Development of a Computerized Adaptive Test for Anxiety Based on the Dutch–Flemish Version of the PROMIS Item Bank - Gerard Flens; Niels Smits; Caroline B. Terwee; Joost Dekker; Irma Huijbrechts; Philip Spinhoven; Edwin de Beurs (Assessment)
We used the Dutch–Flemish version of the USA PROMIS adult V1.0 item bank for Anxiety as input for developing a computerized adaptive test (CAT) to measure the entire latent anxiety continuum. First, psychometric analysis of a combined clinical and general population sample (N = 2,010) showed that the 29-item bank has psychometric properties that are required for a CAT administration. Second, a post hoc CAT simulation showed efficient and highly precise measurement, with an average number of 8.64 items for the clinical sample, and 9.48 items for the general population sample. Furthermore, the accuracy of our CAT version was highly similar to that of the full item bank administration, both in final score estimates and in distinguishing clinical subjects from persons without a mental health disorder. We discuss the future directions and limitations of CAT development with the Dutch–Flemish version of the PROMIS Anxiety item bank.

Measurement efficiency for fixed-precision multidimensional computerized adaptive tests: Comparing health measurement and educational testing using example banks - Paap, Muirne C. S.; Born, Sebastian; Braeken, Johan (Applied Psychological Measurement)

Optimizing cognitive ability measurement with multidimensional computer adaptive testing - Makransky, G.; Glas, C. A. W. (International Journal of Testing)

The Influence of Computerized Adaptive Testing on Psychometric Theory and Practice - Reckase, Mark D. (Journal of Computerized Adaptive Testing, 2024)
The major premise of this article is that part of the stimulus for the evolution of psychometric theory since the 1950s was the introduction of the concept of computerized adaptive testing (CAT) or its earlier non-CAT variations. The conceptual underpinnings of CAT that had the most influence on psychometric theory was the shift of emphasis from the test (or test score) as the focus of analysis to the test item (or item score). The change in focus allowed a change in the way that test results are conceived of as measurements. It also resolved the conflict among a number of ideas that were present in the early work on psychometric theory. Some of the conflicting ideas are summarized below to show how work on the development of CAT resolved some of those conflicts.
Keywords: computerized adaptive testing; Item Response Theory; paradigm shift; scaling theory; test design

Expanding the Meaning of Adaptive Testing to Enhance Validity - Steven L. Wise (Journal of Computerized Adaptive Testing, 2023)
Keywords: Adaptive Testing; CAT; CBT; test-taking disengagement; validity

An Extended Taxonomy of Variants of Computerized Adaptive Testing - Roy Levy; John T. Behrens; Robert J. Mislevy (Journal of Computerized Adaptive Testing, 2023)
Keywords: Adaptive Testing; evidence-centered design; Item Response Theory; knowledge-based model construction; missingness

How Do Trait Change Patterns Affect the Performance of Adaptive Measurement of Change? - Ming Him Tai; Allison W. Cooperman; Joseph N. DeWeese; David J. Weiss (Journal of Computerized Adaptive Testing, 2023)
Keywords: adaptive measurement of change; computerized adaptive testing; longitudinal measurement; trait change patterns

Improving Precision of CAT Measures - John J. Barnard (Journal of Computerized Adaptive Test-ing, 2022)
Keywords: : dichotomously scored items; option probability theory; scoring methods; subjective probability

The (non)Impact of Misfitting Items in Computerized Adaptive Testing - Christine E. DeMars (Journal of Computerized Adaptive Testing, 2022)
Keywords: computerized adaptive testing; item fit; three-parameter logistic model

A Blocked-CAT Procedure for CD-CAT - Mehmet Kaplan; Jimmy de la Torre (Applied Psychological Measurement, 2020)
This article introduces a blocked-design procedure for cognitive diagnosis computerized adaptive testing (CD-CAT), which allows examinees to review items and change their answers during test administration. Four blocking versions of the new procedure were proposed. In addition, the impact of several factors, namely, item quality, generating model, block size, and test length, on the classification rates was investigated. Three popular item selection indices in CD-CAT were used and their efficiency compared using the new procedure. An additional study was carried out to examine the potential benefit of item review. The results showed that the new procedure is promising in that allowing item review resulted only in a small loss in attribute classification accuracy under some conditions. Moreover, using a blocked-design CD-CAT is beneficial to the extent that it alleviates the negative impact of test anxiety on examinees’ true performance.

Computerized adaptive testing to screen children for emotional and behavioral problems by preventive child healthcare - Theunissen, Meninou H.C.; de Wolff, Marianne S.; Deurloo, Jacqueline A.; Vogels, Anton G. C. (BMC Pediatrics, 2020)
Background Questionnaires to detect emotional and behavioral problems (EBP) in Preventive Child Healthcare (PCH) should be short which potentially affects validity and reliability. Simulation studies have shown that Computerized Adaptive Testing (CAT) could overcome these weaknesses. We studied the applicability (using the measures participation rate, satisfaction, and efficiency) and the validity of CAT in routine PCH practice. Methods We analyzed data on 461 children aged 10–11 years (response 41%), who were assessed during routine well-child examinations by PCH professionals. Before the visit, parents completed the CAT and the Child Behavior Checklist (CBCL). Satisfaction was measured by parent- and PCH professional-report. Efficiency of the CAT procedure was measured as number of items needed to assess whether a child has serious problems or not. Its validity was assessed using the CBCL as the criterion. Results Parents and PCH professionals rated the CAT on average as good. The procedure required at average 16 items to assess whether a child has serious problems or not. Agreement of scores on the CAT scales with corresponding CBCL scales was high (range of Spearman correlations 0.59–0.72). Area Under Curves (AUC) were high (range: 0.95–0.97) for the Psycat total, externalizing, and hyperactivity scales using corresponding CBCL scale scores as criterion. For the Psycat internalizing scale the AUC was somewhat lower but still high (0.86). Conclusions CAT is a valid procedure for the identification of emotional and behavioral problems in children aged 10–11 years. It may support the efficient and accurate identification of children with overall, and potentially also specific, emotional and behavioral problems in routine PCH.

A Dynamic Stratification Method for Improving Trait Estimation in Computerized Adaptive Testing Under Item Exposure Control - Jyun-Hong Chen; Hsiu-Yi Chao; Shu-Ying Chen (Applied Psychological Measurement, 2020)
When computerized adaptive testing (CAT) is under stringent item exposure control, the precision of trait estimation will substantially decrease. A new item selection method, the dynamic Stratification method based on Dominance Curves (SDC), which is aimed at improving trait estimation, is proposed to mitigate this problem. The objective function of the SDC in item selection is to maximize the sum of test information for all examinees rather than maximizing item information for individual examinees at a single-item administration, as in conventional CAT. To achieve this objective, the SDC uses dominance curves to stratify an item pool into strata with the number being equal to the test length to precisely and accurately increase the quality of the administered items as the test progresses, reducing the likelihood that a high-discrimination item will be administered to an examinee whose ability is not close to the item difficulty. Furthermore, the SDC incorporates a dynamic process for on-the-fly item–stratum adjustment to optimize the use of quality items. Simulation studies were conducted to investigate the performance of the SDC in CAT under item exposure control at different levels of severity. According to the results, the SDC can efficiently improve trait estimation in CAT through greater precision and more accurate trait estimation than those generated by other methods (e.g., the maximum Fisher information method) in most conditions.

Framework for Developing Multistage Testing With Intersectional Routing for Short-Length Tests - Kyung (Chris) T. Han (Applied Psychological Measurement, 2020)
Multistage testing (MST) has many practical advantages over typical item-level computerized adaptive testing (CAT), but there is a substantial tradeoff when using MST because of its reduced level of adaptability. In typical MST, the first stage almost always performs as a routing stage in which all test takers see a linear test form. If multiple test sections measure different but moderately or highly correlated traits, then a score estimate for one section might be capable of adaptively selecting item modules for following sections without having to administer routing stages repeatedly for each section. In this article, a new framework for developing MST with intersectional routing (ISR) was proposed and evaluated under several research conditions with different MST structures, section score distributions and relationships, and types of regression models for ISR. The overall findings of the study suggested that MST with ISR approach could improve measurement efficiency and test optimality especially with tests with short lengths.

Item Calibration Methods With Multiple Subscale Multistage Testing - Wang, Chun; Chen, Ping; Jiang, Shengyu (Journal of Educational Measurement, 2020)
Abstract Many large-scale educational surveys have moved from linear form design to multistage testing (MST) design. One advantage of MST is that it can provide more accurate latent trait (θ) estimates using fewer items than required by linear tests. However, MST generates incomplete response data by design; hence, questions remain as to how to calibrate items using the incomplete data from MST design. Further complication arises when there are multiple correlated subscales per test, and when items from different subscales need to be calibrated according to their respective score reporting metric. The current calibration-per-subscale method produced biased item parameters, and there is no available method for resolving the challenge. Deriving from the missing data principle, we showed when calibrating all items together the Rubin's ignorability assumption is satisfied such that the traditional single-group calibration is sufficient. When calibrating items per subscale, we proposed a simple modification to the current calibration-per-subscale method that helps reinstate the missing-at-random assumption and therefore corrects for the estimation bias that is otherwise existent. Three mainstream calibration methods are discussed in the context of MST, they are the marginal maximum likelihood estimation, the expectation maximization method, and the fixed parameter calibration. An extensive simulation study is conducted and a real data example from NAEP is analyzed to provide convincing empirical evidence.
Keywords: EM; marginal maximum likelihood; missing data; multistage testing

Item Selection and Exposure Control Methods for Computerized Adaptive Testing with Multidimensional Ranking Items - Chen, Chia-Wen; Wang, Wen-Chung; Chiu, Ming Ming; Ro, Sage (Journal of Educational Measurement, 2020)
Abstract The use of computerized adaptive testing algorithms for ranking items (e.g., college preferences, career choices) involves two major challenges: unacceptably high computation times (selecting from a large item pool with many dimensions) and biased results (enhanced preferences or intensified examinee responses because of repeated statements across items). To address these issues, we introduce subpool partition strategies for item selection and within-person statement exposure control procedures. Simulations showed that the multinomial method reduces computation time while maintaining measurement precision. Both the freeze and revised Sympson-Hetter online (RSHO) methods controlled the statement exposure rate; RSHO sacrificed some measurement precision but increased pool use. Furthermore, preventing a statement's repetition on consecutive items neither hindered the effectiveness of the freeze or RSHO method nor reduced measurement precision.

Multidimensional Test Assembly Using Mixed-Integer Linear Programming: An Application of Kullback–Leibler Information - Dries Debeer; Peter W. van Rijn; Usama S. Ali (Applied Psychological Measurement, 2020)
Many educational testing programs require different test forms with minimal or no item overlap. At the same time, the test forms should be parallel in terms of their statistical and content-related properties. A well-established method to assemble parallel test forms is to apply combinatorial optimization using mixed-integer linear programming (MILP). Using this approach, in the unidimensional case, Fisher information (FI) is commonly used as the statistical target to obtain parallelism. In the multidimensional case, however, FI is a multidimensional matrix, which complicates its use as a statistical target. Previous research addressing this problem focused on item selection criteria for multidimensional computerized adaptive testing (MCAT). Yet these selection criteria are not directly transferable to the assembly of linear parallel test forms. To bridge this gap the authors derive different statistical targets, based on either FI or the Kullback–Leibler (KL) divergence, that can be applied in MILP models to assemble multidimensional parallel test forms. Using simulated item pools and an item pool based on empirical items, the proposed statistical targets are compared and evaluated. Promising results with respect to the KL-based statistical targets are presented and discussed.

New Efficient and Practicable Adaptive Designs for Calibrating Items Online - Yinhong He; Ping Chen; Yong Li (Applied Psychological Measurement, 2020)
When calibrating new items online, it is practicable to first compare all new items according to some criterion and then assign the most suitable one to the current examinee who reaches a seeding location. The modified D-optimal design proposed by van der Linden and Ren (denoted as D-VR design) works within this practicable framework with the aim of directly optimizing the estimation of item parameters. However, the optimal design point for a given new item should be obtained by comparing all examinees in a static examinee pool. Thus, D-VR design still has room for improvement in calibration efficiency from the view of traditional optimal design. To this end, this article incorporates the idea of traditional optimal design into D-VR design and proposes a new online calibration design criterion, namely, excellence degree (ED) criterion. Four different schemes are developed to measure the information provided by the current examinee when implementing this new criterion, and four new ED designs equipped with them are put forward accordingly. Simulation studies were conducted under a variety of conditions to compare the D-VR design and the four proposed ED designs in terms of calibration efficiency. Results showed that the four ED designs outperformed D-VR design in almost all simulation conditions.

The Optimal Item Pool Design in Multistage Computerized Adaptive Tests With the p-Optimality Method - Lihong Yang; Mark D. Reckase (Educational and Psychological Measurement, 2020)
The present study extended the p-optimality method to the multistage computerized adaptive test (MST) context in developing optimal item pools to support different MST panel designs under different test configurations. Using the Rasch model, simulated optimal item pools were generated with and without practical constraints of exposure control. A total number of 72 simulated optimal item pools were generated and evaluated by an overall sample and conditional sample using various statistical measures. Results showed that the optimal item pools built with the p-optimality method provide sufficient measurement accuracy under all simulated MST panel designs. Exposure control affected the item pool size, but not the item distributions and item pool characteristics. This study demonstrated that the p-optimality method can adapt to MST item pool design, facilitate the MST assembly process, and improve its scoring accuracy.

Stratified Item Selection Methods in Cognitive Diagnosis Computerized Adaptive Testing - Jing Yang; Hua-Hua Chang; Jian Tao; Ningzhong Shi (Applied Psychological Measurement, 2020)
Cognitive diagnostic computerized adaptive testing (CD-CAT) aims to obtain more useful diagnostic information by taking advantages of computerized adaptive testing (CAT). Cognitive diagnosis models (CDMs) have been developed to classify examinees into the correct proficiency classes so as to get more efficient remediation, whereas CAT tailors optimal items to the examinee’s mastery profile. The item selection method is the key factor of the CD-CAT procedure. In recent years, a large number of parametric/nonparametric item selection methods have been proposed. In this article, the authors proposed a series of stratified item selection methods in CD-CAT, which are combined with posterior-weighted Kullback–Leibler (PWKL), nonparametric item selection (NPS), and weighted nonparametric item selection (WNPS) methods, and named S-PWKL, S-NPS, and S-WNPS, respectively. Two different types of stratification indices were used: original versus novel. The performances of the proposed item selection methods were evaluated via simulation studies and compared with the PWKL, NPS, and WNPS methods without stratification. Manipulated conditions included calibration sample size, item quality, number of attributes, number of strata, and data generation models. Results indicated that the S-WNPS and S-NPS methods performed similarly, and both outperformed the S-PWKL method. And item selection methods with novel stratification indices performed slightly better than the ones with original stratification indices, and those without stratification performed the worst.

Three Measures of Test Adaptation Based on Optimal Test Information - G. Gage Kingsbury; Steven L. Wise (Journal of Computerized Adaotive Testing, 2020)

Three Measures of Test Adaptation Based on Optimal Test Information - G. Gage Kingsbury; Steven L. Wise (Journal of Computerized Adaotive Testing, 2020)

Adaptive Testing With a Hierarchical Item Response Theory Model - Wenhao Wang; Neal Kingston (Applied Psychological Measurement, 2019)
The hierarchical item response theory (H-IRT) model is very flexible and allows a general factor and subfactors within an overall structure of two or more levels. When an H-IRT model with a large number of dimensions is used for an adaptive test, the computational burden associated with interim scoring and selection of subsequent items is heavy. An alternative approach for any high-dimension adaptive test is to reduce dimensionality for interim scoring and item selection and then revert to full dimensionality for final score reporting, thereby significantly reducing the computational burden. This study compared the accuracy and efficiency of final scoring for multidimensional, local multidimensional, and unidimensional item selection and interim scoring methods, using both simulated and real item pools. The simulation study was conducted under 10 conditions (i.e., five test lengths and two H-IRT models) with a simulated sample of 10,000 students. The study with the real item pool was conducted using item parameters from an actual 45-item adaptive test with a simulated sample of 10,000 students. Results indicate that the theta estimations provided by the local multidimensional and unidimensional item selection and interim scoring methods were relatively as accurate as the theta estimation provided by the multidimensional item selection and interim scoring method, especially during the real item pool study. In addition, the multidimensional method required the longest computation time and the unidimensional method required the shortest computation time.

Application of Dimension Reduction to CAT Item Selection Under the Bifactor Model - Xiuzhen Mao; Jiahui Zhang; Tao Xin (Applied Psychological Measurement, 2019)
Multidimensional computerized adaptive testing (MCAT) based on the bifactor model is suitable for tests with multidimensional bifactor measurement structures. Several item selection methods that proved to be more advantageous than the maximum Fisher information method are not practical for bifactor MCAT due to time-consuming computations resulting from high dimensionality. To make them applicable in bifactor MCAT, dimension reduction is applied to four item selection methods, which are the posterior-weighted Fisher D-optimality (PDO) and three non-Fisher information-based methods—posterior expected Kullback–Leibler information (PKL), continuous entropy (CE), and mutual information (MI). They were compared with the Bayesian D-optimality (BDO) method in terms of estimation precision. When both the general and group factors are the measurement objectives, BDO, PDO, CE, and MI perform equally well and better than PKL. When the group factors represent nuisance dimensions, MI and CE perform the best in estimating the general factor, followed by the BDO, PDO, and PKL. How the bifactor pattern and test length affect estimation accuracy was also discussed.

Computerized Adaptive Testing for Cognitively Based Multiple-Choice Data - Hulya D. Yigit; Miguel A. Sorrel; Jimmy de la Torre (Applied Psychological Measurement, 2019)
Cognitive diagnosis models (CDMs) are latent class models that hold great promise for providing diagnostic information about student knowledge profiles. The increasing use of computers in classrooms enhances the advantages of CDMs for more efficient diagnostic testing by using adaptive algorithms, referred to as cognitive diagnosis computerized adaptive testing (CD-CAT). When multiple-choice items are involved, CD-CAT can be further improved by using polytomous scoring (i.e., considering the specific options students choose), instead of dichotomous scoring (i.e., marking answers as either right or wrong). In this study, the authors propose and evaluate the performance of the Jensen–Shannon divergence (JSD) index as an item selection method for the multiple-choice deterministic inputs, noisy “and” gate (MC-DINA) model. Attribute classification accuracy and item usage are evaluated under different conditions of item quality and test termination rule. The proposed approach is compared with the random selection method and an approximate approach based on dichotomized responses. The results show that under the MC-DINA model, JSD improves the attribute classification accuracy significantly by considering the information from distractors, even with a very short test length. This result has important implications in practical classroom settings as it can allow for dramatically reduced testing times, thus resulting in more targeted learning opportunities.

Computerized Adaptive Testing in Early Education: Exploring the Impact of Item Position Effects on Ability Estimation - Albano, Anthony D.; Cai, Liuhan; Lease, Erin M.; McConnell, Scott R. (Journal of Educational Measurement, 2019)
Abstract Studies have shown that item difficulty can vary significantly based on the context of an item within a test form. In particular, item position may be associated with practice and fatigue effects that influence item parameter estimation. The purpose of this research was to examine the relevance of item position specifically for assessments used in early education, an area of testing that has received relatively limited psychometric attention. In an initial study, multilevel item response models fit to data from an early literacy measure revealed statistically significant increases in difficulty for items appearing later in a 20-item form. The estimated linear change in logits for an increase of 1 in position was .024, resulting in a predicted change of .46 logits for a shift from the beginning to the end of the form. A subsequent simulation study examined impacts of item position effects on person ability estimation within computerized adaptive testing. Implications and recommendations for practice are discussed.

Developing Multistage Tests Using D-Scoring Method - Kyung (Chris) T. Han; Dimiter M. Dimitrov; Faisal Al-Mashary (Educational and Psychological Measurement, 2019)
The D-scoring method for scoring and equating tests with binary items proposed by Dimitrov offers some of the advantages of item response theory, such as item-level difficulty information and score computation that reflects the item difficulties, while retaining the merits of classical test theory such as the simplicity of number correct score computation and relaxed requirements for model sample sizes. Because of its unique combination of those merits, the D-scoring method has seen quick adoption in the educational and psychological measurement field. Because item-level difficulty information is available with the D-scoring method and item difficulties are reflected in test scores, it conceptually makes sense to use the D-scoring method with adaptive test designs such as multistage testing (MST). In this study, we developed and compared several versions of the MST mechanism using the D-scoring approach and also proposed and implemented a new framework for conducting MST simulation under the D-scoring method. Our findings suggest that the score recovery performance under MST with D-scoring was promising, as it retained score comparability across different MST paths. We found that MST using the D-scoring method can achieve improvements in measurement precision and efficiency over linear-based tests that use D-scoring method.

Efficiency of Targeted Multistage Calibration Designs Under Practical Constraints: A Simulation Study - Berger, Stéphanie; Verschoor, Angela J.; Eggen, Theo J. H. M.; Moser, Urs (Journal of Educational Measurement, 2019)
Abstract Calibration of an item bank for computer adaptive testing requires substantial resources. In this study, we investigated whether the efficiency of calibration under the Rasch model could be enhanced by improving the match between item difficulty and student ability. We introduced targeted multistage calibration designs, a design type that considers ability-related background variables and performance for assigning students to suitable items. Furthermore, we investigated whether uncertainty about item difficulty could impair the assembling of efficient designs. The results indicated that targeted multistage calibration designs were more efficient than ordinary targeted designs under optimal conditions. Limited knowledge about item difficulty reduced the efficiency of one of the two investigated targeted multistage calibration designs, whereas targeted designs were more robust.

How Adaptive Is an Adaptive Test: Are All Adaptive Tests Adaptive? - Mark Reckase; Unhee Ju; Sewon Kim (Journal of Computerized Adaptive Testing, 2019)
Keywords: computerized adaptive test; multistage test; statistical indicators of amount of adaptation

Imputation Methods to Deal With Missing Responses in Computerized Adaptive Multistage Testing - Dee Duygu Cetin-Berber; Halil Ibrahim Sari; Anne Corinne Huggins-Manley (Educational and Psychological Measurement, 2019)
Routing examinees to modules based on their ability level is a very important aspect in computerized adaptive multistage testing. However, the presence of missing responses may complicate estimation of examinee ability, which may result in misrouting of individuals. Therefore, missing responses should be handled carefully. This study investigated multiple missing data methods in computerized adaptive multistage testing, including two imputation techniques, the use of full information maximum likelihood and the use of scoring missing data as incorrect. These methods were examined under the missing completely at random, missing at random, and missing not at random frameworks, as well as other testing conditions. Comparisons were made to baseline conditions where no missing data were present. The results showed that imputation and the full information maximum likelihood methods outperformed incorrect scoring methods in terms of average bias, average root mean square error, and correlation between estimated and true thetas.

An Investigation of Exposure Control Methods With Variable-Length CAT Using the Partial Credit Model - Audrey J. Leroux; J. Kay Waid-Ebbs; Pey-Shan Wen; Drew A. Helmer; David P. Graham; Maureen K. O’Connor; Kathleen Ray (Applied Psychological Measurement, 2019)
The purpose of this simulation study was to investigate the effect of several different item exposure control procedures in computerized adaptive testing (CAT) with variable-length stopping rules using the partial credit model. Previous simulation studies on CAT exposure control methods with polytomous items rarely considered variable-length tests. The four exposure control techniques examined were the randomesque with a group of three items, randomesque with a group of six items, progressive-restricted standard error (PR-SE), and no exposure control. The two variable-length stopping rules included were the SE and predicted standard error reduction (PSER), along with three item pools of varied sizes (43, 86, and 172 items). Descriptive statistics on number of nonconvergent cases, measurement precision, testing burden, item overlap, item exposure, and pool utilization were calculated. Results revealed that the PSER stopping rule administered fewer items on average while maintaining measurement precision similar to the SE stopping rule across the different item pool sizes and exposure controls. The PR-SE exposure control procedure surpassed the randomesque methods by further reducing test overlap, maintaining maximum exposure rates at the target rate or lower, and utilizing all items from the pool with a minimal increase in number of items administered and nonconvergent cases.

