bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Selection Procedures for Computerized Adaptive Testing with Polytomous Items
Authors: Van Rijn P. W. , Eggen T. J. H. M. , Hemker B. T. , Sanders P. F.
Published: July 2003

In the present study, a procedure that has been used to select dichotomous items in computerized adaptive testing was applied to polytomous items. This procedure was designed to select the item with maximum weighted information. In a simulation study, the item information function was integrated over a fixed interval of ability values and the item with the maximum area was selected. This maximum interval information item selection procedure was compared to a maximum point information item selection procedure. Substantial differences between the two item selection procedures were not found when computerized adaptive tests were evaluated on bias and the root mean square of the ability estimate.
Different Kinds of DIF: A Distinction Between Absolute and Relative Forms of Measurement Invariance and Bias
Authors: Borsboom Denny , Mellenbergh Gideon J. , Van Heerden Jaap
Published: July 2003

In this article, a distinction is made between absolute and relative measurement. Absolute measurement refers to the measurement of traits on a group-invariant scale, and relative measurement refers to the within-group measurement of traits, where the scale of measurement is expressed in terms of the within-group position on a trait. Relative measurement occurs, for example, if an item induces a within-group comparison in respondents. These distinctions are discussed within the framework of measurement invariance, differentiating between absolute and relative forms of measurement invariance and bias. It is shown that items for relative measurement will produce bias as classically defined if the mean and/or variance of the trait distribution differ between groups. This form of bias, however, does not result from multidimensionality but from the fact that measurement is on a relative scale. A logistic regression procedure for the detection of relative measurement invariance and bias is proposed, as well as a model that allows for the incorporation of items for relative measurement in test analysis. Implications of the distinction between absolute and relative measurement are discussed and prove to be especially relevant for the domain of personality research.
Obtaining a Common Scale for Item Response Theory Item Parameters Using Separate Versus Concurrent Estimation in the Common-Item Equating Design
Authors: Hanson Bradley A. , Béguin Anton A.
Published: July 2003

Item response theory item parameters can be estimated using data from a common-item equating design either separately for each form or concurrently across forms. This paper reports the results of a simulation study of separate versus concurrent item parameter estimation. Using simulated data from a test with 60 dichotomous items, four factors were considered: (a) estimation program (MULTILOG versus BILOG-MG), (b) sample size per form (3,000 versus 1,000), (c) number of common items (20 versus 10), and (d) equivalent versus nonequivalent groups taking the two forms (no mean difference versus a mean difference of 1 SD). In addition, four methods of item parameter scaling were used in the separate estimation condition: two item characteristic curve methods (Stocking-Lord and Haebara) and two moment methods (Mean/Mean and Mean/Sigma). Concurrent estimation generally resulted in lower error than separate estimation, although not universally so. The results suggest that one factor accounting for the lower error when using concurrent estimation may be that the parameter estimates for the common item parameters are based on larger samples. It is argued that the results of this study, together with other research on this topic, are not sufficient to recommend completely avoiding separate estimation in favor of concurrent estimation.
A Comparison of Linking and Concurrent Calibration Under the Graded Response Model
Authors: Kim Seock-Ho , Cohen Allan S.
Published: July 2003

Developing a common metric is essential to successful applications of item response theory to practical testing problems, such as equating, differential item functioning, and computerized adaptive testing. In this study, the authors compared two methods for developing a common metric for the graded response model under item response theory: (a) linking separate calibration runs using equating coefficients from the characteristic curve method and (b) concurrent calibration using the combined data of the base and target groups. Concurrent calibration yielded consistently albeit only slightly smaller root mean square differences for both item discrimination and location parameters. Similar results were observed for distance measures between item parameter estimates and item parameters. Concurrent calibration also yielded consistently though only slightly smaller root mean square differences for ability than linking.
Multidimensional Rasch Measurement via Item Component Models and Faceted Designs
Authors: Rost Jürgen , Carstensen Claus H.
Published: July 2003

A multidimensional Rasch model is presented here, which is a multidimensional extension of item component models. Relations to existing multidimensional item response theory models are discussed. Apart from other applications, it is also suitable for analyzing tests and questionnaires, which are designed according to two or more facets. An application to a 77-item questionnaire on students’ interest in physics with a two-facet structure demonstrates that the model parameters can even be estimated when 17 latent dimensions are to be measured simultaneously (by means of joint maximum likelihood methods).
Application of an Empirical Bayes Enhancement of Mantel-Haenszel Differential Item Functioning Analysis to a Computerized Adaptive Test
Authors: Zwick Rebecca , Thayer Dorothy T.
Published: July 2003

