bluuebunny/crossref_metadata_2025 huggingface dataset:
A Quasi-Nonmetric Method for Multidimensional Scaling Via an Extended Euclidean Model
Authors: Winsberg Suzanne , Douglas Carroll J.
Published: January 2006

An Extended Two-Way Euclidean Multidimensional Scaling (MDS) model which assumes both common and specific dimensions is described and contrasted with the “standard” (Two-Way) MDS model. In this Extended Two-Way Euclidean model the n stimuli (or other objects) are assumed to be characterized by coordinates on R common dimensions. In addition each stimulus is assumed to have a dimension (or dimensions) specific to it alone. The overall distance between object i and object j then is defined as the square root of the ordinary squared Euclidean distance plus terms denoting the specificity of each object. The specificity, sj, can be thought of as the sum of squares of coordinates on those dimensions specific to object i, all of which have nonzero coordinates only for object i. (In practice, we may think of there being just one such specific dimension for each object, as this situation is mathematically indistinguishable from the case in which there are more than one.)

We further assume that δij =F(dij) +eij where δij is the proximity value (e.g., similarity or dissimilarity) of objects i and j, dij is the extended Euclidean distance defined above, while eij is an error term assumed i.i.d. N(0, σ2). F is assumed either a linear function (in the metric case) or a monotone spline of specified form (in the quasi-nonmetric case). A numerical procedure alternating a modified Newton-Raphson algorithm with an algorithm for fitting an optimal monotone spline (or linear function) is used to secure maximum likelihood estimates of the paramstatistics) can be used to test hypotheses about the number of common dimensions, and/or the existence of specific (in addition to R common) dimensions.

This approach is illustrated with applications to both artificial data and real data on judged similarity of nations.
On the Relationship between the Higher-Order Factor Model and the Hierarchical Factor Model
Authors: Yung Yiu-Fai , Thissen David , McLeod Lori D.
Published: January 2006

The relationship between the higher-order factor model and the hierarchical factor model is explored formally. We show that the Schmid-Leiman transformation produces constrained hierarchical factor solutions. Using a generalized Schmid-Leiman transformation and its inverse, we show that for any unconstrained hierarchical factor model there is an equivalent higher-order factor model with direct effects (loadings) on the manifest variables from the higher-order factors. Therefore, the class of higher-order factor models (without direct effects of higher-order factors) is nested within the class of unconstrained hierarchical factor models. In light of these formal results, we discuss some implications for testing the higher-order factor model and the issue of general factor. An interesting aspect concerning the efficient fitting of the higher-order factor model with direct effects is noted.
Logit Models and Logistic Regressions for Social Networks: I. An Introduction to Markov Graphs and p
Authors: Wasserman Stanley , Pattison Philippa
Published: January 2006

Spanning nearly sixty years of research, statistical network analysis has passed through (at least) two generations of researchers and models. Beginning in the late 1930's, the first generation of research dealt with the distribution of various network statistics, under a variety of null models. The second generation, beginning in the 1970's and continuing into the 1980's, concerned models, usually for probabilities of relational ties among very small subsets of actors, in which various simple substantive tendencies were parameterized. Much of this research, most of which utilized log linear models, first appeared in applied statistics publications.

But recent developments in social network analysis promise to bring us into a third generation. The Markov random graphs of Frank and Strauss (1986) and especially the estimation strategy for these models developed by Strauss and Ikeda (1990; described in brief in Strauss, 1992), are very recent and promising contributions to this field. Here we describe a large class of models that can be used to investigate structure in social networks. These models include several generalizations of stochastic blockmodels, as well as models parameterizing global tendencies towards clustering and centralization, and individual differences in such tendencies. Approximate model fits are obtained using Strauss and Ikeda's (1990) estimation strategy.

In this paper we describe and extend these models and demonstrate how they can be used to address a variety of substantive questions about structure in social networks.
Stochastic Ordering Using the Latent Trait and the Sum Score in Polytomous IRT Models
Authors: Hemker Bas T. , Sijtsma Klaas , Molenaar Ivo W. , Junker Brian W.
Published: January 2006

In a restricted class of item response theory (IRT) models for polytomous items the unweighted total score has monotone likelihood ratio (MLR) in the latent trait ϑ. MLR implies two stochastic ordering (SO) properties, denoted SOM and SOL, which are both weaker than MLR, but very useful for measurement with IRT models. Therefore, these SO properties are investigated for a broader class of IRT models for which the MLR property does not hold.