Item Selection Criteria With Practical Constraints in Cognitive Diagnostic Computerized Adaptive Testing - Chuan-Ju Lin; Hua-Hua Chang (Educational and Psychological Measurement, 2019)
For item selection in cognitive diagnostic computerized adaptive testing (CD-CAT), ideally, a single item selection index should be created to simultaneously regulate precision, exposure status, and attribute balancing. For this purpose, in this study, we first proposed an attribute-balanced item selection criterion, namely, the standardized weighted deviation global discrimination index (SWDGDI), and subsequently formulated the constrained progressive index (CP_SWDGDI) by casting the SWDGDI in a progressive algorithm. A simulation study revealed that the SWDGDI method was effective in balancing attribute coverage and the CP_SWDGDI method was able to simultaneously balance attribute coverage and item pool usage while maintaining acceptable estimation precision. This research also demonstrates the advantage of a relatively low number of attributes in CD-CAT applications.

Measurement Efficiency for Fixed-Precision Multidimensional Computerized Adaptive Tests: Comparing Health Measurement and Educational Testing Using Example Banks - Muirne C. S. Paap; Sebastian Born; Johan Braeken (Applied Psychological Measurement, 2019)
It is currently not entirely clear to what degree the research on multidimensional computerized adaptive testing (CAT) conducted in the field of educational testing can be generalized to fields such as health assessment, where CAT design factors differ considerably from those typically used in educational testing. In this study, the impact of a number of important design factors on CAT performance is systematically evaluated, using realistic example item banks for two main scenarios: health assessment (polytomous items, small to medium item bank sizes, high discrimination parameters) and educational testing (dichotomous items, large item banks, small- to medium-sized discrimination parameters). Measurement efficiency is evaluated for both between-item multidimensional CATs and separate unidimensional CATs for each latent dimension. In this study, we focus on fixed-precision (variable-length) CATs because it is both feasible and desirable in health settings, but so far most research regarding CAT has focused on fixed-length testing. This study shows that the benefits associated with fixed-precision multidimensional CAT hold under a wide variety of circumstances.

Multidimensional Computerized Adaptive Testing Using Non-Compensatory Item Response Theory Models - Chia-Ling Hsu; Wen-Chung Wang (Applied Psychological Measurement, 2019)
Current use of multidimensional computerized adaptive testing (MCAT) has been developed in conjunction with compensatory multidimensional item response theory (MIRT) models rather than with non-compensatory ones. In recognition of the usefulness of MCAT and the complications associated with non-compensatory data, this study aimed to develop MCAT algorithms using non-compensatory MIRT models and to evaluate their performance. For the purpose of the study, three item selection methods were adapted and compared, namely, the Fisher information method, the mutual information method, and the Kullback–Leibler information method. The results of a series of simulations showed that the Fisher information and mutual information methods performed similarly, and both outperformed the Kullback–Leibler information method. In addition, it was found that the more stringent the termination criterion and the higher the correlation between the latent traits, the higher the resulting measurement precision and test reliability. Test reliability was very similar across the dimensions, regardless of the correlation between the latent traits and termination criterion. On average, the difficulties of the administered items were found to be at a lower level than the examinees’ abilities, which shed light on item bank construction for non-compensatory items.

Nonparametric CAT for CD in Educational Settings With Small Samples - Yuan-Pei Chang; Chia-Yi Chiu; Rung-Ching Tsai (Applied Psychological Measurement, 2019)
Cognitive diagnostic computerized adaptive testing (CD-CAT) has been suggested by researchers as a diagnostic tool for assessment and evaluation. Although model-based CD-CAT is relatively well researched in the context of large-scale assessment systems, this type of system has not received the same degree of research and development in small-scale settings, such as at the course-based level, where this system would be the most useful. The main obstacle is that the statistical estimation techniques that are successfully applied within the context of a large-scale assessment require large samples to guarantee reliable calibration of the item parameters and an accurate estimation of the examinees’ proficiency class membership. Such samples are simply not obtainable in course-based settings. Therefore, the nonparametric item selection (NPS) method that does not require any parameter calibration, and thus, can be used in small educational programs is proposed in the study. The proposed nonparametric CD-CAT uses the nonparametric classification (NPC) method to estimate an examinee’s attribute profile and based on the examinee’s item responses, the item that can best discriminate the estimated attribute profile and the other attribute profiles is then selected. The simulation results show that the NPS method outperformed the compared parametric CD-CAT algorithms and the differences were substantial when the calibration samples were small.

Routing Strategies and Optimizing Design for Multistage Testing in International Large-Scale Assessments - Svetina, Dubravka; Liaw, Yuan-Ling; Rutkowski, Leslie; Rutkowski, David (Journal of Educational Measurement, 2019)
Abstract This study investigates the effect of several design and administration choices on item exposure and person/item parameter recovery under a multistage test (MST) design. In a simulation study, we examine whether number-correct (NC) or item response theory (IRT) methods are differentially effective at routing students to the correct next stage(s) and whether routing choices (optimal versus suboptimal routing) have an impact on achievement precision. Additionally, we examine the impact of testlet length on both person and item recovery. Overall, our results suggest that no single approach works best across the studied conditions. With respect to the mean person parameter recovery, IRT scoring (via either Fisher information or preliminary EAP estimates) outperformed classical NC methods, although differences in bias and root mean squared error were generally small. Item exposure rates were found to be more evenly distributed when suboptimal routing methods were used, and item recovery (both difficulty and discrimination) was most precisely observed for items with moderate difficulties. Based on the results of the simulation study, we draw conclusions and discuss implications for practice in the context of international large-scale assessments that recently introduced adaptive assessment in the form of MST. Future research directions are also discussed.

Time-Efficient Adaptive Measurement of Change - Matthew Finkelman; Chun Wang (Journal of Computerized Adaptive Testing, 2019)
The adaptive measurement of change (AMC) refers to the use of computerized adaptive testing (CAT) at multiple occasions to efficiently assess a respondent’s improvement, decline, or sameness from occasion to occasion. Whereas previous AMC research focused on administering the most informative item to a respondent at each stage of testing, the current research proposes the use of Fisher information per time unit as an item selection procedure for AMC. The latter procedure incorporates not only the amount of information provided by a given item but also the expected amount of time required to complete it. In a simulation study, the use of Fisher information per time unit item selection resulted in a lower false positive rate in the majority of conditions studied, and a higher true positive rate in all conditions studied, compared to item selection via Fisher information without accounting for the expected time taken. Future directions of research are suggested.
Keywords: adaptive measurement of change; computerized adaptive testing; Fisher information; item selection; response-time modeling

Adaptive Item Selection Under Matroid Constraints - Daniel Bengs; Ulf Brefeld; Ulf Kröhne (Journal of Computerized Adaptive Testing, 2018)

A Comparison of Constraint Programming and Mixed-Integer Programming for Automated Test-Form Generation - Li, Jie; van der Linden, Wim J. (Journal of Educational Measurement, 2018)
Abstract The final step of the typical process of developing educational and psychological tests is to place the selected test items in a formatted form. The step involves the grouping and ordering of the items to meet a variety of formatting constraints. As this activity tends to be time-intensive, the use of mixed-integer programming (MIP) has been proposed to automate it. The goal of this article is to show how constraint programming (CP) can be used as an alternative to automate test-form generation problems with a large variety of formatting constraints, and how it compares with MIP-based form generation as for its models, solutions, and running times. Two empirical examples are presented: (i) automated generation of a computerized fixed-form; and (ii) automated generation of shadow tests for multistage testing. Both examples show that CP works well with feasible solutions and running times likely to be better than that for MIP-based applications.

Constructing Shadow Tests in Variable-Length Adaptive Testing - Qi Diao; Hao Ren (Applied Psychological Measurement, 2018)
Imposing content constraints is very important in most operational computerized adaptive testing (CAT) programs in educational measurement. Shadow test approach to CAT (Shadow CAT) offers an elegant solution to imposing statistical and nonstatistical constraints by projecting future consequences of item selection. The original form of Shadow CAT presumes fixed test lengths. The goal of the current study was to extend Shadow CAT to tests under variable-length termination conditions and evaluate its performance relative to other content balancing approaches. The study demonstrated the feasibility of constructing Shadow CAT with variable test lengths and in operational CAT programs. The results indicated the superiority of the approach compared with other content balancing methods.

A Continuous a-Stratification Index for Item Exposure Control in Computerized Adaptive Testing - Alan Huebner; Chun Wang; Bridget Daly; Colleen Pinkelman (Applied Psychological Measurement, 2018)
The method of a-stratification aims to reduce item overexposure in computerized adaptive testing, as items that are administered at very high rates may threaten the validity of test scores. In existing methods of a-stratification, the item bank is partitioned into a fixed number of nonoverlapping strata according to the items’a, or discrimination, parameters. This article introduces a continuous a-stratification index which incorporates exposure control into the item selection index itself and thus eliminates the need for fixed discrete strata. The new continuous a-stratification index is compared with existing stratification methods via simulation studies in terms of ability estimation bias, mean squared error, and control of item exposure rates.

Evaluation of a New Method for Providing Full Review Opportunities in Computerized Adaptive Testing—Computerized Adaptive Testing With Salt - Cui, Zhongmin; Liu, Chunyan; He, Yong; Chen, Hanwei (Journal of Educational Measurement, 2018)
Abstract Allowing item review in computerized adaptive testing (CAT) is getting more attention in the educational measurement field as more and more testing programs adopt CAT. The research literature has shown that allowing item review in an educational test could result in more accurate estimates of examinees’ abilities. The practice of item review in CAT, however, is hindered by the potential danger of test-manipulation strategies. To provide review opportunities to examinees while minimizing the effect of test-manipulation strategies, researchers have proposed different algorithms to implement CAT with restricted revision options. In this article, we propose and evaluate a new method that implements CAT without any restriction on item review. In particular, we evaluate the new method in terms of the accuracy on ability estimates and the robustness against test-manipulation strategies. This study shows that the newly proposed method is promising in a win-win situation: examinees have full freedom to review and change answers, and the impacts of test-manipulation strategies are undermined.

Factors Affecting the Classification Accuracy and Average Length of a Variable-Length Cognitive Diagnostic Computerized Test - Huebner, Alan; Finkelman, Matthew D.; Weissman, Alexander (Journal of Computerized Adaptive Testing, 2018)

From Simulation to Implementation: Two CAT Case Studies - John J Barnard (Practical Assessment, Research & Evaluation, 2018)

A Hybrid Strategy to Construct Multistage Adaptive Tests - Xinhui Xiong (Applied Psychological Measurement, 2018)
How to effectively construct multistage adaptive test (MST) panels is a topic that has spurred recent advances. The most commonly used approaches for MST assembly use one of two strategies: bottom-up and top-down. The bottom-up approach splits the whole test into several modules, and each module is built first, then all modules are compiled to obtain the whole test, while the top-down approach follows the opposite direction. Both methods have their pros and cons, and sometimes neither is convenient for practitioners. This study provides an innovative hybrid strategy to build optimal MST panels efficiently most of the time. Empirical data and results by using this strategy will be provided.

Implementing Three CATs Within Eighteen Months - Christian Spoden; Andreas Frey; Raphael Bernhardt (Journal of Computerized Adaptive Testing, 2018)

Item Selection Criteria With Practical Constraints in Cognitive Diagnostic Computerized Adaptive Testing - Chuan-Ju Lin; Hua-Hua Chang (Educational and Psychological Measurement, 2018)
For item selection in cognitive diagnostic computerized adaptive testing (CD-CAT), ideally, a single item selection index should be created to simultaneously regulate precision, exposure status, and attribute balancing. For this purpose, in this study, we first proposed an attribute-balanced item selection criterion, namely, the standardized weighted deviation global discrimination index (SWDGDI), and subsequently formulated the constrained progressive index (CP_SWDGDI) by casting the SWDGDI in a progressive algorithm. A simulation study revealed that the SWDGDI method was effective in balancing attribute coverage and the CP_SWDGDI method was able to simultaneously balance attribute coverage and item pool usage while maintaining acceptable estimation precision. This research also demonstrates the advantage of a relatively low number of attributes in CD-CAT applications.

Item Selection Methods in Multidimensional Computerized Adaptive Testing With Polytomously Scored Items - Dongbo Tu; Yuting Han; Yan Cai; Xuliang Gao (Applied Psychological Measurement, 2018)
Multidimensional computerized adaptive testing (MCAT) has been developed over the past decades, and most of them can only deal with dichotomously scored items. However, polytomously scored items have been broadly used in a variety of tests for their advantages of providing more information and testing complicated abilities and skills. The purpose of this study is to discuss the item selection algorithms used in MCAT with polytomously scored items (PMCAT). Several promising item selection algorithms used in MCAT are extended to PMCAT, and two new item selection methods are proposed to improve the existing selection strategies. Two simulation studies are conducted to demonstrate the feasibility of the extended and proposed methods. The simulation results show that most of the extended item selection methods for PMCAT are feasible and the new proposed item selection methods perform well. Combined with the security of the pool, when two dimensions are considered (Study 1), the proposed modified continuous entropy method (MCEM) is the ideal of all in that it gains the lowest item exposure rate and has a relatively high accuracy. As for high dimensions (Study 2), results show that mutual information (MUI) and MCEM keep relatively high estimation accuracy, and the item exposure rates decrease as the correlation increases.

Latent Class Analysis of Recurrent Events in Problem-Solving Items - Haochen Xu; Guanhua Fang; Yunxiao Chen; Jingchen Liu; Zhiliang Ying (Applied Psychological Measurement, 2018)
Computer-based assessment of complex problem-solving abilities is becoming more and more popular. In such an assessment, the entire problem-solving process of an examinee is recorded, providing detailed information about the individual, such as behavioral patterns, speed, and learning trajectory. The problem-solving processes are recorded in a computer log file which is a time-stamped documentation of events related to task completion. As opposed to cross-sectional response data from traditional tests, process data in log files are massive and irregularly structured, calling for effective exploratory data analysis methods. Motivated by a specific complex problem-solving item “Climate Control” in the 2012 Programme for International Student Assessment, the authors propose a latent class analysis approach to analyzing the events occurred in the problem-solving processes. The exploratory latent class analysis yields meaningful latent classes. Simulation studies are conducted to evaluate the proposed approach.

Measuring patient-reported outcomes adaptively: Multidimensionality matters! - Paap, Muirne C. S.; Kroeze, Karel A.; Glas, C. A. W.; Terwee, C. B.; van der Palen, Job; Veldkamp, Bernard P. (Applied Psychological Measurement, 2018)

On-the-Fly Constraint-Controlled Assembly Methods for Multistage Adaptive Testing for Cognitive Diagnosis - Liu, Shuchang; Cai, Yan; Tu, Dongbo (Journal of Educational Measurement, 2018)
Abstract This study applied the mode of on-the-fly assembled multistage adaptive testing to cognitive diagnosis (CD-OMST). Several and several module assembly methods for CD-OMST were proposed and compared in terms of measurement precision, test security, and constrain management. The module assembly methods in the study included the maximum priority index method (MPI), the revised maximum priority index (RMPI), the weighted deviation model (WDM), and the two revised Monte Carlo methods (R1-MC, R2-MC). Simulation results showed that on the whole the CD-OMST performs well in that it not only has acceptable attribute pattern correct classification rates but also satisfies both statistical and nonstatistical constraints; the RMPI method was generally better than the MPI method, the R2-MC method was generally better than the R1-MC method, and the two revised Monte Carlo methods performed best in terms of test security and constraint management, whereas the RMPI and WDM methods worked best in terms of measurement precision. The study is not only expected to provide information about how to combine MST and CD using an on-the-fly method and how do these assembled methods in CD-OMST perform relative to each other but also offer guidance for practitioners to assemble modules in CD-OMST with both statistical and nonstatistical constraints.

Some recommendations for developing multidimensional computerized adaptive tests for patient-reported outcomes - Smits, Niels; Paap, Muirne C. S.; Böhnke, Jan R. (Quality of Life Research, 2018)
Multidimensional item response theory and computerized adaptive testing (CAT) are increasingly used in mental health, quality of life (QoL), and patient-reported outcome measurement. Although multidimensional assessment techniques hold promises, they are more challenging in their application than unidimensional ones. The authors comment on minimal standards when developing multidimensional CATs.

A Top-Down Approach to Designing the Computerized Adaptive Multistage Test - Luo, Xiao; Kim, Doyoung (Journal of Educational Measurement, 2018)
Abstract The top-down approach to designing a multistage test is relatively understudied in the literature and underused in research and practice. This study introduced a route-based top-down design approach that directly sets design parameters at the test level and utilizes the advanced automated test assembly algorithm seeking global optimality. The design process in this approach consists of five sub-processes: (1) route mapping, (2) setting objectives, (3) setting constraints, (4) routing error control, and (5) test assembly. Results from a simulation study confirmed that the assembly, measurement and routing results of the top-down design eclipsed those of the bottom-up design. Additionally, the top-down design approach provided unique insights into design decisions that could be used to refine the test. Regardless of these advantages, it is recommended applying both top-down and bottom-up approaches in a complementary manner in practice.

Using Automatic Item Generation to Create Solutions and Rationales for Computerized Formative Testing - Mark J. Gierl; Hollis Lai (Applied Psychological Measurement, 2018)
Computerized testing provides many benefits to support formative assessment. However, the advent of computerized formative testing has also raised formidable new challenges, particularly in the area of item development. Large numbers of diverse, high-quality test items are required because items are continuously administered to students. Hence, hundreds of items are needed to develop the banks necessary for computerized formative testing. One promising approach that may be used to address this test development challenge is automatic item generation. Automatic item generation is a relatively new but rapidly evolving research area where cognitive and psychometric modeling practices are used to produce items with the aid of computer technology. The purpose of this study is to describe a new method for generating both the items and the rationales required to solve the items to produce the required feedback for computerized formative testing. The method for rationale generation is demonstrated and evaluated in the medical education domain.

What Information Works Best?: A Comparison of Routing Methods - Halil Ibrahim Sari; Anthony Raborn (Applied Psychological Measurement, 2018)
There are many item selection methods proposed for computerized adaptive testing (CAT) applications. However, not all of them have been used in computerized multistage testing (ca-MST). This study uses some item selection methods as a routing method in ca-MST framework. These are maximum Fisher information (MFI), maximum likelihood weighted information (MLWI), maximum posterior weighted information (MPWI), Kullback–Leibler (KL), and posterior Kullback–Leibler (KLP). The main purpose of this study is to examine the performance of these methods when they are used as a routing method in ca-MST applications. These five information methods under four ca-MST panel designs and two test lengths (30 items and 60 items) were tested using the parameters of a real item bank. Results were evaluated with overall findings (mean bias, root mean square error, correlation between true and estimated thetas, and module exposure rates) and conditional findings (conditional absolute bias, standard error of measurement, and root mean square error). It was found that test length affected the outcomes much more than other study conditions. Under 30-item conditions, 1-3 designs outperformed other panel designs. Under 60-item conditions, 1-3-3 designs were better than other panel designs. Each routing method performed well under particular conditions; there was no clear best method in the studied conditions. The recommendations for routing methods in any particular condition were provided for researchers and practitioners as well as the limitations of these results.

Adapting Linear Models for Optimal Test Design to More Complex Test Specifications - Maxim Morin (IACAT 2017 Conference, 2017)
Combinatorial optimization (CO) has proven to be a very helpful approach for addressing test assembly issues and for providing solutions. Furthermore, CO has been applied for several test designs, including: (1) for the development of linear test forms; (2) for computerized adaptive testing and; (3) for multistage testing. In his seminal work, van der Linden (2006) laid out the basis for using linear models for simultaneously assembling exams and item pools in a variety of conditions: (1) for single tests and multiple tests; (2) with item sets, etc. However, for some testing programs, the number and complexity of test specifications can grow rapidly. Consequently, the mathematical representation of the test assembly problem goes beyond most approaches reported either in van der Linden’s book or in the majority of other publications related to test assembly. In this presentation, we extend van der Linden’s framework by including the concept of blocks for test specifications. We modify the usual mathematical notation of a test assembly problem by including this concept and we show how it can be applied to various test designs. Finally, we will demonstrate an implementation of this approach in a stand-alone software, called the ATASolver. Session Video
Keywords: Complex Test Specifications; Linear Models; Optimal Test Design

Adaptive Item and Feedback Selection in Personalized Learning with a Network Approach - Nikky van Buuren; Hendrik Straat; Theo Eggen; Jean-Paul Fox (IACAT 2017 Conference, 2017)
Personalized learning is a term used to describe educational systems that adapt student-specific curriculum sequencing, pacing, and presentation based on their unique backgrounds, knowledge, preferences, interests, and learning goals. (Chen, 2008; Netcoh, 2016). The technological approach to personalized learning provides data-driven models to incorporate these adaptations automatically. Examples of applications include online learning systems, educational games, and revision-aid systems. In this study we introduce Bayesian networks as a methodology to implement an adaptive framework within a personalized learning environment. Existing ideas from Computerized Adaptive Testing (CAT) with Item Response Theory (IRT), where choices about content provision are based on maximizing information, are related to the goals of personalized learning environments. Personalized learning entails other goals besides efficient ability estimation by maximizing information, such as an adaptive configuration of preferences and feedback to the student. These considerations will be discussed and their application in networks will be illustrated. Adaptivity in Personalized Learning.In standard CAT’s there is a focus on selecting items that provide maximum information about the ability of an individual at a certain point in time (Van der Linden & Glas, 2000). When learning is the main goal of testing, alternative adaptive item selection methods were explored by Eggen (2012). The adaptive choices made in personalized learning applications require additional adaptivity with respect to the following aspects; the moment of feedback, the kind of feedback, and the possibility for students to actively influence the learning process. Bayesian Networks and Personalized Learning.Personalized learning aims at constructing a framework to incorporate all the aspects mentioned above. Therefore, the goal of this framework is not only to focus on retrieving ability estimates by choosing items on maximum information, but also to construct a framework that allows for these other factors to play a role. Plajner and Vomlel (2016) have already applied Bayesian Networks to adaptive testing, selecting items with help of entropy reduction. Almond et al. (2015) provide a reference work on Bayesian Networks in Educational Assessment. Both acknowledge the potential of the method in terms of features such as modularity options to build finer-grained models. IRT does not allow to model sub-skills very easily and to gather information on fine-grained level, due to its dependency on the assumption of generally one underlying trait. The local independence assumption in IRT implies being interested in mainly the student’s overall ability on the subject of interest. When the goal is to improve student’s learning, we are not just interested in efficiently coming to their test score on a global subject. One wants a model that is able to map educational problems and talents in detail over the whole educational program, while allowing for dependency between items. The moment in time can influence topics to be better mastered than others, and this is exactly what we can to get out of a model. The possibility to model flexible structures, estimate abilities on a very detailed level for sub-skills and to easily incorporate other variables such as feedback in Bayesian Networks makes it a very promising method for making adaptive choices in personalized learning. It is shown in this research how item and feedback selection can be performed with help of the promising Bayesian Networks. A student involvement possibility is also introduced and evaluated. References Almond, R. G., Mislevy, R. J., Steinberg, L. S., Yan, D., & Williamson, D. M. (2015). Bayesian Networks in Educational Assessment. Test. New York: Springer Science+Business Media. http://doi.org/10.1007/978-0-387-98138-3 Eggen, T.J.H.M. (2012) Computerized Adaptive Testing Item Selection in Computerized Adaptive Learning Systems. In: Eggen. TJHM & Veldkamp, BP.. (Eds). Psychometrics in Practice at RCEC. Enschede: RCEC Netcoh, S. (2016, March). “What Do You Mean by ‘Personalized Learning?’. Croscutting Conversations in Education – Research, Reflections & Practice. Blogpost. Plajner, M., & Vomlel, J. (2016). Student Skill Models in Adaptive Testing. In Proceedings of the Eighth International Conference on Probabilistic Graphical Models (pp. 403-414). Van der Linden, W. J., & Glas, C. A. (2000). Computerized adaptive testing: Theory and practice. Dordrecht: Kluwer Academic Publishers. Session Video
Keywords: feedback selection; item selection; network approach; personalized learning