This study used a simulation to investigate the applicability to computerized adaptive test data of a differential item functioning (DIF) analysis method developed by Zwick, Thayer, and Lewis. The approach involves an empirical Bayes (EB) enhancement of the popular Mantel-Haenszel (MH) DIF analysis method. Results showed the performance of the EB DIF approach to be quite promising, even in extremely small samples. In particular, the EB procedure was found to achieve roughly the same degree of stability for samples averaging 117 and 40 members in the two examinee groups as did the ordinary MH for samples averaging 240 in each of the two groups. Overall, the EB estimates tended to be closer to their target values than did the ordinary MH statistics in terms of root mean square residuals; the EB statistics were also more highly correlated with the target values than were the MH statistics. When combined with a loss-function-based decision rule, the EB method is better at detecting DIF than conventional approaches, but it has a higher Type I error rate.
An IRT-Based Two-Wave Model for Studying Short-Term Stability in Personality Measurement
Authors: Ferrando Pere J.
Published: July 2003

This article describes an item response theorybased structural equation model that allows the short-term stability and the magnitude of retest effects to be assessed for some types of personality traits. The relations between the model estimates and the usual procedures for assessing invariance in IRT are described. An empirical application of the model is given, the substantive implications of the results are discussed, and suggestions for further research and methodological procedures are presented.
Comparison of Four IRT Models When Analyzing Two Tests for Inductive Reasoning
Authors: De Koning Els , Sijtsma Klaas , Hamers Jo H. M.
Published: July 2003

This article discusses the use of the nonparametric IRT Mokken models of monotone homogeneity and double monotonicity and the parametric Rasch and Verhelst models for the analysis of binary test data. First, the four IRT models are discussed and compared at the theoretical level, and for each model, methods are discussed for evaluating the fit of the model to test data. Second, each of the four IRT models is used for analyzing the data collected by means of two versions of a test for inductive reasoning. Finally, the results are discussed and recommendations are given about the practical use of each of the IRT models. It is concluded that the simultaneous use of several IRT models for practical data analysis provides more insight into the structure of tests than the rigid use of only one model.
A Method for Analyzing Sparse Data Matrices in the Generalizability Theory Framework
Authors: Chiu Christopher W. T. , Wolfe Edward W.
Published: July 2003

In generalizability analyses, unstable, and potentially invalid, variance component estimates may result from using only a limited portion of available data. However, missing observations are common in operational performance assessment settings because of the nature of the assessment design. This article describes a procedure for overcoming the computational and technological limitations in analyzing data with missing observations by extracting data from a sparsely .lled data matrix into analyzable smaller subsets of data. This subdividing method is accomplished by creating data sets that exhibit structural designs that are common in generalizability analyses, namely, the crossed, MBIB, and nested designs. The validity of this subdividing method is examined using a Monte Carlo simulation. The method is demonstrated on an operational data set.
Item Parameter Recovery for the Nominal Response Model
Authors: De Ayala R. J. , Sava-Bolesta Monica
Published: July 2003

Establishing guidelines for reasonable item parameter estimation is fundamental to use of the nominal response model. Factors studied were the sample size ratio (SSR), latent trait distribution (LD), and amount of item information. Results showed that the LD accounted for 42.5% of the variability in the accuracy of estimating the slope parameter; the SSR and the maximum item information factors accounted for 29.5% and 3.5% of the accuracy, respectively. In general, as the LD departed from a normal distribution, a larger number of examinees was required to accurately estimate the slope and intercept parameters. Results indicated that an SSR of 10:1 can produce reasonably accurate item parameter estimates when the LD is normal.
Correlates of Person Fit and Effect of Person Fit on Test Validity
Authors: Schmitt Neal , Chan David , Sacco Joshua M , McFarland Lynn A , Jennings Danielle
Published: July 2003

Person-fit indices ( lz and multitest lzm) derived from item response theory and used to identify misfitting examinees were computed based on responses to cognitive ability and personality tests. lz indices from different ability domains within the cognitive tests were uncorrelated with each other; lz indices from different tests within the personality domain were moderately intercorrelated. Cross-domain correlations were near 0. Test-taking motivation and conscientiousness were correlated moderately with multitest lzm for personality tests and to a lesser extent for cognitive tests. Test reactions were uncorrelated with any of the lz measures. Males had higher mean lz s than females. This difference could be partly attributed to differences in conscientiousness. African-Americans had higher mean lz than Whites. This effect could not be accounted for by test-taking motivation or conscientiousness. High values of lz affected the criterion-related validity of the set of cognitive tests such that the validity estimate decreased as lz increased.
Knowledge of Solution Strategies and IRT Modeling of Items for Transitive Reasoning
Authors: Sijtsma Klaas , Verweij Anton C.
Published: July 2003

