bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Measurement of Concordance Among Variables and Its Application
Authors: Lian Ie-Bin , Young Wen-Chin
Published: June 2007

In educational testing problems, some natural restriction on the coefficients may arise in maximizing the correlation or variance of linear combinations of sets of variables. In this article, two statistics based on restricted principal component and restricted canonical correlation (Das & Sen, 1994) are proposed to measure the intra- and inter-group concordance of variables. We also propose using these statistics as criteria for clustering variables. The analysis of encyclopedia data is provided as an illustration.
Two Measures of Change in the Gaps Between the CDFs of Test-Score Distributions
Authors: Holland Paul W.
Published: June 2007

Two ways of measuring the gap between two cumulative distribution functions (CDFs) are examined—vertical and horizontal distance. The applications of interest are to the distributions of scores on tests. Vertical distance leads to gap-measures based on differences between “percents above cut-points” for a given score along the score scale. Horizontal distance leads to gap-measures based on differences between the percentiles of the two distributions for a given percent value. Both methods of measuring gaps between CDFs of score distributions are widely used. My focus here is on how the two methods behave when they are used to measure changes in gaps over time. Using a simple model in which the CDFs simply change by small shifts to the right or left, as they often do in real data (the “shift model”), I demonstrate how these two approaches can easily lead to contradictory conclusions about small changes in gaps. The theoretical analysis is illustrated using real data from the National Assessment of Educational Progress. It is recommended that when considering changes in gaps over time, gap-measures be supplemented with fuller data-displays to clarify the directions that changes in such measures can appear to indicate.
Residual Diagrams Based on a Remarkably Simple Result Concerning the Variances of Maximum Likelihood Estimators
Authors: Andersen Erling B.
Published: June 2007

A remarkably simple result concerning variances of maximum likelihood (ML) estimators is presented. This result allows for construction of residual diagrams to evaluate whether ML estimators derived from independent samples can be assumed to be equal apart from random errors. Such diagrams can be used to supplement, for example, likelihood ratio tests for the equality of the parameters in independent samples. As an application, a model from item response theory is studied, the polytomous Rasch model. Residual diagrams for the equality of the variances in a one-way analysis of variance are also briefly mentioned.
Analysis of Differential Item Functioning (DIF) Using Hierarchical Logistic Regression Models
Authors: Swanson David B. , Clauser Brian E. , Case Susan M. , Nungester Ronald J. , Featherman Carol
Published: June 2007

Over the past 25 years a range of parametric and nonparametric methods have been developed for analyzing Differential Item Functioning (DIF). These procedures are typically performed for each item individually or for small numbers of related items. Because the analytic procedures focus on individual items, it has been difficult to pool information across items to identify potential sources of DIF analytically. In this article, we outline an approach to DIF analysis using hierarchical logistic regression that makes it possible to combine results of logistic regression analyses across items to identify consistent sources of DIF, to quantify the proportion of explained variation in DIF coefficients, and to compare the predictive accuracy of alternate explanations for DIF. The approach can also be used to improve the accuracy of DIF estimates for individual items by applying empirical Bayes techniques, with DIF-related item characteristics serving as collateral information. To illustrate the hierarchical logistic regression procedure, we use a large data set derived from recent computer-based administrations of Step 2, the clinical science component of the United States Medical Licensing Examination (USMLE®). Results of a small Monte Carlo study of the accuracy of the DIF estimates are also reported.
Infinite Parameter Estimates in Logistic Regression: Opportunities, Not Problems
Authors: Rindskopf David
Published: June 2007

Infinite parameter estimates in logistic regression are commonly thought of as a problem. This article shows that in principle an analyst should be happy to have an infinite slope in logistic regression, because it indicates that a predictor is perfect. Using simple approaches, hypothesis tests may be performed and confidence intervals calculated even when a slope is infinite. Some functions of parameters that are infinite are still well defined, and reasonable estimates of these quantities (in particular, LD50) may be obtained even when the maximum likelihood estimates do not, in a strict sense, exist. Surprisingly, these techniques can provide more reasonable and useful results than the most popular alternative method, exact logistic regression.
An Item Response Model for Characterizing Test Compromise
Authors: Segall Daniel O.
Published: June 2007

This article presents an item response model for characterizing test-compromise that enables the estimation of item-preview and score-gain distributions observed in on-demand high-stakes testing programs. Model parameters and posterior distributions are estimated by Markov Chain Monte Carlo (MCMC) procedures. Results of a simulation study suggest that when at least some of the items taken by a small sample of test takers are known to be secure (uncompromised), the procedure can provide useful summaries of test-compromise and its impact on test scores. The article includes discussions of operational use of the proposed procedure, possible model violations and extensions, and application to computerized adaptive testing.
Modeling Incomplete Scaled Questionnaire Data with a Partial Credit Hierarchical Measurement Model
Authors: Maier Kimberly S.
Published: June 2007