Adaptivity in a Diagnostic Educational Test - Sanneke Schouwstra (IACAT 2017 Conference, 2017)
During the past five years a diagnostic educational test for three subjects (writing Dutch, writing English and math) has been developed in the Netherlands. The test informs students and their teachers about the students’ strengths and weaknesses in such a manner that the learning process can be adjusted to their personal needs. It is a computer-based assessment for students in five different educational tracks midway secondary education that can yield diagnoses of many sub-skills. One of the main challenges at the outset of the development was to devise a way to deliver many diagnoses within a reasonably testing time. The answer to this challenge was to make the DET adaptive. In this presentation we will discuss first how the adaptivity is shaped towards the purpose of the Diagnostic Educational Test. The adaptive design, particularly working with item blocks, will be discussed as well as the implemented adaptive rules. We will also show a simulation of different adaptive paths of students and some empirical information on the paths students took through the test Session Video
Keywords: CAT; Diagnostic tests; Education

Analysis of CAT Precision Depending on Parameters of the Item Pool - Anatoly Maslak; Stanislav Pozdniakov (IACAT 2017 Conference, 2017)
The purpose of this research project is to analyze the measurement precision of a latent variable depending on parameters of the item pool. The influence of the following factors is analyzed: Factor A – range of variation of items in the pool. This factor varies on three levels with the following ranges in logits: a1 – [-3.0; +3.0], a2 - [-4.0; +4.0], a3 - [-5.0; +5.0]. Factor B – number of items in the pool. The factor varies on six levels with the following number of items for every factor: b1 - 128, b2 - 256, b3 – 512, b4 - 1024, b5 – 2048, b6 – 4096. The items are evenly distributed in each of the variation ranges. Factor C – examinees’ proficiency varies at 30 levels (c1, c2, …, c30), which are evenly distributed in the range [-3.0; +3.0] logit. The investigation was based on a simulation experiment within the framework of the theory of latent variables. Response Y is the precision of measurement of examinees’ proficiency, which is calculated as the difference between the true levels of examinees’ proficiency and estimates obtained by means of adaptive testing. Three factor ANOVA was used for data processing. The following results were obtained: 1. Factor A is significant. Ceteris paribus, the greater the range of variation of items in the pool, the higher the estimation precision is. 2. Factor B is significant. Ceteris paribus, the greater the number of items in the pool, the higher the estimation precision is. 3. Factor C is statistically insignificant at level α = .05. It means that the precision of estimation of examinees’ proficiency is the same within the range of their variation. 4. The only significant interaction among all interactions is AB. The significance of this interaction is explained by the fact that increasing the number of items in the pool decreases the effect of the range of variation of items in the pool.  Session Video
Keywords: CAT; Item parameters; Precision

Application of Binary Searching for Item Exposure Control in Cognitive Diagnostic Computerized Adaptive Testing - Chanjin Zheng; Chun Wang (Applied Psychological Measurement, 2017)
Cognitive diagnosis has emerged as a new generation of testing theory for educational assessment after the item response theory (IRT). One distinct feature of cognitive diagnostic models (CDMs) is that they assume the latent trait to be discrete instead of continuous as in IRT. From this perspective, cognitive diagnosis bears a close resemblance to searching problems in computer science and, similarly, item selection problem in cognitive diagnostic computerized adaptive testing (CD-CAT) can be considered as a dynamic searching problem. Previously, item selection algorithms in CD-CAT were developed from information indices in information science and attempted to achieve a balance among several objectives by assigning different weights. As a result, they suffered from low efficiency from a tug-of-war competition among multiple goals in item selection and, at the same time, put an undue responsibility of assigning the weights for these goals by trial and error on users. Based on the searching problem perspective on CD-CAT, this article adapts the binary searching algorithm, one of the most well-known searching algorithms in searching problems, to item selection in CD-CAT. The two new methods, the stratified dynamic binary searching (SDBS) algorithm for fixed-length CD-CAT and the dynamic binary searching (DBS) algorithm for variable-length CD-CAT, can achieve multiple goals without any of the aforementioned issues. The simulation studies indicate their performances are comparable or superior to the previous methods.

ATS-PD: An Adaptive Testing System for Psychological Disorders - Ivan Donadello; Andrea Spoto; Francesco Sambo; Silvana Badaloni; Umberto Granziol; Giulio Vidotto (Educational and Psychological Measurement, 2017)
The clinical assessment of mental disorders can be a time-consuming and error-prone procedure, consisting of a sequence of diagnostic hypothesis formulation and testing aimed at restricting the set of plausible diagnoses for the patient. In this article, we propose a novel computerized system for the adaptive testing of psychological disorders. The proposed system combines a mathematical representation of psychological disorders, known as the “formal psychological assessment,” with an algorithm designed for the adaptive assessment of an individual’s knowledge. The assessment algorithm is extended and adapted to the new application domain. Testing the system on a real sample of 4,324 healthy individuals, screened for obsessive-compulsive disorder, we demonstrate the system’s ability to support clinical testing, both by identifying the correct critical areas for each individual and by reducing the number of posed questions with respect to a standard written questionnaire.

Bayesian Perspectives on Adaptive Testing - Wim J. van der Linden; Bingnan Jiang; Hao Ren; Seung W. Choi; Qi Diao (IACAT 2017 Conference, 2017)
Although adaptive testing is usually treated from the perspective of maximum-likelihood parameter estimation and maximum-informaton item selection, a Bayesian pespective is more natural, statistically efficient, and computationally tractable. This observation not only holds for the core process of ability estimation but includes such processes as item calibration, and real-time monitoring of item security as well. Key elements of the approach are parametric modeling of each relevant process, updating of the parameter estimates after the arrival of each new response, and optimal design of the next step. The purpose of the symposium is to illustrates the role of Bayesian statistics in this approach. The first presentation discusses a basic Bayesian algorithm for the sequential update of any parameter in adaptive testing and illustrates the idea of Bayesian optimal design for the two processes of ability estimation and online item calibration. The second presentation generalizes the ideas to the case of 62 IACAT 2017 ABSTRACTS BOOKLET adaptive testing with polytomous items. The third presentation uses the fundamental Bayesian idea of sampling from updated posterior predictive distributions (“multiple imputations”) to deal with the problem of scoring incomplete adaptive tests. Session Video 1 Session Video 2
Keywords: Bayesian Perspective; CAT

Is CAT Suitable for Automated Speaking Test? - Shingo Imai (IACAT 2017 Conference, 2017)
We have developed automated scoring system of Japanese speaking proficiency, namely SJ-CAT (Speaking Japanese Computerized Adaptive Test), which is operational for last few months. One of the unique features of the test is an adaptive test base on polytomous IRT. SJ-CAT consists of two sections; Section 1 has sentence reading aloud tasks and a multiple choicereading tasks and Section 2 has sentence generation tasks and an open answer tasks. In reading aloud tasks, a test taker reads a phoneme-balanced sentence on the screen after listening to a model reading. In a multiple choice-reading task, a test taker sees a picture and reads aloud one sentence among three sentences on the screen, which describe the scene most appropriately. In a sentence generation task, a test taker sees a picture or watches a video clip and describes the scene with his/her own words for about ten seconds. In an open answer tasks, the test taker expresses one’s support for or opposition to e.g., a nuclear power generation with reasons for about 30 seconds. In the course of the development of the test, we found many unexpected and unique characteristics of speaking CAT, which are not found in usual CATs with multiple choices. In this presentation, we will discuss some of such factors that are not previously noticed in our previous project of developing dichotomous J-CAT (Japanese Computerized Adaptive Test), which consists of vocabulary, grammar, reading, and listening. Firstly, we will claim that distribution of item difficulty parameters depends on the types of items. An item pool with unrestricted types of items such as open questions is difficult to achieve ideal distributions, either normal distribution or uniform distribution. Secondly, contrary to our expectations, open questions are not necessarily more difficult to operate in automated scoring system than more restricted questions such as sentence reading, as long as if one can set up suitable algorithm for open question scoring. Thirdly, we will show that the speed of convergence of standard deviation of posterior distribution, or standard error of theta parameter in polytomous IRT used for SJCAT is faster than dichotomous IRT used in J-CAT. Fourthly, we will discuss problems in equation of items in SJ-CAT, and suggest introducing deep learning with reinforcement learning instead of equation. And finally, we will discuss the issues of operation of SJ-CAT on the web, including speed of scoring, operation costs, security among others. Session Video
Keywords: Automated Speaking Test; CAT; language testing

Comparison of Pretest Item Calibration Methods in a Computerized Adaptive Test (CAT) - Huijuan Meng; Chris Han (IACAT 2017 Conference, 2017)
Calibration methods for pretest items in a computerized adaptive test (CAT) are not a new area of research inquiry. After decades of research on CAT, the fixed item parameter calibration (FIPC) method has been widely accepted and used by practitioners to address two CAT calibration issues: (a) a restricted ability range each item is exposed to, and (b) a sparse response data matrix. In FIPC, the parameters of the operational items are fixed at their original values, and multiple expectation maximization (EM) cycles are used to estimate parameters of the pretest items with prior ability distribution being updated multiple times (Ban, Hanson, Wang, Yi, & Harris, 2001; Kang & Peterson, 2009; Pommerich & Segall, 2003). Another calibration method is the fixed person parameter calibration (FPPC) method proposed by Stocking (1988) as “Method A.” Under this approach, candidates’ ability estimates are fixed in the calibration of pretest items and they define the scale on which the parameter estimates are reported. The logic of FPPC is suitable for CAT applications because the person parameters are estimated based on operational items and available for pretest item calibration. In Stocking (1988), the FPPC was evaluated using the LOGIST computer program developed by Wood, Wingersky, and Lord (1976). He reported that “Method A” produced larger root mean square errors (RMSEs) in the middle ability range than “Method B,” which required the use of anchor items (administered non-adaptively) and linking steps to attempt to correct for the potential scale drift due to the use of imperfect ability estimates. Since then, new commercial software tools such as BILOG-MG and flexMIRT (Cai, 2013) have been developed to handle the FPPC method with different implementations (e.g., the MH-RM algorithm with flexMIRT). The performance of the FPPC method with those new software tools, however, has rarely been researched in the literature. In our study, we evaluated the performance of two pretest item calibration methods using flexMIRT, the new software tool. The FIPC and FPPC are compared under various CAT settings. Each simulated exam contains 75% operational items and 25% pretest items, and real item parameters are used to generate the CAT data. This study also addresses the lack of guidelines in existing CAT item calibration literature regarding population ability shift and exam length (more accurate theta estimates are expected in longer exams). Thus, this study also investigates the following four factors and their impact on parameter estimation accuracy, including: (1) candidate population changes (3 ability distributions); (2) exam length (20: 15 OP + 5 PT, 40: 30 OP + 10 PT, and 60: 45 OP + 15 PT); (3) data model fit (3PL and 3PL with fixed C), and (4) pretest item calibration sample sizes (300, 500, and 1000). This study’s findings will fill the gap in this area of research and thus provide new information on which practitioners can base their decisions when selecting a pretest calibration method for their exams. References Ban, J. C., Hanson, B. A., Wang, T., Yi, Q., & Harris, D. J. (2001). A comparative study of online pretest item—Calibration/scaling methods in computerized adaptive testing. Journal of Educational Measurement, 38(3), 191–212. Cai, L. (2013). flexMIRT® Flexible Multilevel Multidimensional Item Analysis and Test Scoring (Version 2) [Computer software]. Chapel Hill, NC: Vector Psychometric Group. Kang, T., & Petersen, N. S. (2009). Linking item parameters to a base scale (Research Report No. 2009– 2). Iowa City, IA: ACT. Pommerich, M., & Segall, D.O. (2003, April). Calibrating CAT pools and online pretest items using marginal maximum likelihood methods. Paper presented at the annual meeting of the National Council on Measurement in Education, Chicago, IL. Stocking, M. L. (1988). Scale drift in online calibration (Research Report No. 88–28). Princeton, NJ: Educational Testing Service. Wood, R. L., Wingersky, M. S., & Lord, F. M. (1976). LOGIST: A computer program for estimating examinee ability and item characteristic curve parameters (RM76-6) [Computer program]. Princeton, NJ: Educational Testing Service. Session Video
Keywords: CAT; Pretest Item Calibration

A Comparison of Three Empirical Reliability Estimates for Computerized Adaptive Testing - Dong Gi Seo (IACAT 2017 conference, 2017)
Reliability estimates in Computerized Adaptive Testing (CAT) are derived from estimated thetas and standard error of estimated thetas. In practical, the observed standard error (OSE) of estimated thetas can be estimated by test information function for each examinee with respect to Item response theory (IRT). Unlike classical test theory (CTT), OSEs in IRT are conditional values given each estimated thetas so that those values should be marginalized to consider test reliability. Arithmetic mean, Harmonic mean, and Jensen equality were applied to marginalize OSEs to estimate CAT reliability. Based on different marginalization method, three empirical CAT reliabilities were compared with true reliabilities. Results showed that three empirical CAT reliabilities were underestimated compared to true reliability in short test length (< 40), whereas the magnitude of CAT reliabilities was followed by Jensen equality, Harmonic mean, and Arithmetic mean in long test length (> 40). Specifically, Jensen equality overestimated true reliability across all conditions in long test length (>50). Session Video
Keywords: CAT; Reliability

Is a Computerized Adaptive Test More Motivating Than a Fixed-Item Test? - Guangming Ling; Yigal Attali; Bridgid Finn; Elizabeth A. Stone (Applied Psychological Measurement, 2017)
Computer adaptive tests provide important measurement advantages over traditional fixed-item tests, but research on the psychological reactions of test takers to adaptive tests is lacking. In particular, it has been suggested that test-taker engagement, and possibly test performance as a consequence, could benefit from the control that adaptive tests have on the number of test items examinees answer correctly. However, previous research on this issue found little support for this possibility. This study expands on previous research by examining this issue in the context of a mathematical ability assessment and by considering the possible effect of immediate feedback of response correctness on test engagement, test anxiety, time on task, and test performance. Middle school students completed a mathematics assessment under one of three test type conditions (fixed, adaptive, or easier adaptive) and either with or without immediate feedback about the correctness of responses. Results showed little evidence for test type effects. The easier adaptive test resulted in higher engagement and lower anxiety than either the adaptive or fixed-item tests; however, no significant differences in performance were found across test types, although performance was significantly higher across all test types when students received immediate feedback. In addition, these effects were not related to ability level, as measured by the state assessment achievement levels. The possibility that test experiences in adaptive tests may not in practice be significantly different than in fixed-item tests is raised and discussed to explain the results of this and previous studies.

Computerized Adaptive Testing for Cognitive Diagnosis in Classroom: A Nonparametric Approach - Yuan-Pei Chang; Chia-Yi Chiu; Rung-Ching Tsai (IACAT 2017 Conference, 2017)
In the past decade, CDMs of educational test performance have received increasing attention among educational researchers (for details, see Fu & Li, 2007, and Rupp, Templin, & Henson, 2010). CDMs of educational test performance decompose the ability domain of a given test into specific skills, called attributes, each of which an examinee may or may not have mastered. The resulting attribute profile documents the individual’s strengths and weaknesses within the ability domain. The Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) has been suggested by researchers as a diagnostic tool for assessment and evaluation (e.g., Cheng & Chang, 2007; Cheng, 2009; Liu, You, Wang, Ding, & Chang, 2013; Tatsuoka & Tatsuoka, 1997). While model-based CD-CAT is relatively well-researched in the context of large-scale assessments, this type of system has not received the same degree of development in small-scale settings, where it would be most useful. The main challenge is that the statistical estimation techniques successfully applied to the parametric CD-CAT require large samples to guarantee the reliable calibration of item parameters and accurate estimation of examinees’ attribute profiles. In response to the challenge, a nonparametric approach that does not require any parameter calibration, and thus can be used in small educational programs, is proposed. The proposed nonparametric CD-CAT relies on the same principle as the regular CAT algorithm, but uses the nonparametric classification method (Chiu & Douglas, 2013) to assess and update the student’s ability state while the test proceeds. Based on a student’s initial responses, 2 a neighborhood of candidate proficiency classes is identified, and items not characteristic of the chosen proficiency classes are precluded from being chosen next. The response to the next item then allows for an update of the skill profile, and the set of possible proficiency classes is further narrowed. In this manner, the nonparametric CD-CAT cycles through item administration and update stages until the most likely proficiency class has been pinpointed. The simulation results show that the proposed method outperformed the compared parametric CD-CAT algorithms and the differences were significant when the item parameter calibration was not optimal. References Cheng, Y. (2009). When cognitive diagnosis meets computerized adaptive testing: CD-CAT. Psychometrika, 74, 619-632. Cheng, Y., & Chang, H. (2007). The modified maximum global discrimination index method for cognitive diagnostic CAT. In D. Weiss (Ed.) Proceedings of the 2007 GMAC Computerized Adaptive Testing Conference. Chiu, C.-Y., & Douglas, J. A. (2013). A nonparametric approach to cognitive diagnosis by proximity to ideal response patterns. Journal of Classification, 30, 225-250. Fu, J., & Li, Y. (2007). An integrative review of cognitively diagnostic psychometric models. Paper presented at the Annual Meeting of the National Council on Measurement in Education. Chicago, Illinois. Liu, H., You, X., Wang, W., Ding, S., & Chang, H. (2013). The development of computerized adaptive testing with cognitive diagnosis for an English achievement test in China. Journal of Classification, 30, 152-172. Rupp, A. A., & Templin, J. L., & Henson, R. A. (2010). Diagnostic Measurement. Theory, Methods, and Applications. New York: Guilford. Tatsuoka, K.K., & Tatsuoka, M.M. (1997), Computerized cognitive diagnostic adaptive testing: Effect on remedial instruction as empirical validation. Journal of Educational Measurement, 34, 3–20. Session Video
Keywords: CD-CAT; non-parametric approach

Concerto 5 Open Source CAT Platform: From Code to Nodes - David Stillwell (IACAT 2017 Conference, 2017)
Concerto 5 is the newest version of the Concerto open source R-based Computer-Adaptive Testing platform, which is currently used in educational testing and in clinical trials. In our quest to make CAT accessible to all, the latest version uses flowchart nodes to connect different elements of a test, so that CAT test creation is an intuitive high-level process that does not require writing code. A test creator might connect an Info Page node, to a Consent Page node, to a CAT node, to a Feedback node. And after uploading their items, their test is done. This talk will show the new flowchart interface, and demonstrate the creation of a CAT test from scratch in less than 10 minutes. Concerto 5 also includes a new Polytomous CAT node, so CATs with Likert items can be easily created in the flowchart interface. This node is currently used in depression and anxiety tests in a clinical trial. Session Video
Keywords: Concerto 5; Open Source CAT

Considerations in Performance Evaluations of Computerized Formative Assessments - Michael Chajewski; John Harnisher (IACAT 2017 Conference, 2017)
Computerized adaptive instruments have been widely established and used in the context of summative assessments for purposes including licensure, admissions and proficiency testing. The benefits of examinee tailored examinations, which can provide estimates of performance that are more reliable and valid, have in recent years attracted a greater audience (i.e. patient oriented outcomes, test prep, etc.). Formative assessment, which are most widely understood in their implementation as diagnostic tools, have recently started to expand to lesser known areas of computerized testing such as in implementations of instructional designs aiming to maximize examinee learning through targeted practice. Using a CAT instrument within the framework of evaluating repetitious examinee performances (in such settings as a Quiz Bank practices for example) poses unique challenges not germane to summative assessments. The scale on which item parameters (and subsequently examinee performance estimates such as Maximum Likelihood Estimates) are determined usually do not take change over time under consideration. While vertical scaling features resolve the learning acquisition problem, most content practice engines do not make use of explicit practice windows which could be vertically aligned. Alternatively, the Multidimensional (MIRT)- and Hierarchical Item Response Theory (HIRT) models allow for the specification of random effects associated with change over time in examinees’ skills, but are often complex and require content and usage resources not often observed. The research submitted for consideration simulated examinees’ repeated variable length Quiz Bank practice in algebra using a 500 1-PL operational item pool. The stability simulations sought to determine with which rolling item interval size ability estimates would provide the most informative insight into the examinees’ learning progression over time. Estimates were evaluated in terms of reduction in estimate uncertainty, bias and RMSD with the true and total item based ability estimates. It was found that rolling item intervals between 20-25 items provided the best reduction of uncertainty around the estimate without compromising the ability to provide informed performance estimates to students. However, while asymptotically intervals of 20-25 items tended to provide adequate estimates of performance, changes over shorter periods of time assessed with shorter quizzes could not be detected as those changes would be suppressed in lieu of the performance based on the full interval considered. Implications for infrastructure (such as recommendation engines, etc.), product and scale development are discussed. Session video
Keywords: algebra; Formative Assessment; Performance Evaluations

Construction of Gratitude Scale Using Polytomous Item Response Theory Model - Nurul Arbiyah (IACAT 2017 Conference, 2017)
Various studies have shown that gratitude is essential to increase the happiness and quality of life of every individual. Unfortunately, research on gratitude still received little attention, and there is no standardized measurement for it. Gratitude measurement scale was developed overseas, and has not adapted to the Indonesian culture context. Moreover, the scale development is generally performed with classical theory approach, which has some drawbacks. This research will develop a gratitude scale using polytomous Item Response Theory model (IRT) by applying the Partial Credit Model (PCM). The pilot study results showed that the gratitude scale (with 44 items) is a reliable measure (α = 0.944) and valid (meet both convergent and discriminant validity requirements). The pilot study results also showed that the gratitude scale satisfies unidimensionality assumptions. The test results using the PCM model showed that the gratitude scale had a fit model. Of 44 items, there was one item that does not fit, so it was eliminated. Second test results for the remaining 43 items showed that they fit the model, and all items were fit to measure gratitude. Analysis using Differential Item Functioning (DIF) showed four items have a response bias based on gender. Thus, there are 39 items remaining in the scale. Session Video
Keywords: Gratitude Scale; polytomous items

Developing a CAT: An Integrated Perspective - Nathan Thompson (IACAT 2017 Conference, 2017)
Most resources on computerized adaptive testing (CAT) tend to focus on psychometric aspects such as mathematical formulae for item selection or ability estimation. However, development of a CAT assessment requires a holistic view of project management, financials, content development, product launch and branding, and more. This presentation will develop such a holistic view, which serves several purposes, including providing a framework for validity, estimating costs and ROI, and making better decisions regarding the psychometric aspects. Thompson and Weiss (2011) presented a 5-step model for developing computerized adaptive tests (CATs). This model will be presented and discussed as the core of this holistic framework, then applied to real-life examples. While most CAT research focuses on developing new quantitative algorithms, this presentation is instead intended to help researchers evaluate and select algorithms that are most appropriate for their needs. It is therefore ideal for practitioners that are familiar with the basics of item response theory and CAT, and wish to explore how they might apply these methodologies to improve their assessments. Steps include: 1. Feasibility, applicability, and planning studies 2. Develop item bank content or utilize existing bank 3. Pretest and calibrate item bank 4. Determine specifications for final CAT 5. Publish live CAT. So, for example, Step 1 will contain simulation studies which estimate item bank requirements, which then can be used to determine costs of content development, which in turn can be integrated into an estimated project cost timeline. Such information is vital in determining if the CAT should even be developed in the first place. References Thompson, N. A., & Weiss, D. J. (2011). A Framework for the Development of Computerized Adaptive Tests. Practical Assessment, Research & Evaluation, 16(1). Retrieved from http://pareonline.net/getvn.asp?v=16&n=1. Session Video
Keywords: CAT Development; integrated approach

Development of a Computer Adaptive Test for Depression Based on the Dutch-Flemish Version of the PROMIS Item Bank - Gerard Flens; Niels Smits; Caroline B. Terwee; Joost Dekker; Irma Huijbrechts; Edwin de Beurs (Evaluation & the Health Professions, 2017)
We developed a Dutch-Flemish version of the patient-reported outcomes measurement information system (PROMIS) adult V1.0 item bank for depression as input for computerized adaptive testing (CAT). As item bank, we used the Dutch-Flemish translation of the original PROMIS item bank (28 items) and additionally translated 28 U.S. depression items that failed to make the final U.S. item bank. Through psychometric analysis of a combined clinical and general population sample (N = 2,010), 8 added items were removed. With the final item bank, we performed several CAT simulations to assess the efficiency of the extended (48 items) and the original item bank (28 items), using various stopping rules. Both item banks resulted in highly efficient and precise measurement of depression and showed high similarity between the CAT simulation scores and the full item bank scores. We discuss the implications of using each item bank and stopping rule for further CAT development.

