CROSS-LINGUISTIC TRANSFER OF SPELLING SKILLS IN SPANISH-SPEAKING ADULT ESL LEARNERS

Spelling is an important literacy skill, and learning to spell is an important component of learning to write. Learners with strong spelling skills also exhibit greater reading, vocabulary, and orthographic knowledge than those with poor spelling skills (Ehri & Rosenthal, 2007; Ehri & Wilce, 1987; Rankin, Bruning, Timme, & Katkanant, 1993). English, being a deep orthography, has inconsistent sound-to-letter correspondences (Seymour, 2005; Ziegler & Goswami, 2005). This poses a great challenge for learners in gaining spelling fluency and accuracy. The purpose of the present study is to examine cross-linguistic transfer of English vowel spellings in Spanish-speaking adult ESL learners. The research participants were 129 Spanish-speaking adult ESL learners and 104 native English-speaking GED students enrolled in a community college located in the South Atlantic region of the United States. The adult ESL participants were in classes at three different levels of English proficiency: advanced, intermediate, and beginning. An experimental English spelling test was administered to both the native English-speaking and ESL participants. In addition, the adult ESL participants took the standardized spelling tests to rank their spelling skills in both English and Spanish. The data were analyzed using robust regression and Poisson regression procedures, Mann-Whitney test, and descriptive statistics. The study found that both Spanish spelling skills and English proficiency are strong predictors of English spelling skills. Spanish spelling is also a strong predictor of level of L1-influenced transfer. More proficient Spanish spellers made significantly fewer L1-influenced spelling errors than less proficient Spanish spellers. L1-influenced transfer of spelling knowledge from Spanish to English likely occurred in three vowel targets (/ɑɪ/ spelled as ae, ai, or ay, /ɑʊ/ spelled as au, and /eɪ/ spelled as e). The ESL participants and the native English-speaking participants produced highly similar error patterns of English vowel spellings when the errors did not indicate L1-influenced transfer, which implies that the two groups might follow similar trajectories of developing English spelling skills. The findings may help guide future researchers or practitioners to modify and develop instructional spelling intervention to meet the needs of adult ESL learners and help them gain English spelling competence.

Social security status and mortality in Belgian and Spanish male workers

Objective: To assess differences in mortality rates between social security statuses in two independent samples of Belgian and Spanish male workers. Methods: Study of two retrospective cohorts (Belgium, n = 23,607; Spain, n = 44,385) of 50-60 year old male employees with 4 years of follow-up. Mortality rate ratios (MRR) were estimated using Poisson regression models. Results: Mortality for subjects with permanent disability was higher than for the employed, for both Belgium [MRR = 4.56 (95% CI: 2.88-7.21)] and Spain [MRR = 7.15 (95% CI: 5.37-9.51)]. For the unemployed/early retirees, mortality was higher in Spain [MRR = 1.64 (95% CI: 1.24-2.17)] than in Belgium [MRR = 0.88 (95% CI: 0.46-1.71)]. Conclusion: MRR differences between Belgium and Spain for unemployed workers could be partly explained because of differences between the two social security systems. Future studies should further explore mortality differences between countries with different social security systems.

Factors influencing dental appearance satisfaction in adolescents: a cross-sectional study conducted in Southern Brazil

The perception of dental aesthetic appearance may affect social interaction and psychological status, influencing dental needs and the search for treatments. Aim: To investigate the satisfaction with dental appearance and influencing factors among adolescents. Methods: The study was carried out among adolescents aged 14 to 19 years attending a private high school in Brazil. Data on demographic information, the perception of dental appearance, previous aesthetic treatments and wish to perform dental treatments were collected in the school. Data were analyzed using Pearson’s chi-square test or Linear Trend. Multivariate analysis was performed using the Poisson regression. Results: A total of 531 adolescents (Response rate = 98.3%) answered the questionnaire. The prevalence of dissatisfaction with dental appearance was 17.4%. Almost 65% had history of previous orthodontic treatment and 16% performed dental bleaching. Approximately 45% of children wished to undergo orthodontics and 54.8% to bleach their teeth. Dissatisfaction with dental appearance was associated with individuals unsatisfied with dental color (95% IC[1.73;4.32]), those perceiving poor dental alignment (PR3.16 95% IC[2.11;4.72]) and those wishing orthodontic treatment (PR2.9; 95% IC[1.79; 4.70]). Conclusions: The prevalence of dissatisfaction was considerable and was associated with aesthetic concerns such as tooth color, dental alignment and with the wish for orthodontics. In this young population, a large part of adolescents had already performed orthodontic and bleaching treatments and wished to perform those treatments again. Satisfaction with dental appearance could affect the adolescents’ behavior regarding search for dental treatment, thus causing possible overtreatment.

