bluuebunny/crossref_metadata_2025 huggingface dataset:
Note on Ultrametric Hierarchical Clustering Algorithms
Authors: Batagelj Vladimir
Published: January 2006

Milligan presented the conditions that are required for a hierarchical clustering strategy to be monotonic, based on a formula by Lance and Williams. In the present paper, the statement of the conditions is improved and shown to provide necessary and sufficient conditions.
Sufficient Statistics and Latent Trait Models
Authors: Andersen Erling B.
Published: January 2006

For questionnaires with two answer categories, it has been proven in complete generality that if a minimal sufficient statistic exists for the individual parameter and if it is the same statistic for all values of the item parameters, then the raw score (or the number of correct answers) is the minimal sufficient statistic. It follows that the model must by of the Rasch type with logistic item characteristic curves and equal item-discriminating powers.

This paper extends these results to multiple choice questionnaires. It is shown that the minimal sufficient statistic for the individual parameter is a function of the so-called score vector. It is also shown that the so-called equidistant scoring is the only scoring of a questionnaire that allows for a real valued sufficient statistic that is independent of the item parameters, if a certain ordering property for the sufficient statistic holds.
Monotonic Weighted Power Transformations to Additivity
Authors: Ramsay J. O.
Published: January 2006

A class of monotonic transformations which generalize the power transformation is fit to the independent and dependent variables in multiple regression so that the resulting additive relationship is optimized. This is achieved by minimizing a quadratic fitting criterion with linear inequality constraints on the parameters. A quadratic programming technique which works reliably and quickly in this application is outlined. Some examples of the analysis of artificial and real data are offered.
Correctness of Kruskal's Algorithms for Monotone Regression with Ties
Authors: de Leeuw Jan
Published: January 2006

Kruskal has proposed two modifications of monotone regression that can be applied if there are ties in nonmetric scaling data. In this note we prove Kruskal's conjecture that his algorithms give the optimal least squares solution of these modified monotone regression problems. We also propose another (third) approach for dealing with ties.
Quantitative Analysis of Qualitative Data
Authors: Young Forrest W.
Published: January 2006

This paper presents an overview of an approach to the quantitative analysis of qualitative data with theoretical and methodological explanations of the two cornerstones of the approach, Alternating Least Squares and Optimal Scaling. Using these two principles, my colleagues and I have extended a variety of analysis procedures originally proposed for quantitative (interval or ratio) data to qualitative (nominal or ordinal) data, including additivity analysis and analysis of variance; multiple and canonical regression; principal components; common factor and three mode factor analysis; and multidimensional scaling. The approach has two advantages: (a) If a least squares procedure is known for analyzing quantitative data, it can be extended to qualitative data; and (b) the resulting algorithm will be convergent. Three completely worked through examples of the additivity analysis procedure and the steps involved in the regression procedures are presented.
Spatial Versus Tree Representations of Proximity Data
Authors: Pruzansky Sandra , Tversky Amos , Carroll J. Douglas
Published: January 2006

In this paper we investigated two of the most common representations of proximities, two-dimensional euclidean planes and additive trees. Our purpose was to develop guidelines for comparing these representations, and to discover properties that could help diagnose which representation is more appropriate for a given set of data. In a simulation study, artificial data generated either by a plane or by a tree were scaled using procedures for fitting either a plane (KYST) or a tree (ADDTREE). As expected, the appropriate model fit the data better than the inappropriate model for all noise levels. Furthermore, the two models were roughly comparable: for all noise levels, KYST accounted for plane data about as well as ADDTREE accounted for tree data. Two properties of the data proved useful in distinguishing between the models: the skewness of the distribution of distances, and the proportion of elongated triangles, which measures departures from the ultrametric inequality, Applications of KYST and ADDTREE to some twenty sets of real data, collected by other investigators, showed that most of these data could be classified clearly as favoring either a tree or a two-dimensional representation.
On the Optimality of the Simultaneous Redundancy Transformations
Authors: Tyler David E.
Published: January 2006

The objective of this paper is to introduce and motivate additional properties and interpretations for the redundancy variables. It is shown that these variables can be derived by application of certain invariance arguments and without reference to the index of redundancy. In addition, an optimality property for the variables is presented which is important whenever one restricts attention in a study to a subset of the redundancy variables. This optimality property pertains to the subset rather than to the individual variables.
Generalized Eigenvalue Problems with Positive Semi-Definite Matrices
Authors: de Leeuw Jan
Published: January 2006

In a recent paper in this journal McDonald, Torii, and Nishisato show that generalized eigenvalue problems in which both matrices are singular can sometimes be solved by reducing them to similar problems of smaller order. In this paper a more extensive analysis of such problems is used to sharpen and clarify the results of McDonald, Torii, and Nishisato. Possible extensions are also indicated. The relevant mathematical literature is reviewed briefly.
A Note on the Investigation of Local and Global Identifiability
Authors: McDonald Roderick P.
Published: January 2006