The Development of a Web-Based CAT in China - Chongli Liang; Danjun Wang; Dan Zhou; Peida Zhan (IACAT 2017 Conference, 2017)
Cognitive ability assessment has been widely used as the recruitment tool in hiring potential employees. Traditional cognitive ability tests have been encountering threats from item-exposures and long time for answering. Especially in China, campus recruitment thinks highly of short answering time and anti-cheating. Beisen, as the biggest native online assessment software provider, developed a web-based CAT for cognitive ability which assessing verbal, quantitative, logical and spatial ability in order to decrease answering times, improve assessment accuracy and reduce threats from cheating and faking in online ability test. The web-based test provides convenient testing for examinees who can access easily to the test via internet just by login the test website at any time and any place through any Internet-enabled devices (e.g., laptops, IPADs, and smart phones). We designed the CAT following strategies of establishing item bank, setting starting point, item selection, scoring and terminating. Additionally, we pay close attention to administrating the test via web. For the CAT procedures, we employed online calibration for establishing a stable and expanding item bank, and integrated maximum Fisher information, α-stratified strategy and randomization for item selection and coping with item exposures. Fixed-length and variable-length strategies were combined in terminating the test. For fulfilling the fluid web-based testing, we employed cloud computing techniques and designed each computing process subtly. Distributed computation was used to process scoring which executes EAP and item selecting at high speed. Caching all items to the servers in advance helps shortening the process of loading items to examinees’ terminal equipment. Horizontally scalable cloud servers function coping with great concurrency. The massive computation in item selecting was conversed to searching items from an information matrix table. We examined the average accuracy, bank usage and computing performance in the condition of laboratory and real testing. According to a test for almost 28000 examinees, we found that bank usage is averagely 50%, and that 80% tests terminate at test information of 10 and averagely at 9.6. In context of great concurrency, the testing is unhindered and the process of scoring and item selection only takes averagely 0.23s for each examiner. Session Video
Keywords: China; Web-Based CAT

The Development of MST Test Information for the Prediction of Test Performances - Ryoungsun Park; Jiseon Kim; Hyewon Chung; Barbara G. Dodd (Educational and Psychological Measurement, 2017)
The current study proposes novel methods to predict multistage testing (MST) performance without conducting simulations. This method, called MST test information, is based on analytic derivation of standard errors of ability estimates across theta levels. We compared standard errors derived analytically to the simulation results to demonstrate the validity of the proposed method in both measurement precision and classification accuracy. The results indicate that the MST test information effectively predicted the performance of MST. In addition, the results of the current study highlighted the relationship among the test construction, MST design factors, and MST performance.

DIF-CAT: Doubly Adaptive CAT Using Subgroup Information to Improve Measurement Precision - Joy Wang; David J. Weiss; Chun Wang (IACAT 2017 Conference, 2017)
Differential item functioning (DIF) is usually regarded as a test fairness issue in high-stakes tests. In low-stakes tests, it is more of an accuracy problem. However, in low-stakes tests, the same method, deleting items that demonstrate significant DIF, is still employed to treat DIF items. When political concerns are not important, such as in low-stakes tests and instruments that are not used to make decisions about people, deleting items might not be optimal. Computerized adaptive testing (CAT) is more and more frequently used in low-stakes tests. The DIF-CAT method evaluated in this research is designed to cope with DIF in a CAT environment. Using this method, item parameters are separately estimated for the focal group and the reference group in a DIF study, then CATs are administered based on different sets of item parameters for the focal and reference groups. To evaluate the performance of the DIF-CAT procedure, it was compared in a simulation study to (1) deleting all the DIF items in a CAT bank and (2) ignoring DIF. A 300-item flat item bank and a 300-item peaked item bank were simulated using the three-parameter logistic IRT model with D = 1,7. 40% of the items in each bank showed DIF. The DIF size was b and/or a = 0.5 while original b ranged from -3 to 3 and a ranged from 0.3 to 2.1. Three types of DIF were considered: (1) uniform DIF caused by differences in b, non-uniform DIF caused by differences in a, and non-uniform DIF caused by differences in both a and b. 500 normally distributed simulees in each of reference and focal groups were used in item parameter re-calibration. In the Delete DIF method, only DIF-free items were calibrated. In the Ignore DIF method, all the items were calibrated using all simulees without differentiating the groups. In the DIF-CAT method, the DIF-free items were used as anchor items to estimate the item parameters for the focal and reference groups and the item parameters from recalibration were used. All simulees used the same item parameters in the Delete method and the Ignore method. CATs for simulees within the two groups used group-specific item parameters in the DIF-CAT method. In the CAT stage, 100 simulees were generated for each of the reference and focal groups, at each of six discrete q levels ranging from -2.5 to 2.5. CAT test length was fixed at 40 items. Bias, average absolute difference, RMSE, standard error of θ estimates, and person fit, were used to compare the performance of the DIF methods. DIF item usage was also recorded for the Ignore method and the DIF-CAT method. Generally, the DIF-CAT method outperformed both the Delete method and the Ignore method in dealing with DIF items in CAT. The Delete method, which is the most frequently used method for handling DIF, performed the worst of the three methods in a CAT environment, as reflected in multiple indices of measurement precision. Even the Ignore method, which simply left DIF items in the item bank, provided θ estimates of higher precision than the Delete method. This poor performance of the Delete method was probably due to reduction in size of the item bank available for each CAT. Session Video
Keywords: DIF-CAT; Doubly Adaptive CAT; Measurement Precision; subgroup information

Dual-Objective Item Selection Criteria in Cognitive Diagnostic Computerized Adaptive Testing - Kang, Hyeon-Ah; Zhang, Susu; Chang, Hua-Hua (Journal of Educational Measurement, 2017)
The development of cognitive diagnostic-computerized adaptive testing (CD-CAT) has provided a new perspective for gaining information about examinees' mastery on a set of cognitive attributes. This study proposes a new item selection method within the framework of dual-objective CD-CAT that simultaneously addresses examinees' attribute mastery status and overall test performance. The new procedure is based on the Jensen-Shannon (JS) divergence, a symmetrized version of the Kullback-Leibler divergence. We show that the JS divergence resolves the noncomparability problem of the dual information index and has close relationships with Shannon entropy, mutual information, and Fisher information. The performance of the JS divergence is evaluated in simulation studies in comparison with the methods available in the literature. Results suggest that the JS divergence achieves parallel or more precise recovery of latent trait variables compared to the existing methods and maintains practical advantages in computation and item pool usage.

Efficiency of Item Selection in CD-CAT Based on Conjunctive Bayesian Network Modeling Hierarchical attributes - Soo-Yun Han; Yun Joo Yoo (IACAT 2017 Conference, 2017)
Cognitive diagnosis models (CDM) aim to diagnosis examinee’s mastery status of multiple fine-grained skills. As new development for cognitive diagnosis methods emerges, much attention is given to cognitive diagnostic computerized adaptive testing (CD-CAT) as well. The topics such as item selection methods, item exposure control strategies, and online calibration methods, which have been wellstudied for traditional item response theory (IRT) based CAT, are also investigated in the context of CD-CAT (e.g., Xu, Chang, & Douglas, 2003; Wang, Chang, & Huebner, 2011; Chen et al., 2012). In CDM framework, some researchers suggest to model structural relationship between cognitive skills, or namely, attributes. Especially, attributes can be hierarchical, such that some attributes must be acquired before the subsequent ones are mastered. For example, in mathematics, addition must be mastered before multiplication, which gives a hierarchy model for addition skill and multiplication skill. Recently, new CDMs considering attribute hierarchies have been suggested including the Attribute Hierarchy Method (AHM; Leighton, Gierl, & Hunka, 2004) and the Hierarchical Diagnostic Classification Models (HDCM; Templin & Bradshaw, 2014). Bayesian Networks (BN), the probabilistic graphical models representing the relationship of a set of random variables using a directed acyclic graph with conditional probability distributions, also provide an efficient framework for modeling the relationship between attributes (Culbertson, 2016). Among various BNs, conjunctive Bayesian network (CBN; Beerenwinkel, Eriksson, & Sturmfels, 2007) is a special kind of BN, which assumes partial ordering between occurrences of events and conjunctive constraints between them. In this study, we propose using CBN for modeling attribute hierarchies and discuss the advantage of CBN for CDM. We then explore the impact of the CBN modeling on the efficiency of item selection methods for CD-CAT when the attributes are truly hierarchical. To this end, two simulation studies, one for fixed-length CAT and another for variable-length CAT, are conducted. For each studies, two attribute hierarchy structures with 5 and 8 attributes are assumed. Among the various item selection methods developed for CD-CAT, six algorithms are considered: posterior-weighted Kullback-Leibler index (PWKL; Cheng, 2009), the modified PWKL index (MPWKL; Kaplan, de la Torre, Barrada, 2015), Shannon entropy (SHE; Tatsuoka, 2002), mutual information (MI; Wang, 2013), posterior-weighted CDM discrimination index (PWCDI; Zheng & Chang, 2016) and posterior-weighted attribute-level CDM discrimination index (PWACDI; Zheng & Chang, 2016). The impact of Q-matrix structure, item quality, and test termination rules on the efficiency of item selection algorithms is also investigated. Evaluation measures include the attribute classification accuracy (fixed-length experiment) and test length of CDCAT until stopping (variable-length experiment). The results of the study indicate that the efficiency of item selection is improved by directly modeling the attribute hierarchies using CBN. The test length until achieving diagnosis probability threshold was reduced to 50-70% for CBN based CAT compared to the CD-CAT assuming independence of attributes. The magnitude of improvement is greater when the cognitive model of the test includes more attributes and when the test length is shorter. We conclude by discussing how Q-matrix structure, item quality, and test termination rules affect the efficiency. References Beerenwinkel, N., Eriksson, N., & Sturmfels, B. (2007). Conjunctive bayesian networks. Bernoulli, 893- 909. Chen, P., Xin, T., Wang, C., & Chang, H. H. (2012). Online calibration methods for the DINA model with independent attributes in CD-CAT. Psychometrika, 77(2), 201-222. Cheng, Y. (2009). When cognitive diagnosis meets computerized adaptive testing: CD-CAT. Psychometrika, 74(4), 619-632. Culbertson, M. J. (2016). Bayesian networks in educational assessment: the state of the field. Applied Psychological Measurement, 40(1), 3-21. Kaplan, M., de la Torre, J., & Barrada, J. R. (2015). New item selection methods for cognitive diagnosis computerized adaptive testing. Applied Psychological Measurement, 39(3), 167-188. Leighton, J. P., Gierl, M. J., & Hunka, S. M. (2004). The attribute hierarchy method for cognitive assessment: a variation on Tatsuoka's rule‐space approach. Journal of Educational Measurement, 41(3), 205-237. Tatsuoka, C. (2002). Data analytic methods for latent partially ordered classification models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51(3), 337-350. Templin, J., & Bradshaw, L. (2014). Hierarchical diagnostic classification models: A family of models for estimating and testing attribute hierarchies. Psychometrika, 79(2), 317-339. Wang, C. (2013). Mutual information item selection method in cognitive diagnostic computerized adaptive testing with short test length. Educational and Psychological Measurement, 73(6), 1017-1035. Wang, C., Chang, H. H., & Huebner, A. (2011). Restrictive stochastic item selection methods in cognitive diagnostic computerized adaptive testing. Journal of Educational Measurement, 48(3), 255-273. Xu, X., Chang, H., & Douglas, J. (2003, April). A simulation study to compare CAT strategies for cognitive diagnosis. Paper presented at the annual meeting of National Council on Measurement in Education, Chicago. Zheng, C., & Chang, H. H. (2016). High-efficiency response distribution–based item selection algorithms for short-length cognitive diagnostic computerized adaptive testing. Applied Psychological Measurement, 40(8), 608-624. Session Video
Keywords: CD-CAT; Conjuctive Bayesian Network Modeling; item selection

Efficiency of Targeted Multistage Calibration Designs under Practical Constraints: A Simulation Study - Stephanie Berger; Angela J. Verschoor; Theo Eggen; Urs Moser (IACAT 2017 Conference, 2017)
Calibration of an item bank for computer adaptive testing requires substantial resources. In this study, we focused on two related research questions. First, we investigated whether the efficiency of item calibration under the Rasch model could be enhanced by calibration designs that optimize the match between item difficulty and student ability (Berger, 1991). Therefore, we introduced targeted multistage calibration designs, a design type that refers to a combination of traditional targeted calibration designs and multistage designs. As such, targeted multistage calibration designs consider ability-related background variables (e.g., grade in school), as well as performance (i.e., outcome of a preceding test stage) for assigning students to suitable items. Second, we explored how limited a priori knowledge about item difficulty affects the efficiency of both targeted calibration designs and targeted multistage calibration designs. When arranging items within a given calibration design, test developers need to know the item difficulties to locate items optimally within the design. However, usually, no empirical information about item difficulty is available before item calibration. Owing to missing empirical data, test developers might fail to assign all items to the most suitable location within a calibration design. Both research questions were addressed in a simulation study in which we varied the calibration design, as well as the accuracy of item distribution across the different booklets or modules within each design (i.e., number of misplaced items). The results indicated that targeted multistage calibration designs were more efficient than ordinary targeted designs under optimal conditions. Especially, targeted multistage calibration designs provided more accurate estimates for very easy and 52 IACAT 2017 ABSTRACTS BOOKLET very difficult items. Limited knowledge about item difficulty during test construction impaired the efficiency of all designs. The loss of efficiency was considerably large for one of the two investigated targeted multistage calibration designs, whereas targeted designs were more robust. References Berger, M. P. F. (1991). On the efficiency of IRT models when applied to different sampling designs. Applied Psychological Measurement, 15(3), 293–306. doi:10.1177/014662169101500310 Session Video
Keywords: CAT; Efficiency; Multistage Calibration

An Empirical Simulation Study Using mstR for MST Designs - Soo Lee (IACAT 2017 Conference, 2017)
Unlike other systems of adaptive testing, multistage testing (MST) provides many benefits of adaptive testing and linear testing, and has become the most sought-after form for computerized testing in educational assessment recently. It is greatly fit for testing educational achievement and can be adapted to practical educational surveys testing. However, there are many practical considerations for MST design for operational implementations including costs and benefits. As a practitioner, we need to start with various simulations to evaluate the various MST designs and their performances before the implementations. A recently developed statistical tool mstR, an open source R package, was released to support the researchers and practitioners to aid their MST simulations for implementations. Conventional MST design has three stages of module (i.e., 1-2-3 design) structure. Alternatively, the composition of modules diverges from one design to another (e.g., 1-3 design). For advance planning of equivalence studies, this paper utilizes both 1-2-3 design and 1-3 design for the MST structures. In order to study the broad structure of these values, this paper evaluates the different MST designs through simulations using the R package mstR. The empirical simulation study provides an introductory overview of mstR and describes what mstR offers using different MST structures from 2PL item bank. Further comparisons will show the advantages of the different MST designs (e.g., 1-2-3 design and 1-3 design) for different practical implementations. As an open-source statistical environment R, mstR provides a great simulation tool and allows psychologists, social scientists, and educational measurement scientists to apply it to innovative future assessments in the operational use of MST.
Keywords: mstR; multistage testing

Evaluation of Parameter Recovery, Drift, and DIF with CAT Data - Nathan Thompson; Jordan Stoeger (IACAT 2017 Conference, 2017)
Parameter drift and differential item functioning (DIF) analyses are frequent components of a test maintenance plan. That is, after a test form(s) is published, organizations will often calibrate postpublishing data at a later date to evaluate whether the performance of the items or the test has changed over time. For example, if item content is leaked, the items might gradually become easier over time, and item statistics or parameters can reflect this. When tests are published under a computerized adaptive testing (CAT) paradigm, they are nearly always calibrated with item response theory (IRT). IRT calibrations assume that range restriction is not an issue – that is, each item is administered to a range of examinee ability. CAT data violates this assumption. However, some organizations still wish to evaluate continuing performance of the items from a DIF or drift paradigm. This presentation will evaluate just how inaccurate DIF and drift analyses might be on CAT data, using a Monte Carlo parameter recovery methodology. Known item parameters will be used to generate both linear and CAT data sets, which are then calibrated for DIF and drift. In addition, we will implement Randomesque item exposure constraints in some CAT conditions, as this randomization directly alleviates the range restriction problem somewhat, but it is an empirical question as to whether this improves the parameter recovery calibrations. Session Video
Keywords: CAT; DIF; Parameter Drift; Parameter Recovery

FastCAT – Customizing CAT Administration Rules to Increase Response Efficiency - Richard C. Gershon (IACAT 2017 Conference, 2017)
A typical pre-requisite for CAT administration is the existence of an underlying item bank completely covering the range of the trait being measured. When a bank fails to cover the full range of the trait, examinees who are close to the floor or ceiling will often never achieve a standard error cut-off and examinees will be forced to answer items increasingly less relevant to their trait level. This scenario is fairly typical for many patients responding to patient reported outcome measures (PROMS). For IACAT 2017 ABSTRACTS BOOKLET 65 example, in the assessment of physical functioning, many item banks ceiling at about the 50%ile. For most healthy patients, after a few items the only items remaining in the bank will represent decreasing ability (even though the patient has already indicated that they are at or above the mean for the population). Another example would be for a patient with no pain taking a Pain CAT. They will probably answer “Never” pain for every succeeding item out to the maximum test length. For this project we sought to reduce patient burden, while maintaining test accuracy, through the reduction of CAT length using novel stopping rules. We studied CAT administration assessment histories for patients who were administered Patient Reported Outcomes Measurement Information System (PROMIS) CATs. In the PROMIS 1 Wave 2 Back Pain/Depression Study, CATs were administered to N=417 cases assessed across 11 PROMIS domains. Original CAT administration rules were: start with a pre-identified item of moderate difficulty; administer a minimum four items per case; stop when an estimated theta’s SE declines to < 0.3 OR a maximum 12 items are administered. Original CAT. 12,622 CAT administrations were analyzed. CATs ranged in number of items administered from 4 to 12 items; 72.5% were 4-item CATs. The second and third most frequently occurring CATs were 5-item (n=1102; 8.7%) and 12-item CATs (n=964; 7.6%). 64,062 items total were administered, averaging 5.1 items per CAT. Customized CAT. Three new CAT stopping rules were introduced, each with potential to increase item-presentation efficiency and maintain required score precision: Stop if a case responds to the first two items administered using an “extreme” response category (towards the ceiling or floor for the in item bank, or at ); administer a minimum two items per case; stop if the change in SE estimate (previous to current item administration) is positive but < 0.01. The three new stopping rules reduced the total number of items administered by 25,643 to 38,419 items (40.0% reduction). After four items were administered, only n=1,824 CATs (14.5%) were still in assessment mode (vs. n=3,477 (27.5%) in the original CATs). On average, cases completed 3.0 items per CAT (vs. 5.1). Each new rule addressed specific inefficiencies in the original CAT administration process: Cases not having or possessing a low/clinically unimportant level of the assessed domain; allow the SE <0.3 stopping criterion to come into effect earlier in the CAT administration process; cases experiencing poor domain item bank measurement, (e.g., “floor,” “ceiling” cases).
Keywords: Administration Rules; Efficiency; FastCAT

From Blueprints to Systems: An Integrated Approach to Adaptive Testing - Gage Kingsbury; Tony Zara (IACAT 2017 Conference, 2017)
For years, test blueprints have told test developers how many items and what types of items will be included in a test. Adaptive testing adopted this approach from paper testing, and it is reasonably useful. Unfortunately, 'how many items and what types of items' are not all the elements one should consider when choosing items for an adaptive test. To fill in gaps, practitioners have developed tools to allow an adaptive test to behave appropriately (i.e. examining exposure control, content balancing, item drift procedures, etc.). Each of these tools involves the use of a separate process external to the primary item selection process. The use of these subsidiary processes makes item selection less optimal and makes it difficult to prioritize aspects of selection. This discussion describes systems-based adaptive testing. This approach uses metadata concerning items, test takers and test elements to select items. These elements are weighted by the stakeholders to shape an expanded blueprint designed for adaptive testing.  Session Video
Keywords: CAT; integrated approach; Keynote

Generating Rationales to Support Formative Feedback in Adaptive Testing - Mark Gierl; Okan Bulut (IACAT 2017 Conference, 2017)
Computer adaptive testing offers many important benefits to support and promote life-long learning. Computers permit testing on-demand thereby allowing students to take the test at any time during instruction; items on computerized tests are scored immediately thereby providing students with instant feedback; computerized tests permit continuous administration thereby allowing students to have more choice about when they write their exams. But despite these important benefits, the advent of computer adaptive testing has also raised formidable challenges, particularly in the area of item development. Educators must have access to large numbers of diverse, high-quality test items to implement computerize adaptive testing because items are continuously administered to students. Hence, hundreds or even thousands of items are needed to develop the test item banks necessary for computer adaptive testing. Unfortunately, educational test items, as they are currently created, are time consuming and expensive to develop because each individual item is written, initially, by a content specialist and, then, reviewed, edited, and revised by groups of content specialists to ensure the items yield reliable and valid information. Hence, item development is one of the most important problems that must be solved before we can migrate to computer adaptive testing to support life-long learning because large numbers of high-quality, content-specific, test items are required. One promising item development method that may be used to address this challenge is with automatic item generation. Automatic item generation is a relatively new but rapidly evolving research area where cognitive and psychometric modelling practices are used produce hundreds of new test items with the aid of computer technology. The purpose of our presentation is to describe a new methodology for generating both the items and the rationales required to solve each generated item in order to produce the feedback needed to support life-long learning. Our item generation methodology will first be described. To ensure our description is practical, the method will also be demonstrated using generated items from the health sciences to demonstrate how item generation can promote life-long learning for medical educators and practitioners.   Session Video
Keywords: Adaptive Testing; formative feedback; Item generation

Grow a Tiger out of Your CAT - Angela Verschoor (IACAT 2017 Conference, 2017)
The main focus in the community of test developers and researchers is on improving adaptive test procedures and methodologies. Yet, the transition from research projects to larger-scale operational CATs is facing its own challenges. Usually, these operational CATs find their origin in government tenders. “Scalability”, “Interoperability” and “Transparency” are three keywords often found in these documents. Scalability is concerned with parallel system architectures which are based upon stateless selection algorithms. Design capacities often range from 10,000 to well over 100,000 concurrent students. Interoperability is implemented in standards like QTI, standards that were not designed with adaptive testing in mind. Transparency is being realized by open source software: the adaptive test should not be a black box. These three requirements often complicate the development of an adaptive test, or sometimes even conflict. Session Video
Keywords: interoparability; Scalability; transparency

Heuristic Constraint Management Methods in Multidimensional Adaptive Testing - Sebastian Born; Andreas Frey (Educational and Psychological Measurement, 2017)
Although multidimensional adaptive testing (MAT) has been proven to be highly advantageous with regard to measurement efficiency when several highly correlated dimensions are measured, there are few operational assessments that use MAT. This may be due to issues of constraint management, which is more complex in MAT than it is in unidimensional adaptive testing. Very few studies have examined the performance of existing constraint management methods (CMMs) in MAT. The present article focuses on the effectiveness of two promising heuristic CMMs in MAT for varying levels of imposed constraints and for various correlations between the measured dimensions. Through a simulation study, the multidimensional maximum priority index (MMPI) and multidimensional weighted penalty model (MWPM), as an extension of the weighted penalty model, are examined with regard to measurement precision and constraint violations. The results show that both CMMs are capable of addressing complex constraints in MAT. However, measurement precision losses were found to differ between the MMPI and MWPM. While the MMPI appears to be more suitable for use in assessment situations involving few to a moderate number of constraints, the MWPM should be used when numerous constraints are involved.

