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We propose a new item response theory growth model with item-specific learning parameters, or ISLP, and two variations of this model. In the ISLP model, either items or blocks of items have their own learning parameters. This model may be used to improve the efficiency of learning in a formative assessment. We show ways that the ISLP model's learning parameters can be estimated in simulation using Markov chain Monte Carlo (MCMC), demonstrate a way that the model could be used in the context of adaptive item selection to increase the rate of learning, and estimate the learning parameters in an empirical data analysis using the ISLP. In the simulation studies, the one-parameter logistic model was used as the measurement model to generate random response data with various test lengths and sample sizes. Ability growth was modeled with a few variations of the ISLP model, and it was verified that the parameters were accurately recovered. Secondly, we generated data using the linear logistic test model with known Q-matrix structure for the item difficulties. Using a two-step procedure gave very comparable results for the estimation of the learning parameters even when item difficulties were unknown. The potential benefit of using an adaptive selection method in conjunction with the ISLP model was shown by comparing total improvement in the examinees' ability parameter to two other methods of item selection that do not utilize this growth model. If the ISLP holds, adaptive item selection consistently led to larger improvements over the other methods. A real data application of the ISLP was given to illustrate its use in a spatial reasoning study designed to promote learning. In this study, interventions were given after each block of ten items to increase ability. Learning parameters were estimated using MCMC.

Soils comprise the largest pool of terrestrial carbon yet have lost significant stocks due to human activity. Changes to land management in cropland and grazing systems present opportunities to sequester carbon in soils at large scales. Uncertainty in the magnitude of this potential impact is largely driven by the difficulties and costs associated with measuring near-surface (0–30 cm) soil carbon concentrations; a key component of soil carbon stock assessments. Many techniques exist to optimize sampling, yet few studies have compared these techniques at varying sample intensities. In this study, we performed ex-ante, high-intensity sampling for soil carbon concentrations at four farms in the eastern United States. We used post hoc Monte-Carlo bootstrapping to investigate the most efficient sampling approaches for soil carbon inventory: K-means stratification, Conditioned Latin Hypercube Sampling (cLHS), simple random, and regular grid. No two study sites displayed similar patterns across all sampling techniques, although cLHS and grid emerged as the most efficient sampling schemes across all sites and strata sizes. The number of strata chosen when using K-means stratification can have a significant impact on sample efficiency, and we caution future inventories from using small strata n, while avoiding even allocation of sample between strata. Our findings reinforce the need for adaptive sampling methodologies where initial site inventory can inform primary, robust inventory with site-specific sampling techniques.

A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are applied to a computer-based assessment with a learning intervention. The results show the potential application of the proposed model to track the change of students' skills directly and provide immediate remediation as well as to evaluate the efficacy of different interventions by investigating how different types of learning interventions impact the transitions from nonmastery to mastery.

Soil organic carbon influences several landscape ecological processes, and soils are becoming recognized as a mechanism to mitigate the negative impacts of climate change. There is a need to define methods and technologies for addressing soils’ spatial variability as well as the time and cost of sampling soil organic carbon (SOC). Visible and near-infrared spectroscopy have been suggested as a sampling tool to reduce inventory cost. We sampled nineteen ranch properties totaling 17,347 ha across Oklahoma and Texas in 2019 to evaluate the effectiveness and accuracy of a handheld reflectometer (Our Sci, Ann Arbor, MI, USA) (370–940 nm) and existing remote sensing approaches to estimate SOC in semi-arid grazing lands. Our data suggest that the Our Sci Reflectometer estimated soil organic carbon with a precision of approximately (±0.3% SOC); however, it was least accurate at higher carbon concentrations. The Our Sci reflectometer, although consistently accurate at lower SOC concentrations, was still less accurate than a model built using only remote sensing and digital soil map data as predictors. Combining the two data sources was the most accurate means of determining SOC. Our results indicated that the Our Sci handheld Vis-NIR reflectometer tested may have only limited applications for reducing inventory costs at scale.

Latent class models for cognitive diagnosis often begin with specification of a matrix that indicates which attributes or skills are needed for each item. Then by imposing restrictions that take this into account, along with a theory governing how subjects interact with items, parametric formulations of item response functions are derived and fitted. Cluster analysis provides an alternative approach that does not require specifying an item response model, but does require an item-by-attribute matrix. After summarizing the data with a particular vector of sum-scores, "K"-means cluster analysis or hierarchical agglomerative cluster analysis can be applied with the purpose of clustering subjects who possess the same skills. Asymptotic classification accuracy results are given, along with simulations comparing effects of test length and method of clustering. An application to a language examination is provided to illustrate how the methods can be implemented in practice.