In general, nonlinear models such as those commonly employed for the analysis of covariance structures, are not globally identifiable. Any investigation of local identifiability must either yield a mapping of identifiability onto the entire parameter space, which will rarely be feasible in any applications of interest, or confine itself to the neighbourhood of such points of special interest as the maximum likelihood point.
Some Contributions to Efficient Statistics in Structural Models: Specification and Estimation of Moment Structures
Authors: Bentler P. M.
Published: January 2006

Current practice in structural modeling of observed continuous random variables is limited to representation systems for first and second moments (e.g., means and covariances), and to distribution theory based on multivariate normality. In psychometrics the multinormality assumption is often incorrect, so that statistical tests on parameters, or model goodness of fit, will frequently be incorrect as well. It is shown that higher order product moments yield important structural information when the distribution of variables is arbitrary. Structural representations are developed for generalizations of the Bentler-Weeks, Jöreskog-Keesling-Wiley, and factor analytic models. Some asymptotically distribution-free efficient estimators for such arbitrary structural models are developed. Limited information estimators are obtained as well. The special case of elliptical distributions that allow nonzero but equal kurtoses for variables is discussed in some detail. The argument is made that multivariate normal theory for covariance structure models should be abandoned in favor of elliptical theory, which is only slightly more difficult to apply in practice but specializes to the traditional case when normality holds. Many open research areas are described.
A Least Squares Algorithm for Fitting Additive Trees to Proximity Data
Authors: De Soete Geert
Published: January 2006

A least squares algorithm for fitting additive trees to proximity data is described. The algorithm uses a penalty function to enforce the four point condition on the estimated path length distances. The algorithm is evaluated in a small Monte Carlo study. Finally, an illustrative application is presented.
A Note on the Nonmetric Analysis of Psychological Intervals for an unknown Ordering of Stimuli
Authors: Bissett Randall , Schneider Bruce
Published: January 2006

A modified version of a coordinate adjustment technique which permits the analysis of comparisons of psychological intervals for an unknown ordering of stimuli is described and compared to the original version and to TORSCA. For configurations with a large number of points, knowledge of the rank order of the stimuli does not improve the solution. For configurations with a small number of points, the performance of the new algorithm with an unknown ordering is equivalent to TORSCA.
Estimation of Stimulus-Response Curves by Bayesian Least Squares
Authors: Brunk H. D.
Published: January 2006

Bayesian least squares techniques are adapted to estimation of stimulus-response curves, rather broadly conceived. Illustrative examples deal with estimation of person characteristic curves and item characteristic curves in the context of mental testing, and estimation of a stimulus-response curve using data from a psychophysical experiment.
Grading Distractor-Identification Tests
Authors: Austin Joe Dan
Published: January 2006

On distractor-identification tests students mark as many distractors as possible on each test item. A grading scale is developed for this type testing. The scale is optimal in that it is the unique scale giving an unbiased estimate of the student's “true score”, i.e., the score that would result if no guessing occurred. If the test is administered as a usual multiple choice test and graded using the usual correction for guessing scale, the expected item score is the same as for the distractor-identification testing using the optimal grading scale. However, the variance of the item score is shown to be less for distractor-identification testing than for usual multiple choice testing under certain conditions.
Analysis of Pairwise Preference Data using Integrated B-Splines
Authors: Winsberg Suzanne , Ramsay James O.
Published: January 2006

Pairwise preference data are represented as a monotone integral transformation of difference on the underlying stimulus-object or utility scale. The class of monotone transformations considered is that in which the kernel of the integral is a linear combination of B-splines. Two types of data are analyzed: binary and continuous. The parameters of the transformation and the underlying scale values or utilities are estimated by maximum likelihood with inequality constraints on the transformation parameters. Various hypothesis tests and interval estimates are developed. Examples of artificial and real data are presented.
The Excitatory State in the Triangular Constant Method
Authors: Frijters J. E. R.
Published: January 2006

The Triangular Constant Method was designed for the measurement of discriminability between sensory stimuli. Its original model assumes a steady excitatory detection state. The purpose of this paper is to elaborate on the consequences of assuming a variable excitatory state and to formulate the concomitant model.
Robust Estimation of Ability in the Rasch Model
Authors: Wainer Howard , Wright Benjamin D.
Published: January 2006

Estimating ability parameters in latent trait models in general, and in the Rasch model in particular is almost always hampered by noise in the data. This noise can be caused by guessing, inattention to easy questions, and other factors which are unrelated to ability. In this study several alternative formulations which attempt to deal with these problems without a reparameterization are tested through a Monte Carlo simulation. It was found that although no one of the tested schemes is uniformly superior to all others, a modified jackknife stood out as the best one in general, it was also super efficient (more efficient than the asymptotically optimal estimator) for tests with forty or fewer items. It is proposed that this sort of jackknifing scheme for estimating ability be considered for practical work.
Extension of Feldt’s Approach to Testing Homogeneity of Coefficients of Reliability
Authors: Kraemer Helena Chmura
Published: January 2006