How Adaptive is an Adaptive Test: Are all Adaptive Tests Adaptive? - Mark D Reckase (2017 IACAT Conference, 2017)
There are many different kinds of adaptive tests but they all have the characteristic that some feature of the test is customized to the purpose of the test. In the time allotted, it is impossible to consider the adaptation of all of this types so this address will focus on the “classic” adaptive test that matches the difficulty of the test to the capabilities of the person being tested. This address will first present information on the maximum level of adaptation that can occur and then compare the amount of adaptation that typically occurs on an operational adaptive test to the maximum level of adaptation. An index is proposed to summarize the amount of adaptation and it is argued that this type of index should be reported for operational adaptive tests to show the amount of adaptation that typically occurs. Click for Presentation Video
Keywords: Adaptive Testing; CAT

The Implementation of Nationwide High Stakes Computerized (adaptive) Testing in the Netherlands - Mia van Boxel; Theo Eggen (IACAT 2017 Conference, 2017)
In this presentation the challenges of implementation of (adaptive) digital testing in the Facet system in the Netherlands is discussed. In the Netherlands there is a long tradition of implementing adaptive testing in educational settings. Already since the late nineties of the last century adaptive testing was used mostly in low stakes testing. Several CATs were implemented in student monitoring systems for primary education and in the general subjects language and arithmetic in vocational education. The only nationwide implemented high stakes CAT is the WISCAT-pabo: an arithmetic test for students in the first year of primary school teacher colleges. The psychometric advantages of item based adaptive testing are obvious. For example efficiency and high measurement precision. But there are also some disadvantages such as the impossibility of reviewing items during and after the test. During the test the student is not in control of his own test; e.q . he can only navigate forward to the next item. This is one of the reasons other methods of testing, such as multistage-testing, with adaptivity not on the item level but on subtest level, has become more popular to use in high stakes testing. A main challenge of computerized (adaptive) testing is the implementation of the item bank and the test workflow in a digital system. Since 2014 a nationwide new digital system (Facet) was introduced in the Netherlands, with connections to the digital systems of different parties based on international standards (LTI and QTI). The first nationwide tests in the Facet-system were flexible exams Dutch and arithmetic for vocational (and secondary) education, taken as item response theory-based equated linear multiple forms tests, which are administered during 5 periods in a year. Nowadays there are some implementations of different methods of (multistage) adaptive testing in the same Facet system (DTT en Acet). In this conference, other presenters of Cito will elaborate on the psychometric characteristics of this other adaptive testing methods. In this contribution, the system architecture and interoperability of the Facet system will be explained. The emphasis is on the implementation and the problems to be solved by using this digital system in all phases of the (adaptive) testing process: item banking, test construction, designing, publication, test taking, analyzing and reporting to the student. An evaluation of the use of the system will be presented. Session Video
Keywords: High stakes CAT; Netherlands; WISCAT

An Imputation Approach to Handling Incomplete Computerized Tests - Troy Chen; Chi-Yu Huang; Chunyan Liu (IACAT 2017 Conference, 2017)
As technology advances, computerized adaptive testing (CAT) is becoming increasingly popular as it allows tests to be tailored to an examinee’s ability.  Nevertheless, examinees might devise testing strategies to use CAT to their advantage.  For instance, if only the items that examinees answer count towards their score, then a higher theta score might be obtained by spending more time on items at the beginning of the test and skipping items at the end if time runs out. This type of gaming can be discouraged if examinees’ scores are lowered or “penalized” based on the amount of non-response. The goal of this study was to devise a penalty function that would meet two criteria: 1) the greater the omit rate, the greater the penalty, and 2) examinees with the same ability and the same omit rate should receive the same penalty. To create the penalty, theta was calculated based on only the items the examinee responded to ( ).  Next, the expected number correct score (EXR) was obtained using  and the test characteristic curve. A penalized expected number correct score (E ) was obtained by multiplying EXR by the proportion of items the examinee responded to. Finally, the penalized theta ( ) was identified using the test characteristic curve. Based on   and the item parameters ( ) of an unanswered item, the likelihood of a correct response,  , is computed and employed to estimate the imputed score ( ) for the unanswered item. Two datasets were used to generate tests with completion rates of 50%, 80%, and 90%.  The first dataset included real data where approximately 4,500 examinees responded to a 21 -item test which provided a baseline/truth. Sampling was done to achieve the three completion rate conditions. The second dataset consisted of simulated item scores for 50,000 simulees under a 1-2-4 multi-stage CAT design where each stage contained seven items. Imputed item scores for unanswered items were computed using a variety of values for G (and therefore T).  Three other approaches to handling unanswered items were also considered: all correct (i.e., T = 0), all incorrect (i.e., T = 1), and random scoring (i.e., T = 0.5). The current study investigated the impact on theta estimates resulting from the proposed approach to handling unanswered items in a fixed-length CAT. In real testing situations, when examinees do not finish a test, it is hard to tell whether they tried diligently but ran out of time or whether they attempted to manipulate the scoring engine.  To handle unfinished tests with penalties, the proposed approach considers examinees’ abilities and incompletion rates. The results of this study provide direction for psychometric practitioners when considering penalties for omitted responses. Session Video
Keywords: CAT; imputation approach; incomplete computerized test

The Information Product Methods: A Unified Approach to Dual-Purpose Computerized Adaptive Testing - Zheng, Chanjin; He, Guanrui; Gao, Chunlei (Applied Psychological MeasurementApplied Psychological Measurement, 2017)
This article gives a brief summary of major approaches in dual-purpose computerized adaptive testing (CAT) in which the test is tailored interactively to both an examinee?s overall ability level, ?, and attribute mastery level, α. It also proposes an information product approach whose connections to the current methods are revealed. An updated comprehensive empirical study demonstrated that the information product approach not only can offer a unified framework to connect all other approaches but also can mitigate the weighting issue in the dual-information approach.

Issues in Trait Range Coverage for Patient Reported Outcome Measure CATs - Extending the Ceiling for Above-average Physical Functioning - Richard C. Gershon (IACAT 2017 Conference, 2017)
The use of a measure which fails to cover the upper range of functioning may produce results which can lead to serious misinterpretation. Scores produced by such a measure may fail to recognize significant improvement, or may not be able to demonstrate functioning commensurate with an important milestone. Accurate measurement of this range is critical for the assessment of physically active adults, e.g., athletes recovering from injury and active military personnel who wish to return to active service. Alternatively, a PF measure with a low ceiling might fail to differentiate patients in rehabilitation who continue to improve, but for whom their score ceilings due to the measurement used. The assessment of physical function (PF) has greatly benefited from modern psychometric theory and resulting scales, such as the Patient-Reported Outcomes Measurement Information System (PROMIS®) PF instruments. While PROMIS PF has extended the range of function upwards relative to older “legacy” instruments, few PROMIS PF items asses high levels of function. We report here on the development of higher functioning items for the PROMIS PF bank. An expert panel representing orthopedics, sports/military medicine, and rehabilitation reviewed existing instruments and wrote new items. After internal review, cognitive interviews were conducted with 24 individuals of average and high levels of physical function. The remaining candidate items were administered along with 50 existing PROMIS anchor items to an internet panel screened for low, average, and high levels of physical function (N = 1,600), as well as members of Boston-area gyms (N= 344). The resulting data was subjected to standard psychometric analysis, along with multiple linking methods to place the new items on the existing PF metric. The new items were added to the full PF bank for simulated computerized adaptive testing (CAT). Item response data was collected on 54 candidate items. Items that exhibited local dependence (LD) or differential item functioning (DIF) related to gender, age, race, education, or PF status. These items were removed from consideration. Of the 50 existing PROMIS PF items, 31 were free of DIF and LD and used as anchors. The parameters for the remaining new candidate items were estimated twice: freelyestimated and linked with coefficients and fixed-anchor calibration. Both methods were comparable and had appropriate fit. The new items were added to the full PF bank for simulated CATs. The resulting CAT was able to extend the ceiling with high precision to a T-score of 68, suggesting accurate measurement for 97% of the general population. Extending the range of items by which PF is measured will substantially improve measurement quality, applicability, and efficiency. The bank has incorporated these extension items and is available for use in research and clinics for brief CAT administration (see www.healthmeasures.net). Future research projects should focus on recovery trajectories of the measure for individuals with above average function who are recovering from injury. Session Video
Keywords: CAT; Issues; Patient Reported Outcome

Item Parameter Drifting and Online Calibration - Hua-Hua Chang; Rui Guo (IACAT 2017 Conference, 2017)
Item calibration is a part of the most important topics in item response theory (IRT). Since many largescale testing programs have switched from paper and pencil (P&P) testing mode to computerized adaptive testing (CAT) mode, developing methods for efficiently calibrating new items have become vital. Among many proposed item calibration processes in CAT, online calibration is the most cost-effective. This presentation introduces an online (re)calibration design to detect item parameter drift for computerized adaptive testing (CAT) in both unidimensional and multidimensional environments. Specifically, for online calibration optimal design in unidimensional computerized adaptive testing model, a two-stage design is proposed by implementing a proportional density index algorithm. For a multidimensional computerized adaptive testing model, a four-quadrant online calibration pretest item selection design with proportional density index algorithm is proposed. Comparisons were made between different online calibration item selection strategies. Results showed that under unidimensional computerized adaptive testing, the proposed modified two-stage item selection criterion with the proportional density algorithm outperformed the other existing methods in terms of item parameter calibration and item parameter drift detection, and under multidimensional computerized adaptive testing, the online (re)calibration technique with the proposed four-quadrant item selection design with proportional density index outperformed other methods. Session Video
Keywords: online calibration; Parameter Drift

Item Pool Design and Evaluation - Mark D Reckase; Wei He; Jing-Ru Xu; Xuechun Zhou (IACAT 2017 Conference, 2017)
Early work on CAT tended to use existing sets of items which came from fixed length test forms. These sets of items were selected to meet much different requirements than are needed for a CAT; decision making or covering a content domain. However, there was also some early work that suggested having items equally distributed over the range of proficiency that was of interest or concentrated at a decision point. There was also some work that showed that there was bias in proficiency estimates when an item pool was too easy or too hard. These early findings eventually led to work on item pool design and, more recently, on item pool evaluation. This presentation gives a brief overview of these topics to give some context for the following presentations in this symposium. Session Video
Keywords: CAT; Item Pool Design

Item Response Time on Task Effect in CAT - Yang Shi (IACAT 2017 Conference, 2017)
Introduction. In addition to reduced test length and increased measurement efficiency, computerized adaptive testing (CAT) can provide new insights into the cognitive process of task completion that cannot be mined via conventional tests. Response time is a primary characteristic of the task completion procedure. It has the potential to inform us about underlying processes. In this study, the relationship between response time and response accuracy will be investigated. Hypothesis. The present study argues that the relationship between response time on task and response accuracy, which may be positive, negative, or curvilinear, will depend on cognitive nature of task items, holding ability of the subjects and difficulty of the items constant. The interpretations regarding the associations are not uniform either. Research question. Is there a homogeneous effect of response time on test outcome across Graduate Proposed explanations. If the accuracy of cognitive test responses decreases with response time, then it is an indication that the underlying cognitive process is a degrading process such as knowledge retrieval. More accessible knowledge can be retrieved faster than less accessible knowledge. It is inherent to knowledge retrieval that the success rate declines with elapsing response time. For instance, in reading tasks, the time on task effect is negative and the more negative, the easier a task is. However, if the accuracy of cognitive test responses increases with response time, then the process is of an upgrading nature, with an increasing success rate as a function of response time. For example, problem-solving takes time, and fast responses are less likely to be well-founded responses. It is of course also possible that the relationship is curvilinear, as when an increasing success rate is followed by a decreasing success rate or vice versa. Methodology. The data are from computer-based GRE quantitative and verbal tests and will be analyzed with generalized linear mixed models (GLMM) framework after controlling the effect of ability and item difficulty as possible confounding factors. A linear model means a linear combination of predictors determining the probability of person p for answering item i correctly. The models are equivalent with advanced IRT models that go beyond the regular modeling of test responses in terms of one or more latent variables and item parameters. The lme4 package for R will be utilized to conduct the statistical calculation. Implications. The right amount of testing time in CAT is important—too much is wasteful and costly, too little impacts score validity. The study is expected to provide new perception on the relationship between response time and response accuracy, which in turn, contribute to a better understanding of time effects and relevant cognitive process in CA. Session Video
Keywords: CAT; Response time; Task effect

Item Selection Strategies for Developing CAT in Indonesia - Istiani Chandra (IACAT 2017 Conference, 2017)
Recently, development of computerized testing in Indonesia is quiet promising for the future. Many government institutions used the technology for recruitment. Starting from Indonesian Army acknowledged the benefits of computerized adaptive testing (CAT) over conventional test administration, ones of the issues of selection the first item have taken place of attention. Due to CAT’s basic philosophy, several methods can be used to select the first item such as educational level, ability estimation from item simulation, or other methods. In this case, the question is remains how apply the methods most effective in the context of constrained adaptive testing. This paper reviews such strategies that appeared in the relevant literature. The focus of this paper is on studies that have been conducted in order to evaluate the effectiveness of item selection strategies for dichotomous scoring. In this paper, also discusses the strength and weaknesses of each strategy group using examples from simulation studies. No new research is presented but rather a compendium of models is reviewed in term of learning in the newcomer context, a wide view of first item selection strategies.
Keywords: CAT; Indonesia; item selection strategies

Item usage in a multidimensional computerized adaptive test (MCAT) measuring health-related quality of life - Paap, Muirne C. S.; Kroeze, Karel A.; Terwee, Caroline B.; van der Palen, Job; Veldkamp, Bernard P. (Quality of Life Research, 2017)

A Large-Scale Progress Monitoring Application with Computerized Adaptive Testing - Okan Bulut; Damien Cormier (IACAT 2017 Conference, 2017)
Many conventional assessment tools are available to teachers in schools for monitoring student progress in a formative manner. The outcomes of these assessment tools are essential to teachers’ instructional modifications and schools’ data-driven educational strategies, such as using remedial activities and planning instructional interventions for students with learning difficulties. When measuring student progress toward instructional goals or outcomes, assessments should be not only considerably precise but also sensitive to individual change in learning. Unlike conventional paper-pencil assessments that are usually not appropriate for every student, computerized adaptive tests (CATs) are highly capable of estimating growth consistently with minimum and consistent error. Therefore, CATs can be used as a progress monitoring tool in measuring student growth. This study focuses on an operational CAT assessment that has been used for measuring student growth in reading during the academic school year. The sample of this study consists of nearly 7 million students from the 1st grade to the 12th grade in the US. The students received a CAT-based reading assessment periodically during the school year. The purpose of these periodical assessments is to measure the growth in students’ reading achievement and identify the students who may need additional instructional support (e.g., academic interventions). Using real data, this study aims to address the following research questions: (1) How many CAT administrations are necessary to make psychometrically sound decisions about the need for instructional changes in the classroom or when to provide academic interventions?; (2) What is the ideal amount of time between CAT administrations to capture student growth for the purpose of producing meaningful decisions from assessment results? To address these research questions, we first used the Theil-Sen estimator for robustly fitting a regression line to each student’s test scores obtained from a series of CAT administrations. Next, we used the conditional standard error of measurement (cSEM) from the CAT administrations to create an error band around the Theil-Sen slope (i.e., student growth rate). This process resulted in the normative slope values across all the grade levels. The optimal number of CAT administrations was established from grade-level regression results. The amount of time needed for progress monitoring was determined by calculating the amount of time required for a student to show growth beyond the median cSEM value for each grade level. The results showed that the normative slope values were the highest for lower grades and declined steadily as grade level increased. The results also suggested that the CAT-based reading assessment is most useful for grades 1 through 4, since most struggling readers requiring an intervention appear to be within this grade range. Because CAT yielded very similar cSEM values across administrations, the amount of error in the progress monitoring decisions did not seem to depend on the number of CAT administrations. Session Video
Keywords: CAT; Large-Scale tests; Process monitoring

Latent-Class-Based Item Selection for Computerized Adaptive Progress Tests - van Buuren, Nikky; Eggen, Theo J. H. M. (Journal of Computerized Adaptive Testing, 2017)
Keywords: computerized adaptive progress test; item selection method; Kullback-Leibler information; Latent class analysis; log-odds scoring

MHK-MST Design and the Related Simulation Study - Ling Yuyu; Zhou Chenglin; Ren Jie (IACAT 2017 Conference, 2017)
The MHK is a national standardized exam that tests and rates Chinese language proficiency. It assesses non-native Chinese minorities’ abilities in using the Chinese language in their daily, academic and professional lives; Computerized multistage adaptive testing (MST) is a combination of conventional paper-and-pencil (P&P) and item level computerized adaptive test (CAT), it is a kind of test forms based on computerized technology, take the item set as the scoring unit. It can be said that, MST estimate the Ability extreme value more accurate than conventional paper-and-pencil (P&P), also used the CAT auto-adapted characteristic to reduce the examination length and the score time of report. At present, MST has used in some large test, like Uniform CPA Examination and Graduate Record Examination(GRE). Therefore, it is necessary to develop the MST of application in China. Based on consideration of the MHK characteristics and its future development, the researchers start with design of MHK-MST. This simulation study is conducted to validate the performance of the MHK －MST system. Real difficulty parameters of MHK items and the simulated ability parameters of the candidates are used to generate the original score matrix and the item modules are delivered to the candidates following the adaptive procedures set according to the path rules. This simulation study provides a sound basis for the implementation of MHK-MST. Session Video
Keywords: language testing; MHK; multistage testing

Multi-stage Testing for a Multi-disciplined End-of primary-school Test - Hendrik Straat; Maaike van Groen; Wobbe Zijlstra; Marie-Anne Keizer-Mittelhaëuser; Michel Lamoré (IACAT 2017 Conference, 2017)
The Dutch secondary education system consists of five levels: basic, lower, and middle vocational education, general secondary education, and pre-academic education. The individual decision for level of secondary education is based on a combination of the teacher’s judgment and an end-of-primaryschool placement test. This placement test encompasses the measurement of reading, language, mathematics and writing; each skill consisting of one to four subdomains. The Dutch end-of-primaryschool test is currently administered in two linear 200-item paper-based versions. The two versions differ in difficulty so as to motivate both less able and more able students, and measure both groups of students precisely. The primary goal of the test is providing a placement advice for five levels of secondary education. The secondary goal is the assessment of six different fundamental reference levels defined on reading, language, and mathematics. Because of the high stakes advice of the test, the Dutch parliament has instructed to change the format to a multistage test. A major advantage of multistage testing is that the tailoring of the tests is more strongly related to the ability of the students than to the teacher’s judgment. A separate multistage test is under development for each of the three skills measured by the reference levels to increase the classification accuracy for secondary education placement and to optimally measure the performance on the reference-level-related skills. This symposium consists of three presentations discussing the challenges in transitioning from a linear paper-based test to a computer-based multistage test within an existing curriculum and the specification of the multistage test to meet the measurement purposes. The transitioning to a multistage test has to improve both classification accuracy and measurement precision. First, we describe the Dutch educational system and the role of the end-of-primary-school placement test within this system. Special attention will be paid to the advantages of multistage testing over both linear testing and computerized adaptive testing, and on practical implications related to the transitioning from a linear to a multistage test. Second, we discuss routing and reporting on the new multi-stage test. Both topics have a major impact on the quality of the placement advice and the reference mastery decisions. Several methods for routing and reporting are compared. Third, the linear test contains 200 items to cover a broad range of different skills and to obtain a precise measurement of those skills separately. Multistage testing creates opportunities to reduce the cognitive burden for the students while maintaining the same quality of placement advice and assessment of mastering of reference levels. This presentation focuses on optimal allocation of items to test modules, optimal number of stages and modules per stage and test length reduction. Session Video 1 Session Video 2
Keywords: mst; Multidisciplined; proficiency

New Challenges (With Solutions) and Innovative Applications of CAT - Chun Wang; David J. Weiss; Xue Zhang; Jian Tao; Yinhong He; Ping Chen; Shiyu Wang; Susu Zhang; Haiyan Lin; Xiaohong Gao; Hua-Hua Chang; Zhuoran Shang (IACAT 2017 Conference, 2017)
Over the past several decades, computerized adaptive testing (CAT) has profoundly changed the administration of large-scale aptitude tests, state-wide achievement tests, professional licensure exams, and health outcome measures. While many challenges of CAT have been successfully addressed due to the continual efforts of researchers in the field, there are still many remaining, longstanding challenges that have yet to be resolved. This symposium will begin with three presentations, each of which provides a sound solution to one of the unresolved challenges. They are (1) item calibration when responses are “missing not at random” from CAT administration; (2) online calibration of new items when person traits have non-ignorable measurement error; (3) establishing consistency and asymptotic normality of latent trait estimation when allowing item response revision in CAT. In addition, this symposium also features innovative applications of CAT. In particular, there is emerging interest in using cognitive diagnostic CAT to monitor and detect learning progress (4th presentation). Last but not least, the 5th presentation illustrates the power of multidimensional polytomous CAT that permits rapid identification of hospitalized patients’ rehabilitative care needs in health outcomes measurement. We believe this symposium covers a wide range of interesting and important topics in CAT. Session Video
Keywords: CAT; challenges; innovative applications

