读者·文本·召唤结构——关于大学英语阅读教学的思考

阅读文本自身所存在的大量空白构成了一个动态、开放并蕴含无限可能的话语结构,它召唤读者通过再创造对其进行建构和填充。这一理论对于大学英语阅读教学具有极大的实践意义,只有充分确立和彰显学生的主体性,尊重文本的多元性解读,促进读者与文本的视域融合并创造积极、平等和和谐的师生对话,才能真正使学生在文本意义构建中发挥建设性作用,从而将阅读知识与能力推进到一个新的高度。     

Bayesian estimation of quantile distributions

Use of Bayesian modelling and analysis has become commonplace in many disciplines (finance, genetics and image analysis, for example). Many complex data sets are collected which do not readily admit standard distributions, and often comprise skew and kurtotic data. Such data is well-modelled by the very flexibly-shaped distributions of the quantile distribution family, whose members are defined by the inverse of their cumulative distribution functions and rarely have analytical likelihood functions defined. Without explicit likelihood functions, Bayesian methodologies such as Gibbs sampling cannot be applied to parameter estimation for this valuable class of distributions without resorting to numerical inversion. Approximate Bayesian computation provides an alternative approach requiring only a sampling scheme for the distribution of interest, enabling easier use of quantile distributions under the Bayesian framework. Parameter estimates for simulated and experimental data are presented.     

On choosing between fixed and random block effects in some no-interaction models

The purpose of this article is to strengthen the understanding of the relationship between a fixed-blocks and random-blocks analysis in models that do not include interactions between treatments and blocks. Treating the block effects as random has been recommended in the literature for balanced incomplete block designs (BIBD) because it results in smaller variances of treatment contrasts. This reduction in variance is large if the block-to-block variation relative to the total variation is small. However, this analysis is also more complicated because it results in a subjective interpretation of results if the block variance component is non-positive. The probability of a non-positive variance component is large precisely in those situations where a random-blocks analysis is useful – that is, when the block-to-block variation, relative to the total variation, is small. In contrast, the analysis in which the block effects are fixed is computationally simpler and less subjective. The loss in power for some BIBD with a fixed effects analysis is trivial. In such cases, we recommend treating the block effects as fixed. For response surface experiments designed in blocks, however, an opposite recommendation is made. When block effects are fixed, the variance of the estimated response surface is not uniquely estimated, and in practice this variance is obtained by ignoring the block effect. It is argued that a more reasonable approach is to treat the block effects to be random than to ignore it.     

Conditions of Slope-Rotatability for Third-Order Polynomial Regression Models

In experimental design for response surface analysis, it is sometimes of interest to estimate the difference of responses at two points. If differences at points close together are involved, the design that reliably estimates the slope of the response surface is important. In particular, Hader and Park (1978 Hader , R. J. , Park , S. H. ( 1978 ). Slope-rotatable central composite designs . Technometrics 20 : 413 – 417 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) suggested the concept of slope-rotatability and studied slope rotatable central composite designs. Until now, many response surface designs including central composite designs have been suggested for fitting second order response surface models. However, we often need to fit third-order polynomial regression models. In this article, we suggest extended central composite designs (ECCDs) to fit third-order models and find the necessary and sufficient conditions for slope-rotatability over all directions in the third-order polynomial models.     

Variance estimation in model assisted survey sampling

Two versions of Yates-Grundy type variance estimators are usually employed for large samples when estimating a survey population total by a generalized regression (Greg, in brief) predictor motivated by consideration of a linear regression model. Their two alternative modifications are developed so that the limiting values of the design expectations of the model expectations of variance estimators 'match' respectively the (I) model expectations of the Taylor approximation of the design variance of the Greg predictor and the (II) limiting value of the design expectation of the model expectation of the squared difference between the Greg predictor and the population total. The exercise is extended to yield modifications needed when randomized response (RR) is only available rather than direct response (DR) when one encounters sensitive issues demanding protection of privacy. A comparative study based on simulation is presented for illustration..     