Componential item response theory (CIRT) is presented as a model-oriented approach to studying processes and strategies underlying the incorrect/correct responses to cognitive test tasks. CIRT is contrasted with a data-oriented approach in which verbal explanations for incorrect/correct responses are collected during the test phase and incorporated in the scoring. Alternatively, the psychologically meaningful data are modeled by unidimensional item response theory models. Verbal explanations for each examinee and task were collected from transitive reasoning tasks in addition to the incorrect/correct responses. Two datasets were compiled, one reflecting the common incorrect/correct scoring and one showing whether a deductive strategy had been used to produce a correct response. The Mokken model of monotone homogeneity, the partial-credit model, and the generalized one-parameter logistic model were used to analyze both polytomous datasets. Results showed that combining knowledge of solution strategies with IRT modeling produced a useful unidimensional scale for transitive reasoning.
Latent Structure Analysis of Classification Errors in Screening and Clinical Diagnosis: An Alternative to Classification Analysis
Authors: Bergan John R , Schwarz Richard D , Reddy Linda A
Published: July 2003

Classification analysis is used widely to detect classification errors determined by evaluating a screening or diagnostic instrument against a criterion measure. The usefulness of classification analysis is limited because it assumes an error-free criterion and provides no statistical test of the validity of that assumption. The classification-analysis model is a special case of a general latent-class model. This paper presents latent-class models that fall within the purview of the general model presented by Clogg & Goodman (1984, 1985) and Walter & Irwig (1988). Variations on the general latent-class model allowthe investigator to determine whether the criterion measure and/or the diagnostic or screening procedure for multiple groups can be considered error-free. Analogous to the problem of differential item functioning, the general model makes it possible to test assumptions regarding classification errors that could occur across groups. The proportion of individuals who may be misclassified by a screening instrument or diagnostic procedure can also be determined using latent structure techniques.
Accuracy of Population Validity and Cross-Validity Estimation: An Empirical Comparison of Formula-Based, Traditional Empirical, and Equal Weights Procedures
Authors: Raju Nambury S. , Bilgic Reyhan , Edwards Jack E. , Fleer Paul F.
Published: July 2003

An empirical monte carlo study was performed using predictor and criterion data from 84,808 U.S. Air Force enlistees. 501 samples were drawn for each of seven sample size conditions: 25, 40, 60, 80, 100, 150, and 200. Using an eight-predictor model, 500 estimates for each of 9 validity and 11 cross-validity estimation procedures were generated for each sample size condition. These estimates were then compared to the actual squared population validity and cross-validity in terms of mean bias and mean squared bias. For the regression models determined using ordinary least squares, the Ezekiel procedure produced the most accurate estimates of squared population validity (followed by the Smith and the Wherry procedures), and Burket’s formula resulted in the best estimates of squared population cross-validity. Other analyses compared the coefficients determined by traditional empirical cross-validation and equal weights; equal weights resulted in no loss of predictive accuracy and less shrinkage. Numerous issues for future basic research on validation and cross-validation are identified.
Correlations Redux: Asymptotic Confidence Limits for Partial and Squared Multiple Correlations
Authors: Graf Richard G. , Alf Edward F.
Published: July 2003

Olkin & Finn (1995) developed expressions for confidence intervals for functions of simple, partial, and multiple correlations. This paper describes procedures and computer programs for solving these problems using the methods described by Olkin and Finn. The programs extendthe methods for any number of predictors or for partialing out any number of variables.
Are Simple Change Scores Obsolete? An Approach to Studying Correlates and Predictors of Change
Authors: Raykov Tenko
Published: July 2003

This paper complements recent discussions about the reliability of observed change scores (Collins, 1996a; Humphreys, 1996; Williams & Zimmerman, 1996a, 1996b). It is argued that modeling change on the latent dimension of interest is a better approach to measuring change than focusing on observed change scores and their properties. It is proposed that research be directed toward correlates and predictors of ability change (Rogosa & Willett, 1985b) and away from recorded change scores and their reliability. A latent variable modeling approach is discussed that focuses on ability change scores. It permits estimation of both individual latent change scores and the relationship of ability change scores to other variables.
A Revised Index of Interrater Agreement for Multi-Item Ratings of a Single Target
Authors: Lindell Michael K. , Brandt Christina J. , Whitney David J.
Published: July 2003

The commonly used form of r wg. (J) can display irregular behavior, so four variants of this index were examined. An alternative index, r* wg. J, is recommended. This index is an inverse linear function of the ratio of the average obtained variance to the variance of uniformly distributed random error. r* wg.Jis superficially similar to Cronbach’s α, but careful examination confirms that r* wg.Jis an index of agreement, not reliability. Based on an examination of the small-sample behavior of r wgand r* wg.J, sample sizes of 10 or more raters are recommended.
Strategies for Computerized Adaptive Grading Testing
Authors: Xiao Beiling
Published: July 2003