The partial credit hierarchical measurement model (HMM) results when a partial credit IRT model and a hierarchical linear model are combined ( Bryk & Raudenbush, 1992 ; Masters, 1982 ). This combined model enables the standard errors of parameters to be estimated accurately. The partial credit HMM is illustrated using a subset of data from the Sloan Study of Youth and Social Development, a five-year longitudinal project studying the career aspirations of adolescents. The data used for this study consisted of a subset of students’ responses to multiple administrations of an attitudinal questionnaire, as well as student-level covariates. Using student responses to seven seven-point semantic differential items tapping student mood, the partial credit HMM was used to explore the effects of gender and classroom activity upon student mood as students were engaged in a mathematics classroom. Gibbs sampling and the Metropolis-Hastings algorithm were used to impute values for the missing data and to estimate the parameters of the model. The results of the data analysis indicated that female students had lower mood than male students did for all classroom activities.
The Hierarchical Rater Model for Rated Test Items and its Application to Large-Scale Educational Assessment Data
Authors: Patz Richard J. , Junker Brian W. , Johnson Matthew S. , Mariano Louis T.
Published: June 2007

Open-ended or “constructed” student responses to test items have become a stock component of standardized educational assessments. Digital imaging of examinee work now enables a distributed rating process to be flexibly managed, and allocation designs that involve as many as six or more ratings for a subset of responses are now feasible. In this article we develop Patz’s (1996) hierarchical rater model (HRM) for polytomous item response data scored by multiple raters, and show how it can be used to scale examinees and items, to model aspects of consensus among raters, and to model individual rater severity and consistency effects. The HRM treats examinee responses to open-ended items as unobsered discrete varibles, and it explicitly models the “proficiency” of raters in assigning accurate scores as well as the proficiency of examinees in providing correct responses. We show how the HRM “fits in” to the generalizability theory framework that has been the traditional tool of analysis for rated item response data, and give some relationships between the HRM, the design effects correction of Bock, Brennan and Muraki (1999), and the rater bundle model of Wilson and Hoskens (2002). Using simulated and real data, we compare the HRM to the conventional IRT Facets model for rating data (e.g., Linacre, 1989; Engelhard, 1994, 1996), and we explore ways that information from HRM analyses may improved the quality of the rating process.
Summarizing Monte Carlo Results in Methodological Research: The Single-Factor, Fixed-Effects ANCOVA Case
Authors: Harwell Michael
Published: June 2007

Results from exact statistical theory and Monte Carlo studies have provided evidence that the test size and power of the F test in analysis of covariance are sensitive to violations of certain assumptions. However, a comprehensive summary of the effect of assumption violations has not been available. In this article, meta-analytic methods are used to summarize the results of Monte Carlo studies of the test size and power of the F test in the single-factor, fixed-effects analysis of covariance model, updating and extending narrative reviews of this literature. Monte Carlo results for the nonparametric rank-transform test in the analysis of covariance model are also analyzed. Guidelines for using these tests when assumptions are violated are presented to promote more judicious use of these procedures.
Estimating Multilevel Linear Models as Structural Equation Models
Authors: Bauer Daniel J.
Published: June 2007

Multilevel linear models (MLMs) provide a powerful framework for analyzing data collected at nested or non-nested levels, such as students within classrooms. The current article draws on recent analytical and software advances to demonstrate that a broad class of MLMs may be estimated as structural equation models (SEMs). Moreover, within the SEM approach it is possible to include measurement models for predictors or outcomes, and to estimate the mediational pathways among predictors explicitly, tasks which are currently difficult with the conventional approach to multilevel modeling. The equivalency of the SEM approach with conventional methods for estimating MLMs is illustrated using empirical examples, including an example involving both multiple indicator latent factors for the outcomes and a causal chain for the predictors. The limitations of this approach for estimating MLMs are discussed and alternative approaches are considered.
Performance of Empirical Bayes Estimators of Level-2 Random Parameters in Multilevel Analysis: A Monte Carlo Study for Longitudinal Designs
Authors: Candel Math J. J. M. , Winkens Bjorn
Published: June 2007

Multilevel analysis is a useful technique for analyzing longitudinal data. To describe a person’s development across time, the quality of the estimates of the random coefficients, which relate time to individual changes in a relevant dependent variable, is of importance. The present study compares three estimators of the random coefficients: the Bayes estimator (BE), the empirical Bayes estimator (EBE), and the ordinary least squares estimator (OLSE). Using MLwiN, Monte Carlo simulations are carried out to study the performance of the estimators, while systematically varying the size of the sample as well as the number of measurement occasions. First, we examine for normally distributed random coefficients to what extent the EBE performs better than the OLSE and to what extent the EBE preserves the good properties of the BE. Second, we examine the robustness of the EBE which is based on a normal distribution of the random parameters, by comparing its performance to the OLSE for data being generated from two distributions other than the normal distribution: a modified t-distribution and a modified exponential distribution. As performance criteria we examine the Bayes risk as well as a criterion based on the frequentist notion of mean squared error.
Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models
Authors: Johnson Matthew S. , Junker Brian W.
Published: June 2007

