大学生自信与成就动机、综合测评成绩的相关研究

通过对289名大学生成就动机、自信的测试,探讨大学生自信与成就动机、综合测评成绩之间的关系。结果表明：(1)大学生成就动机与自信相关显著。其中,追求成功的动机与自信诸维度呈显著正相关,而避免失败的动机与自信诸维度之间则呈显著负相关;(2)综合测评成绩与自信的少数维度有显著相关,与学生成就动机之间相关不显著。     

Exploratory second-order analyses for components and factors

Abstract: Exploratory methods using second‐order components and second‐order common factors were proposed. The second‐order components were obtained from the resolution of the correlation matrix of obliquely rotated first‐order principal components. The standard errors of the estimates of the second‐order component loadings were derived from an augmented information matrix with restrictions for the loadings and associated parameters. The second‐order factor analysis proposed was similar to the classical method in that the factor correlations among the first‐order factors were further resolved by the exploratory method of factor analysis. However, in this paper the second‐order factor loadings were estimated by the generalized least squares using the asymptotic variance‐covariance matrix for the first‐order factor correlations. The asymptotic standard errors for the estimates of the second‐order factor loadings were also derived. A numerical example was presented with simulated results.     

各种分类标准在儿童分类中的竞争

在已有研究的基础上进一步考查类概念,颜色、形状和主题关系四种主要分类标准在儿童分类操作中的竞争力;并衡量各分类标准被选择的次序和差别度。被试是４－８岁儿童１５０名。选取儿童熟悉的７个类概念。采用概念同定匹配和配对法。结果表明：在５岁以后类概念呈快速发展,８岁达基本掌握水平;四种分类标准中感知因素竞争力最弱,主题关系在５岁居高峰;各种分类标准强弱的等第次序自６岁后稳定一致;分类标准的差别度随年龄增长而加大,６－７岁是差别度变化的转折点。     

优、差生组织策略水平的比较研究

本文比较了初一年级优、差生在词表记忆中的组织策略水平的差异,结果表明：１、初一年级优、差生都还没能自发地运用组织策略,优生只在非典型项目的记忆中群集水平明显优于差生,优、差生的群集水平和记忆成绩之间不存在一一对应的关系。２、在典型或非典型项目的记忆中,记忆成绩和知识背景都有着非常显著的正相关。     

Confidence regions for INDSCAL using the jackknife and bootstrap techniques

Bootstrap and jackknife techniques are used to estimate ellipsoidal confidence regions of group stimulus points derived from INDSCAL. The validity of these estimates is assessed through Monte Carlo analysis. Asymptotic estimates of confidence regions based on a MULTISCALE solution are also evaluated. Our findings suggest that the bootstrap and jackknife techniques may be used to provide statements regarding the accuracy of the relative locations of points in space. Our findings also suggest that MULTISCALE asymptotic estimates of confidence regions based on small samples provide an optimistic view of the actual statistical reliability of the solution. The authors wish to thank Geert DeSoete, Richard A. Harshman, William Heiser, Jon Kettenring, Joseph B. Kruskal, Jacqueline Meulman, James O. Ramsay, John W. Tukey, Paul A. Tukey, and Mike Wish. Sharon L. Weinberg is a consultant at AT&T Bell Laboratories, Murray Hill, New Jersey 07974.     

A bayesian procedure for mastery decisions based on multivariate normal test data

A Bayesian framework for making mastery/nonmastery decisions based on multivariate test data is described in this study. Overall, mastery is granted (or denied) if the posterior expected loss associated with such action is smaller than the one incurred by the denial (or grant) of mastery. An explicit form for the cutting contour which separates mastery and nonmastery states in the test score space is given for multivariate normal test scores and for a constant loss ratio. For multiple cutting scores in the true ability space, the test score cutting contour will resemble the boundary defined by multiple test cutting scores when the test reliabilities are reasonably close to unity. For tests with low reliabilities, decisions may very well be based simply on a suitably chosen composite score.This work was performed pursuant to Grant NIE-G-78-0087 with the National Institute of Education, Department of Health, Education, and Welfare, Huynh Huynh, Principal Investigator. Points of view or opinions stated do not necessarily reflect NIE positions or policy and no official endorsement should be inferred. The assistance of Joseph C. Saunders is gratefully acknowledged. The author is indebted to an anonymous referee who pointed out several computational errors in the earlier versions of the paper.     

Modification indices for the 2-PL and the nominal response model

In this paper, it is shown that various violations of the 2-PL model and the nominal response model can be evaluated using the Lagrange multiplier test or the equivalent efficient score test. The tests presented here focus on violation of local stochastic independence and insufficient capture of the form of the item characteristic curves. Primarily, the tests are item-oriented diagnostic tools, but taken together, they also serve the purpose of evaluation of global model fit. A useful feature of Lagrange multiplier statistics is that they are evaluated using maximum likelihood estimates of the null-model only, that is, the parameters of alternative models need not be estimated. As numerical examples, an application to real data and some power studies are presented.     

Precision teaching's unique legacy from B. F. Skinner

Precision Teaching's unique legacy from B. F. Skinner was the monitoring system of rate of response and the cumulative response recorder. This legacy is unique because the other instructional systems derived from Skinner's work do not use his monitoring method exclusively. Rate of response, cumulative recording and their extension to Precision Teaching's standard celeration charting are briefly described. In addition, Precision Teaching's nature, history, costs, distribution, inductive data-base, and academic base are briefly described. Skinner's legacy to education was a sound behavioral scientific base and his unique legacy to Precision Teaching was self-monitoring for real time decision making by learners and teachers.     

Statistical inference for multiple choice tests

Finite sample inference procedures are considered for analyzing the observed scores on a multiple choice test with several items, where, for example, the items are dissimilar, or the item responses are correlated. A discrete p-parameter exponential family model leads to a generalized linear model framework and, in a special case, a convenient regression of true score upon observed score. Techniques based upon the likelihood function, Akaike's information criteria (AIC), an approximate Bayesian marginalization procedure based on conditional maximization (BCM), and simulations for exact posterior densities (importance sampling) are used to facilitate finite sample investigations of the average true score, individual true scores, and various probabilities of interest. A simulation study suggests that, when the examinees come from two different populations, the exponential family can adequately generalize Duncan's beta-binomial model. Extensions to regression models, the classical test theory model, and empirical Bayes estimation problems are mentioned. The Duncan, Keats, and Matsumura data sets are used to illustrate potential advantages and flexibility of the exponential family model, and the BCM technique.The authors wish to thank Ella Mae Matsumura for her data set and helpful comments, Frank Baker for his advice on item response theory, Hirotugu Akaike and Taskin Atilgan, for helpful discussions regarding AIC, Graham Wood for his advice concerning the class of all binomial mixture models, Yiu Ming Chiu for providing useful references and information on tetrachoric models, and the Editor and two referees for suggesting several references and alternative approaches.     

Probability-of-coverage tolerance intervals for the beta binomial

True score tolerance intervals, which are designed to cover a chosen proportion of the conditional distribution of true scores given an observed score, are suggested as alternatives to true score confidence intervals. Using large sample theory, a tolerance interval estimator for the beta binomial is derived. An example indicates that with moderate sample sizes, tolerance intervals with high probability of coverage will not be much wider than when the two beta true score parameters are known.The author acknowledges valuable comments from Richard Sawyer.Most work was completed while the author was at the American College Testing Program.