The purpose of this study was to assess the model fit of a 2PL through comparison with the nonparametric item characteristic curve (ICC) estimation procedures. Results indicate that three nonparametric procedures implemented produced ICCs that are similar to that of the 2PL for items simulated to fit the 2PL. However for misfitting items, especially nonmonotone items, the greatest difference is between the 2PL and kernel smoothing procedures. In general, the differences between ICCs from the nonparametric procedures and the 2PL are reduced as both sample size and test length increase. The false positive rate of the test for model fit is promising for nonparametric ICC estimation methods. Power to detect misfitting items simulated with 4PL is low. Power to detect nonmonotone items is generally much higher. Power is best for kernel smoothing but also good for isotonic regression in the medium to large sample sizes and longer test length conditions. Power for the smoothed isotonic regression is uniformly low. (Contains 4 tables.)

a-stratification is a method that utilizes items with small discrimination (a) parameters early in an exam and those with higher a values when more is learned about the ability parameter. It can achieve much better item usage than the maximum information criterion (MIC). To make a-stratification more practical and more widely applicable, a method for weighting the item selection process in a-stratification as a means of satisfying multiple test constraints is proposed. This method is studied in simulation against an analogous method without stratification as well as a-stratification using descending-rather than ascending-a procedures. In addition, a variation of a-stratification that allows for unbalanced usage of a parameters is included in the study to examine the trade-off between efficiency and exposure control. Finally, MIC and randomized item selection are included as baseline measures. Results indicate that the weighting mechanism successfully addresses the constraints, that stratification helps to a great extent balancing exposure rates, and that the ascending-a design improves measurement precision. (Contains 4 tables and 1 figure.)

In cognitive diagnosis, the test-taking behavior of some examinees may be idiosyncratic so that their test scores may not reflect their true cognitive abilities as much as that of more typical examinees. Statistical tests are developed to recognize the following: (a) nonmasters of the required attributes who correctly answer the item (spuriously high scores) and (b) masters of the attributes who fail to correctly answer the item (spuriously low scores). For a person, nonzero probability of aberrant behavior is tested as the alternative hypothesis, against normal behavior as the null hypothesis. The two generalized likelihood ratio test statistics used, with the null hypothesis parameter on the boundary of the parameter space in each, have asymptotic distributions of a 50:50 mixture of a chi-square distribution with one degree of freedom and a degenerate distribution that is a constant of 0 under the null hypothesis. Simulation results, primarily based on the DINA model (deterministic inputs, noisy "AND" gate), are used to investigate the following: (a) how accurately the statistical tests identify normal/aberrant behaviors, (b) how the power of the tests depends on the length of the cognitive exam and the degree of the inclination toward aberrance, and (c) how sensitive the tests are to inaccurate estimation of model parameters. (Contains 3 figures, 6 tables, and 1 note.)

This paper studies three models for cognitive diagnosis, each illustrated with an application to fraction subtraction data. The objective of each of these models is to classify examinees according to their mastery of skills assumed to be required for fraction subtraction. We consider the DINA model, the NIDA model, and a new model that extends the DINA model to allow for multiple strategies of problem solving. For each of these models the joint distribution of the indicators of skill mastery is modeled using a single continuous higher-order latent trait, to explain the dependence in the mastery of distinct skills. This approach stems from viewing the skills as the specific states of knowledge required for exam performance, and viewing these skills as arising from a broadly defined latent trait resembling the theta of item response models. We discuss several techniques for comparing models and assessing goodness of fit. We then implement these methods using the fraction subtraction data with the aim of selecting the best of the three models for this application. We employ Markov chain Monte Carlo algorithms to fit the models, and we present simulation results to examine the performance of these algorithms.

Clinical and health policy research frequently involves health status measurement using generic or disease specific instruments. These instruments are generally developed to arrive at several scales, each measuring a distinct domain of health quality of life (HQOL). Clinical settings are starting to explore how to integrate patient perspectives of HQOL outcomes into patient care. However, the length of many HQOL instruments poses a challenge in terms of patient burden, as well as clinic flow time. The most popular paradigm for scale construction utilizes classical test theory methodology and can lead to excessive and redundant items in an effort to bolster reliability measurements such as Cronbach's alpha above levels of accepted reliability. This paper presents techniques for utilizing item response theory to arrive at single item scales that are diagnostically informative and short enough to have clinical utility. A danger of such dramatic scale reduction is that validity might be compromised. This danger is addressed in terms of criterion related validity and sensitivity to clinical changes over a 36 months period. The reduction methods are illustrated using selected scales from the Arthritis Impact Measurement Scales 2 (AIMS2) with data obtained from the study "Pharmaceutical Care Outcomes: The Patient Role (PCOPR)."