Unfolding response models, a class of item response theory (IRT) models that assume a unimodal item response function (IRF), are often used for the measurement of attitudes. Verhelst and Verstralen (1993) and Andrich and Luo (1993) independently developed unfolding response models by relating the observed responses to a more common monotone IRT model using a latent response model (LRM; Maris, 1995 ). This article generalizes their approach, and suggests a data augmentation scheme for the estimation of any unfolding response model. The article introduces two Markov chain Monte Carlo (MCMC) estimation procedures for the Bayesian estimation of unfolding model parameters; one is a direct implementation of MCMC, and the second utilizes the data augmentation method. We use the estimation procedure to analyze three data sets, one simulated, and two from real attitudinal surveys.
Some Alternatives to Sympson-Hetter Item-Exposure Control in Computerized Adaptive Testing
Authors: van der Linden Wim J.
Published: June 2007

The Hetter and Sympson (1997 ; 1985 ) method is a method of probabilistic item-exposure control in computerized adaptive testing. Setting its control parameters to admissible values requires an iterative process of computer simulations that has been found to be time consuming, particularly if the parameters have to be set conditional on a realistic set of values for the examinees’ ability parameter. Formal properties of the method are identified that help us explain why this iterative process can be slow and does not guarantee admissibility. In addition, some alternatives to the SH method are introduced. The behavior of these alternatives was estimated for an adaptive test from an item pool from the Law School Admission Test (LSAT). Two of the alternatives showed attractive behavior and converged smoothly to admissibility for all items in a relatively small number of iteration steps.
Comparing Harm Done by Mobility and Class Absence: Missing Students and Missing Data
Authors: Dunn Michelle C. , Kadane Joseph B. , Garrow John R.
Published: June 2007

This article addresses the relationship between academic achievement and the student characteristics of absence and mobility. Mobility is a measure of how often a student changes schools. Absence is how often a student misses class. Standardized test scores are used as proxies for academic achievement. A model for the full joint distribution of the parameters and the data, both missing and observed, is postulated. After priors are elicited, a Metropolis-Hastings algorithm within a Gibbs sampler is used to evaluate the posterior distributions of the model parameters for the Pittsburgh Public Schools. Results are given in two stages. First, mobility and absence are shown to have, with high probability, negative relationships with academic achievement. Second, the posterior for mobility is viewed in terms of the equivalent harm done by absence: changing schools at least once in the three year period, 1998–2000, has an impact on standardized tests administered in the spring of 2000 equivalent to being absent about 14 days in 1999–2000 or 32 days in 1998–1999.
Bayesian Hierarchical Modeling of Heterogeneity in Multiple Contingency Tables: An Application to Behavioral Observation Data
Authors: Dagne Getachew A. , Brown C. Hendricks , Howe George W.
Published: June 2007

Intervention studies often rely on microcoded data of social interactions to provide evidence of change due to development or treatment. Traditionally these data have been collapsed into small contingency tables. Such an approach can introduce spurious findings. Instead of treating each unit’s contingency table independently, or collapsing the tables into single aggregate table, it is more efficient to analyze associations in all units simultaneously using hierarchical models. This article presents Bayesian hierarchical models to analyze several two-way categorical data with random effects that allow different levels of variation across several events. To illustrate this approach, the authors present an analysis of couples’ interaction data from a recent study investigating how couples cope when one partner has become unemployed.
Introduction to a Special Issue of the Journal of Educational and Behavioral Statistics on Value-Added Assessment
Authors: Wainer Howard
Published: June 2007

“Some professors are justly renowned for their bravura performances as Grand Expositor on the podium, Agent Provocateur in the preceptorial, or Kindly Old Mentor in the corridors. These familiar roles in the standard faculty repertoire, however, should not be mistaken for teaching, except as they are validated by the transformation of the minds and persons of the intended audience.”

“Good teachers evaluate themselves with a pitiless gaze and measure their successes not by their virtuosity as performers but by their contribution to the transformation of students.”

