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Background: Teachers' roles in the emotional socialization of preschoolers have been less studied. Less attention has been given to the contributions of both child- and teacher-level variables. Objective: This study aimed at exploring the association between child- and teacher-level factors with growth in children's emotion regulation (ER) and emotion knowledge (EK). Method: We used hierarchical linear modeling and two-time-point (fall & spring) data consisting of 187 Taiwanese preschool children and 15 teachers. The child-level predictors included temperament characteristics and how the child's negative emotion is responded to with six aspects of the Coping with Children's Negative Emotions Scale (CCNES). The teacher-level variables consisted of teacher-reported reactions to negative emotions of children and duration of the teacher-child interaction as a group. Results: ER growth was predicted by three child-level predictors--teachers' punitive reactions to an individual child were negatively associated with, whereas children's negative affectivity and effortful control were positively associated with the change in ER. EK growth was predicted by both child- and teacher-level predictors--children's surgency/extraversion and teachers' minimization reactions to children as a group were negatively associated with, whereas both group-level child-teacher interactions and expressive encouragement were positively associated with EK growth Conclusion: This study supports that teacher's individual-level punitive reactions to a child's negative emotions negatively predicted ER growth, and that teacher's group-level minimization reactions negatively predicted, whereas group-level expressive encouragement and child-teacher interactions positively predicted children's EK growth, indicating children could put together repeated experiences of watching other children's emotions being reacted to.

With a non-clinical sample of pre-schoolers, we used partial least squares regression to assess the extent to which children's seven sensory regulatory factors (X) account for variations in the three temperament factors (Y) and Y explains variations in X. The results suggested predicting temperament factors from sensory processing factors receive better outcomes. Finally, multiple regression analyses using the sensory processing measures to predict each of the three temperamental variables showed that (1) significant predictors of negative affectivity included difficulties in tactile sensitivity, taste/smell sensitivity, and low energy; (2) surgency was predicted by underresponsive/seeks sensation, auditory filtering, tactile sensitivity, and movement sensitivity; and (3) effortful control was predicted by low energy and auditory filtering. The hypothesis supported children's sensory processing preferences form the basis for the manifestation of temperament. Parents and educators need to notice environmental stimuli can cause challenges for children with sensory processing difficulties and manifesti difficult temperament.

Doubly truncated (DT) data occur when event times are observed only if they fall within subject-specific, possibly random, intervals. In this article, we consider conditional maximum likelihood estimation for the regression parameters of the Cox–Aalen model with DT data. Based on gradient projection method (GPM), we propose computational algorithms for obtaining the conditional maximum likelihood estimator (cMLE). The proposed cMLE is shown to be consistent and asymptotically normal. Simulation studies show that the cMLE performs well in finite samples. [ABSTRACT FROM AUTHOR]

Field data provide important information on product reliability. Interval sampling is widely used for collection of field data, which typically report incident cases during a certain time period. Such sampling scheme induces doubly truncated (DT) data if the exact failure time is known. In many situations, the exact failure date is known only to fall within an interval, leading to doubly truncated and interval censored (DTIC) data. This article considers analysis of DTIC data under parametric failure time models. We consider a conditional likelihood approach and propose interval estimation for parameters and the cumulative distribution functions. Simulation studies show that the proposed method performs well for finite sample size. [ABSTRACT FROM AUTHOR]

AbstractIn this article, we propose equivalence tests for the difference of two survival functions in the presence of cure fraction. We consider both the non mixture cure model (NMCM) and mixture cure model (MCM). We first demonstrate that the existing tests can be used to test equivalence of two improper survival functions under the class of log transformation (LT) and Box-Cox transformation (BCT) NMCM. For the MCM, a logistic regression model is assumed for the probability of being uncured and the class of LT or BCT models are used to describe the distribution of event time for the uncured subjects. Under the MCM, we consider testing separately the null hypothesis of identical cure rate and that of equivalence of two survival functions of the uncured subjects. We also develop a Wald statistic for simultaneously testing whether or not both the maximal difference between cure rates and that between two short-term survival functions fall within some equivalence margins. Simulation results indicate that the proposed tests have a satisfactory size and an adequate power in finite sample. [ABSTRACT FROM AUTHOR]

Abstract Interval sampling is often used in epidemiological studies. With interval sampling, data are collected only from individuals who fail within a certain calendar time interval. Such interval sampling induces doubly truncated (DT) data if the calendar time of the failure event can be observed exactly. In practice, the failure time may not be directly observed and it is only recorded within time intervals, leading to doubly truncated and interval censored (DTIC) data. In this article, we analyze DTIC data using a class of semiparametric transformation models. By iterating between an imputation step and an optimization step for DT data, we propose two imputation methods for obtaining parameter estimates for regression parameters and cumulative hazard function of models. Simulation studies indicate that the proposed estimators perform adequately. [ABSTRACT FROM AUTHOR]

Based on data collected from 211 elementary school children in central Taiwan over four years, the role of temperament in science achievement was examined with multivariate analysis of covariance (MANCOVA) with repeated measures design. The results revealed that the students' science achievement is stable over time. The task orientation characteristics (i.e., distractibility, hyperactivity, and persistence) identified by previous research as important in mathematics and reading achievement are not consistent with the findings of this study that although the impacts of distractibility and persistence are significant, the simple effect of activity level on science achievement was not significant. However, the interaction effect of activity level and persistence is significant. Further break down analyses conducted to pursuit the interaction effect revealed that: (1) persistence has a consistent significant positive effects on all children over the years, except for the ones with high activity level (the upper 25th percentile); and (2) activity level has significant negative effects on high persistence (the upper 25th percentile) children only, and the interaction effect of activity level increases over time--the older the child the more significant the influence. Also, the tendency of approach/withdraw has significant impact on science achievement The educational implications of the findings include: (1) teachers of high activity students with high persistence may need to consider the match between children's interests and contents of instruction; (2) a good match between children's individual differences in distractibility, persistence, and approach need to be considered in contents, expectations, and interactions of the instruction. (Contains 1 figure and 3 tables.)

One important topic in clinical trials is to show that the effects of new and standard treatments are equivalent in terms of clinical relevance. In literature, many equivalence tests based on the maximal difference between two survival functions for the two treatments over the whole time axis have been proposed. However, since survival times can only be observed until the end of follow-up, an equivalence test should be based on a comparison only in the observed time-window dictated by the end of follow-up. In this article, under the class of log transformation model, we propose an asymptotical α -level equivalence test for the difference between two survival functions that only addresses equivalence until the end of follow-up. We demonstrate that the hypothesis of equivalence of two survival functions before the end of follow-up can be formulated as interval-based hypothesis testing which involves the treatment effect parameter. Simulation results indicate that when sample size is sufficiently large the proposed test controls the type I error effectively and performs well at detecting the equivalence. The proposed test is applied to a dataset from veteran's administration lung cancer trial. [ABSTRACT FROM AUTHOR]