IRT-based adaptive grading tests are designed to assign examinees to one of several grading categories. The item search algorithm in these tests can be based on either a golden section search, a Z-score, or an EAP-based search; these methods result, respectively, in the golden search grading test (GGT), the Z-score grading test (ZGT), and the EAP grading test (EGT). Grade assignments are evaluated after each item is administered and after the current trait estimate ([UNKNOWN]) has been determined. A test is terminated based on one of three conditions: (1) [UNKNOWN] is between two cutoff scores; (2) [UNKNOWN] is above or below the highest or lowest cutoff scores, respectively; or (3) a prespecified maximum number of items has been administered. Monte carlo studies using actual ACT Mathematics test item parameters showed that all three strategies effectively assigned examinees into multiple achievement grade levels. EGT had more correct classifications in the middle range of grade levels and more classification errors for extreme true grade levels than CGT and ZGT. However, when assuming simulees with a relatively larger variance priordistribution, or using maximum likelihood estimation for final [UNKNOWN] estimates, the systematic classification errors disappeared in EGT. GGT and ZGT required fewer items than EGT to terminate the test.
Detecting Item Memorization in the CAT Environment
Authors: McLeod Lori Davis , Lewis Charles
Published: July 2003

The purpose of appropriateness/person-fit indices is to identify response patterns for which a given item response theory model is inappropriate for an examinee even though that model is appropriate for a group. This study was concerned with those cases in which examinees had prior knowledge of items from an item bank used to generate a computerized adaptive test (CAT) and used the memorized information to inflate their test scores. The objective was to evaluate procedures that could identify these individuals by examining the application of person-fit indices in the CAT environment. The lzand ECI4 zindices were selected for comparison. Using information from these indices, a new method was developed. All three indices showed little power to detect the use of memorization. Some possibilities for altering a test when the model becomes inappropriate for an examinee are also discussed.
Likert Scaling Using Continuous, Censored, and Graded Response Models: Effects on Criterion-Related Validity
Authors: Ferrando Pere Joan
Published: July 2003

This study examined how three item response models performed when they were applied to data collected from a conventionally developed Likert-type personality scale. Each model examined isbased on a different response assumption: the multiple linear factor analysis model (continuous responses), the TOBIT factor analysis model (censored responses), and the multidimensional graded response model. The item and examinee parameters of the models were estimated using different discrepancy functions and current software implementations. Comparisons were made in terms of the goodness-of-fit of the model, parameter estimates, and criterion-related validity. Results showed that the models’ response assumptions were reasonably tenable and that the solutions obtained in all cases were similar. More complex models did not lead to substantial improvement in predictive validity.
Computerized Adaptive Testing: Overview and Introduction
Authors: Meijer Rob R. , Nering Michael L.
Published: July 2003

Use of computerized adaptive testing (CAT) has increased substantially since it was first formulated in the 1970s. This paper provides an overview of CAT and introduces the contributions to this Special Issue. The elements of CAT discussed here include item selection procedures, estimation of the latent trait, item exposure, measurement precision, and item bank development. Some topics for future research are also presented.
a-Stratified Multistage Computerized Adaptive Testing
Authors: Chang Hua-Hua , Ying Zhiliang
Published: July 2003

Computerized adaptive tests (CAT) commonly use item selection methods that select the item which provides maximum information at an examinees estimated trait level. However, these methods can yield extremely skewed item exposure distributions. For tests based on the three-parameter logistic model, it was found that administering items with low discrimination parameter (a) values early in the test and administering those with high a values later was advantageous; the skewness of item exposure distributions was reduced while efficiency was maintained in trait level estimation. Thus, a new multistage adaptive testing approach is proposed that factors a into the item selection process. In this approach, the items in the item bank are stratified into a number of levels based on their a values. The early stages ofa test use items with lower as and later stages use items with higher as. At each stage, items are selected according to an optimization criterion from the corresponding level. Simulation studies were performed to compare a-stratified CATs with CATs based on the Sympson-Hetter method for controlling item exposure. Results indicated that this new strategy led to tests that were well-balanced, with respect to item exposure, and efficient. The a-stratifiedCATs achieved a lower average exposure rate than CATs based on Bayesian or information-based item selection and the Sympson-Hetter method.
Graphical Models and Computerized Adaptive Testing
Authors: Almond Russell G. , Mislevy Robert J.
Published: July 2003