In this study, first a taxonomy is given for nonparametric and parametric models for polytomous items based on the hierarchical relationship between the models. Next, it is investigated which models have the MLR property and which have the SO properties. It is shown that all models in the taxonomy possess the SOM property. However, counterexamples illustrate that many models do not, in general, possess the even more useful SOL property.
Constrained Maximum Likelihood Estimation of Two-Level Covariance Structure Model Via EM Type Algorithms
Authors: Lee Sik-Yum , Tsang Sin-Yu
Published: January 2006

In this paper, the constrained maximum likelihood estimation of a two-level covariance structure model with unbalanced designs is considered. The two-level model is reformulated as a single-level model by treating the group level latent random vectors as hypothetical missing-data. Then, the popular EM algorithm is extended to obtain the constrained maximum likelihood estimates. For general nonlinear constraints, the multiplier method is used at the M-step to find the constrained minimum of the conditional expectation. An accelerated EM gradient procedure is derived to handle linear constraints. The empirical performance of the proposed EM type algorithms is illustrated by some artifical and real examples.
Mixtures of Conditional Mean- and Covariance-Structure Models
Authors: Arminger Gerhard , Stein Petra , Wittenberg Jörg
Published: January 2006

Models and parameters of finite mixtures of multivariate normal densities conditional on regressor variables are specified and estimated. We consider mixtures of multivariate normals where the expected value for each component depends on possibly nonnormal regressor variables. The expected values and covariance matrices of the mixture components are parameterized using conditional mean- and covariance-structures. We discuss the construction of the likelihood function, estimation of the mixture model with regressors using three different EM algorithms, estimation of the asymptotic covariance matrix of parameters and testing for the number of mixture components. In addition to simulation studies, data on food preferences are analyzed.
Approximating a Symmetric Matrix
Authors: Bailey R. A. , Gower J. C.
Published: January 2006

We examine the least squares approximation C to a symmetric matrix B, when all diagonal elements get weight w relative to all nondiagonal elements. When B has positivity p and C is constrained to be positive semi-definite, our main result states that, when w ≥1/2, then the rank of C is never greater than p, and when w ≤1/2 then the rank of C is at least p. For the problem of approximating a given n × n matrix with a zero diagonal by a squared-distance matrix, it is shown that the sstress criterion leads to a similar weighted least squares solution with w =(n+2)/4; the main result remains true. Other related problems and algorithmic consequences are briefly discussed.
A Dynamic Generalization of the Rasch Model
Authors: Verhelst N. D. , Glas C. A. W.
Published: January 2006

In the present paper a model for describing dynamic processes is constructed by combining the common Rasch model with the concept of structurally incomplete designs. This is accomplished by mapping each item on a collection of virtual items, one of which is assumed to be presented to the respondent dependent on the preceding responses and/or the feedback obtained. It is shown that, in the case of subject control, no unique conditional maximum likelihood (CML) estimates exist, whereas marginal maximum likelihood (MML) proves a suitable estimation procedure. A hierarchical family of dynamic models is presented, and it is shown how to test special cases against more general ones. Furthermore, it is shown that the model presented is a generalization of a class of mathematical learning models, known as Luce's beta-model.
A Scale-Invariant Treatment for Recursive Path Models
Authors: McDonald Roderick P. , Parker Prudence M. , Ishizuka Tomoichi
Published: January 2006

A reparameterization is formulated that yields estimates of scale-invariant parameters in recursive path models with latent variables, and (asymptotically) correct standard errors, without the use of constrained optimization. The method is based on the logical structure of the reticular action model.
A General Approach to Categorical Data Analysis with Missing Data, using Generalized Linear Models with Composite Links
Authors: Rindskopf David
Published: January 2006

A general approach for analyzing categorical data when there are missing data is described and illustrated. The method is based on generalized linear models with composite links. The approach can be used (among other applications) to fill in contingency tables with supplementary margins, fit loglinear models when data are missing, fit latent class models (without or with missing data on observed variables), fit models with fused cells (including many models from genetics), and to fill in tables or fit models to data when variables are more finely categorized for some cases than others. Both Newton-like and EM methods are easy to implement for parameter estimation.
Structural Equation Models with Continuous and Polytomous Variables
Authors: Lee Sik-Yum , Poon Wai-Yin , Bentler P. M.
Published: January 2006

A two-stage procedure is developed for analyzing structural equation models with continuous and polytomous variables. At the first stage, the maximum likelihood estimates of the thresholds, polychoric covariances and variances, and polyserial covariances are simultaneously obtained with the help of an appropriate transformation that significantly simplifies the computation. An asymptotic covariance matrix of the estimates is also computed. At the second stage, the parameters in the structural covariance model are obtained via the generalized least squares approach. Basic statistical properties of the estimates are derived and some illustrative examples and a small simulation study are reported.
Sensitivity of a Linear Composite of Predictor Items to differential Item Weighting
Authors: Rozeboom William W.
Published: January 2006

Many authors have demonstrated for idealized item configurations that equal item weights are often virtually as good for a particular predictive purpose as the item weights that are theoretically optimal. What has not been heretofore clear, however, is what happens to the similarity between weighted and unweighted composites of the same items when the item configuration's variance structure is complex.
Estimating the Standard Errors of Rotated Factor Loadings by Jackknifing
Authors: Clarkson Douglas B.
Published: January 2006