( Bressler, 1991 )
An Empirical Comparison of Statistical Models for Value-Added Assessment of School Performance
Authors: Tekwe Carmen D. , Carter Randy L. , Ma Chang-Xing , Algina James , Lucas Maurice E. , Roth Jeffrey , Ariet Mario , Fisher Thomas , Resnick Michael B.
Published: June 2007

Hierarchical Linear Models (HLM) have been used extensively for value-added analysis, adjusting for important student and school-level covariates such as socioeconomic status. A recently proposed alternative, the Layered Mixed Effects Model (LMEM) also analyzes learning gains, but ignores sociodemographic factors. Other features of LMEM, such as its ability to apportion credit for learning gains among multiple schools and its utilization of incomplete observations, make it appealing. A third model that is appealing due to its simplicity is the Simple Fixed Effects Models (SFEM). Statistical and computing specifications are given for each of these models. The models were fitted to obtain value-added measures of school performance by grade and subject area, using a common data set with two years of test scores. We investigate the practical impact of differences among these models by comparing their value-added measures. The value-added measures obtained from the SFEM were highly correlated with those from the LMEM. Thus, due to its simplicity, the SFEM is recommended over LMEM. Results of comparisons of SFEM with HLM were equivocal. Inclusion of student level variables such as minority status and poverty leads to results that differ from those of the SFEM. The question of whether to adjust for such variables is, perhaps, the most important issue faced when developing a school accountability system. Either inclusion or exclusion of them is likely to lead to a biased system. Which bias is most tolerable may depend on whether the system is to be a high-stakes one.
Controlling for Student Background in Value-Added Assessment of Teachers
Authors: Ballou Dale , Sanders William , Wright Paul
Published: June 2007

The Tennessee Value-Added Assessment System measures teacher effectiveness on the basis of student gains, implicitly controlling for socioeconomic status and other background factors that influence initial levels of achievement. The absence of explicit controls for student background has been criticized on the grounds that these factors influence gains as well. In this research we modify the TVAAS by introducing commonly used controls for student SES and demographics. The introduction of controls at the student level has a negligible impact on estimated teacher effects in the TVAAS, though not in a simple fixed effects estimator with which the TVAAS is compared. The explanation lies in the TVAAS’s exploitation of the covariance of tests in different subjects and grades, whereby a student’s history of test performance substitutes for omitted background variables.
Models for Value-Added Modeling of Teacher Effects
Authors: McCaffrey Daniel F. , Lockwood J. R. , Koretz Daniel , Louis Thomas A. , Hamilton Laura
Published: June 2007

The use of complex value-added models that attempt to isolate the contributions of teachers or schools to student development is increasing. Several variations on these models are being applied in the research literature, and policy makers have expressed interest in using these models for evaluating teachers and schools. In this article, we present a general multivariate, longitudinal mixed-model that incorporates the complex grouping structures inherent to longitudinal student data linked to teachers. We summarize the principal existing modeling approaches, show how these approaches are special cases of the proposed model, and discuss possible extensions to model more complex data structures. We present simulation and analytical results that clarify the interplay between estimated teacher effects and repeated outcomes on students over time. We also explore the potential impact of model misspecifications, including missing student covariates and assumptions about the accumulation of teacher effects over time, on key inferences made from the models. We conclude that mixed models that account for student correlation over time are reasonably robust to such misspecifications when all the schools in the sample serve similar student populations. However, student characteristics are likely to confound estimated teacher effects when schools serve distinctly different populations.
A Note on the Noncentrality Parameter and Effect Size Estimates for the F Test in ANOVA
Authors: Liu Xiaofeng , Raudenbush Stephen
Published: June 2007

The noncentrality parameter for the noncentral F is a precision-weighted sum of squares of treatment means, which is closely related to the test statistic and effect size. The two common effect size estimates are not based on the uniformly minimum variance unbiased (UMVU) estimate of the noncentrality parameter. The UMVU estimate of the noncentrality parameter implies a new and more conservative effect size estimate.
Adaptive Testing With Regression Trees in the Presence of Multidimensionality
Authors: Yan Duanli , Lewis Charles , Stocking Martha
Published: June 2007

It is unrealistic to suppose that standard item response theory (IRT) models will be appropriate for all the new and currently considered computer-based tests. In addition to developing new models, we also need to give attention to the possibility of constructing and analyzing new tests without the aid of strong models. Computerized adaptive testing currently relies heavily on IRT. Alternative, empirically based, nonparametric adaptive testing algorithms exist, but their properties are little known. This article introduces a nonparametric, tree-based algorithm for adaptive testing and shows that it may be superior to conventional, IRT-based adaptive testing in cases where the IRT assumptions are not satisfied. In particular, it shows that the tree-based approach clearly outperformed (one-dimensional) IRT when the pool was strongly two-dimensional.
Estimating Ability and Item-Selection Strategy in Self-Adapted Testing: A Latent Class Approach
Authors: Revuelta Javier
Published: June 2007