Computerized adaptive testing (CAT) based on item response theory (IRT) is viewed from the perspective of graphical modeling (GM). GM provides methods for making inferences about multifaceted skills and knowledge, and for extracting data from complex performances. However, simply incorporating variables for all sources of variation is rarely successful. Thus, researchers must closely analyze the substance and structure of the problem to create more effective models. Researchers regularly employ sophisticated strategies to handle many sources of variability outside the IRT model. Relevant variables can play many roles without appearing in the operational IRT model per se, e.g., in validity studies, assembling tests, and constructing and modeling tasks. Some of these techniques are described from a GM perspective, as well as how to extend them to more complex assessment situations. Issues are illustrated in the context of language testing.
Some Reliability Estimates for Computerized Adaptive Tests
Authors: Nicewander W. Alan , Thomasson Gary L.
Published: July 2003

Three reliability estimates are derived for the Bayes modal estimate (BME) and the maximum likelihood estimate (MLE) of θin computerized adaptive tests (CAT). Each reliability estimate is a function of test information. Two of the estimates are shown to be upper bounds to true reliability. The three reliability estimates and the true reliabilities of both MLE and BME were computed for seven simulated CATs. Results showed that the true reliabilities for MLE and BME were nearly identical in all seven tests. The three reliability estimates never differed from the true reliabilities by more than .02 (.01 in most cases). A simple implementation of one reliability estimate was found to accurately estimate reliability in CATs.
Item Selection in Adaptive Testing with the Sequential Probability Ratio Test
Authors: Eggen T. J. H. M.
Published: July 2003

Wald’s (1947) sequential probability ratio test can be implemented as an adaptive test for classifying examinees into categories. However, current implementations use an item selection method that is either random or based on Fisher information (FI), a criterion related to optimized examinee trait estimates. In this study, a method based on Kullback-Leibler information (KLI) was evaluated. Simulation studies were conducted for two- and three-category classifications in which item selection methods based on FI and KLI were compared. Results showed that testing algorithms using KLI-based item selection performed better than or as well as those using FI-based item selection.
Reducing Bias in CAT Trait Estimation: A Comparison of Approaches
Authors: Wang Tianyou , Hanson Bradley A. , Lau Che-Ming A.
Published: July 2003

The use of a beta prior in trait estimation was extended to the maximum expected a posteriori (MAP) method of Bayesian estimation. This new method, called essentially unbiased MAP (EU-MAP), was compared with MAP (using a standard normal prior), essentially unbiased expected a posteriori, weighted likelihood, and maximum likelihood estimation methods. Comparisons were made based on the effects that the shape of prior distributions, different item bank characteristics, and practical constraints had on bias, standard error, and root-mean-square error (RMSE). Overall, EU-MAP performed best. This new method significantly reduced bias in fixed-length tests (though with a slight increase in RMSE) and performed reasonably well when a fixed posterior variance termination rule was used. Practical constraints had little effect on the bias of this method.
Distinguishing Constant and Dimension-Dependent Interaction: A Simulation Study
Authors: Tuerlinckx Francis , De Boeck Paul
Published: July 2003

A simulation study was conducted to determine how well two models for local item dependency (LID), called interaction models, could be distinguished. The models examined were the constantorder interaction model (COIM) and the dimension dependent interaction model (DDIM). Data were simulated according to the latter model. Three factors were manipulated: sample size, the weight of the difference between the latent trait value of the examinee and the interaction parameter, and the value of the interaction parameter. Results indicated that (1) if the interaction parameter is not too extreme, the COIM will be rejected in favor of the true model (the Rasch model fit poorly for all levels of the interaction parameter); (2) a larger weight of the difference between the latent trait value and the interaction parameter facilitated the rejection of the COIM, although finding the true weight required a large sample size; and (3) the value for the interaction parameter with an optimal discrimination between the COIM and DDIM was not 0, as expected.
A Description and Demonstration of the Polytomous-DFIT Framework
Authors: Flowers Claudia P. , Oshima T. C. , Raju Nambury S.
Published: July 2003

Raju, van der Linden, & Fleer (1995) proposed an item response theory based, parametric differential item functioning (DIF) and differential test functioning (DTF) procedure known as differential functioning of items and tests (DFIT). According to Raju et al., the DFIT framework can be used with unidimensional and multidimensional data that are scored dichotomously and/or polytomously. This study examined the polytomous-DFIT framework. Factors manipulated in the simulation were: (1) length of test (20 and 40 items), (2) focal group distribution, (3) number of DIF items, (4) direction of DIF, and (5) type of DIF. The findings provided promising results and indicated directions for future research. The polytomous DFIT framework was effective in identifying DTF and DIF for the simulated conditions. The DTF index did not perform as consistently as the DIF index. The findings are similar to those of unidimensional and multidimensional DFIT studies.
The Null Distribution of Person-Fit Statistics for Conventional and Adaptive Tests
Authors: Van Krimpen-Stoop Edith M. L. A. , Meijer Rob R.
Published: July 2003

