Clusteringn objects intok groups under optimal scaling of variables

We propose a method to reduce many categorical variables to one variable withk categories, or stated otherwise, to classifyn objects intok groups. Objects are measured on a set of nominal, ordinal or numerical variables or any mix of these, and they are represented asn points inp-dimensional Euclidean space. Starting from homogeneity analysis, also called multiple correspondence analysis, the essential feature of our approach is that these object points are restricted to lie at only one ofk locations. It follows that thesek locations must be equal to the centroids of all objects belonging to the same group, which corresponds to a sum of squared distances clustering criterion. The problem is not only to estimate the group allocation, but also to obtain an optimal transformation of the data matrix. An alternating least squares algorithm and an example are given.The authors thank Eveline Kroezen and Teije Euverman for their comments on a previous draft of this paper.     

Multidimensional Unfolding by Nonmetric Multidimensional Scaling of Spearman Distances in the Extended Permutation Polytope

A multidimensional unfolding technique that is not prone to degenerate solutions and is based on multidimensional scaling of a complete data matrix is proposed: distance information about the unfolding data and about the distances both among judges and among objects is included in the complete matrix. The latter information is derived from the permutation polytope supplemented with the objects, called the preference sphere. In this sphere, distances are measured that are closely related to Spearman's rank correlation and that are comparable among each other so that an unconditional approach is reasonable. In two simulation studies, it is shown that the proposed technique leads to acceptable recovery of given preference structures. A major practical advantage of this unfolding technique is its relatively easy implementation in existing software for multidimensional scaling.     

Feature selection in feature network models: Finding predictive subsets of features with the Positive Lasso

A set of features is the basis for the network representation of proximity data achieved by feature network models (FNMs). Features are binary variables that characterize the objects in an experiment, with some measure of proximity as response variable. Sometimes features are provided by theory and play an important role in the construction of the experimental conditions. In some research settings, the features are not known a priori. This paper shows how to generate features in this situation and how to select an adequate subset of features that takes into account a good compromise between model fit and model complexity, using a new version of least angle regression that restricts coefficients to be non‐negative, called the Positive Lasso. It will be shown that features can be generated efficiently with Gray codes that are naturally linked to the FNMs. The model selection strategy makes use of the fact that FNM can be considered as univariate multiple regression model. A simulation study shows that the proposed strategy leads to satisfactory results if the number of objects is less than or equal to 22. If the number of objects is larger than 22, the number of features selected by our method exceeds the true number of features in some conditions.     

自我面孔识别的神经机制：基于fMRI研究的ALE元分析

自我面孔识别反映了个体通过自我与他人的区分识别出自我面孔的过程。本文采用ALE元分析的方法, 对自我面孔识别的fMRI研究进行系统的定量分析, 探究自我面孔识别的神经基础。结果显示, 自我面孔识别的关键脑区包括顶上小叶、额中回、额下回、脑岛、梭状回、楔前叶和枕叶皮层。另外, 自我面孔识别可能包括两个层面的加工过程：知觉层面的加工整合过程以及由知觉引发的评价和情绪反应过程。知觉加工整合涵盖了自我面孔识别的各个加工阶段, 主要涉及枕叶、梭状回和楔前叶的功能; 而评价加工及情绪反应过程则发生在自我面孔识别的中晚期, 主要涉及顶上小叶、额中回、额下回及脑岛的功能。未来研究可结合时间和空间数据并关注脑区间的协同功能, 考察与内感受的神经关联, 开展临床研究并探索威胁信息的影响机制。     

A Creation Narrative for the Psychometric Society and <Emphasis Type="Italic">Psychometrika</Emphasis>: In the Beginning There Was Paul Horst

A review is provided for the creation of the Psychometric Society in 1935, and the establishment of its journal, Psychometrika, in 1936. This document is part of the 80th anniversary celebration for Psychometrika’s founding, held during the annual meeting of the Psychometric Society in July of 2016 in Asheville, NC.     

How to Measure and Explain Achievement Change in Large-Scale Assessments: A Rejoinder

In this rejoinder, we discuss substantive and methodological validity issues of large-scale assessments of trends in student achievement, commenting on the discussion paper by Van den Heuvel-Panhuizen, Robitzsch, Treffers, and Köller (2009). We focus on methodological challenges in deciding what to measure, how to measure it, and how to foster stability. Next, we discuss what to do with trends that are found. Finally, we reflect on how the research findings were received.     

Interpreting degenerate solutions in unfolding by use of the vector model and the compensatory distance model

In this paper, we reconsider the merits of unfolding solutions based on loss functions involving a normalization on the variance per subject. In the literature, solutions based on Stress-2 are often diagnosed to be degenerate in the majority of cases. Here, the focus lies on two frequently occurring types of degeneracies. The first type typically locates some subject points far away from a compact cluster of the other points. In the second type of solution, the object points lie on a circle. In this paper, we argue that these degenerate solutions are well fitting and informative. To reveal the information, we introduce mixtures of plots based on the ideal point model of unfolding, the vector model, and on the signed distance model. In addition to a different representation, we provide a new iterative majorization algorithm to optimize the average squared correlation between the distances in the configuration and the transformed data per individual. It is shown that this approach is equivalent to minimizing Kruskal’s Stress-2.This revised article was published online in August 2005 with the PDF paginated correctly.     

A Model-Free Diagnostic for Single-Peakedness of Item Responses Using Ordered Conditional Means

In this article we propose a model-free diagnostic for single-peakedness (unimodality) of item responses. Presuming a unidimensional unfolding scale and a given item ordering, we approximate item response functions of all items based on ordered conditional means (OCM). The proposed OCM methodology is based on Thurstone &; Chave's (1929) Thurstone, L. L. and Chave, E. J. 1929. The measurement of attitude: A psychophysical method and some experiments with a scale for measuring attitude toward the church. Chicago, IL: University of Chicago Press.. [Crossref] , [Google Scholar] criterion of irrelevance, which is a graphical, exploratory method for evaluating the “relevance” of dichotomous attitude items. We generalized this criterion to graded response items and quantified the relevance by fitting a unimodal smoother. The resulting goodness-of-fit was used to determine item fit and aggregated scale fit. Based on a simulation procedure, cutoff values were proposed for the measures of item fit. These cutoff values showed high power rates and acceptable Type I error rates. We present 2 applications of the OCM method. First, we apply the OCM method to personality data from the Developmental Profile; second, we analyze attitude data collected by Roberts and Laughlin (1996) Roberts, J. S. and Laughlin, J. E. 1996. A unidimensional item response model for unfolding responses from a graded disagree-agree response scale. Applied Psychological Measurement, 20: 231–255. [Crossref], [Web of Science ®] , [Google Scholar] concerning opinions of capital punishment.