This article presents a psychometric model for estimating ability and item-selection strategies in self-adapted testing. In contrast to computer adaptive testing, in self-adapted testing the examinees are allowed to select the difficulty of the items. The item-selection strategy is defined as the distribution of difficulty conditional on the responses given to previous items. The article shows that missing responses in self-adapted testing are missing at random and can be ignored in the estimation of ability. However, the item-selection strategy cannot always be ignored in such an estimation. An EM algorithm is presented to estimate an examinee’s ability and strategies, and a model fit is evaluated using Akaike’s information criterion. The article includes an application with real data to illustrate how the model can be used in practice for evaluating hypotheses, estimating ability, and identifying strategies. In the example, four strategies were identified and related to examinees’ ability. It was shown that individual examinees tended not to follow a consistent strategy throughout the test.
Causal Inference and the Heckman Model
Authors: Briggs Derek C.
Published: June 2007

In the social sciences, evaluating the effectiveness of a program or intervention often leads researchers to draw causal inferences from observational research designs. Bias in estimated causal effects becomes an obvious problem in such settings. This article presents the Heckman Model as an approach sometimes applied to observational data for the purpose of estimating an unbiased causal effect and shows how the Heckman Model can be used to correct for the problem of selection bias. It discusses in detail the assumptions necessary before the approach can be used to make causal inferences. The Heckman Model makes assumptions about the relationship between two equations in an underlying behavioral model: a response schedule and a selection function. This article shows that the Heckman Model is particularly sensitive to the choice of variables included in the selection function. This is demonstrated empirically in the context of estimating the effect of commercial coaching programs on the SAT performance of high school students. Coaching effects for both sections of the SAT are estimated using data from the National Education Longitudinal Study of 1988. Small changes in the selection function are shown to have a big impact on estimated coaching effects under the Heckman Model.
Experiences With Markov Chain Monte Carlo Convergence Assessment in Two Psychometric Examples
Authors: Sinharay Sandip
Published: June 2007

There is an increasing use of Markov chain Monte Carlo (MCMC) algorithms for fitting statistical models in psychometrics, especially in situations where the traditional estimation techniques are very difficult to apply. One of the disadvantages of using an MCMC algorithm is that it is not straightforward to determine the convergence of the algorithm. Using the output of an MCMC algorithm that has not converged may lead to incorrect inferences on the problem at hand. The convergence is not one to a point, but that of the distribution of a sequence of generated values to another distribution, and hence is not easy to assess; there is no guaranteed diagnostic tool to determine convergence of an MCMC algorithm in general. This article examines the convergence of MCMC algorithms using a number of convergence diagnostics for two real data examples from psychometrics. Findings from this research have the potential to be useful to researchers using the algorithms. For both the examples, the number of iterations required (suggested by the diagnostics) to be reasonably confident that the MCMC algorithm has converged may be larger than what many practitioners consider to be safe.
Using Propensity Score Subclassification for Multiple Treatment Doses to Evaluate a National Antidrug Media Campaign
Authors: Zanutto Elaine , Lu Bo , Hornik Robert
Published: June 2007

In 1998, the U.S. Office of National Drug Control Policy launched a national media campaign in an effort to reduce and prevent drug use among young Americans. Because the campaign was implemented nationwide, there is no control group available for use in evaluating the effects of the campaign. Nevertheless, it is possible to use propensity score methods to evaluate the effects of the campaign. However, because teens receive varying degrees of exposure to the media campaign, it is necessary to apply propensity score methods that accommodate multiple treatment doses. This work extends that of previous authors to subclassification on the propensity score for observational studies with multiple treatment doses, rather than matching on the propensity score, and proposes modifications to accommodate complex survey data. This methodology is illustrated using data from a pilot study for the media campaign evaluation.
Prediction in Multilevel Models
Authors: Afshartous David , de Leeuw Jan
Published: June 2007

Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This article addresses the problem of predicting a future observable y*j in thej th group of a hierarchical data set. Three prediction rules are considered and several analytical results on the relative performance of these prediction rules are demonstrated. In addition, the prediction rules are assessed by means of a Monte Carlo study that extensively covers both the sample size and parameter space. Specifically, the sample size space concerns the various combinations of Level 1 (individual) and Level 2 (group) sample sizes, while the parameter space concerns different intraclass correlation values. The three prediction rules employ OLS, prior, and multilevel estimators for the Level 1 coefficientsβj The multilevel prediction rule performs the best across all design conditions, and the prior prediction rule degrades as the number of groups, J, increases. Finally, this article investigates the robustness of the multilevel prediction rule to misspecifications of the Level 2 model.
Inferential Methods for the Tetrachoric Correlation Coefficient
Authors: Bonett Douglas G. , Price Robert M.
Published: June 2007