The jackknife by groups and modifications of the jackknife by groups are used to estimate standard errors of rotated factor loadings for selected populations in common factor model maximum likelihood factor analysis. Simulations are performed in which t-statistics based upon these jackknife estimates of the standard errors are computed. The validity of the t-statistics and their associated confidence intervals is assessed. Methods are given through which the computational efficiency of the jackknife may be greatly enhanced in the factor analysis model.
On the Equivalence of Two Oblique Congruence Rotation Methods, and Orthogonal Approximations
Authors: ten Berge Jos M. F.
Published: January 2006

Tucker's method of oblique congruence rotation is shown to be equivalent to a procedure by Meredith. This implies that Monte Carlo studies on congruence by Nesselroade, Baltes and Labouvie and by Korth and Tucker are highly comparable. The problem of rotating two matrices orthogonally to maximal congruence has not yet been solved. An approximate solution to this problem can be derived from Tucker's method. Even better results can be obtained from a Procrustes rotation followed by rotation to simple structure.
Robust Inference with Binary Data
Authors: Victoria-Feser Maria-Pia
Published: January 2006

In this paper robustness properties of the maximum likelihood estimator (MLE) and several robust estimators for the logistic regression model when the responses are binary are analysed. It is found that the MLE and the classical Rao's score test can be misleading in the presence of model misspecification which in the context of logistic regression means either misclassification's errors in the responses, or extreme data points in the design space. A general framework for robust estimation and testing is presented and a robust estimator as well as a robust testing procedure are presented. It is shown that they are less influenced by model misspecifications than their classical counterparts. They are finally applied to the analysis of binary data from a study on breastfeeding.
Heterogeneous Factor Analysis Models: A Bayesian Approach
Authors: Ansari Asim , Jedidi Kamel , Dube Laurette
Published: January 2006

Multilevel factor analysis models are widely used in the social sciences to account for heterogeneity in mean structures. In this paper we extend previous work on multilevel models to account for general forms of heterogeneity in confirmatory factor analysis models. We specify various models of mean and covariance heterogeneity in confirmatory factor analysis and develop Markov Chain Monte Carlo (MCMC) procedures to perform Bayesian inference, model checking, and model comparison.

We test our methodology using synthetic data and data from a consumption emotion study. The results from synthetic data show that our Bayesian model perform well in recovering the true parameters and selecting the appropriate model. More importantly, the results clearly illustrate the consequences of ignoring heterogeneity. Specifically, we find that ignoring heterogeneity can lead to sign reversals of the factor covariances, inflation of factor variances and underappreciation of uncertainty in parameter estimates. The results from the emotion study show that subjects vary both in means and covariances. Thus traditional psychometric methods cannot fully capture the heterogeneity in our data.
Statistical Inference of Minimum Rank Factor Analysis
Authors: Shapiro Alexander , Ten Berge Jos M. F.
Published: January 2006

For any given number of factors, Minimum Rank Factor Analysis yields optimal communalities for an observed covariance matrix in the sense that the unexplained common variance with that number of factors is minimized, subject to the constraint that both the diagonal matrix of unique variances and the observed covariance matrix minus that diagonal matrix are positive semidefinite. As a result, it becomes possible to distinguish the explained common variance from the total common variance. The percentage of explained common variance is similar in meaning to the percentage of explained observed variance in Principal Component Analysis, but typically the former is much closer to 100 than the latter. So far, no statistical theory of MRFA has been developed. The present paper is a first start. It yields closed-form expressions for the asymptotic bias of the explained common variance, or, more precisely, of the unexplained common variance, under the assumption of multivariate normality. Also, the asymptotic variance of this bias is derived, and also the asymptotic covariance matrix of the unique variances that define a MRFA solution. The presented asymptotic statistical inference is based on a recently developed perturbation theory of semidefinite programming. A numerical example is also offered to demonstrate the accuracy of the expressions.
Variational Approximations for Categorical Causal Modeling With Latent Variables
Authors: Humphreys K. , Titterington D. M.
Published: January 2006

Latent class models in the social and behavioral sciences have remained structurally simple. One reason for this is that inference in statistical models can be computationally difficult. Methods for approximate inference, known as variational approximations, which have been developed in the machine learning, graphical modeling and statistical physics literatures, can be used to alleviate the computational difficulties of inference for latent variable models. The aim of the present article is to set these methods alongside some social and behavioral science literature to which they are relevant, and in particular to consider their potential for “categorical causal modeling”, using latent class analysis. We have collated a number of popular categorical-data models with latent variables and causal structure, typically incorporating a Markovian structure. The efficacy of the approximation methods has been demonstrated through simulations related to an important behavioral science model.
Using the Conditional Grade-Of-Membership Model to Assess Judgment Accuracy
Authors: Cooil Bruce , Varki Sajeev
Published: January 2006