Limitations and extensions of Feldt's approach to testing the equality of Cronbach's alpha coefficients in independent and matched samples are discussed. In particular, this approach is used to test equality of intraclass correlation coefficients.
An Extension of Wollenberg’s Redundancy Analysis
Authors: Johansson J. K.
Published: January 2006

An extension of Wollenberg's redundancy analysis is proposed to derive Y- variates corresponding to the optimal X- variates. These variates are maximally correlated with the given X- variates, and depending upon the standardization chosen they also have certain properties of orthogonality.
A Note on Sufficient Conditions that a Common Factor will be Determinate in an Infinite Domain of Variables
Authors: Mulaik Stanley A.
Published: January 2006

It is proved for the common factor model with r common factors that under certain condition s which maintain the distinctiveness of each common factor a given common factor will be determinate if there exists an unlimited number of variables in the model each having an absolute correlation with the factor greater than some arbitrarily small positive quantity.
Improper Solutions in the Analysis of Covariance Structures: Their Interpretability and a Comparison of Alternate Respecifications
Authors: Gerbing David W. , Anderson James C.
Published: January 2006

A Monte Carlo approach was employed to investigate the interpretability of improper solutions caused by sampling error in maximum likelihood confirmatory factor analysis. Four models were studied with two sample sizes. Of the overall goodness-of-fit indices provided by the LISREL VI program significant differences between improper and proper solutions were found only for the root mean square residual. As expected, indicators of the factor on which the negative uniqueness estimate occurred had biased loadings, and the correlations of its factor with other factors were also biased. In contrast, the loadings of indicators on other factors and those factor intercorrelations did not have any bias of practical significance. For initial solutions with one negative uniqueness estimate, three respecifications were studied: Fix the uniqueness at .00, fix it at .20, or constrain the domain of the solution to be proper. For alternate, respecified solutions that were converged and proper, the constrained solutions and uniqueness fixed at .00 solutions were equivalent. The mean goodness-of-fit and pattern coefficient values for the original improper solutions were not meaningfully different from those obtained under the constrained and uniqueness fixed at .00 respecifications.
Trend in Correlated Proportions
Authors: Gibbons Robert D. , Bock R. Darrell
Published: January 2006

A random effects probit model is developed for the case in which the same units are sampled repeatedly at each level of an independent variable. Because the observed proportions may be correlated under these conditions, estimating their trend with respect to the independent variable is no longer a standard problem for probit, logit or loglinear analysis. Using a qualitative analogue of a random regressions model, we employ instead marginal maximum likelihood to estimate the average latent trend line. Likelihood ratio tests of the hypothesis of no trend in the average line, and the hypothesis of no differences in average trend lines between experimental treatments, are proposed. We illustrate the model both with simulated data and with observed data from a clinical experiment in which psychiatric patients on two drug therapies are rated on five occasions for the presence or absence of symptoms.
The Rank of Reduced Dispersion Matrices
Authors: Bekker Paul A. , de Leeuw Jan
Published: January 2006

Psychometricians working in factor analysis and econometricians working in regression with measurement error in all variables are both interested in the rank of dispersion matrices under variation of the diagonal elements. Psychometricians concentrate on cases in which low rank can be attained, preferably rank one, the Spearman case. Econometricians concentrate on cases in which the rank cannot be reduced below the number of variables minus one, the Frisch case. In this paper we give an extensive historial discussion of both fields, we prove the two key results in a more satisfactory and uniform way, we point out various small errors and misunderstandings, and we present a methodological comparison of factor analysis and regression on the basis of our results.
Generalized Concordance
Authors: Hubert Lawrence J.
Published: January 2006

Based on a simple nonparametric procedure for comparing two proximity matrices, a measure of concordance is introduced that is appropriate when K independent proximity matrices are available. In addition to the development of a general concept of concordance and specific techniques for its evaluation within and between the subsets of a partition of the K matrices, several methods are also suggested for comparing and/or for fitting a particular structure to the given data. Finally, brief indications are provided as to how the well-known notion of concordance for K rank orders can be included within the more general framework.
Factor Indeterminacy in the 1930’s and the 1970’s Some Interesting Parallels
Authors: Steiger James H.
Published: January 2006

The issue of factor indeterminacy, and its meaning and significance for factor analysis, has been the subject of considerable debate in recent years. Interestingly, the identical issue was discussed widely in the literature of the late 1920's and early 1930's, but this early discussion was somehow lost or forgotten during the development and popularization of multiple factor analysis. There are strong parallels between the arguments in the early literature, and those which have appeared in recent papers. Here I review the history of this early literature, briefly survey the more recent work, and discuss these parallels where they are especially illuminating.
Some Results on Proper Eigenvalues and Eigenvectors with Applications to Scaling
Authors: McDonald Roderick P. , Torii Yukihiko , Nishisato Shizuhiko
Published: January 2006