Several person-fit statistics have been proposed to detect item score patterns that do not fit an item response theory model. To classify response patterns as misfitting, the distribution of a person-fit statistic is needed. The theoretical null distributions of several fit statistics have been derived for paper-and-pencil (P&P) tests. However, it is unknown whether these distributions also hold for computerized adaptive tests (CAT). A three-part simulation study was conducted. In the first study, the theoretical distribution of the l z statistic across trait. θlevels for CAT and P&P tests was investigated. The distribution of the l* zstatistic proposed by Snijders (in press) was also investigated. Results indicated that the distribution of both l zand l* zdiffered from the theoretical distribution in CAT. The second study examined the distributions of l zand l* zusing simulation. These simulated distributions, when based on O [UNKNOWN], were found to be problematic in CAT. In the third study, the detection rates of l* zand l zwere compared. The rates for both statistics were found to be similar in most cases.
A Rationale for Defining Achievement Levels Using IRT-Estimated Domain Scores
Authors: Schulz E. Matthew , Kolen Michael J. , Nicewander W. Alan
Published: July 2003

A new procedure for defining achievement levels on continuous scales was developed using aspects of Guttman scaling and item response theory. This procedure assigns examinees to levels of achievement when the levels are represented by separate pools of multiple-choice items. Items were assigned to levels on the basis of their content and hierarchically defined level descriptions. The resulting level response functions were well-spaced and noncrossing. This result allowed well-spaced levels of achievement to be defined by a common percent-correct standard of mastery on the level pools. Guttman patterns of mastery could be inferred from level scores. The new scoring procedure was found to have higher reliability, higher classification consistency, and lower classification error, when compared to two Guttman scoring procedures.
Software Note
Authors: Childs Ruth A. , Chen Wen-Hung
Published: July 2003

The logistic versions of Samejima’s (1969) graded response model and Muraki’s (1992) generalized partial-credit model are parameterized differently by MULTILOG (Thissen, 1991) and PARSCALE (Muraki & Bock, 1996). Procedures for obtaining comparable item parameter estimates from MULTILOG and PARSCALE are described and example command files are provided.
Regional Involvement in Defense Industry Restructuring in Belgium and the Netherlands
Authors: Hassink Robert
Published: July 2003

Based on intensive interviews with industrial and regional actors, this article shows that in two smaller European countries, Belgium and the Netherlands, regional defense conversion efforts have not been common or markedly successful. Defense industrial activity, while regionally concentrated, employs only a small share of the workforce and is predominantly owned by foreign defense concerns without much commitment to the regions. Furthermore, defense downsizing has taken place amid a more generalized, ongoing structural crisis that militates against special defense industrial programs. Organized regional responses are as apt to oppose defense spending cuts as to pursue conversion. Nevertheless, there have been some successes. European Union KONVER funds for regional and industrial diversification, though not always sought or properly deployed, have encouraged diversification.
The Regional Role in Post-Cold War Military Industrial Conversion
Authors: Markusen Ann , Brzoska Michael
Published: July 2003

Regionally concentrated post-cold war military spending cuts offer an opportunity to compare regional conversion across countries. The authors briefly lay out the challenge and distinguish regional from national and industrial conversion strategies. Several factors facilitate or impede region-level conversion efforts: regional contextual factors—defense dependency, isolation, diversity, and vigor in the regional economy; military industrial features—sectoral specialization, size, and ownership patterns; and political structure and behavior—federal/unitary systems, regional public sector capacity, political party structure, leadership, and coalition building. The authors illustrate each with examples drawn from eighteen regions in eight countries, the subjects of articles collected in this special issue.
Regional Conversion in the Russian Case
Authors: Gonchar Ksenia , Opitz Petra
Published: July 2003

Decentralization has put increasing power at the disposal of regions to support conversion processes in Russia since 1992. There is, however, no clear-cut relation between the defense dependency of regions and their conversion success. Other factors, such as exports of any kind, the composition of regional industrial demand for nondefense goods, and the availability of shock absorbers—such as small businesses, the informal economy, networks, and institutions—had stronger effects on conversion outcomes. Case studies of Nizniy Novgorod, St. Petersburg, and Novosibirsk reveal wide regional differences in both conversion policies and their outcomes.
Regional Conversion Under Conditions of Defense Industry Centralization: The French Case
Authors: de Penanros Roland , Serfati Claude
Published: July 2003