Consider the case where J instruments are used to classify each of I objects relative to K nominal categories. The conditional grade-of-membership (GoM) model provides a method of estimating the classification probabilities of each instrument (or “judge”) when the objects being classified consist of both pure types that lie exclusively in one of K nominal categories, and mixtures that lie in more than one category. Classification probabilities are identifiable whenever the sample of GoM vectors includes pure types from each category. When additional, relatively mild, assumptions are made about judgment accuracy, the identifiable correct classification probabilities are the greatest lower bounds among all solutions that might correspond to the observed multinomial process, even when the unobserved GoM vectors do not include pure types from each category. Estimation using the conditional GoM model is illustrated on a simulated data set. Further simulations show that the estimates of the classification probabilities are relatively accurate, even when the sample contains only a small percentage of approximately pure objects.
A Monte Carlo Investigation of Conjoint Analysis Index-of-Fit: Goodness of Fit, Significance and Power
Authors: Umesh U. N. , Mishra Sanjay
Published: January 2006

Researchers in the field of conjoint analysis know the index-of-fit values worsen as the judgmental error of evaluation increases. This simulation study provides guidelines on the goodness of fit based on distribution of index-of-fit for different conjoint analysis designs. The study design included the following factors: number of profiles, number of attributes, algorithm used and judgmental model used. Critical values are provided for deciding the statistical significance of conjoint analysis results. Using these cumulative distributions, the power of the test used to reject the null hypothesis of random ranking is calculated. The test is found to be quite powerful except for the case of very small residual degrees of freedom.
A Three-Stage Estimation Procedure for Structural Equation Models with Polytomous Variables
Authors: Lee Sik-Yum , Poon Wai-Yin , Bentler P. M.
Published: January 2006

This paper is concerned with the analysis of structural equation models with polytomous variables. A computationally efficient three-stage estimator of the thresholds and the covariance structure parameters, based on partition maximum likelihood and generalized least squares estimation, is proposed. An example is presented to illustrate the method.
A Latent-Trait Based Reliability Estimate and Upper Bound
Authors: Nicewander W. Alan
Published: January 2006

An estimate and an upper-bound estimate for the reliability of a test composed of binary items is derived from the multidimensional latent trait theory proposed by Bock and Aitkin (1981). The estimate derived here is similar to internal consistency estimates (such as coefficient alpha) in that it is a function of the correlations among test items; however, it is not a lowerbound estimate as are all other similar methods.

An upper bound to reliability that is less than unity does not exist in the context of classical test theory. The richer theoretical background provided by Bock and Aitkin's latent trait model has allowed the development of an index (called δ here) that is always greater-than or equal-to the reliability coefficient for a test (and is less-than or equal-to one). The upper bound estimate of reliability has practical uses—one of which makes use of the “greatest lower bound”.
A Maximum Likelihood Approach to Test Validation with Missing and Censored Dependent Variables
Authors: Gross Alan L.
Published: January 2006

A maximum likelihood approach is described for estimating the validity of a test (x) as a predictor of a criterion variable (y) when there are both missing and censored y scores present in the data set. The missing data are due to selection on a latent variable (ys) which may be conditionally related to y given x. Thus, the missing data may not be missing random. The censoring process in due to the presence of a floor or ceiling effect. The maximum likelihood estimates are constructed using the EM algorithm. The entire analysis is demonstrated in terms of hypothetical data sets.
Asymptotic Standard Errors of IRT Observed-Score Equating Methods
Authors: Ogasawara Haruhiko
Published: January 2006

A method of the IRT observed-score equating using chain equating through a third test without equating coefficients is presented with the assumption of the three-parameter logistic model. The asymptotic standard errors of the equated scores by this method are obtained using the results given by M. Liou and P.E. Cheng. The asymptotic standard errors of the IRT observed-score equating method using a synthetic examinee group with equating coefficients, which is a currently used method, are also provided. Numerical examples show that the standard errors by these observed-score equating methods are similar to those by the corresponding true score equating methods except in the range of low scores.
Some Properties of the Correlation Matrix of Dichotomous Guttman Items
Authors: Zwick Rebecca
Published: January 2006

Both the elements and the eigenvalues of the Pearson correlation matrix of dichotomous Guttman-scalable items can be expressed as simple functions of the number of items if the score distribution is uniform and there is an equal number of items at each difficulty level. Even when these special conditions do not hold, the correlations can often be expressed in a simple form by assuming a particular score distribution.
Measurement Error and ANCOVA: Functional and Structural Relationship Approaches
Authors: Raaijmakers Jeroen G. W. , Pieters Jo P. M.
Published: January 2006