Conditions are given under which the stationary points and values of a ratio of quadratic forms in two singular matrices can be obtained by a series of simple matrix operations. It is shown that two classes of optimal weighting problems, based respectively on the grouping of variables and on the grouping of observations, satisfy these conditions. The classical treatment of optimal scaling of forced-choice multicategory data is extended for these cases. It is shown that previously suggested methods based on reparameterization will work only under very special conditions.
An Efficient Alternating Least-Squares Algorithm to Perform Multidimensional Unfolding
Authors: Greenacre Michael J. , Browne Michael W.
Published: January 2006

We consider the problem of least-squares fitting of squared distances in unfolding. An alternating procedure is proposed which fixes the row or column configuration in turn and finds the global optimum of the objective criterion with respect to the free parameters, iterating in this fashion until convergence is reached. A considerable simplification in the algorithm results, namely that this conditional global optimum is identified by performing a single unidimensional search for each point, irrespective of the dimensionality of the unfolding solution.
Principal Components Analysis of Sampled Functions
Authors: Besse Philippe , Ramsay J. O.
Published: January 2006

This paper describes a technique for principal components analysis of data consisting of n functions each observed at p argument values. This problem arises particularly in the analysis of longitudinal data in which some behavior of a number of subjects is measured at a number of points in time. In such cases information about the behavior of one or more derivatives of the function being sampled can often be very useful, as for example in the analysis of growth or learning curves. It is shown that the use of derivative information is equivalent to a change of metric for the row space in classical principal components analysis. The reproducing kernel for the Hilbert space of functions plays a central role, and defines the best interpolating functions, which are generalized spline functions. An example is offered of how sensitivity to derivative information can reveal interesting aspects of the data.
A Direct Solution for Pairwise Rotations in Kaiser's Varimax Method
Authors: Nevels Klaas
Published: January 2006

The present note contains a completing-the-squares type approach to the varimax rotation problem. This approach yields a direct proof of global optimality of a solution for optimal rotation in a plane. Because varimax rotation can be interpreted as diagonalization of a set of symmetric matrices, the present solution also applies to the diagonalization problem.
Test Theory and Psychometrika: The Past Twenty-Five Years
Authors: Lewis Charles
Published: January 2006

On the occasion of Psychometrika's fiftieth anniversary, the past twenty-five years' developments in mental test theory are reviewed, with special emphasis on the topics receiving attention in the pages of this journal. (Analogous reviews for Psychometrika's first quarter century were given by Gulliksen and Guilford in 1961.) Much of the recent progress in test theory (and in other branches of psychometrics as well) has been made by treating the problems in this field as being essentially ones of statistical inference. It is concluded that (a) research in test theory is in a healthy state and (b) Psychometrika is an important source of information about that research.
Factor Analysis and Psychometrika: Major Developments
Authors: Mulaik Stanley A.
Published: January 2006

Papers on factor analysis appearing in Psychometrika reflect the initial efforts of the Thurstonians to reformulate psychology as a quantitative science. The Thurstonians' emphasis on the development of factor analysis as an exploratory methodology was not new with them but was taken from British statisticians and psychologists who preceded them, whose literature the Thurstonians otherwise tended to ignore. The Thurstonians' rejection of general factors and focus on rotation to simple structure reflected an attempt to avoid statistical artifact and to identify factors with psychological substance. Much of the literature on factor analysis in Psychometrika concerned solving technical problems in the exploratory factor analysis method. Factor analysis took a major shift in direction in the 1970's with the development of confirmatory methodologies, many of which now receive greater attention than the method of exploratory factor analysis, most of the problems of which are now resolved.
Maximum Likelihood Estimation of Polyserial Correlations
Authors: Lee Sik-Yum , Poon Wai-Yin
Published: January 2006

This paper considers a multivariate normal model with one of the component variables observable only in polytomous form. The maximum likelihood approach is used for estimation of the parameters in the model. The Newton-Raphson algorithm is implemented to obtain the solution of the problem. Examples based on real and simulated data are reported.
Metric and Nonmetric Plotting Models
Authors: Kuhfeld Warren F.
Published: January 2006

A problem with standard printer scatterplot routines is that points are not adequately labeled. Four methods of generating printer plots are presented. All four methods allow multi-character point labels, and three of the methods can guarantee that no portion of the labels are hidden. This is accomplished by using varying amounts of metric and nonmetric distance information to locate the points.
Evaluating Performance in Continuous Experiments with Feedback to Subjects
Authors: Samaniego Francisco J. , Utts Jessica M.
Published: January 2006