The tetrachoric correlation describes the linear relation between two continuous variables that have each been measured on a dichotomous scale. The treatment of the point estimate, standard error, interval estimate, and sample size requirement for the tetrachoric correlation is cursory and incomplete in modern psychometric and behavioral statistics texts. A new and simple method of accurately approximating the tetrachoric correlation is introduced. The tetrachoric approximation is then used to derive a simple standard error, confidence interval, and sample size planning formula. The new confidence interval is shown to perform far better than the confidence interval computed by SAS. A method to improve the SAS confidence interval is proposed. All of the new results are computationally simple and are ideally suited for textbook and classroom presentations.
A Unified Approach to Linear Equating for the Nonequivalent Groups Design
Authors: von Davier Alina A. , Kong Nan
Published: June 2007

This article describes a new, unified framework for linear equating in a non-equivalent groups anchor test (NEAT) design. The authors focus on three methods for linear equating in the NEAT design—Tucker, Levine observed-score, and chain—and develop a common parameterization that shows that each particular equating method is a special case of the linear equating function in the NEAT design. A new concept, the method function, is used to distinguish among the linear equating functions, in general, and among the three equating methods, in particular. This approach leads to a general formula for the standard error of equating for all linear equating functions in the NEAT design. A new tool, the standard error of equating difference , is presented to investigate if the observed difference in the equating functions is statistically significant.
No Humble Pie: The Origins and Usage of a Statistical Chart
Authors: Spence Ian
Published: June 2007

William Playfair’s pie chart is more than 200 years old and yet its intellectual origins remain obscure. The inspiration likely derived from the logic diagrams of Llull, Bruno, Leibniz, and Euler, which were familiar to William because of the instruction of his mathematician brother John. The pie chart is broadly popular but—despite its common appeal—most experts have not been seduced, and the academy has advised avoidance; nonetheless, the masses have chosen to ignore this advice. This commentary discusses the origins of the pie chart and the appropriate uses of the form.
A Multilevel Bayesian Item Response Theory Method for Scaling Socioeconomic Status in International Studies of Education
Authors: May Henry
Published: June 2007

A new method is presented and implemented for deriving a scale of socioeconomic status (SES) from international survey data using a multilevel Bayesian item response theory (IRT) model. The proposed model incorporates both international anchor items and nation-specific items and is able to (a) produce student family SES scores that are internationally comparable, (b) reduce the influence of irrelevant national differences in culture on the SES scores, and (c) effectively and efficiently deal with the problem of missing data in a manner similar to Rubin’s (1987) multiple imputation approach. The results suggest that this model is superior to conventional models in terms of its fit to the data and its ability to use information collected via international surveys.
Assembling a Computerized Adaptive Testing Item Pool as a Set of Linear Tests
Authors: van der Linden Wim J. , Ariel Adelaide , Veldkamp Bernard P.
Published: June 2007

Test-item writing efforts typically results in item pools with an undesirable correlational structure between the content attributes of the items and their statistical information. If such pools are used in computerized adaptive testing (CAT), the algorithm may be forced to select items with less than optimal information, that violate the content constraints, and/or have unfavorable exposure rates. Although at first sight somewhat counterintuitive, it is shown that if the CAT pool is assembled as a set of linear test forms, undesirable correlations can be broken down effectively. It is proposed to assemble such pools using a mixed integer programming model with constraints that guarantee that each test meets all content specifications and an objective function that requires them to have maximal information at a well-chosen set of ability values. An empirical example with a previous master pool from the Law School Admission Test (LSAT) yielded a CAT with nearly uniform bias and mean-squared error functions for the ability estimator and item-exposure rates that satisfied the target for all items in the pool.
Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis
Authors: Azen Razia , Budescu David V.
Published: June 2007

Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R2 contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria for an appropriate generalization of R2 to the case of multiple response variables. The DA results obtained by univariate regression (with each criterion separately) are analytically compared with results obtained by multivariate DA and illustrated with an example. It is shown that univariate dominance does not necessarily imply multivariate dominance (and vice versa), and it is recommended that researchers who wish to account for the correlation among the response variables use multivariate DA to determine the relative importance of predictors.
Fitting Value-Added Models in R
Authors: Doran Harold C. , Lockwood J. R.
Published: June 2007

Value-added models of student achievement have received widespread attention in light of the current test-based accountability movement. These models use longitudinal growth modeling techniques to identify effective schools or teachers based upon the results of changes in student achievement test scores. Given their increasing popularity, this article demonstrates how to perform the data analysis necessary to fit a general value-added model using the nlme package available for the R statistics environment. We demonstrate techniques for inspecting the data prior to fitting the model, walk a practitioner through a sample analysis, and discuss general extensions commonly found across the literature that may be incorporated to enhance the basic model presented, including the estimation of multiple outcomes and teacher effects.
Modeling Heterogeneity of Latent Growth Depending on Initial Status
Authors: Klein Andreas G. , Muthén Bengt O.
Published: June 2007