This article discusses alternative procedures to the standard F-test for ANCOVA in case the covariate is measured with error. Both a functional and a structural relationship approach are described. Examples of both types of analysis are given for the simple two-group design. Several cases are discussed and special attention is given to issues of model identifiability. An approximate statistical test based on the functional relationship approach is described. On the basis of Monte Carlo simulation results it is concluded that this testing procedure is to be preferred to the conventional F-test of the ANCOVA null hypothesis. It is shown how the standard null hypothesis may be tested in a structural relationship approach. It is concluded that some knowledge of the reliability of the covariate is necessary in order to obtain meaningful results.
On Quantifying Different Types of Categorical Data
Authors: Nishisato Shizuhiko
Published: January 2006

In quantifying categorical data, constraints play an important role in characterizing the outcome. In the Guttman-type quantification of contingency tables and multiple-choice data (incidence data), the trivial solution due to the marginal constraints is typically removed before quantification; this removal, however, has the effect of distorting the shape of the total space. Awareness of this is important for the interpretation of the quantified outcome. The present study provides some relevant formulas for those cases that are affected by the trivial solution and those cases that are not. The characterization of the total space used by the Guttman-type quantification and pertinent discussion are presented.
The Person Response Function as a Tool in Person-Fit Research
Authors: Sijtsma Klaas , Meijer Rob R.
Published: January 2006

Item responses that do not fit an item response theory (IRT) model may cause the latent trait value to be inaccurately estimated. In the past two decades several statistics have been proposed that can be used to identify nonfitting item score patterns. These statistics all yield scalar values. Here, the use of the person response function (PRF) for identifying nonfitting item score patterns was investigated. The PRF is a function and can be used for diagnostic purposes. First, the PRF is defined in a class of IRT models that imply an invariant item ordering. Second, a person-fit method proposed by Trabin & Weiss (1983) is reformulated in a nonparametric IRT context assuming invariant item ordering, and statistical theory proposed by Rosenbaum (1987a) is adapted to test locally whether a PRF is nonincreasing. Third, a simulation study was conducted to compare the use of the PRF with the person-fit statistic ZU3. It is concluded that the PRF can be used as a diagnostic tool in person-fit research.
Bayesian Estimation of a Multilevel IRT Model Using Gibbs Sampling
Authors: Fox Jean-Paul , Glas Cees A. W.
Published: January 2006

In this article, a two-level regression model is imposed on the ability parameters in an item response theory (IRT) model. The advantage of using latent rather than observed scores as dependent variables of a multilevel model is that it offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to observed scores, latent scores are test-independent, which offers the possibility of using results from different tests in one analysis where the parameters of the IRT model and the multilevel model can be concurrently estimated. The two-parameter normal ogive model is used for the IRT measurement model. It will be shown that the parameters of the two-parameter normal ogive model and the multilevel model can be estimated in a Bayesian framework using Gibbs sampling. Examples using simulated and real data are given.
On Robusiness of the Normal-Theory Based Asymptotic Distributions of Three Reliability Coefficient Estimates
Authors: Yuan Ke-Hai , Bentler Peter M.
Published: January 2006

This paper studies the asymptotic distributions of three reliability coefficient estimates: Sample coefficient alpha, the reliability estimate of a composite score following a factor analysis, and the estimate of the maximal reliability of a linear combination of item scores following a factor analysis. Results indicate that the asymptotic distribution for each of the coefficient estimates, obtained based on a normal sampling distribution, is still valid within a large class of nonnormal distributions. Therefore, a formula for calculating the standard error of the sample coefficient alpha, recently obtained by van Zyl, Neudecker and Nel, applies to other reliability coefficients and can still be used even with skewed and kurtotic data such as are typical in the social and behavioral sciences.
Optimal Measurement Conditions for Spatiotemporal Eeg/Meg Source Analysis
Authors: Huizenga Hilde M. , Heslenfeld Dirk J. , Molenaar Peter C. M.
Published: January 2006

Electromagnetic source analysis yields estimates of the sources of the Electro- and/or MagnetoEncephaloGram (EEG/MEG) and thus generates a functional description of the human brain. The standard errors of the source estimates are influenced by the number and position of EEG/MEG sensors, by the number of time samples, and by the number of trials in which EEG/MEG is measured. Therefore, optimal design theory is applied to determine the required number and position of sensors, the required number of samples, and the required number of trials. To that end, the Fedorov exchange algorithm is extended to incorporate multi-response models. A simulation study and an empirical study on visual evoked potentials indicate that the proposed method is fast and reliable, and improves source precision considerably.
Errata for “Nonparametric Goodness-of-Fit Tests for the Rasch Model”
Authors: Ponocny Ivo
Published: January 2006

