A variable neighborhood search method for generalized blockmodeling of two-mode binary matrices

The clustering of two-mode proximity matrices is a challenging combinatorial optimization problem that has important applications in the quantitative social sciences. We focus on one particular type of problem related to the clustering of a two-mode binary matrix, which is relevant to the establishment of generalized blockmodels for social networks. In this context, clusters for the rows of the two-mode matrix intersect with clusters of the columns to form blocks, which should ideally be either complete (all 1s) or null (all 0s). A new procedure based on variable neighborhood search is presented and compared to an existing two-mode K-means clustering algorithm. The new procedure generally provided slightly greater explained variation; however, both methods yielded exceptional recovery of cluster structure.     

ADPROCLUS: a graphical user interface for fitting additive profile clustering models to object by variable data matrices

In many areas of psychology, one is interested in disclosing the underlying structural mechanisms that generated an object by variable data set. Often, based on theoretical or empirical arguments, it may be expected that these underlying mechanisms imply that the objects are grouped into clusters that are allowed to overlap (i.e., an object may belong to more than one cluster). In such cases, analyzing the data with Mirkin’s additive profile clustering model may be appropriate. In this model: (1) each object may belong to no, one or several clusters, (2) there is a specific variable profile associated with each cluster, and (3) the scores of the objects on the variables can be reconstructed by adding the cluster-specific variable profiles of the clusters the object in question belongs to. Until now, however, no software program has been publicly available to perform an additive profile clustering analysis. For this purpose, in this article, the ADPROCLUS program, steered by a graphical user interface, is presented. We further illustrate its use by means of the analysis of a patient by symptom data matrix.     

Model based clustering of large data sets: Tracing the development of spelling ability

There are two main theories with respect to the development of spelling ability: the stage model and the model of overlapping waves. In this paper exploratory model based clustering will be used to analyze the responses of more than 3500 pupils to subsets of 245 items. To evaluate the two theories, the resulting clusters will be ordered along a developmental dimension using an external criterion. Solutions for three statistical problems will be given: (1) an algorithm that can handle large data sets and only renders non-degenerate clusters; (2) a goodness of fit test that is not affected by the fact that the number of possible response vectors by far out-weights the number of observed response vectors; and (3) a new technique,data expunction, that can be used to evaluate goodness-of-fit tests if the missing data mechanism is known. Research supported by a grant (NWO 411-21-006) of the Dutch Organization for Scientific Research.     

Investigating the anticipatory nature of pattern perception in sport

The aim of the present study was to examine the anticipatory nature of pattern perception in sport by using static and moving basketball patterns across three different display types. Participants of differing skill levels were included in order to determine whether the effects would be moderated by the knowledge and experience of the observer in the same manner reported previously for simple images. The results from a pattern recognition task showed that both expert and recreational participants were more likely to anticipate the next likely state of a pattern when it was presented as a moving video, but only the experts appeared to have the depth of understanding required to elicit the same anticipatory encoding for patterns presented as schematic images. The results extend those reported in previous research and provide further evidence of an anticipatory encoding in pattern perception for images containing complex, interrelated patterns.     

A graphical method to aid the sequential analysis of observational data

In this paper, two methods of sequential analysis are applied to hypothetical observational data. The first method employs the conventional “conditional probability” approach, illustrated using the GSEQ program (Bakeman & Quera, 1995). In order to overcome some of the difficulties associated with the conditional probability approach, the second method employs a new “normalized and pooled” approach. Essentially, by normalizing periods of time preceding, during, and following each occurrence of a nominated “given” behavior, the proportion of time units devoted to a “target” behavior can be estimated and then pooled across all occurrences of the given behavior. A summary diagram representing the likelihood that the target behavior precedes, occurs concurrently with, and follows the given behavior can then be constructed. Elements of this summary diagram can also be quantified. Given the graphical nature of the output, and its ease of use, the normalized and pooled approach may help to promote the use of sequential analysis in applied settings.     

On methods in the analysis of profile data

This paper is concerned with methods for analyzing quantitative, non-categorical profile data, e.g., a battery of tests given to individuals in one or more groups. It is assumed that the variables have a multinormal distribution with an arbitrary variance-covariance matrix. Approximate procedures based on classical analysis of variance are presented, including an adjustment to the degrees of freedom resulting in conservativeF tests. These can be applied to the case where the variance-covariance matrices differ from group to group. In addition, exact generalized multivariate analysis methods are discussed. Examples are given illustrating both techniques.We are indebted to Mrs. Norma French for performing all the calculations appearing in this paper.     