Consider an experiment in which a subject guesses repeatedly at a randomly chosen target on a continuum. To guarantee a positive probability of success, the continuum is partitioned into a finite but large number of segments. The subject is given directional feedback. General guessing strategies are characterized, and an optimal strategy is identified. The hypothesis that the subject's performance can be explained by “chance alone” is of interest in such experiments. A test is developed based on comparing the subject's performance to expected performance using the optimal strategy. A “skill-scoring” procedure is developed for assessing a subject's performance in light of the strategy used, and a test based on skill-scoring is advanced.
The Analytical Solution of the Additive Constant Problem
Authors: Cailliez Francis
Published: January 2006

If d is a measure of dissimilarity on a finite set with n elements, the smallest positive constant c* such that d + c has an euclidean representation for all c ≥c* is shown to be the largest eigen-value of a matrix of size 2n.
A New Procedure for Detection of Crossing DIF
Authors: Li Hsin-Hung , Stout William
Published: January 2006

The purpose of this paper is to present a hypothesis testing and estimation procedure, Crossing SIBTEST, for detecting crossing DIF. Crossing DIF exists when the difference in the probabilities of a correct answer for the two examinee groups changes signs as ability level is varied. In item response theory terms, crossing DIF is indicated by two crossing item characteristic curves. Our new procedure, denoted as Crossing SIBTEST, first estimates the matching subtest score at which crossing occurs using least squares regression analysis. A Crossing SIBTEST statistic then is used to test the hypothesis of crossing DIF. The performance of Crossing SIBTEST is evaluated in this study.
Polytomous IRT Models and Monotone Likelihood Ratio of the Total Score
Authors: Hemker Bas T. , Sijtsma Klaas , Molenaar Ivo W. , Junker Brian W.
Published: January 2006

In a broad class of item response theory (IRT) models for dichotomous items the unweighted total score has monotone likelihood ratio (MLR) in the latent trait θ. In this study, it is shown that for polytomous items MLR holds for the partial credit model and a trivial generalization of this model. MLR does not necessarily hold if the slopes of the item step response functions vary over items, item steps, or both. MLR holds neither for Samejima's graded response model, nor for nonparametric versions of these three polytomous models. These results are surprising in the context of Grayson's and Huynh's results on MLR for nonparametric dichotomous IRT models, and suggest that establishing stochastic ordering properties for nonparametric polytomous IRT models will be much harder.
A Method of Estimating Item Characteristic Functions using the Maximum Likelihood Estimate of Ability
Authors: Samejima Fumiko
Published: January 2006

A method of estimating item characteristic functions is proposed, in which a set of test items, whose operating characteristics are known and which give a constant test information function for a substantially wide range of ability, are used. The method is based on the maximum likelihood estimates of ability for a group of several hundred examinees. Throughout the present study the Monte Carlo method is used.
Function Invariant and Parameter Scale-Free Transformation Methods
Authors: Bentler P. M. , Wingard Joseph A.
Published: January 2006

The parameter matrices of factor analysis and principal component analysis are arbitrary with respect to the scale of the factors or components; typically, the scale is fixed so that the factors have unit variance. Oblique transformations to optimize an objective statement of a principle such as simple structure or factor simplicity yield arbitrary solutions, unless the criterion function is invariant with respect to the scale of the factors, or the parameter matrix is scale free with respect to the factors. Criterion functions that are factor scale-free have a number of invariance characteristics, such as being equally applicable to primary pattern or reference structure matrices. A scale-invariant simple structure function of previously studied function components is defined. First and second partial derivatives are obtained, and Newton-Raphson iterations are utilized. The resulting solutions are locally optimal and subjectively pleasing.
Factor Simplicity Index and Transformations
Authors: Bentler P. M.
Published: January 2006

A scale-invariant index of factorial simplicity is proposed as a summary statistic for principal components and factor analysis. The index ranges from zero to one, and attains its maximum when all variables are simple rather than factorially complex. A factor scale-free oblique transformation method is developed to maximize the index. In addition, a new orthogonal rotation procedure is developed. These factor transformation methods are implemented using rapidly convergent computer programs. Observed results indicate that the procedures produce meaningfully simple factor pattern solutions.
Dynamic Structural Systems Under Indirect Observation: Identifiability and Estimation Aspects from a System Theoretic Perspective
Authors: Otter Pieter W.
Published: January 2006

In this—partly—expository paper the parameter identifiability and estimation of a general dynamic structural model under indirect observation will be considered from a system theoretic perspective. The general dynamic model covers (dynamic) factor analytic models, (dynamic) MIMIC models and Jöreskog's linear structural model as special cases. Its reduced form is—under a slightly different specification—known in system theory and econometrics as the stochastic, stationary version of the state-space model. By using concepts and methods from system theory, such as the observability and controllability concept, the (steady-state) Kalman filter and a general nonlinear ML-estimation procedure known as prediction-error estimation the general dynamic model will be identified. It will be shown that Jöreskog's LISREL-procedure is a special case of the prediction-error estimation procedure.
An Individual Differences Additive Model: An Alterating Least Squares Method with Optimal Scaling Features
Authors: Takane Yoshio , Young Forrest W. , de Leeuw Jan
Published: January 2006