A Monte Carlo-algorithm realizing a family of nonpaxametric tests for the Rasch model is introduced which are conditional on the item and subject marginals. The algorithm is based on random changes of elements of data matrices without changing the maxginals; most powerful tests against all alternative hypotheses are given for which a monotone characteristic may be computed from the data matrix; alternatives may also be composed. Computation times are long, but exact p-values are approximated with the quality of approximation only depending on calculation time, but not on the number of persons. The power and the flexibility of the procedure is demonstrated by means of an empirical example where, among others, indicators for increased item similarities, the existence of subscales, violations of sufficiency of the raw score as well as learning processes were found. Many of the features described are implemented in the program T-Rasch 1.0 by Ponocny and Ponocny-Seliger (1999).
Bayesian Estimation and Model Selection in Ordered Latent Class Models for Polytomous Items
Authors: van Onna M. J. H.
Published: January 2006

In a latent class IRT model in which the latent classes are ordered on one dimension, the class specific response probabilities are subject to inequality constraints. The number of these inequality constraints increase dramatically with the number of response categories per item, if assumptions like monotonicity or double monotonicity of the cumulative category response functions are postulated. A Markov chain Monte Carlo method, the Gibbs sampler, can sample from the multivariate posterior distribution of the parameters under the constraints. Bayesian model selection can be done by posterior predictive checks and Bayes factors. A simulation study is done to evaluate results of the application of these methods to ordered latent class models in three realistic situations. Also, an example of the presented methods is given for existing data with polytomous items. It can be concluded that the Bayesian estimation procedure can handle the inequality constraints on the parameters very well. However, the application of Bayesian model selection methods requires more research.
Multidimensional Adaptive Testing with Constraints on Test Content
Authors: Veldkamp Bernard P. , van der Linden Wim J.
Published: January 2006

The case of adaptive testing under a multidimensional response model with large numbers of constraints on the content of the test is addressed. The items in the test are selected using a shadow test approach. The 0–1 linear programming model that assembles the shadow tests maximizes posterior expected Kullback-Leibler information in the test. The procedure is illustrated for five different cases of multidimensionality. These cases differ in (a) the numbers of ability dimensions that are intentional or should be considered as “nuisance dimensions” and (b) whether the test should or should not display a simple structure with respect to the intentional ability dimensions.
Combining Standardized Mean Differences Using the Method of Maximum Likelihood
Authors: Yuan Ke-Hai , Bushman Brad J.
Published: January 2006

A maximum likelihood procedure for combining standardized mean differences based on a noncentratt-distribution is proposed. With a proper data augmentation technique, an EM-algorithm is developed. Information and likelihood ratio statistics are discussed in detail for reliable inference. Simulation results favor the proposed procedure over both the existing normal theory maximum likelihood procedure and the commonly used generalized least squares procedure.
Simple Additivity of Stochastic Psychological Processes: Tests and Measures
Authors: Balakrishnan J. D.
Published: January 2006

In this article, some additive models of behavioral measures are defined, and distributional tests of them are proposed. Major theoretical results include (a) conditions for additivity of components to predict the highest level of dominance in a model-free stochastic dominance hierarchy, (b) methods of identifying the shape of the hazard rate function of an added component from certain relationships among the observable density and distribution functions, and (c) effects of stochastic dependence between components on the distributional tests. Feasibility and usefulness of the methods are demonstrated by application to choice RT and ratings experiments.
A Conservative Inverse Normal Test Procedure for Combining P-Values in Integrative Research
Authors: Saner Hilary
Published: January 2006

The use of p-values in combining the results of independent studies often involves studies that are potentially aberrant either in quality or in actual values. A robust data analysis suggests the use of a statistic that takes these aberrations into account by trimming some of the largest and smallest p-values. We present a trimmed statistic based on an inverse cumulative normal transformation of the ordered p-values, together with a simple and convenient method for approximating the distribution and first two moments of this statistic.
A Nonspatial Methodology for the Analysis of Two-Way Proximity Data Incorporating the Distance-Density Hypothesis
Authors: DeSarbo Wayne S. , Manrai Ajay K. , Burke Raymond R.
Published: January 2006

This paper presents a nonspatial operationalization of the Krumhansl (1978, 1982) distance-density model of similarity. This model assumes that the similarity between two objects i and j is a function of both the interpoint distance between i and j and the density of other stimulus points in the regions surrounding i and j. We review this conceptual model and associated empirical evidence for such a specification. A nonspatial, tree-fitting methodology is described which is sufficiently flexible to fit a number of competing hypotheses of similarity formation. A sequential, unconstrained minimization algorithm is technically presented together with various program options. Three applications are provided which demonstrate the flexibility of the methodology. Finally, extensions to spatial models, three-way analyses, and hybrid models are discussed.
Noniterative Estimation and the Choice of the Number of Factors in Exploratory Factor Analysis
Authors: Kano Yutaka
Published: January 2006

