Incorporating eye tracking into cognitive interviewing to pretest survey questions

In this study, we investigated whether incorporating eye tracking into cognitive interviewing is effective when pretesting survey questions. In the control condition, a cognitive interview was conducted using a standardized interview protocol that included pre-defined probing questions for about one-quarter of the questions in a 52-item questionnaire. In the experimental condition, participants’ eye movements were tracked while they completed an online version of the questionnaire. Simultaneously, their reading patterns were monitored for evidence of response problems. Afterward, a cognitive interview was conducted using an interview protocol identical to that in the control condition. We compared both approaches with regard to the number and types of problems they detected. We found support for our hypothesis that cognitive interviewing and eye tracking complement each other effectively. As expected, the hybrid method was more productive in identifying both questionnaire problems and problematic questions than applying cognitive interviewing alone.     

学习视角下教师教育课程的认知困境

教师教育课程的安排是以学科逻辑结构为依据的,使得教育理论远离了学生的经验,人为增加学习难度,教师教育课程应该从学科课程与教学法为切入点。教师教育课程内部的知识结构从理论到理论,割裂了认知过程,应该通过对他人实践过程的学习来弥补这个缺陷。学生的教育实习与教育理论学习是脱节的,使得教育理论无法指导学生的教育实践,也无法通过自身的教育实践来检验教育理论的合理性。所以,教育实习指导教师应该有意识地加强这方面的指导,帮助学生最后完成教育理论的学习。     

长时工作记忆的模块优化机制——基于理想认知模型的分析

记忆术是在新异信息与固有信息间强制建立一种人为意义联系的记忆策略。记忆术之所以能够有效提升记忆品质，是因为它能帮助构建稳定的理想认知模型，而理想化认知模型可被用作完整的记忆组块被个体提取到工作记忆中。组块中处于个体意识中央的那部分信息作为提取线索，将组块内的其它信息牵引到长时工作记忆中，这一方面增强了信息的可得性，另一方面还有效克服了短时工作记忆的容量限制。总之，记忆术通过理想认知模型优化了长时工作记忆，从而改善了记忆效率，提升了记忆品质。     

Quasi-minimax estimation in the partial linear model

This article discusses the minimax estimator in partial linear model y = Zβ + f + ε under ellipsoidal restrictions on the parameter space and quadratic loss function. The superiority of the minimax estimator over the two-step estimator is studied in the mean squared error matrix criterion.     

Why do we observe misclassification errors smaller than the Bayes error?

In simulation studies for discriminant analysis, misclassification errors are often computed using the Monte Carlo method, by testing a classifier on large samples generated from known populations. Although large samples are expected to behave closely to the underlying distributions, they may not do so in a small interval or region, and thus may lead to unexpected results. We demonstrate with an example that the LDA misclassification error computed via the Monte Carlo method may often be smaller than the Bayes error. We give a rigorous explanation and recommend a method to properly compute misclassification errors.     

Aggregating classifiers via Rademacher–Walsh polynomials

Methods are proposed to combine several individual classifiers in order to develop more accurate classification rules. The proposed algorithm uses Rademacher–Walsh polynomials to combine M (≥2) individual classifiers in a nonlinear way. The resulting classifier is optimal in the sense that its misclassification error rate is always less than, or equal to, that of each constituent classifier. A number of numerical examples (based on both real and simulated data) are also given. These examples demonstrate some new, and far-reaching, benefits of working with combined classifiers.     

‘Miss mum’: mind and affective experience of Korean learners identified with autism spectrum and cognitive difficulties

Responding to the call for culturally informed research on autism spectrum (AS) to allow for the provision of appropriate services, this paper qualitatively explores the affective experience of five Korean learners identified with AS and cognitive difficulties by listening to their voices. The learners, supported by the use of visual aids and a choice of their preferred mode of communication, expressed the complexity of their inner experience that encompassed abilities as well as difficulties. The findings highlight the commonalities of experience between Korean and non-Korean individuals with AS, along with some aspects specific to the Korean cultural context.     

Mean shift and influence measures in linear measurement error models with stochastic linear restrictions

We present influence diagnostics for linear measurement error models with stochastic linear restrictions using the corrected likelihood of Nakamura in 1990. The case deletion and mean shift outlier models are developed to identify outlying and influential observations. We derive a corrected score test statistic for outlier detection based on mean shift outlier models. The analogs of Cook's distance and likelihood distance are proposed to determine influential observations based on case deletion models. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics and a simulation study has been used to evaluate the performance of the proposed estimators based on the mean squares error criterion and the score test statistic. Finally, a numerical example is given to illustrate the theoretical results.     

A goodness of fit test for multilevel survival data

No satisfactory goodness of fit test is available for multilevel survival data which occur when survival data are clustered or hierarchical in nature. Hence the aim of this research is to develop a new goodness of fit test for multilevel survival data and to examine the properties of the newly developed test. Simulation studies were carried out to evaluate the type ? error and the power. The results showed that the type I error holds for every combination tested and that the test is powerful against the alternative hypothesis of nonproportional hazards for all combinations tested.     

Nonparametric Shrinkage Estimation for Aalen's Additive Hazards Model

Aalen's nonparametric additive model in which the regression coefficients are assumed to be unspecified functions of time is a flexible alternative to Cox's proportional hazards model when the proportionality assumption is in doubt. In this paper, we incorporate a general linear hypothesis into the estimation of the time‐varying regression coefficients. We combine unrestricted least squares estimators and estimators that are restricted by the linear hypothesis and produce James‐Stein‐type shrinkage estimators of the regression coefficients. We develop the asymptotic joint distribution of such restricted and unrestricted estimators and use this to study the relative performance of the proposed estimators via their integrated asymptotic distributional risks. We conduct Monte Carlo simulations to examine the relative performance of the estimators in terms of their integrated mean square errors. We also compare the performance of the proposed estimators with a recently devised LASSO estimator as well as with ridge‐type estimators both via simulations and data on the survival of primary billiary cirhosis patients.