Consider test data, a specified set of dichotomous skills measured by the test, and an IRT cognitive diagnosis model (ICDM). Statistical estimation of the data set using the ICDM can provide examinee estimates of mastery for these skills, referred to generally as attributes. With such detailed information about each examinee, future instruction can be tailored specifically for each student, often referred to as formative assessment. However, use of such cognitive diagnosis models to estimate skills in classrooms can require computationally intensive and complicated statistical estimation algorithms, which can diminish the breadth of applications of attribute level diagnosis. We explore the use of sum-scores (each attribute measured by a sum-score) combined with estimated model-based sum-score mastery/nonmastery cutoffs as an easy-to-use and intuitive method to estimate attribute mastery in classrooms and other settings where simple skills diagnostic approaches are desirable. Using a simulation study of skills diagnosis test settings and assuming a test consisting of a model-based calibrated set of items, correct classification rates (CCRs) are compared among four model-based approaches for estimating attribute mastery, namely using full model-based estimation and three different methods of computing sum-scores (simple sum-scores, complex sum-scores, and weighted complex sum-scores) combined with model-based mastery sum-score cutoffs. In summary, the results suggest that model-based sum-scores and mastery cutoffs can be used to estimate examinee attribute mastery with only moderate reductions in CCRs in comparison with the full model-based estimation approach. Certain topics are mentioned that are currently being investigated, especially applications in classroom and textbook settings.

Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly defined latent trait resembling the [theta] of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example.

Developed models to investigate the fit of parametric item response models by comparing them to models fitted under nonparametric assumptions. Illustrated these techniques through simulation studies and real-data examples. Discusses the identifiability and estimation consistency of item response theory models. (SLD)

Proposes a method for performing survival analysis with latent variables with dual aims of scoring subjects with respect to a psychological variable of interest and distinguishing the varying extents to which items are capable of measuring the construct. Illustrates this discrete proportional hazards model. (SLD)

Two multidimensional item response theory model-based methods of selecting item bundles (clusters of items chosen by some organizational principle) suspected of displaying amplification of differential item functioning (DIF) are proposed. A distinction is made between benign and adverse DIF. Three real data examples illustrate the two methods. (SLD)

A procedure for detection of differential item functioning (DIF) is proposed that amalgamates SIBTEST and kernel-smoothed item response function estimation to assess DIF as a function of the latent trait theta that the test is designed to measure. Smoothed SIBTEST is studied through simulation and real data analysis. (SLD)

In his 1996 essay 'Zen and the Art of Mario Maintenance: Cycles of Death and Rebirth in Video Games and Children's Subliterature,' Gary Westfahl begins an examination of the compulsion [...]

Byline: Young-Sun Lee (1), Jeffrey Douglas (2), Betty Chewning (3) Keywords: Arthritis Impact Measurement Scales 2 (AIMS2); health quality of life; item response theory Abstract: Clinical and health policy research frequently involves health status measurement using generic or disease specific instruments. These instruments are generally developed to arrive at several scales, each measuring a distinct domain of health quality of life (HQOL). Clinical settings are starting to explore how to integrate patient perspectives of HQOL outcomes into patient care. However, the length of many HQOL instruments poses a challenge in terms of patient burden, as well as clinic flow time. The most popular paradigm for scale construction utilizes classical test theory methodology and can lead to excessive and redundant items in an effort to bolster reliability measurements such as Cronbach's alpha above levels of accepted reliability. This paper presents techniques for utilizing item response theory to arrive at single item scales that are diagnostically informative and short enough to have clinical utility. A danger of such dramatic scale reduction is that validity might be compromised. This danger is addressed in terms of criterion related validity and sensitivity to clinical changes over a 36 months period. The reduction methods are illustrated using selected scales from the Arthritis Impact Measurement Scales 2 (AIMS2) with data obtained from the study Pharmaceutical Care Outcomes: The Patient Role (PCOPR). Author Affiliation: (1) Department of Human Development, Teachers College Columbia University, 525 West 120th Street, Box 212, New York, NY, 10027, USA (2) University of Illinois, Urbana-Champaign, IL, USA (3) University of Wisconsin, Madison, WI, USA Article History: Registration Date: 28/08/2006 Received Date: 06/06/2006 Accepted Date: 21/08/2006 Online Date: 10/10/2006

The pyridinone non-nucleoside reverse transcriptase inhibitor L-967,661 shows significant dose-related activity against HIV-1 and is also well tolerated by patients, but the rapid emergence of resistant strains of the virus may limit the effectiveness of this and other non-nucleoside reverse transcriptase inhibitors. The non-nucleoside reverse transcriptase inhibitors are atypical antiretroviral agents with selective activity in vitro against human immunodeficiency virus type 1 (HIV-1). They perform through direct inhibition of reverse transcriptase and are not incorporated into DNA. However, in vitro studies have indicated additive or synergistic activity between nucleoside and non-nucleoside antiretroviral agents. These agents may continue to be useful in combination regimens. When the genetic complexity of the virus is extensive and subpopulations of resistant virus are more probable, the use of non-nucleoside agents for very early infection or post-exposure prophylaxis may be especially beneficial.