In this article, a heterogeneous latent growth curve model for modeling heterogeneity of growth rates is proposed. The suggested model is an extension of a conventional growth curve model and a complementary tool to mixed growth modeling. It allows the modeling of heterogeneity of growth rates as a continuous function of latent initial status and time-invariant covariates. A quasi-ML parameter estimation method and a likelihood-ratio test for heterogeneity of growth is developed, and the new methodology is applied to an empirical example on math achievement data. The example illustrates that the proposed model gives more accurate confidence intervals for the prediction of future math achievement than a conventional growth curve model and identifies the subgroup of subjects which show the most homogeneous growth.
Population-Sample Regression in the Estimation of Population Proportions
Authors: Weitzman R. A.
Published: June 2007

Focusing on a single sample obtained randomly with replacement from a single population, this article examines the regression of population on sample proportions and develops an unbiased estimator of the square of the correlation between them. This estimator turns out to be the regression coefficient. Use of the squared-correlation estimator as a shrinkage coefficient applied to sample proportions in a Bayesian context results in credibility intervals that are narrower, sometimes considerably narrower, than conventional confidence intervals. On illustrative data involving 285 respondents who selected one of two optional responses, the 95% credibility interval was 33% narrower than the corresponding conventional confidence interval, while the regression or shrinkage effect, which decreases with increasing sample size, was about±0.02. In a review of prior work, this article also shows that shrinkage coefficients, applied to multisample means as well as single-sample proportions, correspond generally to estimators of squared population-sample correlations.
The Potential for Electricity Market Restructuring in Quebec
Authors: Robert Clark C. , Leach Andrew
Published: June 2007

In this paper we discuss the potential for future electricity market restructuring in Quebec. In particular, we consider the political economy of the abolishment of the Heritage Pool requirement, which caps wholesale prices. Its elimination is challenging since it would result in higher retail prices. However, if the extra revenue that would be earned from selling at the market price is properly redistributed, a majority of voters could benefit. We also examine the possibility of establishing a competitive wholesale market. The usual model of forced divestiture may not lead to a competitive outcome since most of Quebec's electricity is produced at large hydroelectric complexes cascaded on a few individual river systems. Consequently, Quebec might consider adopting the approach taken by Norway and join an expanded production pool.
Benchmarking Canada's Performance in the Global Competition for Mobile Talent
Authors: Gera Surendra , Songsakul Thitima
Published: June 2007

Competing in the international arena for skilled workers is crucial for Canada to foster its innovation and economic prosperity. Attracting and retaining skilled workers are among the most important challenges facing Canadian policy makers. The key objective of the paper is to assess how Canada is faring in this matter in comparison to key OECD (Organisation for Economic Co-operation and Development) countries. The paper examines international mobility in terms of the stock and flow of high-skilled workers, assesses whether Canada attracts a “fair share” of internationally mobile skilled workers, and discusses immigration policies that influence a country's relative performance in attracting these workers from abroad.
Les prix élevés de l'essence avant les longues fins de semaine : mythe ou réalité ?
Authors: Erutku Can
Published: June 2007

Some consumers believe that the retail price of gasoline increases before long holiday weekends. In this study, we seek to verify whether or not this perception is justified, and we attempt to explain our results. We have recorded the retail price of gasoline on Thursdays and Fridays in 11 Canadian cities over a period of four years. Although our procedure differs from that adopted by the Conference Board (2001) in an earlier study, we arrive at the same result: the data do not allow us to corroborate the supposed “long weekend effect.”
Total Syntheses in Solution of TOAC‐Labelled Alamethicin F50/5 Analogues
Authors: Peggion Cristina , Jost Micha , Baldini Chiara , Formaggio Fernando , Toniolo Claudio
Published: June 2007

Total syntheses in solution of a set of four selected analogues of the 19‐mer component F50/5 of alamethicin, the most extensively studied among the channel‐former peptaibol antibiotics, are planned and reported. All analogues bear three Glu(OMe) residues, replacing the Gln residues at positions 7, 18, and 19 of the naturally occurring compound. Three analogues are mono‐labelled with the free‐radical‐containing amino acid residue TOAC at the strategic positions 1, 8, or 16. The fourth analogue is bis‐labelled with the same EPR‐active residue at both positions 1 and 16. In the native sequence, all of the positions where TOAC replacements have been introduced are characterized by residues of Aib, the prototype of the class of helicogenic Cα‐tetrasubstituted α‐amino acids. All of the TOAC analogues synthesized exhibit significant membrane‐modifying properties.
Solvent Dependence of the Rotational Diffusion of TOAC‐Spin‐Labeled Alamethicin
Authors: Marsh Derek , Jost Micha , Peggion Cristina , Toniolo Claudio
Published: June 2007