Based on the usual factor analysis model, this paper investigates the relationship between improper solutions and the number of factors, and discusses the properties of the noniterative estimation method of Ihara and Kano in exploratory factor analysis. The consistency of the Ihara and Kano estimator is shown to hold even for an overestimated number of factors, which provides a theoretical basis for the rare occurrence of improper solutions and for a new method of choosing the number of factors. The comparative study of their estimator and that based on maximum likelihood is carried out by a Monte Carlo experiment.
Generalized Structured Component Analysis
Authors: Hwang Heungsun , Takane Yoshio
Published: January 2006

We propose an alternative method to partial least squares for path analysis with components, called generalized structured component analysis. The proposed method replaces factors by exact linear combinations of observed variables. It employs a well-defined least squares criterion to estimate model parameters. As a result, the proposed method avoids the principal limitation of partial least squares (i.e., the lack of a global optimization procedure) while fully retaining all the advantages of partial least squares (e.g., less restricted distributional assumptions and no improper solutions). The method is also versatile enough to capture complex relationships among variables, including higher-order components and multi-group comparisons. A straightforward estimation algorithm is developed to minimize the criterion.
Dietary rescue of fumble—a Drosophila model for pantothenate‐kinase‐associated neurodegeneration
Authors: Yang Y. , Wu Z. , Kuo Y. M. , Zhou B.
Published: January 2006

Hallervorden–Spatz syndrome (HSS) is a devastating neurological disease, characterized by iron accumulation in the globus pallidus in the basal ganglia. Most HSS cases are caused by mutations in one of the four human pantothenate kinases (PANK2). This PANK2‐caused subgroup of HSS is sometimes referred as PKAN (pantothenate‐kinase‐associated neurodegeneration). No effective treatment for PKAN or HSS is currently available. fumble, a Drosophila mutant that carries a mutation in Drosophila Pank, has many features similar to those of PKAN patients. In this study, we used fumble as a model to evaluate various compounds or nutritional products for their possible therapeutic efficacy. While no product was found to dramatically improve the symptoms, GKE (containing Ginkgo biloba extract and flavone) and vitamin E showed statistically significant beneficial effects. Our studies indicate that pantothenate is of limited value in alleviating fumble phenotypes and also suggest that some compounds might have deleterious effects.
Ontogeny of Drevermannia and the origin of blindness in Late Devonian proetoid trilobites
Authors: LEROSEY-AUBRIL RUDY
Published: January 2006

Numerous silicified and calcareous sclerites of various sizes, recovered from the latest Famennian of Thuringia (Germany), allow the description of the first complete growth series of a blind proetoid trilobite: Drevermannia richteri. In addition, the partial ontogenetic development of Drevermannia antecurvata sp. nov. and undetermined species, Drevermannia sp. 1, are described. The proetoid anaprotaspides, associated with D. richteri, illustrate that a marked increase in larval size occurred prior to the terminal Devonian extinction event. Considering the homogeneity of larval size in older Devonian proetoids, it is interpreted as evidence that the developmental strategy of these trilobites was significantly modified. Though largely speculative, two alternative hypotheses are proposed to explain this modification. Finally, all three ontogenetic sequences show that ocular structures never develop externally in Drevermannia, but also illustrate that the development of optical nerves is not completely lost in this group. This suggests that blindness in the Drevermannia lineage followed a centripetal mode of eye reduction.
Influence of high hydrostatic pressure on the proteolysis of β-lactoglobulin A by trypsin
Authors: Chicón Rosa , Belloque Josefina , Recio Isidra , López-Fandiño Rosina
Published: January 2006

This work describes the effect of the hydrolysis time and pressure (0·1–400 MPa) on the proteolysis of β-lactoglobulin A (β-lg A) with trypsin, either conducting hydrolysis of β-lg under pressure or hydrolysing β-lg that was previously pressure treated. Pressurisation, before or during enzyme treatments, enhanced tryptic hydrolysis of β-lg. Trypsin degraded pressure-modified β-lg and pressure-induced β-lg aggregates, favouring proteolysis to the intermediate degradation products: (Val15-Arg40), (Val41-Lys69)S-S(Leu149-Ile162) and (Val41-Lys70)S-S(Leu149-Ile162). These were further cleaved at the later stages of proteolysis to yield: (Val15-Tyr20), (Ser21-Arg40), (Val41-Tyr60), (Trp61-Lys69)S-S(Leu149-Ile162) and (Trp61-Lys70)S-S(Leu149-Ile162). Particularly, in the tryptic hydrolysates of pre-pressurized β-lg, two other fragments linked by disulphide bonds: (Lys101-Arg124)S-S(Leu149-Ile162) and (Tyr102-Arg124)S-S(Leu149-Ile162), were found. These corresponded to rearrangement products induced by SH/SS exchange between the free thiol group of Cys121 and Cys160, that normally forms the disulphide bond Cys66-Cys160. In the light of these results, structural modifications of β-lg under high pressure are discussed.
Energy saving passive-dynamic gait for a one-legged hopping robot
Authors: Cherouvim Nicholas , Papadopoulos Evangelos
Published: January 2006