An individual differences additive model is discussed which represents individual differences in additivity by differential weighting of additive factors. A procedure for estimating the model parameters for various data measurement characteristics is developed. The procedure is evaluated using both Monte Carlo and real data. The method is found to be very useful in describing certain types of developmental change in cognitive structure, as well as being numerically robust and efficient.
A Simple Technique for Improving the Quality of Parameter Estimates in Learning Hierarchy Validation Studies
Authors: Barton Alan R.
Published: January 2006

Learning hierarchy research has been characterized by the use of ad hoc statistical procedures to determine the validity of postulated hierarchical connections. The two most substantial attempts to legitimize the procedure are due to White & Clark and Dayton & Macready, although both of these methods suffer from serious inadequacies. Data from a number of sources is analyzed using a restricted maximum likelihood estimation procedure and the results are compared with those obtained using the method suggested by Dayton and Macready. Improved estimates are evidenced by an increase in the computed value of the log likelihood function.
Random Versus Rational Strategies for Initial Configurations in Nonmetric Multidimensional Scaling
Authors: Arabie Phipps
Published: January 2006

An examination is made concerning the utility and design of studies comparing nonmetric scaling algorithms and their initial configurations, as well as the agreement between the results of such studies. Various practical details of nonmetric scaling are also considered.
Some Comments on Selecting the Best of Several Binomial Populations or the Bivariate Normal Population Having the Largest Correlation Coefficient
Authors: Wilcox Rand R.
Published: January 2006

Using variance stabilizing transformations, this note describes approximate solutions to the ranking and selection problem of selecting the best binomial population or the bivariate normal population with the largest correlation coefficient.
Factor Analysis for Non-Normal Variables
Authors: Mooijaart Ab
Published: January 2006

Factor analysis for nonnormally distributed variables is discussed in this paper. The main difference between our approach and more traditional approaches is that not only second order cross-products (like covariances) are utilized, but also higher order cross-products. It turns out that under some conditions the parameters (factor loadings) can be uniquely determined. Two estimation procedures will be discussed. One method gives Best Generalized Least Squares (BGLS) estimates, but is computationally very heavy, in particular for large data sets. The other method is a least squares method which is computationally less heavy. In one example the two methods will be compared by using the bootstrap method. In another example real life data are analyzed.
Optimal Appropriateness Measurement
Authors: Levine Michael V. , Drasgow Fritz
Published: January 2006

The test-taking behavior of some examinees may be so idiosyncratic that their test scores may not be comparable to the scores of more typical examinees. Appropriateness measurement attempts to use answer patterns to recognize atypical examinees. In this report appropriateness measurement procedures are viewed as statistical tests for choosing between a null hypothesis of normal test-taking behavior and an alternative hypothesis of atypical test-taking behavior. Most powerful tests for inappropriateness are described together with methods for computing their power. A recursion greatly simplifying the calculation of optimal test statistics is described and illustrated.
Comment on “Correspondence Analysis used Complementary to Loglinear Analysis”
Authors: van der Heijden Peter G. M. , Worsley Keith J.
Published: January 2006

In van der Heijden and de Leeuw (1985) it was proposed to use loglinear analysis to detect interactions in a multiway contingency table, and to explore the form of these interactions with correspondence analysis. After performing the exploratory phase of the analysis, we will show here how the results found in this phase can be used for confirmation.
Estimating Latent Correlations between Repeated Testings
Authors: Andersen Erling B.
Published: January 2006

A model for longitudinal latent structure analysis is proposed. We assume that test scores for a given mental or attitudinal test are observed for the same individuals at two different points in time. The purpose of the analysis is to fit a model that combines the values of the latent variable at the two time points in a two-dimensional latent density. The correlation coefficient between the two values of the latent variable can then be estimated. The theory and methods are illustrated by a Danish dataset concerning psychic vulnerability.
A Latent Class Model for Rating Data
Authors: Rost Jürgen
Published: January 2006

A latent class model for rating data is presented which is the analogue of Andrich's binomial Rasch model for Lazarsfeld's latent class analysis (LCA). The response probabilities for the rating categories follow a binomial distribution and depend on class-specific item parameters. The EM-algorithm for parameter estimation as well as goodness of fit tests for the model are described. An example using questionnaire items on interest in physics illustrates the use of the model as an alternative to the latent trait approach of analyzing test data.
Power of the Likelihood Ratio Test in Covariance Structure Analysis
Authors: Satorra Albert , Saris Willem E.
Published: January 2006