French defense-industrial and defense-technological policies have been important factors in regional economic development. The process of downward adjustment begun in the late 1990s will have major effects on defense-dependent regions. However, conversion has only been given a minor role. The study of two quite different defense-dependent regions, Brittany and Paris, indicates that the strong, vested idiosyncratic interests of large sections of the defense industry, combined with an overly high concentration of political, economic, and social activities at the national level, are the major obstacles to a significant regional conversion policy.
Conversion Planning in Two Military Shipbuilding Regions: Hampton Roads, Virginia, and Bremen, Germany
Authors: Accordino John , Elsner Wolfram
Published: July 2003

Analysis of the responses of Bremen and Hampton Roads to cutbacks in military spending illustrates how conversion planning—and economic development planning generally—are embedded within a sociopolitical context. This context is largely determined by the regions’ industrial structure, but it is strongly influenced by the structure and power of local government and the region’s social cohesion. National and international economic conversion policies should be flexible enough not only to rapidly respond to severe regional adjustment problems to maintain qualified technological and human potentials, but also to be useful to regions with diverse sociopolitical structures.
Regional Aspects of Defense-Industrial Transformation in East-Central Europe
Authors: Kiss Yudit
Published: July 2003

The article highlights regional aspects of defense industry transformation in Slovakia and Hungary. Two case studies reveal different enterprise survival strategies in different local contexts. The Slovak case demonstrates how lack of restructuring and communication blocks development at both the enterprise and regional levels. The Hungarian case presents a successfully restructured enterprise and region through coordinated efforts. The main conclusion is that the combined restructuring efforts of enterprises and regions can provide long-term solutions for the problems caused by the crisis of the defense industry.
Forecast Accuracy and Efficiency: An Evaluation of Ex Ante Substate Long-Term Forecasts
Authors: Lenze David G.
Published: July 2003

This article identifies regional characteristics that account for differential forecast accuracy. It is found that population, income, employment, and housing start forecast accuracy depend significantly on county size, growth rate, and elderly population; industrial specialization is irrelevant. This finding has implications for making generalizations from a subset of regions. The informational efficiency of regional forecasts is also examined. It is found that employment forecasts are efficient, income forecasts tend to be overly reliant on previous period growth, and naïve population projections ignore information exploited by structural economic-demographic forecasts. These findings have important implications for the optimal combination of forecasts and the improvement of forecasting methodologies.
Urban Sprawl: Diagnosis and Remedies
Authors: Brueckner Jan K.
Published: July 2003

This article argues that urban spatial expansion results mainly from three powerful forces: a growing population, rising incomes, and falling commuting costs. Urban growth occurring purely in response to these fundamental forces cannot be faulted as socially undesirable, but three market failures may distort their operation, upsetting the allocation of land between agricultural and urban uses and justifying criticism of urban sprawl. These are the failure to account for the benefits of open space, excessive commuting because of a failure to account for the social costs of congestion, and failure to make new development pay for the infrastructure costs it generates. Precise remedies for these market failures are two types of development taxes and congestion tolls levied on commuters. Each of these remedies leads to a reduction in the spatial size of the city.
Manufacturing Employment Change, Output Demand, and Labor Productivity in the Regions of Britain
Authors: Graham Daniel J. , Spence Nigel
Published: July 2003

This article presents an analysis of manufacturing employment change in the regions of Britain between 1987 and 1994 achieved through the implementation of a labor demand shift-share model. It develops and estimates a model that is capable of determining the parameters of a labor demand function in the absence of capital data. The principal focus is on the regional results generated, which quantify and compare the employment consequences of region-specific change in industrial output, wage rates, the price of capital, and the level of technology. The authors draw implications from these results for regional manufacturing output and labor productivity performance. The article demonstrates that output, price, and technology effects have been instrumental in creating regional differences in employment and labor productivity change in British manufacturing industries.
The Role of Space in Public Capital Research
Authors: Mikelbank Brian A. , Jackson Randall W.
Published: July 2003

The role of public capital in the economy is an emerging research area with theoretical significance and societal and policy relevance. Several academic disciplines and public institutions are regular contributors to this research field. The range of findings of these studies extends from virtually no role to a very strong role for public capital in the economy. Reconciling the varied and sometimes contradictory results has not been a high priority among researchers. The lack of attention to spatial forces, an issue gaining attention only relatively recently, also has been troublesome. This article reviews public capital research along three primary dimensions: the dependent variable, scale of analysis, and attention to spatial processes. The authors emphasize scale and space as among the most important but least discussed differences among public capital studies and then focus on the nature of the economic processes at work and detectable at different scales of analysis.
The Time-Series Relatedness of State and National Indexes of Leading Indicators and Implications for Regional Forecasting
Authors: Shoesmith Gary L.
Published: July 2003

