Cees A. W. Glas

Online calibration enables continuous replenishment of computerized adaptive testing (CAT) item banks by embedding new pretest items within operational test sessions. However, the relative performance of different calibration components and their interactions have not been fully examined in a unified framework. This study compared three pretest item selection methods (maximum Fisher information [MFI], D-optimal value design, and Bayesian D-optimal design), two parameter estimation methods (a fixed-ability joint maximum likelihood procedure [JML-itpar] and one expectation-maximization cycle [OEM]), three levels of random calibration stage sample size (250, 500, and 1,000), and three levels of calibration sample size per pretest item (250, 500, and 1,000) under the one-parameter logistic and two-parameter logistic item response theory models through a Monte Carlo simulation with 108 conditions and 100 replications per condition. The results revealed a trade-off among item selection methods: MFI yielded the most accurate difficulty parameter estimates, while D-optimal value design and Bayesian D-optimal design produced better discrimination parameter recovery. Bayesian D-optimal design offered the best balance between accuracy and calibration efficiency. For the first time in the standard online calibration context, JML-itpar was systematically evaluated and found to be a viable estimation method. JML-itpar paired with MFI produced lower discrimination parameter root mean squared error than OEM at calibration sample sizes of 500 and 1,000. OEM remained superior for the difficulty parameter. The calibration sample size per pretest item was the most influential factor, with substantial accuracy gains from 250 to 500 responses and diminishing returns beyond 500. The random calibration stage sample size had negligible effects. These findings provide practical guidance for selecting calibration components in operational CAT programs.

Existing observation instruments to measure differentiated instruction often lack insight into the degree to which teachers’ decisions match the actual needs of their students, and neglect the importance of preparation and evaluation. This article describes the psychometric evaluation of a comprehensive instrument (Assessing Differentiation in All Phases of Teaching; ADAPT) that does not suffer from these shortcomings. To assess its quality, 41 raters used it to score videos of lessons and interviews of 86 primary school teachers. A 5-dimensional item-response model showed good fit and high internal consistency, and a decision study was conducted to determine the reliability and agreement coefficients for different numbers of raters. For the intended low-stakes use, a single rater would be enough to provide a reliable estimate of a teacher’s overall score. Finally, rater experiences showed that ADAPT has high practical value due to the comprehensive manual and detailed score descriptions and examples. The instrument can therefore not only be used for research purposes, but can also serve as a valuable resource for teachers and teacher educators in practice.

Many studies in fields such as psychology and educational sciences obtain information about attributes of subjects through observational studies, in which raters score subjects using multiple-item rating scales. Error variance due to measurement effects, such as items and raters, attenuate the regression coefficients and lower the power of (hierarchical) linear models. A modeling procedure is discussed to reduce the attenuation. The procedure consists of (1) an item response theory (IRT) model to map the discrete item responses to a continuous latent scale and (2) a generalizability theory (GT) model to separate the variance in the latent measurement into variance components of interest and nuisance variance components. It will be shown how measurements obtained from this mixture of IRT and GT models can be embedded in (hierarchical) linear models, both as predictor or criterion variables, such that error variance due to nuisance effects are partialled out. Using examples from the field of educational measurement, it is shown how general-purpose software can be used to implement the modeling procedure.

In recent years, there has been a growing interest and extensive use of computerized adaptive testing (CAT) especially in large-scale assessments. Numerous simulation studies have been conducted on both real and simulated data sets to determine the optimum conditions and develop CAT versions. Being one of the most popular large-scale assessment programs, Trends in International Mathematics and Science Study (TIMSS) has been implemented as paper and pencil tests to monitor student achievement in mathematics and science at fourth and eighth grade levels since 1995. The purpose of this study is to investigate the optimum CAT algorithm for TIMSS eighth grade mathematics assessments. Since Turkey and USA participated in 2007, 2011 and 2015 administrations, their data were combined and then 393 items were calibrated on the same scale by using marginal maximum likelihood estimation method. With this item pool, several scenarios were proposed and tested to determine not only the optimum starting rule, ability estimation method, test termination rule but also the efficiency of exposure control method. The results of the study indicated that estimating abilities with expected a posteriori method after 6 random items, terminating the fixed-length test after 20 items seemed to be the optimum algorithm for TIMSS eighth grade mathematics assessments. Also, it was found that using item exposure control had a prior importance for the effective use of the item pool. This study has some implications for both national and international large-scale test developers in determining the optimum CAT algorithm and its consequences compared with paper and pencil versions.