A New Cognitive Diagnostic Computerized Adaptive Testing for Simultaneously Diagnosing Skills and Misconceptions - Bor-Chen Kuo; Chun-Hua Chen (IACAT 2017 Conference, 2017)
In education diagnoses, diagnosing misconceptions is important as well as diagnosing skills. However, traditional cognitive diagnostic computerized adaptive testing (CD-CAT) is usually developed to diagnose skills. This study aims to propose a new CD-CAT that can simultaneously diagnose skills and misconceptions. The proposed CD-CAT is based on a recently published new CDM, called the simultaneously identifying skills and misconceptions (SISM) model (Kuo, Chen, & de la Torre, in press). A new item selection algorithm is also proposed in the proposed CD-CAT for achieving high adaptive testing performance. In simulation studies, we compare our new item selection algorithm with three existing item selection methods, including the Kullback–Leibler (KL) and posterior-weighted KL (PWKL) proposed by Cheng (2009) and the modified PWKL (MPWKL) proposed by Kaplan, de la Torre, and Barrada (2015). The results show that our proposed CD-CAT can efficiently diagnose skills and misconceptions; the accuracy of our new item selection algorithm is close to the MPWKL but less computational burden; and our new item selection algorithm outperforms the KL and PWKL methods on diagnosing skills and misconceptions. References Cheng, Y. (2009). When cognitive diagnosis meets computerized adaptive testing: CD-CAT. Psychometrika, 74(4), 619–632. doi: 10.1007/s11336-009-9123-2 Kaplan, M., de la Torre, J., & Barrada, J. R. (2015). New item selection methods for cognitive diagnosis computerized adaptive testing. Applied Psychological Measurement, 39(3), 167–188. doi:10.1177/0146621614554650 Kuo, B.-C., Chen, C.-H., & de la Torre, J. (in press). A cognitive diagnosis model for identifying coexisting skills and misconceptions. Applied Psychological Measurement. Session Video
Keywords: CD-CAT; Misconceptions; Simultaneous diagnosis

New Results on Bias in Estimates due to Discontinue Rules in Intelligence Testing - Matthias von Davier; Youngmi Cho; Tianshu Pan (IACAT 2017 Conference, 2017)
The presentation provides new results on a form of adaptive testing that is used frequently in intelligence testing. In these tests, items are presented in order of increasing difficulty, and the presentation of items is adaptive in the sense that each subtest session is discontinued once a test taker produces a certain number of incorrect responses in sequence. The subsequent (not observed) responses are commonly scored as wrong for that subtest, even though the test taker has not seen these. Discontinuation rules allow a certain form of adaptiveness both in paper-based and computerbased testing, and help reducing testing time. Two lines of research that are relevant are studies that directly assess the impact of discontinuation rules, and studies that more broadly look at the impact of scoring rules on test results with a large number of not administered or not reached items. He & Wolf (2012) compared different ability estimation methods for this type of discontinuation rule adaptation of test length in a simulation study. However, to our knowledge there has been no rigorous analytical study of the underlying distributional changes of the response variables under discontinuation rules. It is important to point out that the results obtained by He & Wolf (2012) agree with results presented by, for example, DeAyala, Plake & Impara (2001) as well as Rose, von Davier & Xu (2010) and Rose, von Davier & Nagengast (2016) in that ability estimates are biased most when scoring the not observed responses as wrong. Discontinuation rules combined with scoring the non-administered items as wrong is used operationally in several major intelligence tests, so more research is needed in order to improve this particular type of adaptiveness in the testing practice. The presentation extends existing research on adaptiveness by discontinue-rules in intelligence tests in multiple ways: First, a rigorous analytical study of the distributional properties of discontinue-rule scored items is presented. Second, an extended simulation is presented that includes additional alternative scoring rules as well as bias-corrected ability estimators that may be suitable to improve results for discontinue-rule scored intelligence tests. References: DeAyala, R. J., Plake, B. S., & Impara, J. C. (2001). The impact of omitted responses on the accuracy of ability estimation in item response theory. Journal of Educational Measurement, 38, 213-234. He, W. & Wolfe, E. W. (2012). Treatment of Not-Administered Items on Individually Administered Intelligence Tests. Educational and Psychological Measurement, Vol 72, Issue 5, pp. 808 – 826. DOI: 10.1177/0013164412441937 Rose, N., von Davier, M., & Xu, X. (2010). Modeling non-ignorable missing data with item response theory (IRT; ETS RR-10-11). Princeton, NJ: Educational Testing Service. Rose, N., von Davier, M., & Nagengast, B. (2016) Modeling omitted and not-reached items in irt models. Psychometrika. doi:10.1007/s11336-016-9544-7 Session Video
Keywords: Bias; CAT; Intelligence Testing

Projection-Based Stopping Rules for Computerized Adaptive Testing in Licensure Testing - Luo, Xiao; Kim, Doyoung; Dickison, Philip (Applied Psychological MeasurementApplied Psychological Measurement, 2017)
The confidence interval (CI) stopping rule is commonly used in licensure settings to make classification decisions with fewer items in computerized adaptive testing (CAT). However, it tends to be less efficient in the near-cut regions of the ? scale, as the CI often fails to be narrow enough for an early termination decision prior to reaching the maximum test length. To solve this problem, this study proposed the projection-based stopping rules that base the termination decisions on the algorithmically projected range of the final ? estimate at the hypothetical completion of the CAT. A simulation study and an empirical study were conducted to show the advantages of the projection-based rules over the CI rule, in which the projection-based rules reduced the test length without jeopardizing critical psychometric qualities of the test, such as the ? and classification precision. Operationally, these rules do not require additional regularization parameters, because the projection is simply a hypothetical extension of the current test within the existing CAT environment. Because these new rules are specifically designed to address the decreased efficiency in the near-cut regions as opposed to for the entire scale, the authors recommend using them in conjunction with the CI rule in practice.

Response Time and Response Accuracy in Computerized Adaptive Testing - Yang Shi (IACAT 2017 Conference, 2017)
Introduction. This study explores the relationship between response speed and response accuracy in Computerized Adaptive Testing (CAT). CAT provides a score as well as item response times, which can offer additional diagnostic information regarding behavioral processes of task completion that cannot be uncovered by paper-based instruments. The goal of this study is to investigate how the accuracy rate evolves as a function of response time. If the accuracy of cognitive test responses decreases with response time, then it is an indication that the underlying cognitive process is a degrading process such as knowledge retrieval. More accessible knowledge can be retrieved faster than less accessible knowledge. For instance, in reading tasks, the time on task effect is negative and the more negative, the easier a task is. However, if the accuracy of cognitive test responses increases with response time, then the process is of an upgrading nature, with an increasing success rate as a function of response time. For example, problem-solving takes time, and fast responses are less likely to be well-founded responses. It is of course also possible that the relationship is curvilinear, as when an increasing success rate is followed by a decreasing success rate or vice versa. Hypothesis. The present study argues the relationship between response time on task and response accuracy can be positive, negative, or curvilinear, which depends on cognitive nature of task items holding ability of the subjects and difficulty of the items constant. Methodology. Data from a subsection of GRE quantitative test were available. We will use generalized linear mixed models. A linear model means a linear combination of predictors determining the probability of person p for answering item i correctly. Modeling mixed effects means both random effects and fixed effects are included. Fixed effects refer to constants across test takers. The models are equivalent with advanced IRT models that go beyond the regular modeling of test responses in terms of one or more latent variables and item parameters. The lme4 package for R will be utilized to conduct the statistical calculation. Research questions. 1. What is the relationship between response accuracy and response speed? 2. What is the correlation between response accuracy and type of response time (fast response vs slow response) after controlling ability of people? Preliminary Findings. 1. There is a negative relationship between response time and response accuracy. The success rate declines with elapsing response time. 2. The correlation between the two response latent variables (fast and slow) is 1.0, indicating the time on task effects between respond time types are not different. Implications. The right amount of testing time in CAT is important—too much is wasteful and costly, too little impacts score validity. The study is expected to provide new perception on the relationship between response time and response accuracy, which in turn, contribute to the best timing strategy in CAT—with or without time constraints. Session Video
Keywords: CAT; response accuracy; Response time

Robust Automated Test Assembly for Testlet-Based Tests: An Illustration with Analytical Reasoning Items - Veldkamp, Bernard P.; Paap, Muirne C. S. (Frontiers in Education, 2017)

Scripted On-the-fly Multistage Testing - Edison Choe; Bruce Williams; Sung-Hyuck Lee (IACAT 2017 Conference, 2017)
On-the-fly multistage testing (OMST) was introduced recently as a promising alternative to preassembled MST. A decidedly appealing feature of both is the reviewability of items within the current stage. However, the fundamental difference is that, instead of routing to a preassembled module, OMST adaptively assembles a module at each stage according to an interim ability estimate. This produces more individualized forms with finer measurement precision, but imposing nonstatistical constraints and controlling item exposure become more cumbersome. One recommendation is to use the maximum priority index followed by a remediation step to satisfy content constraints, and the Sympson-Hetter method with a stratified item bank for exposure control. However, these methods can be computationally expensive, thereby impeding practical implementation. Therefore, this study investigated the script method as a simpler solution to the challenge of strict content balancing and effective item exposure control in OMST. The script method was originally devised as an item selection algorithm for CAT and generally proceeds as follows: For a test with m items, there are m slots to be filled, and an item is selected according to pre-defined rules for each slot. For the first slot, randomly select an item from a designated content area (collection). For each subsequent slot, 1) Discard any enemies of items already administered in previous slots; 2) Draw a designated number of candidate items (selection length) from the designated collection according to the current ability estimate; 3) Randomly select one item from the set of candidates. There are two distinct features of the script method. First, a predetermined sequence of collections guarantees meeting content specifications. The specific ordering may be determined either randomly or deliberately by content experts. Second, steps 2 and 3 depict a method of exposure control, in which selection length balances item usage at the possible expense of ability estimation accuracy. The adaptation of the script method to OMST is straightforward. For the first module, randomly select each item from a designated collection. For each subsequent module, the process is the same as in scripted CAT (SCAT) except the same ability estimate is used for the selection of all items within the module. A series of simulations was conducted to evaluate the performance of scripted OMST (SOMST, with 3 or 4 evenly divided stages) relative to SCAT under various item exposure restrictions. In all conditions, reliability was maximized by programming an optimization algorithm that searches for the smallest possible selection length for each slot within the constraints. Preliminary results indicated that SOMST is certainly a capable design with performance comparable to that of SCAT. The encouraging findings and ease of implementation highly motivate the prospect of operational use for large-scale assessments. Presentation Video
Keywords: CAT; multistage testing; On-the-fly testing

A Simulation Study to Compare Classification Method in Cognitive Diagnosis Computerized Adaptive Testing - Jing Yang; Jian Tao; Hua-Hua Chang; Ning-Zhong Shi (IACAT 2017 Conference, 2017)
Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) combines the strengths of both CAT and cognitive diagnosis. Cognitive diagnosis models that can be viewed as restricted latent class models have been developed to classify the examinees into the correct profile of skills that have been mastered and those that have not so as to get more efficient remediation. Chiu & Douglas (2013) introduces a nonparametric procedure that only requires specification of Q-matrix to classify by proximity to ideal response pattern. In this article, we compare nonparametric procedure with common profile estimation method like maximum a posterior (MAP) in CD-CAT. Simulation studies consider a variety of Q-matrix structure, the number of attributes, ways to generate attribute profiles, and item quality. Results indicate that nonparametric procedure consistently gets the higher pattern and attribute recovery rate in nearly all conditions. References Chiu, C.-Y., & Douglas, J. (2013). A nonparametric approach to cognitive diagnosis by proximity to ideal response patterns. Journal of Classification, 30, 225-250. doi: 10.1007/s00357-013-9132-9 Session Video

Using Automated Item Generation in a Large-scale Medical Licensure Exam Program: Lessons Learned. - André F. De Champlain (2017 IACAT Conference, 2017)
On-demand testing has become commonplace with most large-scale testing programs. Continuous testing is appealing for candidates in that it affords greater flexibility in scheduling a session at the desired location. Furthermore, the push for more comprehensive systems of assessment (e.g. CBAL) is predicated on the availability of more frequently administered tasks given the purposeful link between instruction and assessment in these frameworks. However, continuous testing models impose several challenges to programs, including overexposure of items. Robust item banks are therefore needed to support routine retirement and replenishment of items. In a traditional approach to developing items, content experts select a topic and then develop an item consisting of a stem, lead-in question, a correct answer and list of distractors. The item then undergoes review by a panel of experts to validate the content and identify any potential flaws. The process involved in developing quality MCQ items can be time-consuming as well as costly, with estimates as high as $1500-$2500 USD per item (Rudner, 2010). The Medical Council of Canada (MCC) has been exploring a novel item development process to supplement traditional approaches. Specifically, the use of automated item generation (AIG), which uses technology to generate test items from cognitive models, has been studied for over five years. Cognitive models are representations of the knowledge and skills that are required to solve any given problem. While developing a cognitive model for a medical scenario, for example, content experts are asked to deconstruct the (clinical) reasoning process involved via clearly stated variables and related elements. The latter information is then entered into a computer program that uses algorithms to generate MCQs. The MCC has been piloting AIG –based items for over five years with the MCC Qualifying Examination Part I (MCCQE I), a pre-requisite for licensure in Canada. The aim of this presentation is to provide an overview of the practical lessons learned in the use and operational rollout of AIG with the MCCQE I. Psychometrically, the quality of the items is at least equal, and in many instances superior, to that of traditionally written MCQs, based on difficulty, discrimination, and information. In fact, 96% of the AIG based items piloted in a recent administration were retained for future operational scoring based on pre-defined inclusion criteria. AIG also offers a framework for the systematic creation of plausible distractors, in that the content experts not only need to provide the clinical reasoning underlying a correct response but also the cognitive errors associated with each of the distractors (Lai et al. 2016). Consequently, AIG holds great promise in regard to improving and tailoring diagnostic feedback for remedial purposes (Pugh, De Champlain, Gierl, Lai, Touchie, 2016). Furthermore, our test development process has been greatly enhanced by the addition of AIG as it requires that item writers use metacognitive skills to describe how they solve problems. We are hopeful that sharing our experiences with attendees might not only help other testing organizations interested in adopting AIG, but also foster discussion which might benefit all participants. References Lai, H., Gierl, M.J., Touchie, C., Pugh, D., Boulais, A.P., & De Champlain, A.F. (2016). Using automatic item generation to improve the quality of MCQ distractors. Teaching and Learning in Medicine, 28, 166-173. Pugh, D., De Champlain, A.F., Lai, H., Gierl, M., & Touchie, C. (2016). Using cognitive models to develop quality multiple choice questions. Medical Teacher, 38, 838-843. Rudner, L. (2010). Implementing the Graduate Management Admission Test Computerized Adaptive Test. In W. van der Linden & C. Glass (Eds.), Elements of adaptive testing (pp. 151-165). New York, NY: Springer.  Presentation Video
Keywords: Automated item generation; large scale; medical licensure

Using Bayesian Decision Theory in Cognitive Diagnosis Computerized Adaptive Testing - Chia-Ling Hsu; Wen-Chung Wang; ShuYing Chen (IACAT 2017 Conference, 2017)
Cognitive diagnosis computerized adaptive testing (CD-CAT) purports to provide each individual a profile about the strengths and weaknesses of attributes or skills with computerized adaptive testing. In the CD-CAT literature, researchers dedicated to evolving item selection algorithms to improve measurement efficiency, and most algorithms were developed based on information theory. By the discontinuous nature of the latent variables in CD-CAT, this study introduced an alternative for item selection, called the minimum expected cost (MEC) method, which was derived based on Bayesian decision theory. Using simulations, the MEC method was evaluated against the posterior weighted Kullback-Leibler (PWKL) information, the modified PWKL (MPWKL), and the mutual information (MI) methods by manipulating item bank quality, item selection algorithm, and termination rule. Results indicated that, regardless of item quality and termination criterion, the MEC, MPWKL, and MI methods performed very similarly and they all outperformed the PWKL method in classification accuracy and test efficiency, especially in short tests; the MEC method had more efficient item bank usage than the MPWKL and MI methods. Moreover, the MEC method could consider the costs of incorrect decisions and improve classification accuracy and test efficiency when a particular profile was of concern. All the results suggest the practicability of the MEC method in CD-CAT. Session Video
Keywords: Bayesian Decision Theory; CD-CAT

Using Computerized Adaptive Testing to Detect Students’ Misconceptions: Exploration of Item Selection - Yawei Shen; Yu Bao; Shiyu Wang; Laine Bradshaw (IACAT 2017 Conference, 2017)
Owning misconceptions impedes learning, thus detecting misconceptions through assessments is crucial to facilitate teaching. However, most computerized adaptive testing (CAT) applications to diagnose examinees’ attribute profiles focus on whether examinees mastering correct concepts or not. In educational scenario, teachers and students have to figure out the misconceptions underlying incorrect answers after obtaining the scores from assessments and then correct the corresponding misconceptions. The Scaling Individuals and Classifying Misconceptions (SICM) models proposed by Bradshaw and Templin (2014) fill this gap. SICMs can identify a student’s misconceptions directly from the distractors of multiple-choice questions and report whether s/he own the misconceptions or not. Simultaneously, SICM models are able to estimate a continuous ability within the item response theory (IRT) framework to fulfill the needs of policy-driven assessment systems relying on scaling examinees’ ability. However, the advantage of providing estimations for two types of latent variables also causes complexity of model estimation. More items are required to achieve the same accuracies for both classification and estimation compared to dichotomous DCMs and to IRT, respectively. Thus, we aim to develop a CAT using the SICM models (SICM-CAT) to estimate students’ misconceptions and continuous abilities simultaneously using fewer items than a linear test. To achieve this goal, in this study, our research questions mainly focus on establishing several item selection rules that target on providing both accurate classification results and continuous ability estimations using SICM-CAT. The first research question is which information criterion to be used. The Kullback–Leibler (KL) divergence is the first choice, as it can naturally combine the continuous and discrete latent variables. Based on this criterion, we propose an item selection index that can nicely integrate the two types of information. Based on this index, the items selected in real time could discriminate the examinee’s current misconception profile and ability estimates from other possible estimates to the most extent. The second research question is about how to adaptively balance the estimations of the misconception profile and the continuous latent ability. Mimic the idea of the Hybrid Design proposed by Wang et al. (2016), we propose a design framework which makes the item selection transition from the group-level to the item-level. We aim to explore several design questions, such as how to select the transiting point and which latent variable estimation should be targeted first. Preliminary results indicated that the SICM-CAT based on the proposed item selection index could classify examinees into different latent classes and measure their latent abilities compared with the random selection method more accurately and reliably under all the simulation conditions. We plan to compare different CAT designs based on our proposed item selection rules with the best linear test as the next step. We expect that the SICM-CAT is able to use shorter test length while retaining the same accuracies and reliabilities. References Bradshaw, L., & Templin, J. (2014). Combining item response theory and diagnostic classification models: A psychometric model for scaling ability and diagnosing misconceptions. Psychometrika, 79(3), 403-425. Wang, S., Lin, H., Chang, H. H., & Douglas, J. (2016). Hybrid computerized adaptive testing: from group sequential design to fully sequential design. Journal of Educational Measurement, 53(1), 45-62. Session Video
Keywords: CAT; incorrect answering; Student Misconception

Using Determinantal Point Processes for Multistage Testing - Jill-Jênn Vie (IACAT 2017 Conference, 2017)
Multistage tests are a generalization of computerized adaptive tests (CATs), that allow to ask batches of questions before starting to adapt the process, instead of asking questions one by one. In order to be provided in real-world scenarios, they should be assembled on the fly, and recent models have been designed accordingly (Zheng & Chang, 2015). We will present a new algorithm for assembling multistage tests, based on a recent technique in machine learning called determinantal point processes. We will illustrate this technique on various student data that come from fraction subtraction items, or massive online open courses. In multidimensional CATs, feature vectors are estimated for students and questions, and the probability that a student gets a question correct depends on how much their feature vector is correlated with the question feature vector. In other words, questions that are close in space lead to similar response patterns from the students. Therefore, in order to maximize the information of a batch of questions, the volume spanned by their feature vectors should be as large as possible. Determinantal point processes allow to sample efficiently batches of items from a bank that are diverse, i.e., that span a large volume: it is actually possible to draw k items among n with a O(nk3 ) complexity, which is convenient for large databases of 10,000s of items. References Zheng, Y., & Chang, H. H. (2015). On-the-fly assembled multistage adaptive testing. Applied Psychological Measurement, 39(2), 104-118. Session Video
Keywords: Multidimentional CAT; multistage testing

The validation of a computer-adaptive test (CAT) for assessing health-related quality of life in children and adolescents in a clinical sample: study design, methods and first results of the Kids-CAT study - Barthel, D.; Otto, C.; Nolte, S.; Meyrose, A.-K.; Fischer, F.; Devine, J.; Walter, O.; Mierke, A.; Fischer, K. I.; Thyen, U.; Klein, M.; Ankermann, T.; et al. (Quality of Life Research, 2017)
Recently, we developed a computer-adaptive test (CAT) for assessing health-related quality of life (HRQoL) in children and adolescents: the Kids-CAT. It measures five generic HRQoL dimensions. The aims of this article were (1) to present the study design and (2) to investigate its psychometric properties in a clinical setting.

Bayesian Networks in Educational Assessment: The State of the Field - Culbertson, Michael J. (Applied Psychological Measurement, 2016)
Bayesian networks (BN) provide a convenient and intuitive framework for specifying complex joint probability distributions and are thus well suited for modeling content domains of educational assessments at a diagnostic level. BN have been used extensively in the artificial intelligence community as student models for intelligent tutoring systems (ITS) but have received less attention among psychometricians. This critical review outlines the existing research on BN in educational assessment, providing an introduction to the ITS literature for the psychometric community, and points out several promising research paths. The online appendix lists 40 assessment systems that serve as empirical examples of the use of BN for educational assessment in a variety of domains.

A Comparison of Constrained Item Selection Methods in Multidimensional Computerized Adaptive Testing - Su, Ya-Hui (Applied Psychological Measurement, 2016)
The construction of assessments in computerized adaptive testing (CAT) usually involves fulfilling a large number of statistical and non-statistical constraints to meet test specifications. To improve measurement precision and test validity, the multidimensional priority index (MPI) and the modified MPI (MMPI) can be used to monitor many constraints simultaneously under a between-item and a within-item multidimensional framework, respectively. As both item selection methods can be implemented easily and computed efficiently, they are important and useful for operational CATs; however, no thorough simulation study has compared the performance of these two item selection methods under two different item bank structures. The purpose of this study was to investigate the efficiency of the MMPI and the MPI item selection methods under the between-item and within-item multidimensional CAT through simulations. The MMPI and the MPI item selection methods yielded similar performance in measurement precision for both multidimensional pools and yielded similar performance in exposure control and constraint management for the between-item multidimensional pool. For the within-item multidimensional pool, the MPI method yielded slightly better performance in exposure control but yielded slightly worse performance in constraint management than the MMPI method.

On Computing the Key Probability in the Stochastically Curtailed Sequential Probability Ratio Test - Huebner, Alan R.; Finkelman, Matthew D. (Applied Psychological Measurement, 2016)
The Stochastically Curtailed Sequential Probability Ratio Test (SCSPRT) is a termination criterion for computerized classification tests (CCTs) that has been shown to be more efficient than the well-known Sequential Probability Ratio Test (SPRT). The performance of the SCSPRT depends on computing the probability that at a given stage in the test, an examinee’s current interim classification status will not change before the end of the test. Previous work discusses two methods of computing this probability, an exact method in which all potential responses to remaining items are considered and an approximation based on the central limit theorem (CLT) requiring less computation. Generally, the CLT method should be used early in the test when the number of remaining items is large, and the exact method is more appropriate at later stages of the test when few items remain. However, there is currently a dearth of information as to the performance of the SCSPRT when using the two methods. For the first time, the exact and CLT methods of computing the crucial probability are compared in a simulation study to explore whether there is any effect on the accuracy or efficiency of the CCT. The article is focused toward practitioners and researchers interested in using the SCSPRT as a termination criterion in an operational CCT.