Increasing the energy autonomy of a hopping one-legged robot is studied in this paper. For a particular passive gait, of all those possible, the energy dissipated per unit length of travel is shown to be less than for any other gait. This optimal gait is identified analytically, by exploiting the commonly used SLIP model to simplify real robot dynamics. Both mechanical and electrical losses are considered. The accuracy of the optimal gait analytical prediction is evaluated by a numerical analysis of a realistic robot model. Finally, restrictions imposed on executing the optimal gait due to motor limitations are studied.
Environments in multiagent systems
Authors: WEYNS DANNY , SCHUMACHER MICHAEL , RICCI ALESSANDRO , VIROLI MIRKO , HOLVOET TOM
Published: January 2006

There is a growing awareness in the multiagent systems research community that the environment plays a prominent role in multiagent systems. Originating from research on behavior-based agent systems and situated multiagent systems, the importance of the environment is now gradually being accepted in the multiagent system community in general. In this paper, we put forward the environment as a first-order abstraction in multiagent systems. This position is motivated by the fact that several aspects of multiagent systems that conceptually do not belong to agents themselves should not be assigned to, or hosted inside the agents. Examples are infrastructure for communication, the topology of a spatial domain or support for the action model. These and other aspects should be considered explicitly. The environment is the natural candidate to encapsulate these aspects. We elaborate on environment engineering, and we illustrate how the environment plays a central role in a real-world multiagent system application.
Towards Semantic Web agents: Knowledge Web and AgentLink
Authors: PAYNE TERRY , TAMMA VALENTINA
Published: January 2006

This paper presents an overview on the role of agents in the Semantic Web, which was the topic of the AgentLink Technical Forum Group on ‘Semantic Web Agents’, aimed at fostering closer collaboration between the European communities working in these areas. The paper is structured in three main sections. In the first, we argue how agents are an essential component of the Semantic Web, then we provide a brief history, by no means comprehensive, of how the Semantic Web vision—that includes agents—has evolved over the past fifteen years. We then conclude, reporting on the topics presented and discussed during the Technical Forum.
Electric field-induced astrocyte alignment directs neurite outgrowth
Authors: ALEXANDER JOHN K. , FUSS BABETTE , COLELLO RAYMOND J.
Published: January 2006

The extension and directionality of neurite outgrowth are key to achieving successful target connections during both CNS development and during the re-establishment of connections lost after neural trauma. The degree of axonal elongation depends, in large part, on the spatial arrangement of astrocytic processes rich in growth-promoting proteins. Because astrocytes in culture align their processes on exposure to an electrical field of physiological strength, we sought to determine the extent to which aligned astrocytes affect neurite outgrowth. To this end, dorsal root ganglia cells were seeded onto cultured rat astrocytes that were pre-aligned by exposure to an electric field of physiological strength (500 mV mm−1). Using confocal microscopy and digital image analysis, we found that neurite outgrowth at 24 hours and at 48 hours is enhanced significantly and directed consistently along the aligned astrocyte processes. Moreover, this directed neurite outgrowth is maintained when grown on fixed, aligned astrocytes. Collectively, these results indicate that endogenous electric fields present within the developing CNS might act to align astrocyte processes, which can promote and direct neurite growth. Furthermore, these results demonstrate a simple method to produce an aligned cellular substrate, which might be used to direct regenerating neurites.
Leitlinien für die rehabilitative Behandlung von Patienten mit psychischen und psychosomatischen Störungen
Authors: Schulz Holger , Büscher Cathrin , Koch Uwe , Watzke Birgit
Published: January 2006

Zusammenfassung: Bei der Behandlung von Patienten mit psychischen Störungen ist die Berücksichtigung rehabilitativer Elemente von großer Relevanz, da ein hoher Anteil chronischer Verlaufsformen besteht, mit erheblichen Einschränkungen der Aktivitäten und Partizipation. In diesem Beitrag wird die Frage untersucht, inwieweit rehabilitative Elemente in nationalen wie internationalen Leitlinien zur Behandlung psychischer Störungen berücksichtigt sind. Dieses erfolgt für die fünf sehr häufig vorkommenden Diagnosegruppen Depression, Panikstörung, Somatoforme Störungen, Posttraumatische Belastungsstörung sowie Borderline-Persönlichkeitsstörung und wird auf der Grundlage einer systematischen Leitlinienrecherche und der Entwicklung von Beurteilungskriterien für generische und rehabilitationsspezifische Aspekte vorgenommen. Die Analysen zeigen, dass in den vorliegenden Leitlinien Elemente mit besonders hoher Spezifität für die Rehabilitation bisher nur sehr wenig berücksichtigt sind. Daraus wird ein Entwicklungsbedarf deutlich, der neben einer weiteren Konkretisierung rehabilitativer Elemente die systematische Aufbereitung der Evidenz für die Wirksamkeit dieser Elemente fokussieren sollte.