Inducing a blockmodel structure of two-mode binary data using seriation procedures

We show that seriation of the rows and columns of a two-mode, binary matrix can be an effective method for producing a reordering of the matrix that reveals a blockmodel structure of the data. The objective criterion of the seriation process is based on Robinson patterning of matrix elements. The key advantages of the proposed method are: (a) it can be used in conjunction with existing two-mode blockmodeling algorithms by facilitating selection of the number of classes for the rows and columns of the matrix and the appropriate types of ideal blocks; (b) the model uses a well-grounded index based on Robinson structure, (c) guaranteed optimal solutions can be obtained for problems of practical size, and (d) the seriation method is frequently capable of producing a solution that has a substantive interpretation with respect to the orderings of the row objects and column items.     

Robust and powerful nonorthogonal analyses

Numerous types of analyses for factorial designs having unequal cell frequencies have been discussed in the literature. These analyses test either weighted or unweighted marginal means which, in turn, correspond to different model comparisons. Previous research has indicated, however, that these analyses result in biased (liberal or conservative) tests when cell variances are heterogeneous. We show how to obtain a generally robust and powerful analysis with any of the recommended nonorthogonal solutions by adapting a modification of the Welch-James procedure for comparing means when population variances are heterogeneous.This research was supported by a Social Sciences and Humanities Research Council (SSHRC) grant (# 410-92-0430) to the first author, a Manitoba Health Research Council Scholar Award and a grant from Natural Sciences and Engineering Research Council to the second author, and a SSHRC Doctoral Fellowship (# 752-92-1628) to the third author. The authors would like to express their gratitude to Joanne Keselman and three anonymous reviewers for their many helpful substantive comments on earlier drafts of this paper.     

Bluffing and betting behavior in a simplified poker game

A pure‐strategy, simplified poker (PSP) game is proposed, where two players draw from a small and discrete number of hands. Equilibrium strategies of the game are described and an experiment is conducted where 120 subjects played the PSP against a computer, which was programmed to play either the equilibrium solution or a fictitious play (FP) learning algorithm designed to take advantage of poor play. The results show that players did not adopt the cutoff‐type strategies predicted by the equilibrium solution; rather they made considerable “errors” by: Betting when they should have checked, checking when they should have bet, and calling when they should have folded. There is no evidence that aggregate performance improved over time in either condition although considerable individual differences were observed among subjects. Behavioral learning theory (BLT) cannot easily explain these individual differences and cognitive learning theory (CLT) is introduced to explain the apparent anomalies. Copyright © 2009 John Wiley & Sons, Ltd.     

服务学习促进大学生批判性思维的干预研究

采用准实验设计教学干预,将75名心理学专业本科生分为实验组(n=54)和对照组(n=21),实验组进行为期10周的结构化服务学习活动(每周2小时),对照组给予匹配任务(文献阅读和翻译总结)。采用反思日志中的开放性问题和非服务学习领域的结构不良情景问题为测验材料,利用自编的问题解决中的批判性思维表现编码表,对服务学习活动前后和过程中相关材料进行编码,探讨服务学习中服务组学生批判性思维的变化及其在非服务学习领域结构不良问题解决中的迁移效果。结果表明服务学习对大学生的批判性思维表现具有显著促进作用,主要体现在方案改进维度上,这种促进作用可以迁移到非服务学习领域问题解决的批判性思维表现中。     

Outranking Relations: Do They Have Special Properties?

Outranking relations are most often built using a concordance–discordance principle. Such relations are in general neither transitive nor complete. This is not to say that the concordance–discordance principle does not impose some ‘structural’ restrictions on these relations. We show why this question may be of some importance for analysing the various techniques designed to build a recommendation on the basis of such relations. These restrictions are studied for the ELECTRE and PROMETHEE methods.     

Adjusting for information inflation due to local dependency in moderately large item clusters

When multiple items are clustered around a reading passage, the local independence assumption in item response theory is often violated. The amount of information contained in an item cluster is usually overestimated if violation of local independence is ignored and items are treated as locally independent when in fact they are not. In this article we provide a general method that adjusts for the inflation of information associated with a test containing item clusters. A computational scheme was presented for the evaluation of the factor of adjustment for clusters in the restrictive case of two items per cluster, and the general case of more than two items per cluster. The methodology was motivated by a study of the NAEP Reading Assessment. We present a simulated study along with an analysis of a NAEP data set.The research was supported under the National Assessment of Educational Progress (Grant No. R999G30002) as administered by the Office of Educational Research and Improvement, U.S. Department of Education. This work was started when the author was at the Division of Statistics and Psychometrics at the Educational Testing Service. The author thanks Juliet Shaffer, Bob Mislevy, Eric Bradlow, three reviewers and an associate editor for their helpful comments on the paper.     

Models for ordinal hierarchical classes analysis

This paper proposes an ordinal generalization of the hierarchical classes model originally proposed by De Boeck and Rosenberg (1998). Any hierarchical classes model implies a decomposition of a two-way two-mode binary arrayM into two component matrices, called bundle matrices, which represent the association relation and the set-theoretical relations among the elements of both modes inM. Whereas the original model restricts the bundle matrices to be binary, the ordinal hierarchical classes model assumes that the bundles are ordinal variables with a prespecified number of values. This generalization results in a classification model with classes ordered along ordinal dimensions. The ordinal hierarchical classes model is shown to subsume Coombs and Kao's (1955) model for nonmetric factor analysis. An algorithm is described to fit the model to a given data set and is subsequently evaluated in an extensive simulation study. An application of the model to student housing data is discussed.     

The analysis of structural equation models by means of derivative free nonlinear least squares

It is shown that the PAR Derivative-Free Nonlinear Regression program in BMDP can be used to fit structural equation models, producing generalized least squares estimates, standard errors, and goodness-of-fit test statistics. Covariance structure models more general than LISREL can be analyzed. The approach is particularly useful for dealing with new non-standard models and experimenting with alternate methods of estimation. The research of the second author was supported by the NSF grant MCS 83-01587. We wish to thank our referees for some very valuable suggestions.     

A new proof for monotone likelihood ratio for the sum of independent bernoulli random variables

By use of an inequality of Marcus and Lopes for elementary symmetric functions, a new proof is presented for the following result by Ghurye and Wallace: Given that the independent random variablesX _j are Bernoulli with success probabilityp _j () strictly between 0 and 1 and nondecreasing in, the sum X _j has monotone likelihood ratio.