the presence of correlated multivariate exposures and outcomes. In this paper, we first propose a modeling strategy, based on LV models, to examine the association between repeated outcome measures (e.g., child weight) and a set of correlated exposure biomarkers (e.g., prenatal lead exposure). We then construct novel tests for G × E effects within the LV framework to examine effect modification of outcome-exposure association by genetic factors (e.g., the hemochromatosis gene). We consider two scenarios: one allowing dependence of the LV models on genes and the other assuming independence between the LV models and genes. We combine the two sets of estimates by shrinkage estimation to trade off bias and efficiency in a data-adaptive way. Using simulations, we evaluate the properties of the shrinkage estimates, and in particular, we demonstrate the need for this data-adaptive shrinkage given repeated outcome measures, exposure measures possibly repeated and time-varying gene-environment association.

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Researchers dealing with the task of estimating locations of individuals on continuous latent variables may rely on several statistical models described in the literature. However, weighting costs and benefits of using one specific model over alternative models depends on empirical information that is not always clearly available. Therefore, the aim of this simulation study was to compare the performance of seven popular statistical models in providing adequate latent trait estimates in conditions of items difficulties targeted at the sample mean or at the tails of the latent trait distribution. Results suggested an overall tendency of models to provide more accurate estimates of true latent scores when using items targeted at the sample mean of the latent trait distribution. Rating Scale Model, Graded Response Model, and Weighted Least Squares Mean- and Variance-adjusted Confirmatory Factor Analysis yielded the most reliable latent trait estimates, even when applied to inadequate items for the sample distribution of the latent variable. These findings have important implications concerning some popular methodological practices in Psychology and related areas. (AU)

Pesquisadores interessados em estimar a localização de indivíduos em variáveis latentes contínuas podem se beneficiar de diversos modelos estatísticos disponíveis na literatura. Entretanto, ponderar os custos e os benefícios de usar um modelo em detrimento de outros depende de informações empíricas que nem sempre estão diretamente disponíveis. Em virtude disso, o objetivo deste estudo foi comparar o desempenho de sete modelos estatísticos populares quanto a proporcionar adequadas estimativas de traço latente em condições de itens com dificuldades condizentes com a distribuição latente amostral versus apenas condizentes com as caudas dessa distribuição. Os resultados sugeriram uma tendência de todos os modelos de proporcionar estimativas mais precisas ao serem usados itens adequados para o nível de traço latente da amostra. Os modelos da Teoria de Resposta ao Item Rating Scale e Graded Response e a análise fatorial confirmatória com estimação Weighted Least Squares Mean- and Variance-adjusted forneceram as estimativas mais fidedignas de traço latente, mesmo quando os itens utilizados, de fato, correspondiam ao nível latente de poucos casos da amostra. Os resultados possuem importantes implicações no que diz respeito a algumas práticas metodológicas populares na Psicologia e em áreas próximas. (AU)

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