Fuzzy partition models for fitting a set of partitions

Methodology is described for fitting a fuzzy consensus partition to a set of partitions of the same set of objects. Three models defining median partitions are described: two of them are obtained from a least-squares fit of a set of membership functions, and the third (proposed by Pittau and Vichi) is acquired from a least-squares fit of a set of joint membership functions. The models are illustrated by application to both a set of hard partitions and a set of fuzzy partitions and comparisons are made between them and an alternative approach to obtaining a consensus fuzzy partition proposed by Sato and Sato; a discussion is given of some interesting differences in the results.We are grateful to Dr. M.G. Pittau for carrying out the analyses of the macroeconomic data using the method of Sato and Sato (1994).     

Compact integer-programming models for extracting subsets of stimuli from confusion matrices

This paper presents an integer linear programming formulation for the problem of extracting a subset of stimuli from a confusion matrix. The objective is to select stimuli such that total confusion among the stimuli is minimized for a particular subset size. This formulation provides a drastic reduction in the number of variables and constraints relative to a previously proposed formulation for the same problem. An extension of the formulation is provided for a biobjective problem that considers both confusion and recognition in the objective function. Demonstrations using an empirical interletter confusion matrix from the psychological literature revealed that a commercial branch-and-bound integer programming code was always able to identify optimal solutions for both the single-objective and biobjective formulations within a matter of seconds. A further extension and demonstration of the model is provided for the extraction of multiple subsets of stimuli, wherein the objectives are to maximize similarity within subsets and minimize similarity between subsets.     

Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions

Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called “double semi-partialing”, or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman–Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions. Special thanks go to Cajo Ter Braak, Philip Hans Franses, Patrick Houweling, Pierre Legendre, three anonymous reviewers, the associate editor, and the editor for comments.     

A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning

Perhaps the most common criterion for partitioning a data set is the minimization of the within-cluster sums of squared deviation from cluster centroids. Although optimal solution procedures for within-cluster sums of squares (WCSS) partitioning are computationally feasible for small data sets, heuristic procedures are required for most practical applications in the behavioral sciences. We compared the performances of nine prominent heuristic procedures for WCSS partitioning across 324 simulated data sets representative of a broad spectrum of test conditions. Performance comparisons focused on both percentage deviation from the “best-found” WCSS values, as well as recovery of true cluster structure. A real-coded genetic algorithm and variable neighborhood search heuristic were the most effective methods; however, a straightforward two-stage heuristic algorithm, HK-means, also yielded exceptional performance. A follow-up experiment using 13 empirical data sets from the clustering literature generally supported the results of the experiment using simulated data. Our findings have important implications for behavioral science researchers, whose theoretical conclusions could be adversely affected by poor algorithmic performances.     

Ethical Problems Inherent in Psychological Research based on Internet Communication as Stored Information

This paper deals with certain ethical problems inherent in psychological research based on internet communication as stored information. Section 1 contains an analysis of research on Internet debates. In particular, it takes into account a famous example of deception for psychology research purposes. In section 2, the focus is on research on personal data in texts published on the Internet. Section 3 includes an attempt to formulate some ethical principles and guidelines, which should be regarded as fundamental in research on stored information.     

Distance patterns and disposal sites in rural area homicides committed in Finland

The present study examined offender characteristics, distance patterns, and the nature of disposal sites in rural area homicides. Pre‐trial investigation files of cases where victims' bodies were found in rural areas in 1994–2005 (n = 46) and forensic psychiatric examination reports of the offenders were content‐analysed. Psychopathy Check List‐Revised was used to assess psychopathy. Investigators of these homicides filled out a questionnaire on the offender's familiarity with the body disposal area, and MapInfo was used to measure offender/victim‐residence‐to‐crime‐to‐body‐disposal‐site distances. Rural area homicides more frequently involved multiple offenders who were significantly younger than offenders in other homicides. Of the victims, 73% were found in woods and 27% in water. Offenders were familiar with disposal sites in over half of the cases. The victim's gender, close relationship with the offender, and the offender's violent crime history were associated with longer homicide‐scene‐to‐body‐disposal‐site distances. The number of inhabitants and offender's violent crime history were related to longer offender‐residence‐to‐body‐disposal‐site distances. Offender's age, intelligence, or psychopathology bore no significant association with the distance patterns. The results can be applied when searching missing persons in homicide investigations and in prioritising suspects in rural area homicides. Copyright © 2008 John Wiley & Sons, Ltd.     

A mobile computing solution for collecting functional analysis data on a Pocket PC

The present paper provides a task analysis for creating a computerized data system using a Pocket PC and Microsoft Visual Basic. With Visual Basic software and any handheld device running the Windows Moble operating system, this task analysis will allow behavior analysts to program and customize their own functional analysis data-collection system. The program will allow the user to select the type of behavior to be recorded, choose between interval and frequency data collection, and summarize data for graphing and analysis. We also provide suggestions for customizing the data-collection system for idiosyncratic research and clinical needs.     

元数据管理中元仓储库的实现方法

元数据管理是数据仓库的关键技术之一,是数据仓库建设实施过程中极其重要的一个环节。元仓储库的设计与实现工作是元数据管理工作的基础。本文以OMG组织的CWM标准为基础,提出了一套元仓储库的实现方法及福建电力元仓储库的建设方法。     

Dual imputation model for incomplete longitudinal data

Missing values are a practical issue in the analysis of longitudinal data. Multiple imputation (MI) is a well‐known likelihood‐based method that has optimal properties in terms of efficiency and consistency if the imputation model is correctly specified. Doubly robust (DR) weighing‐based methods protect against misspecification bias if one of the models, but not necessarily both, for the data or the mechanism leading to missing data is correct. We propose a new imputation method that captures the simplicity of MI and protection from the DR method. This method integrates MI and DR to protect against misspecification of the imputation model under a missing at random assumption. Our method avoids analytical complications of missing data particularly in multivariate settings, and is easy to implement in standard statistical packages. Moreover, the proposed method works very well with an intermittent pattern of missingness when other DR methods can not be used. Simulation experiments show that the proposed approach achieves improved performance when one of the models is correct. The method is applied to data from the fireworks disaster study, a randomized clinical trial comparing therapies in disaster‐exposed children. We conclude that the new method increases the robustness of imputations.