A method is proposed for constructing indices as linear functions of variables such that the reliability of the compound score is maximized. Reliability is defined in the framework of latent variable modeling [i.e., item response theory (IRT)] and optimal weights of the components of the index are found by maximizing the posterior variance relative to the total latent variable variance. Three methods for estimating the weights are proposed. The first is a likelihood-based approach, that is, marginal maximum likelihood (MML). The other two are Bayesian approaches based on Markov chain Monte Carlo (MCMC) computational methods. One is based on an augmented Gibbs sampler specifically targeted at IRT, and the other is based on a general purpose Gibbs sampler such as implemented in OpenBugs and Jags. Simulation studies are presented to demonstrate the procedure and to compare the three methods. Results are very similar, so practitioners may be suggested the use of the easily accessible latter method. A real-data set pertaining to the 28-joint Disease Activity Score is used to show how the methods can be applied in a complex measurement situation with multiple time points and mixed data formats.

This paper demonstrates the mechanism of a multidimensional computerized adaptive test (CAT) to measure fatigue in patients with rheumatoid arthritis (RA). A CAT can be used to precisely measure patient-reported outcomes at an individual level as items are consequentially selected based on the patient’s previous answers. The item bank of the CAT Fatigue RA has been developed from the patients’ perspective and consists of 196 items pertaining to three fatigue dimensions: severity, impact and variability of fatigue. The CAT Fatigue RA was completed by fifteen patients. To test the CAT’s working mechanism, we applied the flowchart-check-method. The adaptive item selection procedure for each patient was checked by the researchers. The estimated fatigue levels and the measurement precision per dimension were illustrated with the selected items, answers and flowcharts. The CAT Fatigue RA selected all items in a logical sequence and those items were selected which provided the most information about the patient’s individual fatigue. Flowcharts further illustrated that the CAT reached a satisfactory measurement precision, with less than 20 items, on the dimensions severity and impact and to somewhat lesser extent also for the dimension variability. Patients’ fatigue scores varied across the three dimensions; sometimes severity scored highest, other times impact or variability. The CAT’s ability to display different fatigue experiences can improve communication in daily clinical practice, guide interventions, and facilitate research into possible predictors of fatigue. The results indicate that the CAT Fatigue RA measures precise and comprehensive. Once it is examined in more detail in a consecutive, elaborate validation study, the CAT will be available for implementation in daily clinical practice and for research purposes.

Little is known about the acceptance and usability of computerized adaptive tests (CATs) among patients with rheumatoid arthritis (RA). The main difference between completing a CAT and a traditional questionnaire concerns item presentation. CATs only provide one item at a time on the screen, and skipping forward or backward to review and change already given answers is often not possible. The objective of this study was to examine how patients with RA experience a Web-based CAT for fatigue. In individual sessions, participants filled in the CAT while thinking aloud, and were subsequently interviewed about their experience with the new instrument. The technology acceptance model (TAM) was used to structure the results. The participants were 15 patients with RA. They perceived the CAT as clear, brief, and easy to use. They were positive about answering one question per screen, the changing response options, layout, progress bar, and item number. There were 40% (6/15) of the participants that also mentioned that they experienced the completion of the CAT as useful and pleasant, and liked the adaptive test mechanism. However, some participants noted that not all items were applicable to everybody, and that the wordings of questions within the severity dimension were often similar. Participants perceived the “CAT Fatigue RA” as easy to use, and also its usefulness was expressed. A 2.0 version has been improved according to the participants’ comments, and is currently being used in a validation study before it will be implemented in daily clinical practice. Our results give a first indication that CAT methodology may outperform traditional questionnaires not merely on measurement precision, but also on usability and acceptance valuation.