A methodology for the capture and analysis of hybrid data: A case study of program debugging

This article describes a methodology for the capture and analysis of hybrid data. A case study in the field of reasoning with multiple representations—specifically, in computer programming—is presented to exemplify the use of the methodology. The hybrid data considered comprise computer interaction logs, audio recordings, and data about visual attention focus. The capture of the focus of visual attention data is performed with software. The software employed tracks the user’s visual attention by blurring parts of the stimuli presented on the screen and allowing the participant to see only a small region of it at any one time. These hybrid data are analyzed via a methodology that combines qualitative and quantitative approaches. The article describes the software tool employed and the analytic methodology, and also discusses data capture issues and limitations of the approach.     

A set-theoretical approach to the problem of hierarchical clustering

A set-theoretical formalization is developed for the problem of generating hierarchically organized collections of subsets, or to use a phrase common in the applied substantive literature, for the problem of hierarchical clustering. A number of terms are introduced to characterize those clustering methods that attempt to limit the size of the overlap between each pair of subsets constructed at a specific “compactness” level. Several examples, motivated primarily by graph theory, are discussed briefly to illustrate the various set-theoretical concepts presented.     

The Repeated Evaluation Technique (RET). A method to capture dynamic effects of innovativeness and attractiveness

Innovativeness is defined as ‘originality by virtue of introducing new ideas’. Thus, innovative designs often break common visual habits and are evaluated as relatively unattractive at first sight (Leder & Carbon, 2005 ). In most empirical studies, attractiveness is measured only once. These measures do not capture the dynamic aspects of innovation. This paper presents a dynamic procedure, the Repeated Evaluation Technique (RET), that improves the validity of attractiveness evaluations. RET simulates time and exposure effects of everyday life. Using RET, we investigated the appreciation of different car designs varying in innovativeness and curvature. While the mere exposure theory (Zajonc, 1968 ) would predict a general increase of liking in increasing exposure, RET revealed dissociate effects depending on innovativeness. Only innovative designs showed an increase in attractiveness. Low innovative designs were rated as being relatively attractive in the beginning, but did not profit from elaboration due to RET. Copyright © 2005 John Wiley & Sons, Ltd.     

A semiautomatic method for adding behavioral data to videotape records in combination with the Noldus Observer system

The Observer 3.0 (Noldus, 1991) was used as the base for a time-saving fine analysis of animal behavior. A PC controlled a shuttlebox and a shuttlebox control unit. Learning behavior was recorded on videotapes by an S-VHS camcorder with vertical interval time code (VITC), which was used to generate an observational data file (ODF). The observational system was enhanced by automatically recorded status signals from the PC as event markers on the soundtrack of the videotape. These signals were used to subdivide a training session into numbered trials during generation of the ODF. They were later used for a PC-aided fine analysis of behavioral events, such as attention responses (AR) and orienting responses (OR) during shuttlebox learning. In this way, a large number of both automatically inserted PC event markers and manually inserted, key-defined behavioral events were registered and analyzed effectively in a semiautomatic fashion. The system is illustrated with an analysis of shuttlebox avoidance data from gerbils (Meriones unguiculatus).     

A marginal maximum likelihood method for the vector threshold model to analyze dichotomous choice data

Abstract: At least two types of models, the vector model and the unfolding model can be used for the analysis of dichotomous choice data taken from, for example, the pick any/ n method. The previous vector threshold models have a difficulty with estimation of the nuisance parameters such as the individual vectors and thresholds. This paper proposes a new probabilistic vector threshold model, where, unlike the former vector models, the angle that defines an individual vector is a random variable, and where the marginal maximum likelihood estimation method using the expectation-maximization algorithm is adopted to avoid incidental parameters. The paper also attempts to discuss which of the two models is more appropriate to account for dichotomous choice data. Two sets of dichotomous choice data are analyzed by the model.     

A theoretical solution to the problem of personality-situation interaction

There has been much debate and research on the relative roles and the interaction of personality and situational factors in the determination of behaviour, and more broadly on the relative status of psychology and social science as explanations for human action. Much of this is based on a misconception, to which a branch of applied mathematics called automata theory suggests the answer. If our objective is redefined as the explanation of situation-behaviour relations by reference to psychological processes, rather than the explanation of behaviour by reference to situational and psychological factors, then many of the confusions and inconsistencies disappear. On this view, behaviour varies from situation to situation in a way which varies from person to person. However, the two processes are of different kinds and different orders, and they are only misrepresented as commensurable causes working interactively to produce a common effect. Alternative formulations are presented, and their implications discussed for research in individual and social psychology.