On the effect of adding clinical samples to validation studies of patient-reported outcome item banks: a simulation study - Smits, Niels (Quality of Life Research, 2016)
To increase the precision of estimated item parameters of item response theory models for patient-reported outcomes, general population samples are often enriched with samples of clinical respondents. Calibration studies provide little information on how this sampling scheme is incorporated into model estimation. In a small simulation study the impact of ignoring the oversampling of clinical respondents on item and person parameters is illustrated.

Effect of Imprecise Parameter Estimation on Ability Estimation in a Multistage Test in an Automatic Item Generation Context - Colvin, Kimberly; Keller, Lisa A; Robin, Frederic (Journal of Computerized Adaptive Testing, 2016)
Keywords: Adaptive Testing; automatic item generation; errors in item parameters; item clones; multistage testing

Exploration of Item Selection in Dual-Purpose Cognitive Diagnostic Computerized Adaptive Testing: Based on the RRUM - Dai, Buyun; Zhang, Minqiang; Li, Guangming (Applied Psychological Measurement, 2016)
Cognitive diagnostic computerized adaptive testing (CD-CAT) can be divided into two broad categories: (a) single-purpose tests, which are based on the subject’s knowledge state (KS) alone, and (b) dual-purpose tests, which are based on both the subject’s KS and traditional ability level ( ). This article seeks to identify the most efficient item selection method for the latter type of CD-CAT corresponding to various conditions and various evaluation criteria, respectively, based on the reduced reparameterized unified model (RRUM) and the two-parameter logistic model of item response theory (IRT-2PLM). The Shannon entropy (SHE) and Fisher information methods were combined to produce a new synthetic item selection index, that is, the “dapperness with information (DWI)” index, which concurrently considers both KS and within one step. The new method was compared with four other methods. The results showed that, in most conditions, the new method exhibited the best performance in terms of KS estimation and the second-best performance in terms of estimation. Item utilization uniformity and computing time are also considered for all the competing methods.

High-Efficiency Response Distribution–Based Item Selection Algorithms for Short-Length Cognitive Diagnostic Computerized Adaptive Testing - Zheng, Chanjin; Chang, Hua-Hua (Applied Psychological Measurement, 2016)
Cognitive diagnostic computerized adaptive testing (CD-CAT) purports to obtain useful diagnostic information with great efficiency brought by CAT technology. Most of the existing CD-CAT item selection algorithms are evaluated when test length is fixed and relatively long, but some applications of CD-CAT, such as in interim assessment, require to obtain the cognitive pattern with a short test. The mutual information (MI) algorithm proposed by Wang is the first endeavor to accommodate this need. To reduce the computational burden, Wang provided a simplified scheme, but at the price of scale/sign change in the original index. As a result, it is very difficult to combine it with some popular constraint management methods. The current study proposes two high-efficiency algorithms, posterior-weighted cognitive diagnostic model (CDM) discrimination index (PWCDI) and posterior-weighted attribute-level CDM discrimination index (PWACDI), by modifying the CDM discrimination index. They can be considered as an extension of the Kullback–Leibler (KL) and posterior-weighted KL (PWKL) methods. A pre-calculation strategy has also been developed to address the computational issue. Simulation studies indicate that the newly developed methods can produce results comparable with or better than the MI and PWKL in both short and long tests. The other major advantage is that the computational issue has been addressed more elegantly than MI. PWCDI and PWACDI can run as fast as PWKL. More importantly, they do not suffer from the problem of scale/sign change as MI and, thus, can be used with constraint management methods together in a straightforward manner.

Hybrid Computerized Adaptive Testing: From Group Sequential Design to Fully Sequential Design - Wang, Shiyu; Lin, Haiyan; Chang, Hua-Hua; Douglas, Jeff (Journal of Educational Measurement, 2016)
Computerized adaptive testing (CAT) and multistage testing (MST) have become two of the most popular modes in large-scale computer-based sequential testing.  Though most designs of CAT and MST exhibit strength and weakness in recent large-scale implementations, there is no simple answer to the question of which design is better because different modes may fit different practical situations. This article proposes a hybrid adaptive framework to combine both CAT and MST, inspired by an analysis of the history of CAT and MST. The proposed procedure is a design which transitions from a group sequential design to a fully sequential design. This allows for the robustness of MST in early stages, but also shares the advantages of CAT in later stages with fine tuning of the ability estimator once its neighborhood has been identified. Simulation results showed that hybrid designs following our proposed principles provided comparable or even better estimation accuracy and efficiency than standard CAT and MST designs, especially for examinees at the two ends of the ability range.

On the Issue of Item Selection in Computerized Adaptive Testing With Response Times - Veldkamp, Bernard P. (Journal of Educational Measurement, 2016)
Many standardized tests are now administered via computer rather than paper-and-pencil format. The computer-based delivery mode brings with it certain advantages. One advantage is the ability to adapt the difficulty level of the test to the ability level of the test taker in what has been termed computerized adaptive testing (CAT). A second advantage is the ability to record not only the test taker's response to each item (i.e., question), but also the amount of time the test taker spends considering and answering each item. Combining these two advantages, various methods were explored for utilizing response time data in selecting appropriate items for an individual test taker.Four strategies for incorporating response time data were evaluated, and the precision of the final test-taker score was assessed by comparing it to a benchmark value that did not take response time information into account. While differences in measurement precision and testing times were expected, results showed that the strategies did not differ much with respect to measurement precision but that there were differences with regard to the total testing time.

Maximum Likelihood Score Estimation Method With Fences for Short-Length Tests and Computerized Adaptive Tests - Han, Kyung T. (Applied Psychological Measurement, 2016)
A critical shortcoming of the maximum likelihood estimation (MLE) method for test score estimation is that it does not work with certain response patterns, including ones consisting only of all 0s or all 1s. This can be problematic in the early stages of computerized adaptive testing (CAT) administration and for tests short in length. To overcome this challenge, test practitioners often set lower and upper bounds of theta estimation and truncate the score estimation to be one of those bounds when the log likelihood function fails to yield a peak due to responses consisting only of 0s or 1s. Even so, this MLE with truncation (MLET) method still cannot handle response patterns in which all harder items are correct and all easy items are incorrect. Bayesian-based estimation methods such as the modal a posteriori (MAP) method or the expected a posteriori (EAP) method can be viable alternatives to MLE. The MAP or EAP methods, however, are known to result in estimates biased toward the center of a prior distribution, resulting in a shrunken score scale. This study introduces an alternative approach to MLE, called MLE with fences (MLEF). In MLEF, several imaginary “fence” items with fixed responses are introduced to form a workable log likelihood function even with abnormal response patterns. The findings of this study suggest that, unlike MLET, the MLEF can handle any response patterns and, unlike both MAP and EAP, results in score estimates that do not cause shrinkage of the theta scale.

Modeling Student Test-Taking Motivation in the Context of an Adaptive Achievement Test - Wise, Steven L.; Kingsbury, G. Gage (Journal of Educational Measurement, 2016)
This study examined the utility of response time-based analyses in understanding the behavior of unmotivated test takers. For the data from an adaptive achievement test, patterns of observed rapid-guessing behavior and item response accuracy were compared to the behavior expected under several types of models that have been proposed to represent unmotivated test taking behavior. Test taker behavior was found to be inconsistent with these models, with the exception of the effort-moderated model. Effort-moderated scoring was found to both yield scores that were more accurate than those found under traditional scoring, and exhibit improved person fit statistics. In addition, an effort-guided adaptive test was proposed and shown by a simulation study to alleviate item difficulty mistargeting caused by unmotivated test taking.

Monitoring Items in Real Time to Enhance CAT Security - Zhang, Jinming; Li, Jie (Journal of Educational Measurement, 2016)
An IRT-based sequential procedure is developed to monitor items for enhancing test security. The procedure uses a series of statistical hypothesis tests to examine whether the statistical characteristics of each item under inspection have changed significantly during CAT administration. This procedure is compared with a previously developed CTT-based procedure through simulation studies. The results show that when the total number of examinees is fixed both procedures can control the rate of type I errors at any reasonable significance level by choosing an appropriate cutoff point and meanwhile maintain a low rate of type II errors. Further, the IRT-based method has a much lower type II error rate or more power than the CTT-based method when the number of compromised items is small (e.g., 5), which can be achieved if the IRT-based procedure can be applied in an active mode in the sense that flagged items can be replaced with new items.

Multidimensional Computerized Adaptive Testing for Classifying Examinees With Within-Dimensionality - van Groen, Maaike M.; Eggen, Theo J. H. M.; Veldkamp, Bernard P. (Applied Psychological Measurement, 2016)
A classification method is presented for adaptive classification testing with a multidimensional item response theory (IRT) model in which items are intended to measure multiple traits, that is, within-dimensionality. The reference composite is used with the sequential probability ratio test (SPRT) to make decisions and decide whether testing can be stopped before reaching the maximum test length. Item-selection methods are provided that maximize the determinant of the information matrix at the cutoff point or at the projected ability estimate. A simulation study illustrates the efficiency and effectiveness of the classification method. Simulations were run with the new item-selection methods, random item selection, and maximization of the determinant of the information matrix at the ability estimate. The study also showed that the SPRT with multidimensional IRT has the same characteristics as the SPRT with unidimensional IRT and results in more accurate classifications than the latter when used for multidimensional data.

Online Calibration of Polytomous Items Under the Generalized Partial Credit Model - Zheng, Yi (Applied Psychological Measurement, 2016)
Online calibration is a technology-enhanced architecture for item calibration in computerized adaptive tests (CATs). Many CATs are administered continuously over a long term and rely on large item banks. To ensure test validity, these item banks need to be frequently replenished with new items, and these new items need to be pretested before being used operationally. Online calibration dynamically embeds pretest items in operational tests and calibrates their parameters as response data are gradually obtained through the continuous test administration. This study extends existing formulas, procedures, and algorithms for dichotomous item response theory models to the generalized partial credit model, a popular model for items scored in more than two categories. A simulation study was conducted to investigate the developed algorithms and procedures under a variety of conditions, including two estimation algorithms, three pretest item selection methods, three seeding locations, two numbers of score categories, and three calibration sample sizes. Results demonstrated acceptable estimation accuracy of the two estimation algorithms in some of the simulated conditions. A variety of findings were also revealed for the interacted effects of included factors, and recommendations were made respectively.

Optimal Reassembly of Shadow Tests in CAT - Choi, Seung W.; Moellering, Karin T.; Li, Jie; van der Linden, Wim J. (Applied Psychological Measurement, 2016)
Even in the age of abundant and fast computing resources, concurrency requirements for large-scale online testing programs still put an uninterrupted delivery of computer-adaptive tests at risk. In this study, to increase the concurrency for operational programs that use the shadow-test approach to adaptive testing, we explored various strategies aiming for reducing the number of reassembled shadow tests without compromising the measurement quality. Strategies requiring fixed intervals between reassemblies, a certain minimal change in the interim ability estimate since the last assembly before triggering a reassembly, and a hybrid of the two strategies yielded substantial reductions in the number of reassemblies without degradation in the measurement accuracy. The strategies effectively prevented unnecessary reassemblies due to adapting to the noise in the early test stages. They also highlighted the practicality of the shadow-test approach by minimizing the computational load involved in its use of mixed-integer programming.

Parameter Drift Detection in Multidimensional Computerized Adaptive Testing Based on Informational Distance/Divergence Measures - Kang, Hyeon-Ah; Chang, Hua-Hua (Applied Psychological Measurement, 2016)
An informational distance/divergence-based approach is proposed to detect the presence of parameter drift in multidimensional computerized adaptive testing (MCAT). The study presents significance testing procedures for identifying changes in multidimensional item response functions (MIRFs) over time based on informational distance/divergence measures that capture the discrepancy between two probability functions. To approximate the MIRFs from the observed response data, the k-nearest neighbors algorithm is used with the random search method. A simulation study suggests that the distance/divergence-based drift measures perform effectively in identifying the instances of parameter drift in MCAT. They showed moderate power with small samples of 500 examinees and excellent power when the sample size was as large as 1,000. The proposed drift measures also adequately controlled for Type I error at the nominal level under the null hypothesis.

Stochastic Curtailment of Questionnaires for Three-Level Classification: Shortening the CES-D for Assessing Low, Moderate, and High Risk of Depression - Smits, Niels; Finkelman, Matthew D.; Kelderman, Henk (Applied Psychological Measurement, 2016)
In clinical assessment, efficient screeners are needed to ensure low respondent burden. In this article, Stochastic Curtailment (SC), a method for efficient computerized testing for classification into two classes for observable outcomes, was extended to three classes. In a post hoc simulation study using the item scores on the Center for Epidemiologic Studies–Depression Scale (CES-D) of a large sample, three versions of SC, SC via Empirical Proportions (SC-EP), SC via Simple Ordinal Regression (SC-SOR), and SC via Multiple Ordinal Regression (SC-MOR) were compared at both respondent burden and classification accuracy. All methods were applied under the regular item order of the CES-D and under an ordering that was optimal in terms of the predictive power of the items. Under the regular item ordering, the three methods were equally accurate, but SC-SOR and SC-MOR needed less items. Under the optimal ordering, additional gains in efficiency were found, but SC-MOR suffered from capitalization on chance substantially. It was concluded that SC-SOR is an efficient and accurate method for clinical screening. Strengths and weaknesses of the methods are discussed.

Using Response Time to Detect Item Preknowledge in Computer?Based Licensure Examinations - Qian H.; Staniewska, D.; Reckase, M.; Woo, A. (Educational Measurement: Issues and Practice., 2016)
This article addresses the issue of how to detect item preknowledge using item response time data in two computer-based large-scale licensure examinations. Item preknowledge is indicated by an unexpected short response time and a correct response. Two samples were used for detecting item preknowledge for each examination. The first sample was from the early stage of the operational test and was used for item calibration. The second sample was from the late stage of the operational test, which may feature item preknowledge. The purpose of this research was to explore whether there was evidence of item preknowledge and compromised items in the second sample using the parameters estimated from the first sample. The results showed that for one nonadaptive operational examination, two items (of 111) were potentially exposed, and two candidates (of 1,172) showed some indications of preknowledge on multiple items. For another licensure examination that featured computerized adaptive testing, there was no indication of item preknowledge or compromised items. Implications for detected aberrant examinees and compromised items are discussed in the article.

Assessing Individual-Level Impact of Interruptions During Online Testing - Sinharay, Sandip; Wan, Ping; Choi, Seung W.; Kim, Dong-In (Journal of Educational Measurement, 2015)
With an increase in the number of online tests, the number of interruptions during testing due to unexpected technical issues seems to be on the rise. For example, interruptions occurred during several recent state tests. When interruptions occur, it is important to determine the extent of their impact on the examinees' scores. Researchers such as Hill and Sinharay et al. examined the impact of interruptions at an aggregate level. However, there is a lack of research on the assessment of impact of interruptions at an individual level. We attempt to fill that void. We suggest four methodological approaches, primarily based on statistical hypothesis testing, linear regression, and item response theory, which can provide evidence on the individual-level impact of interruptions. We perform a realistic simulation study to compare the Type I error rate and power of the suggested approaches. We then apply the approaches to data from the 2013 Indiana Statewide Testing for Educational Progress-Plus (ISTEP+) test that experienced interruptions.

a-Stratified Computerized Adaptive Testing in the Presence of Calibration Error - Cheng, Ying; Patton, Jeffrey M.; Shao, Can (Educational and Psychological Measurement, 2015)
a-Stratified computerized adaptive testing with b-blocking (AST), as an alternative to the widely used maximum Fisher information (MFI) item selection method, can effectively balance item pool usage while providing accurate latent trait estimates in computerized adaptive testing (CAT). However, previous comparisons of these methods have treated item parameter estimates as if they are the true population parameter values. Consequently, capitalization on chance may occur. In this article, we examined the performance of the AST method under more realistic conditions where item parameter estimates instead of true parameter values are used in the CAT. Its performance was compared against that of the MFI method when the latter is used in conjunction with Sympson–Hetter or randomesque exposure control. Results indicate that the MFI method, even when combined with exposure control, is susceptible to capitalization on chance. This is particularly true when the calibration sample size is small. On the other hand, AST is more robust to capitalization on chance. Consistent with previous investigations using true item parameter values, AST yields much more balanced item pool usage, with a small loss in the precision of latent trait estimates. The loss is negligible when the test is as long as 40 items.

Best Design for Multidimensional Computerized Adaptive Testing With the Bifactor Model - Seo, Dong Gi; Weiss, David J. (Educational and Psychological Measurement, 2015)
Most computerized adaptive tests (CATs) have been studied using the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CATs. This study investigated the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm (MCAT) with a bifactor model using simulated data. Four item selection methods in MCAT were examined for three bifactor pattern designs using two multidimensional item response theory models. To compare MCAT item selection and estimation methods, a fixed test length was used. The Ds-optimality item selection improved θ estimates with respect to a general factor, and either D- or A-optimality improved estimates of the group factors in three bifactor pattern designs under two multidimensional item response theory models. The MCAT model without a guessing parameter functioned better than the MCAT model with a guessing parameter. The MAP (maximum a posteriori) estimation method provided more accurate θ estimates than the EAP (expected a posteriori) method under most conditions, and MAP showed lower observed standard errors than EAP under most conditions, except for a general factor condition using Ds-optimality item selection.

Comparing Simple Scoring With IRT Scoring of Personality Measures: The Navy Computer Adaptive Personality Scales - Oswald, Frederick L.; Shaw, Amy; Farmer, William L. (Applied Psychological Measurement, 2015)
This article analyzes data from U.S. Navy sailors (N = 8,956), with the central measure being the Navy Computer Adaptive Personality Scales (NCAPS). Analyses and results from this article extend and qualify those from previous research efforts by examining the properties of the NCAPS and its adaptive structure in more detail. Specifically, this article examines item exposure rates, the efficiency of item use based on item response theory (IRT)–based Expected A Posteriori (EAP) scoring, and a comparison of IRT-EAP scoring with much more parsimonious scoring methods that appear to work just as well (stem-level scoring and dichotomous scoring). The cutting-edge nature of adaptive personality testing will necessitate a series of future efforts like this: to examine the benefits of adaptive scoring schemes and novel measurement methods continually, while pushing testing technology further ahead.

A Comparison of IRT Proficiency Estimation Methods Under Adaptive Multistage Testing - Kim, Sooyeon; Moses, Tim; Yoo, Hanwook (Henry) (Journal of Educational Measurement, 2015)
This inquiry is an investigation of item response theory (IRT) proficiency estimators’ accuracy under multistage testing (MST). We chose a two-stage MST design that includes four modules (one at Stage 1, three at Stage 2) and three difficulty paths (low, middle, high). We assembled various two-stage MST panels (i.e., forms) by manipulating two assembly conditions in each module, such as difficulty level and module length. For each panel, we investigated the accuracy of examinees’ proficiency levels derived from seven IRT proficiency estimators. The choice of Bayesian (prior) versus non-Bayesian (no prior) estimators was of more practical significance than the choice of number-correct versus item-pattern scoring estimators. The Bayesian estimators were slightly more efficient than the non-Bayesian estimators, resulting in smaller overall error. Possible score changes caused by the use of different proficiency estimators would be nonnegligible, particularly for low- and high-performing examinees.

Considering the Use of General and Modified Assessment Items in Computerized Adaptive Testing - Wyse, A. E.; Albano, A. D. (Applied Measurement in Education, 2015)
This article used several data sets from a large-scale state testing program to examine the feasibility of combining general and modified assessment items in computerized adaptive testing (CAT) for different groups of students. Results suggested that several of the assumptions made when employing this type of mixed-item CAT may not be met for students with disabilities that have typically taken alternate assessments based on modified achievement standards (AA-MAS). A simulation study indicated that the abilities of AA-MAS students can be underestimated or overestimated by the mixed-item CAT, depending on students’ location on the underlying ability scale. These findings held across grade levels and test lengths. The mixed-item CAT appeared to function well for non-AA-MAS students.

The Effect of Upper and Lower Asymptotes of IRT Models on Computerized Adaptive Testing - Cheng, Ying; Liu, Cheng (Applied Psychological Measurement, 2015)
In this article, the effect of the upper and lower asymptotes in item response theory models on computerized adaptive testing is shown analytically. This is done by deriving the step size between adjacent latent trait estimates under the four-parameter logistic model (4PLM) and two models it subsumes, the usual three-parameter logistic model (3PLM) and the 3PLM with upper asymptote (3PLMU). The authors show analytically that the large effect of the discrimination parameter on the step size holds true for the 4PLM and the two models it subsumes under both the maximum information method and the b-matching method for item selection. Furthermore, the lower asymptote helps reduce the positive bias of ability estimates associated with early guessing, and the upper asymptote helps reduce the negative bias induced by early slipping. Relative step size between modeling versus not modeling the upper or lower asymptote under the maximum Fisher information method (MI) and the b-matching method is also derived. It is also shown analytically why the gain from early guessing is smaller than the loss from early slipping when the lower asymptote is modeled, and vice versa when the upper asymptote is modeled. The benefit to loss ratio is quantified under both the MI and the b-matching method. Implications of the analytical results are discussed.

Evaluating Content Alignment in Computerized Adaptive Testing - Wise, S. L.; Kingsbury, G. G.; Webb, N. L. (Educational Measurement: Issues and Practice, 2015)
The alignment between a test and the content domain it measures represents key evidence for the validation of test score inferences. Although procedures have been developed for evaluating the content alignment of linear tests, these procedures are not readily applicable to computerized adaptive tests (CATs), which require large item pools and do not use fixed test forms. This article describes the decisions made in the development of CATs that influence and might threaten content alignment. It outlines a process for evaluating alignment that is sensitive to these threats and gives an empirical example of the process.

Implementing a CAT: The AMC Experience - Barnard, John J (Journal of Computerized Adaptive Testing, 2015)
Keywords: adaptive; Assessment; computer; medical; online; Testing

Investigation of Response Changes in the GRE Revised General Test - Liu, Ou Lydia; Bridgeman, Brent; Gu, Lixiong; Xu, Jun; Kong, Nan (Educational and Psychological Measurement, 2015)
Research on examinees’ response changes on multiple-choice tests over the past 80 years has yielded some consistent findings, including that most examinees make score gains by changing answers. This study expands the research on response changes by focusing on a high-stakes admissions test—the Verbal Reasoning and Quantitative Reasoning measures of the GRE revised General Test. We analyzed data from 8,538 examinees for Quantitative and 9,140 for Verbal sections who took the GRE revised General Test in 12 countries. The analyses yielded findings consistent with prior research. In addition, as examinees’ ability increases, the benefit of response changing increases. The study yielded significant implications for both test agencies and test takers. Computer adaptive tests often do not allow the test takers to review and revise. Findings from this study confirm the benefit of such features.

New Item Selection Methods for Cognitive Diagnosis Computerized Adaptive Testing - Kaplan, Mehmet; de la Torre, Jimmy; Barrada, Juan Ramón (Applied Psychological Measurement, 2015)
This article introduces two new item selection methods, the modified posterior-weighted Kullback–Leibler index (MPWKL) and the generalized deterministic inputs, noisy “and” gate (G-DINA) model discrimination index (GDI), that can be used in cognitive diagnosis computerized adaptive testing. The efficiency of the new methods is compared with the posterior-weighted Kullback–Leibler (PWKL) item selection index using a simulation study in the context of the G-DINA model. The impact of item quality, generating models, and test termination rules on attribute classification accuracy or test length is also investigated. The results of the study show that the MPWKL and GDI perform very similarly, and have higher correct attribute classification rates or shorter mean test lengths compared with the PWKL. In addition, the GDI has the shortest implementation time among the three indices. The proportion of item usage with respect to the required attributes across the different conditions is also tracked and discussed.