This study investigates the time-series relatedness of state and national indexes of leading indicators and the implications of these results regarding state employment forecasting. Composite indexes of leading indicators are constructed for the United States and each of the fifty states based on housing permits, initial claims for state unemployment compensation, and average weekly hours in manufacturing. The three-component U.S. index (USLI3 ) is shown to reflect much of the cyclical variation in the U.S. composite of eleven leading indicators (USLI11 ). Final prediction error (FPE) causality tests and cointegration/long-memory components statistics show thatUSLI3 generally causes or “drives” the state indexes. These results suggest that national leading indicators may be more useful than similar state indicators in predicting state-level activity. Experimental forecasts confirm that bothUSLI3 andUSLI11 are generally more effective in improving forecasts of state nonfarm employment than the respective fifty state composite indexes.
Investing in Rural Infrastructure
Authors: Fox William F. , Porca Sanela
Published: July 2003

Infrastructure is best envisioned as services that come from a set of public works, not as physical facilities. In concept, infrastructure can expand rural economies by raising productivity levels, allowing expansion in the use of other resources, and attracting resources. Empirical research evidences that at the margin, the net effect of the three factors is for infrastructure to have only a modest effect on economic performance. Infrastructure investments will normally have their biggest impact in economically integrated and intermediate rural areas. Nonetheless, a basic complement of infrastructure is essential to support economic activity, but this set should be seen as accommodating rather than causing growth. Local, market-based approaches are normally the best way to develop the infrastructure and to ensure that an area has the basic infrastructure that can be maintained, supported, and used effectively.
Enhancing Rural Leadership and Institutions: What Can We Learn from American Indian Nations?
Authors: Cornell Stephen
Published: July 2003

Since the 1970s, a small but significant number of American Indian nations have put together some of the most impressive stories of economic development in the United States. What insights do these stories have to offer for rural economic development generally? This article analyzes recent Indian reservation experience, concluding that local control, strategic thinking, effective governing institutions, and leadership all have played key roles in reservation development success. It considers implications of these findings for leadership and institutions in rural America.
Competitive Advantages of Rural America in the Next Century
Authors: Isserman Andrew M.
Published: July 2003

Judging by statistics from the past half-century, rural America will continue to grow faster than urban America. The spread of large cities, the creation of new ones, the addition of 30 million more senior citizens, and the diffusion of immigrants into rural areas are powerful forces contributing to rural growth. Rural areas are competitive in a broad range of industries, eventually including significant elements of today’s high wage, urban-oriented growth industries. Some rural places will be left behind, but past policy experience shows that alleviating conditions in distressed rural areas is well within the nation’s capacity. The official statistical system that divides the nation into metropolitan and rural areas misdirects policy discussions by hiding rural growth and obscuring the intertwined nature of urban and rural activities. Four-fifths of rural growth has occurred in areas reclassified as metropolitan, and two-fifths of all farmers farm in metropolitan America.
Rural Policy Lessons from OECD Countries
Authors: Pezzini Mario
Published: July 2003

Rural policy has seen significant shifts in the past two decades. Governance efforts have focused on improving central coordination, creating more flexible arrangements for central support, forming new institutional arrangements at local and regional levels, and building local capacity through leadership and community development programs. Policies objectives and instruments have focused on improving the competitiveness of rural areas, investing in human and social capital, diversifying economic activities, enhancing business assistance, commercializing natural and cultural amenities, finding market niches for local products, providing public services, and incorporating program evaluation procedures. Thus, rural policy has now gone beyond agricultural policy in many countries, offering new trajectories of development for rural areas. Yet these refinements and innovations tend to be recent and limited in scope. Additional work will be necessary to ascertain their effectiveness, durability, and transferability.
New Goals for New Rural Policies
Authors: Hewings Geoffrey J. D.
Published: July 2003

The basic question is whether rural America is sustainable in its present form. America’s hegemonic position in world agricultural commodity trade is eroding. The lessons from urban America suggest that structural transformation is both painful and necessary. Improving rural infrastructure will be an important objective, but the benefits may be smaller than similar investments in metropolitan areas. Expanding agricultural opportunities will be important, through value-added processing and new specialized crops. Lifting rural human capital will be an essential ingredient in spurring new rural business formation. Finally, improving rural economic networks will be an important step in harnessing the potential of small businesses that typify the rural economic landscape. There will not be one way to address the myriad problems in rural America, but any new rural policies must recognize that not all rural communities may be viable in the future.
Using Input-Output Information for Bayesian Forecasting of Industry Employment in a Regional Econometric Model
Authors: Rickman Dan S.
Published: July 2003

Bayesian estimation is used to incorporate regional input-output information into the employment block of a regional econometric model. The Bayesian approach used borrows both from previous work on embedding input-output information within econometric models and from Bayesian vector autoregression forecasting. The accuracy of out-of-sample forecasts produced by the alternative Bayesian models and some traditional forecast approaches are compared.