Three derivatives of the hydrophobic, channel‐forming peptaibiotic alamethicin (F50/5) have been synthesized, the original Aib residue at position 1, 8, or 16 being replaced with the spin‐labeled amino acid TOAC (=2,2,6,6‐tetramethylpiperidin‐1‐oxyl‐4‐amino‐4‐carboxylic acid). Electron‐paramagnetic‐resonance (EPR) spectroscopy was used to characterize the rotational diffusion of these compounds in five isotropic solvents of differing viscosity and polarity, including MeOH, EtOH, PrOH, i‐PrOH, and hexanol (HxOH). In MeOH, the labeled alamethicins were found to rotate anisotropically as a monomer (axial ratio a/b=3). In aliphatic alcohols of increasing viscosity (and hydrophobicity), the rotational correlation times progressively increased. Even in HxOH, the (fivefold) increase in correlation time was no greater than the increase in viscosity. We conclude that TOAC‐labeled alamethicins remain monomeric in these solvents of relatively high polarity.
Dual‐modality in vivo monitoring of subventricular zone stem cell migration and metabolism
Authors: Cicchetti Francesca , Gross Robert E. , Bulte Jeff W.M. , Owen Mary , Chen Iris , Saint‐Pierre Martine , Wang Xukui , Yu Meixiang , Brownell Anna‐Liisa
Published: June 2007

Rat subventricular zone (SVZ) stem cells were labeled with superparamagnetic iron oxide particles (SPIO) to follow their fate and migratory potential with magnetic resonance imaging (MRI) and positron emission tomography (PET). Labeled cells were transplanted into either the right rostral migratory stream (RMS) or striatum of normal adult Sprague–Dawley rats and serially followed for 3 months. Minimal migration of the cells implanted into the striatum was observed after 3 weeks whereas SVZ cells implanted into the RMS migrated toward the olfactory bulb at 1 week post‐transplantation. PET studies of glucose metabolism using 18F‐FDG demonstrated enhanced glucose utilization in the striatum of transplanted animals. PET studies conducted 3 months after transplantation showed elevated accumulation of 11C‐raclopride (dopamine receptor type 2) and 11C‐CFT (dopamine transporter) binding in the striatal grafts. Implanted SVZ cells did not induce significant inflammation as identified by PET using 11C‐PK11195, a ligand detecting activated microglia. Histological analysis identified viable SPIO‐labeled cells (some of which were nestin‐positive) 7 weeks post‐transplantation, suggesting a prolonged presence of undifferentiated neural stem cells within transplants. In addition, double immunostaining for neuronal and astrocytic markers (NeuN and GFAP) indicated that differentiation into neuronal and astrocytic phenotypes also occurred. Thus, combining MRI and PET enables monitoring of cell migration and metabolism non‐invasively in vivo for extended periods of time. Copyright © 2007 John Wiley & Sons, Ltd.
Agglomeration of NTO on the surface of HMX particles in water‐NMP solvent
Authors: Kim Kwang‐Joo , Kim Hyoun‐Soo
Published: June 2007

A sensitive explosive was coated with a less sensitive explosive in order to improve stability while maintaining explosion performance. Agglomeration of 3‐nitro‐1,2,4‐triazole‐5‐one (NTO) on the surface of cyclotetramethylene tetranitramine (HMX) crystals in water‐N ‐methyl‐2‐pyrrolidone (NMP) solvent was performed by cooling crystallization. Phenomena for coating by crystallization and agglomeration were studied by in‐situ measurement. The agglomeration kinetic for the coating of NTO on HMX crystals was correlated with the 3rd power of the solution supersaturation and the 2nd power of the number of the suspended particles. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Fatty acid profiles of 80 vegetable oils with regard to their nutritional potential
Authors: Dubois Virginie , Breton Sylvie , Linder Michel , Fanni Jacques , Parmentier Michel
Published: June 2007

The current concern for fat intake in western countries has raised the question of the individual fatty acid (FA) impact on health. This important issue has strengthened the awareness of nutritionists and food manufacturers for the control of the FA profile of food products. The aim of this review is to provide a classification of the FA profiles of 80 vegetable oil sources, according to their nutritional potential. The first part of the review focuses on lipoprotein metabolism, and on the impact of each dietary FA on blood lipid composition (LDL‐cholesterol, HDL‐cholesterol and circulating triacylglycerols). In the second part of the review, the oil sources are clustered by similar FA profiles, and the classification is discussed with regard to the individual FA action on blood lipid composition. Apart from the major vegetable seeds, the clustering highlighted some interesting nutritional oil sources containing mainly α‐linolenic acid (camelina, linseed, perilla and stock oils), or interesting amounts of the two essential FA (purslane, chia, raspberry seed, sea buckthorn seed and salicorn oils). Furthermore, this classification provides a useful tool for the formulation of the FA profile of food products.