Environmental risk assessment model for low dose extrapolation ∗

Use of experimental data from animal studies to estimate human risk due to long-term exposure to very low doses of chemicals in the environment poses a number of biological and statistical problems. One of the statistical problems is to extrapolate the animal dose-response relation from the high dose levels where data are available to low dose, which humans might encounter. Here, a quantal dose-response model is developed based on a multi-hit theory of toxic response. The development of the model utilizes a weighted Lagrange-Poisson distribution for the number of hits. When spontaneous background toxic response is included, the model involves three unknown parameters. The maximum likelihood estimators for these parameters are given as the solution of a nonlinear iterative algorithm. The use of this model for low-dose extrapolation is indicated. The results are applied to nine sets of toxic response data.     

Covariance Analysis for Split-Plot and Split-Block Designs

Commentaries are informative essays dealing with viewpoints of statistical practice, statistical education, and other topics considered to be of general interest to the broad readership of The American Statistician. Commentaries are similar in spirit to Letters to the Editor, but they involve longer discussions of background, issues, and perspectives. All commentaries will be refereed for their merit and compatibility with these criteria.

Proper methodology for the analysis of covariance for experiments designed in a split-plot or split-block design is not found in the statistical literature. Analyses for these designs are often performed incompletely or even incorrectly. This is especially true when popular statistical computer software packages are used for the analysis of these designs. This article provides several appropriate models, ANOVA tables, and standard errors for comparisons from experiments arranged in a standard split-plot, split–split-plot, or split-block design where a covariate has been measured on the smallest size experimental unit.     

Robust Model-Based Inference for Incomplete Data via Penalized Spline Propensity Prediction

Parametric model-based regression imputation is commonly applied to missing-data problems, but is sensitive to misspecification of the imputation model. Little and An (2004 Little , R. J. A. , An , H. ( 2004 ). Robust likelihood-based analysis of multivariate data with missing values . Statistica Sinica 14 : 949 – 968 .[Web of Science ®] , [Google Scholar]) proposed a semiparametric approach called penalized spline propensity prediction (PSPP), where the variable with missing values is modeled by a penalized spline (P-Spline) of the response propensity score, which is logit of the estimated probability of being missing given the observed variables. Variables other than the response propensity are included parametrically in the imputation model. However they only considered point estimation based on single imputation with PSPP. We consider here three approaches to standard errors estimation incorporating the uncertainty due to non response: (a) standard errors based on the asymptotic variance of the PSPP estimator, ignoring sampling error in estimating the response propensity; (b) standard errors based on the bootstrap method; and (c) multiple imputation-based standard errors using draws from the joint posterior predictive distribution of missing values under the PSPP model. Simulation studies suggest that the bootstrap and multiple imputation approaches yield good inferences under a range of simulation conditions, with multiple imputation showing some evidence of closer to nominal confidence interval coverage when the sample size is small.     

A Proportional Random Utility Approach to Qualitative Response Models

A new functional form of the response probability for a qualitative response model is proposed. The new model is flexible enough to avoid the constraint of independence from irrelevant alternatives, which is perceived as a weakness of the multinomial logit model in some applications. It is computationally simpler than the multinomial probit model and is promising for analyzing problems with a moderate number of alternatives.     

Understanding the resilience of mail-back survey methods: An analysis of 20 years of change in response rates to national park surveys

Coverage and response rate challenges facing telephone and internet surveys have encouraged scientists to reconsider mail data collection methods. Although response rates to telephone surveys have declined sharply in the last 20 years, it is unclear how response rates to mail have fared during this time. This study analyzes 179 mail-back surveys of visitors to US National Parks from 1988 to 2007, which used virtually the same administration procedures throughout the period. Results show that response rates, based on only those who initially agreed to return a questionnaire, have remained at a high level with a 76% average while the number of questions and pages steadily increased. Despite this rise in response burden, rates have declined only moderately from about 80% in the late 1980s to about 70% more recently. The roles of additional contacts and survey salience in maintaining high response rates are examined. Results suggest that mail-back surveys for obtaining information from quasi-general public populations remain an effective data collection procedure.