A procedure for computing the power of the likelihood ratio test used in the context of covariance structure analysis is derived. The procedure uses statistics associated with the standard output of the computer programs commonly used and assumes that a specific alternative value of the parameter vector is specified. Using the noncentral Chi-square distribution, the power of the test is approximated by the asymptotic one for a sequence of local alternatives. The procedure is illustrated by an example. A Monte Carlo experiment also shows how good the approximation is for a specific case.
An Analysis and Synthesis of Multiple Correspondence Analysis, Optimal Scaling, Dual Scaling, Homogeneity Analysis and Other Methods for Quantifying Categorical Multivariate Data
Authors: Tenenhaus Michel , Young Forrest W.
Published: January 2006

We discuss a variety of methods for quantifying categorical multivariate data. These methods have been proposed in many different countries, by many different authors, under many different names. In the first major section of the paper we analyze the many different methods and show that they all lead to the same equations for analyzing the same data. In the second major section of the paper we introduce the notion of a duality diagram, and use this diagram to synthesize the many superficially different methods into a single method.
On the Relationship between Fortier's Simultaneous Linear Prediction and van den Wollenberg's Redundancy Analysis
Authors: ten Berge Jos M. F.
Published: January 2006

The purpose of this note is to show that Van den Wollenberg's method of redundancy analysis is a special case of a simultaneous linear prediction method offered by Fortier.
Maximum Likelihood Additivity Analysis
Authors: Takane Yoshio
Published: January 2006

A maximum likelihood estimation procedure was developed to fit unweighted and weighted additive models to conjoint data obtained by the categorical rating, the pair comparison or the directional ranking method. The scoring algorithm used to fit the models was found to be both reliable and efficient, and the program MAXADD is capable of handling up to 300 parameters to be estimated. Practical uses of the procedure are reported to demonstrate various advantages of the procedure as a statistical method.
A Bayesian Procedure for Mastery Decisions based on Multivariate Normal Test Data
Authors: Huynh Huynh
Published: January 2006

A Bayesian framework for making mastery/nonmastery decisions based on multivariate test data is described in this study. Overall, mastery is granted (or denied) if the posterior expected loss associated with such action is smaller than the one incurred by the denial (or grant) of mastery. An explicit form for the cutting contour which separates mastery and nonmastery states in the test score space is given for multivariate normal test scores and for a constant loss ratio. For multiple cutting scores in the true ability space, the test score cutting contour will resemble the boundary defined by multiple test cutting scores when the test reliabilities are reasonably close to unity. For tests with low reliabilities, decisions may very well be based simply on a suitably chosen composite score.
Sequential Estimation in Multidimensional Scaling
Authors: Miyano Hisao , Inukai Yukio
Published: January 2006

The concept of sequential estimation is introduced in multidimensional scaling (MDS). The sequential estimation method developed in this paper refers to continually updating estimates of a configuration as new observations are added. This method has a number of advantages, such as a locally optimal design of the experiment can be easily constructed, and dynamic experimentation is made possible. Using artificial data, the performance of our sequential method is illustrated.
The Effect of Sampling Error on Convergence, Improper Solutions, and Goodness-of-Fit Indices for Maximum Likelihood Confirmatory Factor Analysis
Authors: Anderson James C. , Gerbing David W.
Published: January 2006

A Monte Carlo study assessed the effect of sampling error and model characteristics on the occurrence of nonconvergent solutions, improper solutions and the distribution of goodness-of-fit indices in maximum likelihood confirmatory factor analysis. Nonconvergent and improper solutions occurred more frequently for smaller sample sizes and for models with fewer indicators of each factor. Effects of practical significance due to sample size, the number of indicators per factor and the number of factors were found for GFI, AGFI, and RMR, whereas no practical effects were found for the probability values associated with the chi-square likelihood ratio test.
A General Latent Trait Model for Response Processes
Authors: Embretson (Whitely) Susan
Published: January 2006

The purpose of the current paper is to propose a general multicomponent latent trait model (GLTM) for response processes. The proposed model combines the linear logistic latent trait (LLTM) with the multicomponent latent trait model (MLTM). As with both LLTM and MLTM, the general multicomponent latent trait model can be used to (1) test hypotheses about the theoretical variables that underlie response difficulty and (2) estimate parameters that describe test items by basic substantive properties. However, GLTM contains both component outcomes and complexity factors in a single model and may be applied to data that neither LLTM nor MLTM can handle. Joint maximum likelihood estimators are presented for the parameters of GLTM and an application to cognitive test items is described.
Constrained Classification: The use of a Priori Information in Cluster Analysis
Authors: DeSarbo Wayne S. , Mahajan Vijay
Published: January 2006

