Stevens’ Direct Scaling Methods and the Uniqueness Problem

Stevens postulated that we can use the responses of a participant in a ratio scaling experiment directly to construct a psychophysical function representing the participant's sensations. Although Stevens' methods of constructing measurement scales are widely used in the behavioral sciences, the problem of which scale type is appropriate to describe ratio scaling data is still unresolved. To deal with this problem, we develop a theoretical framework to specify the scale type attained by Stevens' direct scaling methods. It is shown, under fairly mild background assumptions, that the behavioral axioms presented in this paper are necessary and sufficient for the psychophysical functions to be ordinal-, interval-, log-interval-, or ratio-scales. Furthermore, suggestions on how to test these behavioral axioms are provided. Requests for reprints should be sent to thomas.     

主体关联性对贝叶斯推理概率估计的影响

用贝叶斯推理问题为实验材料,探讨了主体关联性对贝叶斯推理概率估计的影响。结果表明,当估计的事件与主体有关时,被试对消极事件概率估计较低,对积极事件概率估计值;当估计的事件与主体无关时,被试对消极事件和积极事件的概率估计无显著差异。反应时分析表明,被试对消极事件的概率估计比对积极事件的概率估计时间显著地长,当消极事件与主体有关时概率估计时间就更长;而对积极事件的概率估计,与主体有关和与主体无关时反应时差异不显著。这表明,被试对消极事件的概率估计(特别是消极事件与己有关时)更为慎重。     

短时距估计中刺激物的动态变化效应

用两个实验考查了时距估计中刺激物的动态变化效应。结果发现,刺激物的运动、速度和旋转角度显著地影响着被试的时间判断,刺激物速度的时距延长效应对于较长时距(18s-24s)来说更加明显;运动刺激与较短、较准确的时距判断相关联;旋转角度的减少使得被试低估时距,而旋转角度的增加使得时距估计呈高估倾向。被试在时间估计过程中表现出长时距低估和短时距高估现象,本研究得到的时间估计转换点是11.4s,此点与先前的有关研究基本一致。     

概化理论不同评分方案缺失数据方差分量估计

包含评分者侧面的测验通常不符合任意一种概化理论设计,因此从概化理论的角度来看这类测验下的数据应属于缺失数据,而决定缺失结构的就是测验的评分方案。用R软件模拟出三种评分方案下的数据,并比较传统法、评价法和拆分法在各评分方案下的估计效果,结果表明：（1）传统法估计准确性较差;（2）评分者一致性较高时,适宜用评价法进行估计;（3）拆分法的估计结果最准确,仅在固定评分者评分方案下需注意评分者与考生数量之比,该比值小于等于0.0047 时估计结果较为准确。     

Probabilistic Disjoint Principal Component Analysis

One of the most relevant problems in principal component analysis and factor analysis is the interpretation of the components/factors. In this paper, disjoint principal component analysis model is extended in a maximum-likelihood framework to allow for inference on the model parameters. A coordinate ascent algorithm is proposed to estimate the model parameters. The performance of the methodology is evaluated on simulated and real data sets.     

Evaluating Factorial Invariance: An Interval Estimation Approach Using Bayesian Structural Equation Modeling

In this study, we introduce an interval estimation approach based on Bayesian structural equation modeling to evaluate factorial invariance. For each tested parameter, the size of noninvariance with an uncertainty interval (i.e. highest density interval [HDI]) is assessed via Bayesian parameter estimation. By comparing the most credible values (i.e. 95% HDI) with a region of practical equivalence (ROPE), the Bayesian approach allows researchers to (1) support the null hypothesis of practical invariance, and (2) examine the practical importance of the noninvariant parameter. Compared to the traditional likelihood ratio test, simulation results suggested that the proposed Bayesian approach could offer additional insight into evaluating factorial invariance, thus, leading to more informative conclusions. We provide an empirical example to demonstrate the procedures necessary to implement the proposed method in applied research. The importance of and influences on the choice of an appropriate ROPE are discussed.     

Social discounting and delay discounting

Social discounting was measured as the amount of money a participant was willing to forgo to give a fixed amount (usually $75) to another person. In the first experiment, amount forgone was a hyperbolic function of the social distance between the giver and receiver. In the second experiment, degree of social discounting was an increasing function of reward magnitude whereas degree of delay discounting was a decreasing function of reward magnitude. In the third experiment, the shape of the function relating delayed rewards to equally valued immediate rewards for another person was predicted from individual delay and social discount functions. All in all, the studies show that the social discount function, like delay and probability discount functions, is hyperbolic in form. Copyright © 2007 John Wiley & Sons, Ltd.     

小学儿童算术估算能力的发展

本研究深入考察了我国现阶段小学儿童算术估算能力的发展状况。通过运用自编材料对1027名儿童的团体与个别测试,发现其估算能力的发展具有如下特点：（1）明显受到题目类型的影响,但在不同年龄阶段影响又不尽相同。（2）三年级可能是整数和小数估算能力发展的一个关键期,五年级是发展分数估算能力的较好时期。（3）不同估算策略的发展进程差别相当大,不同时期都有发展的侧重点。（4）小学儿童在估算时易发生多种错误,而且不同年级均有一些典型错误。另外研究还对儿童估算能力、策略与错误类型的发展进行了深入讨论。     

Nonparametric Estimation of Standard Errors in Covariance Analysis Using the Infinitesimal Jackknife

The infinitesimal jackknife provides a simple general method for estimating standard errors in covariance structure analysis. Beyond its simplicity and generality what makes the infinitesimal jackknife method attractive is that essentially no assumptions are required to produce consistent standard error estimates, not even the requirement that the population sampled has the covariance structure assumed. Commonly used covariance structure analysis software uses parametric methods for estimating parameters and standard errors. When the population sampled has the covariance structure assumed, but fails to have the distributional form assumed, the parameter estimates usually remain consistent, but the standard error estimates do not. This has motivated the introduction of a variety of nonparametric standard error estimates that are consistent when the population sampled fails to have the distributional form assumed. The only distributional assumption these require is that the covariance structure be correctly specified. As noted, even this assumption is not required for the infinitesimal jackknife. The relation between the infinitesimal jackknife and other nonparametric standard error estimators is discussed. An advantage of the infinitesimal jackknife over the jackknife and the bootstrap is that it requires only one analysis to produce standard error estimates rather than one for every jackknife or bootstrap sample.     

Regularized Partial and/or Constrained Redundancy Analysis

Methods of incorporating a ridge type of regularization into partial redundancy analysis (PRA), constrained redundancy analysis (CRA), and partial and constrained redundancy analysis (PCRA) were discussed. The usefulness of ridge estimation in reducing mean square error (MSE) has been recognized in multiple regression analysis for some time, especially when predictor variables are nearly collinear, and the ordinary least squares estimator is poorly determined. The ridge estimation method was extended to PRA, CRA, and PCRA, where the reduced rank ridge estimates of regression coefficients were obtained by minimizing the ridge least squares criterion. It was shown that in all cases they could be obtained in closed form for a fixed value of ridge parameter. An optimal value of the ridge parameter is found by G-fold cross validation. Illustrative examples were given to demonstrate the usefulness of the method in practical data analysis situations. We thank Jim Ramsay for his insightful comments on an earlier draft of this paper. The work reported in this paper is supported by Grants 10630 from the Natural Sciences and Engineering Research Council of Canada to the first author.