Estudio de la causalidad entre fecundidad y pobreza en el Ecuador a partir del análisis de datos provinciales, años 2006 y 2014

La presente investigación tiene como objetivo principal determinar la existencia de una relación de causalidad entre Fecundidad y Pobreza en el Ecuador a partir del análisis de datos provinciales para los años 2006 y 2014. Para evaluar la relación de estas variables, se hizo uso de dos modelos econométricos: el Modelo de Regresión Poisson para evaluar el impacto de la Pobreza sobre la Fecundidad; y el Modelo de Regresión Probit para analizar el impacto que tiene la Fecundidad sobre la pobreza. Los modelos mencionados fueron estimados para un total de 13.580 hogares en el año 2006 y 28.399 hogares en el año 2014, datos que fueron obtenidos a partir de la cuarta y quinta versión de la Encuesta de Condiciones de Vida del Ecuador (ECV) realizadas por el INEC. Se encontró una fuerte relación positiva entre las variables mencionadas en ambos años de estudio, sin embargo,debido a la falta de información y a la estructuración de la base de datos empleada no se pudo determinar de forma precisa la existencia de una relación causal entre ambas variables. A pesar de no haberse determinado la dirección de la causalidad es importante mencionar que la influencia que ejerce la Pobreza sobre los niveles de Fecundidad en el Ecuador es mucho mayor a la que se encontró al analizar el impacto que tiene la Fecundidad sobre la Pobreza, es decir, elevados niveles de pobreza causan un mayor número de hijos en los hogares.

The role of area-level deprivation and gender in participation in population-based faecal immunochemical test (FIT) colorectal cancer screening

This study aimed to investigate the effects of sex and deprivation on participation in a population-based faecal immunochemical test (FIT) colorectal cancer screening programme. The study population included 9785 individuals invited to participate in two rounds of a population-based biennial FIT-based screening programme, in a relatively deprived area of Dublin, Ireland. Explanatory variables included in the analysis were sex, deprivation category of area of residence and age (at end of screening). The primary outcome variable modelled was participation status in both rounds combined (with “participation” defined as having taken part in either or both rounds of screening). Poisson regression with a log link and robust error variance was used to estimate relative risks (RR) for participation. As a sensitivity analysis, data were stratified by screening round. In both the univariable and multivariable models deprivation was strongly associated with participation. Increasing affluence was associated with higher participation; participation was 26% higher in people resident in the most affluent compared to the most deprived areas (multivariable RR = 1.26: 95% CI 1.21–1.30). Participation was significantly lower in males (multivariable RR = 0.96: 95%CI 0.95–0.97) and generally increased with increasing age (trend per age group, multivariable RR = 1.02: 95%CI, 1.01–1.02). No significant interactions between the explanatory variables were found. The effects of deprivation and sex were similar by screening round. Deprivation and male gender are independently associated with lower uptake of population-based FIT colorectal cancer screening, even in a relatively deprived setting. Development of evidence-based interventions to increase uptake in these disadvantaged groups is urgently required.

The Poisson-exponential regression model under different latent activation schemes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The Conway-Maxwell-Poisson-generalized gamma regression model with long-term survivors

In this paper, we proposed a flexible cure rate survival model by assuming the number of competing causes of the event of interest following the Conway-Maxwell distribution and the time for the event to follow the generalized gamma distribution. This distribution can be used to model survival data when the hazard rate function is increasing, decreasing, bathtub and unimodal-shaped including some distributions commonly used in lifetime analysis as particular cases. Some appropriate matrices are derived in order to evaluate local influence on the estimates of the parameters by considering different perturbations, and some global influence measurements are also investigated. Finally, data set from the medical area is analysed.

The zero-inflated Conway-Maxwell-Poisson distribution: Bayesian inference, regression modeling and influence diagnostic

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The Poisson-exponential regression model under different latent activation schemes

In this paper, a new family of survival distributions is presented. It is derived by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution. Three different activation schemes are also considered. Moreover, we propose the inclusion of covariates in the model formulation in order to study their effect on the expected value of the number of causes and on the failure rate function. Inferential procedure based on the maximum likelihood method is discussed and evaluated via simulation. The developed methodology is illustrated on a real data set on ovarian cancer.

On estimation and influence diagnostics for zero-inflated negative binomial regression models

The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-Inflated Poisson model A frequentist analysis a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered In addition an EM-type algorithm is developed for performing maximum likelihood estimation Then the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived In order to study departures from the error assumption as well as the presence of outliers residual analysis based on the standardized Pearson residuals is discussed The relevance of the approach is illustrated with a real data set where It is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart (C) 2010 Elsevier B V All rights reserved

Tests pour la dépendance entre les sections dans un modèle de Poisson

Les simulations et figures ont été réalisées avec le logiciel R.

A Poisson mixed model with nonnormal random effect distribution

In this paper, we propose a random intercept Poisson model in which the random effect is assumed to follow a generalized log-gamma (GLG) distribution. This random effect accommodates (or captures) the overdispersion in the counts and induces within-cluster correlation. We derive the first two moments for the marginal distribution as well as the intraclass correlation. Even though numerical integration methods are, in general, required for deriving the marginal models, we obtain the multivariate negative binomial model from a particular parameter setting of the hierarchical model. An iterative process is derived for obtaining the maximum likelihood estimates for the parameters in the multivariate negative binomial model. Residual analysis is proposed and two applications with real data are given for illustration. (C) 2011 Elsevier B.V. All rights reserved.

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.