On the use of MCMC computerized adaptive testing with empirical prior information to improve efficiency

The paper deals with the introduction of empirical prior information in the estimation of candidate’s ability within computerized adaptive testing (CAT). CAT is generally applied to improve efficiency of test administration. In this paper, it is shown how the inclusion of background variables both in the initialization and the ability estimation is able to improve the accuracy of ability estimates. In particular, a Gibbs sampler scheme is proposed in the phases of interim and final ability estimation. By using both simulated and real data, it is proved that the method produces more accurate ability estimates, especially for short tests and when reproducing boundary abilities. This implies that operational problems of CAT related to weak measurement precision under particular conditions, can be reduced as well. In the empirical examples, the methods were applied to CAT for intelligence testing in the area of personnel selection and to educational measurement. Other promising applications would be in the medical world, where testing efficiency is of paramount importance as well.     

An Investigation of Parameter Recovery in MCMC Estimation for the Additive IRT Model

The article aims at evaluating the parameter recovery for the multidimensional additive IRT model (Sheng, 2005 Sheng , Y. ( 2005 ). Bayesian Analysis of Hierarchical IRT Models: Comparing and Combining the Unidimensional & Multi-Unidimensional IRT Models. PhD thesis , Faculty of the Graduate SchoolUniversity of Missouri-Columbia . [Google Scholar]; Sheng and Wikle, 2009 Sheng , Y. , Wikle , C. K. ( 2009 ). Bayesian IRT models incorporating general and specific abilities . Behaviormetrika 36 : 27 – 48 .[Crossref] , [Google Scholar]). By estimating the model parameters via Gibbs sampler, a simulation study is conducted under different testing conditions, e.g., dimensionality, test and subtest lengths, correlation matrices, and different values of discrimination parameters. The results show that, especially when the test length is short and the abilities are highly correlated, the accuracy of the parameter estimates is reduced and more iterations are required to convergence. An application in educational testing is also described to show the effectiveness of the model in use.     

Bayesian Estimation of a Multidimensional Additive Graded Response Model for Correlated Traits

The aim of the article is to propose a Bayesian estimation through Markov chain Monte Carlo of a multidimensional item response theory model for graded responses with an additive structure with correlated latent traits. A simulation study is conducted to evaluate the model parameter recovery under different conditions (sample size, test and subtest length, number of response categories, and correlation structure). The results show that the parameters are well reproduced when the sample size is sufficiently large (n = 1, 000), while the worst recovery is observed for small sample size (n = 500), and four response categories with a short number of test items.     

Environmental consequences of exurban expansion in an agricultural area: the case of the Argentinian Pampas ecoregion

Exurban land use growth has been documented for at least thirty years in various regions around the world. Land use planners and land use/land cover change researchers have been concerned about the low-density residential developments scattered on a natural or agricultural matrix, due to their ecological and environmental impacts. In this paper, exurban sprawl in the Pampas ecoregion (Buenos Aires province, Argentina), is characterized and assessments are taken of the magnitude of two important consequences: agricultural land conversion, and excess CO₂ emission resulting from commute. The exurban developments concentrate between 50 and 70 km from Buenos Aires city, mainly at walking distances from compact towns, from 1 to 6 km of main roads, and no further than 25 km from them. Most of the exurban developments are located on the higher elevations in the study area (25 to 30 m above sea level). Even though exurban land use covers a very small proportion of land, the results show a tendency for conversion of land in the highest soil productivity capacity classes. While the best agricultural lands accounted for 29% of the land in the study area, it accounted for 54% of the exurban development. Preference of exurban land use conversion for good quality agricultural soils is observed even in those counties with a scarcity of good soils. However, there are great differences among counties in the relative proportion of land converted to exurban land use in each soil productivity capacity class. At the county level, from 0 to 100% of the highest production capacity lands have been converted to exurban uses, regardless of the proportion of land in each soil productivity capacity class for individual counties. Excess CO₂ equivalents emission was calculated for both the actual and the potencial number of households in the exurban developments. According to the automobile marque, range and fuel type, the actual emission ranges from 81,065 to 190,446 tons/year, and the potential from 296,643 to 696,908 tons/year. The excess emission per household is higher than that produced by domestic energy consumption within the Buenos Aires city during the same time period. The amount of CO₂ emitted in a year by personal vehicle transportation, in the study area, is equivalent to that captured by 16,000 ha of temperate forests or 27,600 ha of temperate grasslands during the same time period.

The use of predicted values for item parameters in item response theory models: an application in intelligence tests

In testing, item response theory models are widely used in order to estimate item parameters and individual abilities. However, even unidimensional models require a considerable sample size so that all parameters can be estimated precisely. The introduction of empirical prior information about candidates and items might reduce the number of candidates needed for parameter estimation. Using data for IQ measurement, this work shows how empirical information about items can be used effectively for item calibration and in adaptive testing. First, we propose multivariate regression trees to predict the item parameters based on a set of covariates related to the item-solving process. Afterwards, we compare the item parameter estimation when tree-fitted values are included in the estimation or when they are ignored. Model estimation is fully Bayesian, and is conducted via Markov chain Monte Carlo methods. The results are two-fold: (a) in item calibration, it is shown that the introduction of prior information is effective with short test lengths and small sample sizes and (b) in adaptive testing, it is demonstrated that the use of the tree-fitted values instead of the estimated parameters leads to a moderate increase in the test length, but provides a considerable saving of resources.