In many classification problems, one often possesses external and/or internal information concerning the objects or units to be analyzed which makes it appropriate to impose constraints on the set of allowable classifications and their characteristics. CONCLUS, or CONstrained CLUStering, is a new methodology devised to perform constrained classification in either an overlapping or nonoverlapping (hierarchical or nonhierarchial) manner. This paper initially reviews the related classification literature. A discussion of the use of constraints in clustering problems is then presented. The CONCLUS model and algorithm are described in detail, as well as their flexibility for use in various applications. Monte Carlo results are presented for two synthetic data sets with appropriate discussion of the resulting implications. An illustration of CONCLUS is presented with respect to a sales territory design problem where the objects classified are various Forbes-500 companies. Finally, the discussion section highlights the main contribution of the paper and offers some areas for future research.
A Family of Differentiation Indices
Authors: Iachan Ronaldo
Published: January 2006

A general class of differentiation indices is introduced for profiles of scores. Their ordinal properties and an application to Holland's classification system are examined. Comparison with other suggested indices is performed both theoretically and empirically.
Loglinear Rasch Model Tests
Authors: Kelderman Hendrikus
Published: January 2006

Existing statistical tests for the fit of the Rasch model have been criticized, because they are only sensitive to specific violations of its assumptions. Contingency table methods using loglinear models have been used to test various psychometric models. In this paper, the assumptions of the Rasch model are discussed and the Rasch model is reformulated as a quasi-independence model. The model is a quasi-loglinear model for the incomplete subgroup × score × item 1 × item 2 × ... × item k contingency table. Using ordinary contingency table methods the Rasch model can be tested generally or against less restrictive quasi-loglinear models to investigate specific violations of its assumptions.
A Comment on ‘Some Standard Errors in Item Response Theory’
Authors: de Gruijter Dato N. M.
Published: January 2006

In maximum likelihood estimation the standard error of the location parameter of the three parameter logistic model can be large, due to inaccurate estimation of the lower asymptote. Thissen and Wainer who demonstrated this effect, suggested that the introduction of a prior distribution for the lower asymptote might alleviate the problems. Here it is demonstrated in some detail that the standard error of the location parameter can be made acceptably small in this way.
An Alternative Test for Multisample Sphericity
Authors: Harris P.
Published: January 2006

A test for multisample sphericity based on the efficient scores criterion is obtained as an alternative to the likelihood ratio test developed by Mendoza.
A Method of Optimal Scaling for Multivariate Ordinal Data and its Extensions
Authors: Saito Takayuki , Otsu Tatsuo
Published: January 2006

This paper develops a method of optimal scaling for multivariate ordinal data, in the framework of a generalized principal component analysis. This method yields a multidimensional configuration of items, a unidimensional scale of category weights for each item and, optionally, a multidimensional configuration of subjects. The computation is performed by alternately solving an eigenvalue problem and executing a quasi-Newton projection method. The algorithm is extended for analysis of data with mixed measurement levels or for analysis with a combined weighting of items. Numerical examples and simulations are provided. The algorithm is discussed and compared with some related methods.
Caution Indices based on Item Response Theory
Authors: Tatsuoka Kikumi K.
Published: January 2006

A new family of indices was introduced earlier as a link between two approaches: One based on item response theory and the other on sample statistics. In this study, the statistical properties of these indices are investigated and then the relationships to Guttman Scales, and to item and person response curves are discussed. Further, these indices are standardized, and an example of their potential usefulness for diagnosing students' misconceptions is shown.
Differentiability of Kruskal's Stress at a Local Minimum
Authors: De Leeuw Jan
Published: January 2006

It is shown that Kruskal's multidimensional scaling loss function is differentiable at a local minimum. Or, to put it differently, that in multidimensional scaling solutions using Kruskal's stress distinct points cannot coincide.
A General Structural Equation Model with Dichotomous, Ordered Categorical, and Continuous Latent Variable Indicators
Authors: Muthén Bengt
Published: January 2006

A structural equation model is proposed with a generalized measurement part, allowing for dichotomous and ordered categorical variables (indicators) in addition to continuous ones. A computationally feasible three-stage estimator is proposed for any combination of observed variable types. This approach provides large-sample chi-square tests of fit and standard errors of estimates for situations not previously covered. Two multiple-indicator modeling examples are given. One is a simultaneous analysis of two groups with a structural equation model underlying skewed Likert variables. The second is a longitudinal model with a structural model for multivariate probit regressions.
The Nonconvergence of Factals: A nonmetric Common Factor Analysis
Authors: Mooijaart Ab
Published: January 2006

FACTALS is a nonmetric common factor analysis model for multivariate data whose variables may be nominal, ordinal or interval. In FACTALS an Alternating Least Squares algorithm is utilized which is claimed to be monotonically convergent.

In this paper it is shown that this algorithm is based upon an erroneous assumption, namely that the least squares loss function (which is in this case a nonscale free loss function) can be transformed into a scalefree loss function. A consequence of this is that monotonical convergence of the algorithm can not be guaranteed.