Online Item Calibration for Q-Matrix in CD-CAT - Chen, Yunxiao; Liu, Jingchen; Ying, Zhiliang (Applied Psychological Measurement, 2015)
Item replenishment is important for maintaining a large-scale item bank. In this article, the authors consider calibrating new items based on pre-calibrated operational items under the deterministic inputs, noisy-and-gate model, the specification of which includes the so-called -matrix, as well as the slipping and guessing parameters. Making use of the maximum likelihood and Bayesian estimators for the latent knowledge states, the authors propose two methods for the calibration. These methods are applicable to both traditional paper–pencil–based tests, for which the selection of operational items is prefixed, and computerized adaptive tests, for which the selection of operational items is sequential and random. Extensive simulations are done to assess and to compare the performance of these approaches. Extensions to other diagnostic classification models are also discussed.

On-the-Fly Assembled Multistage Adaptive Testing - Zheng, Yi; Chang, Hua-Hua (Applied Psychological Measurement, 2015)
Recently, multistage testing (MST) has been adopted by several important large-scale testing programs and become popular among practitioners and researchers. Stemming from the decades of history of computerized adaptive testing (CAT), the rapidly growing MST alleviates several major problems of earlier CAT applications. Nevertheless, MST is only one among all possible solutions to these problems. This article presents a new adaptive testing design, “on-the-fly assembled multistage adaptive testing” (OMST), which combines the benefits of CAT and MST and offsets their limitations. Moreover, OMST also provides some unique advantages over both CAT and MST. A simulation study was conducted to compare OMST with MST and CAT, and the results demonstrated the promising features of OMST. Finally, the “Discussion” section provides suggestions on possible future adaptive testing designs based on the OMST framework, which could provide great flexibility for adaptive tests in the digital future and open an avenue for all types of hybrid designs based on the different needs of specific tests.

Stochastic Curtailment in Adaptive Mastery Testing: Improving the Efficiency of Confidence Interval–Based Stopping Rules - Sie, Haskell; Finkelman, Matthew D.; Bartroff, Jay; Thompson, Nathan A. (Applied Psychological Measurement, 2015)
A well-known stopping rule in adaptive mastery testing is to terminate the assessment once the examinee’s ability confidence interval lies entirely above or below the cut-off score. This article proposes new procedures that seek to improve such a variable-length stopping rule by coupling it with curtailment and stochastic curtailment. Under the new procedures, test termination can occur earlier if the probability is high enough that the current classification decision remains the same should the test continue. Computation of this probability utilizes normality of an asymptotically equivalent version of the maximum likelihood ability estimate. In two simulation sets, the new procedures showed a substantial reduction in average test length while maintaining similar classification accuracy to the original method.

Using Out-of-Level Items in Computerized Adaptive Testing - Wei,H.; Lin,J. (International Journal of Testing, 2015)
Out-of-level testing refers to the practice of assessing a student with a test that is intended for students at a higher or lower grade level. Although the appropriateness of out-of-level testing for accountability purposes has been questioned by educators and policymakers, incorporating out-of-level items in formative assessments for accurate feedback is recommended. This study made use of a commercial item bank with vertically scaled items across grades and simulated student responses in a computerized adaptive testing (CAT) environment. Results of the study suggested that administration of out-of-level items improved measurement accuracy and test efficiency for students who perform significantly above or below their grade-level peers. This study has direct implications with regards to the relevance, applicability, and benefits of using out-of-level items in CAT.

Utilizing Response Times in Computerized Classification Testing - Sie, Haskell; Finkelman, Matthew D.; Riley, Barth; Smits, Niels (Applied Psychological Measurement, 2015)
A well-known approach in computerized mastery testing is to combine the Sequential Probability Ratio Test (SPRT) stopping rule with item selection to maximize Fisher information at the mastery threshold. This article proposes a new approach in which a time limit is defined for the test and examinees’ response times are considered in both item selection and test termination. Item selection is performed by maximizing Fisher information per time unit, rather than Fisher information itself. The test is terminated once the SPRT makes a classification decision, the time limit is exceeded, or there is no remaining item that has a high enough probability of being answered before the time limit. In a simulation study, the new procedure showed a substantial reduction in average testing time while slightly improving classification accuracy compared with the original method. In addition, the new procedure reduced the percentage of examinees who exceeded the time limit.

Variable-Length Computerized Adaptive Testing Using the Higher Order DINA Model - Hsu, Chia-Ling; Wang, Wen-Chung (Journal of Educational Measurement, 2015)
Cognitive diagnosis models provide profile information about a set of latent binary attributes, whereas item response models yield a summary report on a latent continuous trait. To utilize the advantages of both models, higher order cognitive diagnosis models were developed in which information about both latent binary attributes and latent continuous traits is available. To facilitate the utility of cognitive diagnosis models, corresponding computerized adaptive testing (CAT) algorithms were developed. Most of them adopt the fixed-length rule to terminate CAT and are limited to ordinary cognitive diagnosis models. In this study, the higher order deterministic-input, noisy-and-gate (DINA) model was used as an example, and three criteria based on the minimum-precision termination rule were implemented: one for the latent class, one for the latent trait, and the other for both. The simulation results demonstrated that all of the termination criteria were successful when items were selected according to the Kullback-Leibler information and the posterior-weighted Kullback-Leibler information, and the minimum-precision rule outperformed the fixed-length rule with a similar test length in recovering the latent attributes and the latent trait.

Cognitive Diagnostic Models and Computerized Adaptive Testing: Two New Item-Selection Methods That Incorporate Response Times - Finkelman, M. D.; Kim, W.; Weissman, A.; Cook, R.J. (Journal of Computerized Adaptive Testing, 2014)

A Comparison of Four Item-Selection Methods for Severely Constrained CATs - He, Wei; Diao, Qi; Hauser, Carl (Educational and Psychological Measurement, 2014)
This study compared four item-selection procedures developed for use with severely constrained computerized adaptive tests (CATs). Severely constrained CATs refer to those adaptive tests that seek to meet a complex set of constraints that are often not conclusive to each other (i.e., an item may contribute to the satisfaction of several constraints at the same time). The procedures examined in the study included the weighted deviation model (WDM), the weighted penalty model (WPM), the maximum priority index (MPI), and the shadow test approach (STA). In addition, two modified versions of the MPI procedure were introduced to deal with an edge case condition that results in the item selection procedure becoming dysfunctional during a test. The results suggest that the STA worked best among all candidate methods in terms of measurement accuracy and constraint management. For the other three heuristic approaches, they did not differ significantly in measurement accuracy and constraint management at the lower bound level. However, the WPM method appears to perform considerably better in overall constraint management than either the WDM or MPI method. Limitations and future research directions were also discussed.

A Comparison of Multi-Stage and Linear Test Designs for Medium-Size Licensure and Certification Examinations - Brossman, Bradley. G.; Guille, R.A. (Journal of Computerized Adaptive Testing, 2014)

Computerized Adaptive Testing for the Random Weights Linear Logistic Test Model - Crabbe, Marjolein; Vandebroek, Martina (Applied Psychological Measurement, 2014)
This article discusses four-item selection rules to design efficient individualized tests for the random weights linear logistic test model (RWLLTM): minimum posterior-weighted -error minimum expected posterior-weighted -error maximum expected Kullback–Leibler divergence between subsequent posteriors (KLP), and maximum mutual information (MUI). The RWLLTM decomposes test items into a set of subtasks or cognitive features and assumes individual-specific effects of the features on the difficulty of the items. The model extends and improves the well-known linear logistic test model in which feature effects are only estimated at the aggregate level. Simulations show that the efficiencies of the designs obtained with the different criteria appear to be equivalent. However, KLP and MUI are given preference over and due to their lesser complexity, which significantly reduces the computational burden.

Computerized multistage testing: Theory and applications - Duanli Yan; Alina A von Davier; Charles Lewis (CRC Press, 2014)

Detecting Item Preknowledge in Computerized Adaptive Testing Using Information Theory and Combinatorial Optimization - Belov, D. I. (Journal of Computerized Adaptive Testing, 2014)
Keywords: combinatorial optimization; hypothesis testing; item preknowledge; Kullback-Leibler divergence; simulated annealing.; test security

Determining the Overall Impact of Interruptions During Online Testing - Sinharay, Sandip; Wan, Ping; Whitaker, Mike; Kim, Dong-In; Zhang, Litong; Choi, Seung W. (Journal of Educational Measurement, 2014)
With an increase in the number of online tests, interruptions during testing due to unexpected technical issues seem unavoidable. For example, interruptions occurred during several recent state tests. When interruptions occur, it is important to determine the extent of their impact on the examinees’ scores. There is a lack of research on this topic due to the novelty of the problem. This article is an attempt to fill that void. Several methods, primarily based on propensity score matching, linear regression, and item response theory, were suggested to determine the overall impact of the interruptions on the examinees’ scores. A realistic simulation study shows that the suggested methods have satisfactory Type I error rate and power. Then the methods were applied to data from the Indiana Statewide Testing for Educational Progress-Plus (ISTEP+) test that experienced interruptions in 2013. The results indicate that the interruptions did not have a significant overall impact on the student scores for the ISTEP+ test.

An Enhanced Approach to Combine Item Response Theory With Cognitive Diagnosis in Adaptive Testing - Wang, Chun; Zheng, Chanjin; Chang, Hua-Hua (Journal of Educational Measurement, 2014)
Computerized adaptive testing offers the possibility of gaining information on both the overall ability and cognitive profile in a single assessment administration. Some algorithms aiming for these dual purposes have been proposed, including the shadow test approach, the dual information method (DIM), and the constraint weighted method. The current study proposed two new methods, aggregate ranked information index (ARI) and aggregate standardized information index (ASI), which appropriately addressed the noncompatibility issue inherent in the original DIM method. More flexible weighting schemes that put different emphasis on information about general ability (i.e., θ in item response theory) and information about cognitive profile (i.e., α in cognitive diagnostic modeling) were also explored. Two simulation studies were carried out to investigate the effectiveness of the new methods and weighting schemes. Results showed that the new methods with the flexible weighting schemes could produce more accurate estimation of both overall ability and cognitive profile than the original DIM. Among them, the ASI with both empirical and theoretical weights is recommended, and attribute-level weighting scheme is preferred if some attributes are considered more important from a substantive perspective.

Enhancing Pool Utilization in Constructing the Multistage Test Using Mixed-Format Tests - Park, Ryoungsun; Kim, Jiseon; Chung, Hyewon; Dodd, Barbara G. (Applied Psychological Measurement, 2014)
This study investigated a new pool utilization method of constructing multistage tests (MST) using the mixed-format test based on the generalized partial credit model (GPCM). MST simulations of a classification test were performed to evaluate the MST design. A linear programming (LP) model was applied to perform MST reassemblies based on the initial MST construction. Three subsequent MST reassemblies were performed. For each reassembly, three test unit replacement ratios (TRRs; 0.22, 0.44, and 0.66) were investigated. The conditions of the three passing rates (30%, 50%, and 70%) were also considered in the classification testing. The results demonstrated that various MST reassembly conditions increased the overall pool utilization rates, while maintaining the desired MST construction. All MST testing conditions performed equally well in terms of the precision of the classification decision.

General Test Overlap Control: Improved Algorithm for CAT and CCT - Chen, Shu-Ying; Lei, Pui-Wa; Chen, Jyun-Hong; Liu, Tzu-Chen (Applied Psychological Measurement, 2014)
This article proposed a new online test overlap control algorithm that is an improvement of Chen’s algorithm in controlling general test overlap rate for item pooling among a group of examinees. Chen’s algorithm is not very efficient in that not only item pooling between current examinee and prior examinees is controlled for but also item pooling between previous examinees, which would have been controlled for when they were current examinees. The proposed improvement increases efficiency by only considering item pooling between current and previous examinees, and its improved performance over Chen is demonstrated in a simulated computerized adaptive testing (CAT) environment. Moreover, the proposed algorithm is adapted for computerized classification testing (CCT) using the sequential probability ratio test procedure and is evaluated against some existing exposure control procedures. The proposed algorithm appears to work best in controlling general test overlap rate among the exposure control procedures examined without sacrificing much classification precision, though longer tests might be required for more stringent control of item pooling among larger groups. Given the capability of the proposed algorithm in controlling item pooling among a group of examinees of any size and its ease of implementation, it appears to be a good test overlap control method.

Improving Measurement Precision of Hierarchical Latent Traits Using Adaptive Testing - Wang, Chun (Journal of Educational and Behavioral Statistics, 2014)
Many latent traits in social sciences display a hierarchical structure, such as intelligence, cognitive ability, or personality. Usually a second-order factor is linearly related to a group of first-order factors (also called domain abilities in cognitive ability measures), and the first-order factors directly govern the actual item responses. Because only a subtest of items is used to measure each domain, the lack of sufficient reliability becomes the primary impediment for generating and reporting domain abilities. In recent years, several item response theory (IRT) models have been proposed to account for hierarchical factor structures, and these models are also shown to alleviate the low reliability issue by using in-test collateral information to improve measurement precision. This article advocates using adaptive item selection together with a higher order IRT model to further increase the reliability of hierarchical latent trait estimation. Two item selection algorithms are proposed—the constrained D-optimal method and the sequencing domain method. Both are shown to yield improved measurement precision as compared to the unidimensional item selection (by treating each dimension separately). The improvement is more prominent when the test length is short and when the correlation between dimensions is high (e.g., higher than .64). Moreover, two reliability indices for hierarchical latent traits are discussed and their use for quantifying the reliability of hierarchical traits measured by adaptive testing is demonstrated.

Item Pool Design for an Operational Variable-Length Computerized Adaptive Test - He, Wei; Reckase, Mark D. (Educational and Psychological Measurement, 2014)
For computerized adaptive tests (CATs) to work well, they must have an item pool with sufficient numbers of good quality items. Many researchers have pointed out that, in developing item pools for CATs, not only is the item pool size important but also the distribution of item parameters and practical considerations such as content distribution and item exposure issues. Yet, there is little research on how to design item pools to have those desirable features. The research reported in this article provided step-by-step hands-on guidance on the item pool design process by applying the bin-and-union method to design item pools for a large-scale licensure CAT employing complex adaptive testing algorithm with variable test length, a decision based on stopping rule, content balancing, and exposure control. The design process involved extensive simulations to identify several alternative item pool designs and evaluate their performance against a series of criteria. The design output included the desired item pool size and item parameter distribution. The results indicate that the mechanism used to identify the desirable item pool features functions well and that two recommended item pool designs would support satisfactory performance of the operational testing program.

Item Selection Methods Based on Multiple Objective Approaches for Classifying Respondents Into Multiple Levels - van Groen, Maaike M.; Eggen, Theo J. H. M.; Veldkamp, Bernard P. (Applied Psychological Measurement, 2014)
Computerized classification tests classify examinees into two or more levels while maximizing accuracy and minimizing test length. The majority of currently available item selection methods maximize information at one point on the ability scale, but in a test with multiple cutting points selection methods could take all these points simultaneously into account. If for each cutting point one objective is specified, the objectives can be combined into one optimization function using multiple objective approaches. Simulation studies were used to compare the efficiency and accuracy of eight selection methods in a test based on the sequential probability ratio test. Small differences were found in accuracy and efficiency between different methods depending on the item pool and settings of the classification method. The size of the indifference region had little influence on accuracy but considerable influence on efficiency. Content and exposure control had little influence on accuracy and efficiency.

Multidimensional CAT Item Selection Methods for Domain Scores and Composite Scores With Item Exposure Control and Content Constraints - Yao, Lihua (Journal of Educational Measurement, 2014)
The intent of this research was to find an item selection procedure in the multidimensional computer adaptive testing (CAT) framework that yielded higher precision for both the domain and composite abilities, had a higher usage of the item pool, and controlled the exposure rate. Five multidimensional CAT item selection procedures (minimum angle; volume; minimum error variance of the linear combination; minimum error variance of the composite score with optimized weight; and Kullback-Leibler information) were studied and compared with two methods for item exposure control (the Sympson-Hetter procedure and the fixed-rate procedure, the latter simply refers to putting a limit on the item exposure rate) using simulated data. The maximum priority index method was used for the content constraints. Results showed that the Sympson-Hetter procedure yielded better precision than the fixed-rate procedure but had much lower item pool usage and took more time. The five item selection procedures performed similarly under Sympson-Hetter. For the fixed-rate procedure, there was a trade-off between the precision of the ability estimates and the item pool usage: the five procedures had different patterns. It was found that (1) Kullback-Leibler had better precision but lower item pool usage; (2) minimum angle and volume had balanced precision and item pool usage; and (3) the two methods minimizing the error variance had the best item pool usage and comparable overall score recovery but less precision for certain domains. The priority index for content constraints and item exposure was implemented successfully.

A Numerical Investigation of the Recovery of Point Patterns With Minimal Information - Cox, M. A. A. (Applied Psychological Measurement, 2014)
A method has been proposed (Tsogo et al. 2001) in order to reconstruct the geometrical configuration of a large point set using minimal information. This paper employs numerical examples to investigate the proposed procedure. The suggested method has two great advantages. It reduces the volume of the data collection exercise and eases the computational effort involved in analyzing the data. It is suggested, however, that the method while possibly providing a useful starting point for a solution, does not provide a universal panacea.

The Sequential Probability Ratio Test and Binary Item Response Models - Nydick, Steven W. (Journal of Educational and Behavioral Statistics, 2014)
The sequential probability ratio test (SPRT) is a common method for terminating item response theory (IRT)-based adaptive classification tests. To decide whether a classification test should stop, the SPRT compares a simple log-likelihood ratio, based on the classification bound separating two categories, to prespecified critical values. As has been previously noted (Spray & Reckase, 1994), the SPRT test statistic is not necessarily monotonic with respect to the classification bound when item response functions have nonzero lower asymptotes. Because of nonmonotonicity, several researchers (including Spray & Reckase, 1994) have recommended selecting items at the classification bound rather than the current ability estimate when terminating SPRT-based classification tests. Unfortunately, this well-worn advice is a bit too simplistic. Items yielding optimal evidence for classification depend on the IRT model, item parameters, and location of an examinee with respect to the classification bound. The current study illustrates, in depth, the relationship between the SPRT test statistic and classification evidence in binary IRT models. Unlike earlier studies, we examine the form of the SPRT-based log-likelihood ratio while altering the classification bound and item difficulty. These investigations motivate a novel item selection algorithm based on optimizing the expected SPRT criterion given the current ability estimate. The new expected log-likelihood ratio algorithm results in test lengths noticeably shorter than current, commonly used algorithms, and with no loss in classification accuracy.

A Sequential Procedure for Detecting Compromised Items in the Item Pool of a CAT System - Zhang, Jinming (Applied Psychological Measurement, 2014)
To maintain the validity of a continuous testing system, such as computerized adaptive testing (CAT), items should be monitored to ensure that the performance of test items has not gone through any significant changes during their lifetime in an item pool. In this article, the author developed a sequentially monitoring procedure based on a series of statistical hypothesis tests to examine whether the statistical characteristics of individual items have changed significantly during test administration. Simulation studies show that under the simulated setting, by choosing an appropriate cutoff point, the procedure can control the rate of Type I errors at any reasonable significance level and meanwhile, has a very low rate of Type II errors.

Stratified Item Selection and Exposure Control in Unidimensional Adaptive Testing in the Presence of Two-Dimensional Data - Kalinowski, Kevin E.; Natesan, Prathiba; Henson, Robin K. (Applied Psychological Measurement, 2014)
It is not uncommon to use unidimensional item response theory models to estimate ability in multidimensional data with computerized adaptive testing (CAT). The current Monte Carlo study investigated the penalty of this model misspecification in CAT implementations using different item selection methods and exposure control strategies. Three item selection methods—maximum information (MAXI), a-stratification (STRA), and a-stratification with b-blocking (STRB) with and without Sympson–Hetter (SH) exposure control strategy—were investigated. Calibrating multidimensional items as unidimensional items resulted in inaccurate item parameter estimates. Therefore, MAXI performed better than STRA and STRB in estimating the ability parameters. However, all three methods had relatively large standard errors. SH exposure control had no impact on the number of overexposed items. Existing unidimensional CAT implementations might consider using MAXI only if recalibration as multidimensional model is too expensive. Otherwise, building a CAT pool by calibrating multidimensional data as unidimensional is not recommended.

Using Multidimensional CAT to Administer a Short, Yet Precise, Screening Test - Yao, Lihua; Pommerich, Mary; Segall, Daniel O. (Applied Psychological Measurement, 2014)
Multidimensional computerized adaptive testing (MCAT) provides a mechanism by which the simultaneous goals of accurate prediction and minimal testing time for a screening test could both be met. This article demonstrates the use of MCAT to administer a screening test for the Computerized Adaptive Testing–Armed Services Vocational Aptitude Battery (CAT-ASVAB) under a variety of manipulated conditions. CAT-ASVAB is a test battery administered via unidimensional CAT (UCAT) that is used to qualify applicants for entry into the U.S. military and assign them to jobs. The primary research question being evaluated is whether the use of MCAT to administer a screening test can lead to significant reductions in testing time from the full-length selection test, without significant losses in score precision. Different stopping rules, item selection methods, content constraints, time constraints, and population distributions for the MCAT administration are evaluated through simulation, and compared with results from a regular full-length UCAT administration.

The Utility of Adaptive Testing in Addressing the Problem of Unmotivated Examinees - Steven L. Wise (Journal of Computerized Adaptive Testing, 2014)

The Applicability of Multidimensional Computerized Adaptive Testing for Cognitive Ability Measurement in Organizational Assessment - Makransky, Guido; Glas, Cees A. W. (International Journal of Testing, 2013)

The applicability of multidimensional computerized adaptive testing to cognitive ability measurement in organizational assessment - Makransky, G.; Glas, C. A. W. (International Journal of Testing, 2013)

The Application of the Monte Carlo Approach to Cognitive Diagnostic Computerized Adaptive Testing With Content Constraints - Mao, Xiuzhen; Xin, Tao (Applied Psychological Measurement, 2013)
The Monte Carlo approach which has previously been implemented in traditional computerized adaptive testing (CAT) is applied here to cognitive diagnostic CAT to test the ability of this approach to address multiple content constraints. The performance of the Monte Carlo approach is compared with the performance of the modified maximum global discrimination index (MMGDI) method on simulations in which the only content constraint is on the number of items that measure each attribute. The results of the two simulation experiments show that (a) the Monte Carlo method fulfills all the test requirements and produces satisfactory measurement precision and item exposure results and (b) the Monte Carlo method outperforms the MMGDI method when the Monte Carlo method applies either the posterior-weighted Kullback–Leibler algorithm or the hybrid Kullback–Leibler information as the item selection index. Overall, the recovery rate of the knowledge states, the distribution of the item exposure, and the utilization rate of the item bank are improved when the Monte Carlo method is used.

Comparing the Performance of Five Multidimensional CAT Selection Procedures With Different Stopping Rules - Yao, Lihua (Applied Psychological Measurement, 2013)
Through simulated data, five multidimensional computerized adaptive testing (MCAT) selection procedures with varying test lengths are examined and compared using different stopping rules. Fixed item exposure rates are used for all the items, and the Priority Index (PI) method is used for the content constraints. Two stopping rules, standard error (SE) and predicted standard error reduction (PSER), are proposed; each MCAT selection process is stopped if either the required precision has been achieved or the selected number of items has reached the maximum limit. The five procedures are as follows: minimum angle (Ag), volume (Vm), minimize the error variance of the linear combination (V 1), minimize the error variance of the composite score with the optimized weight (V 2), and Kullback–Leibler (KL) information. The recovery for the domain scores or content scores and their overall score, test length, and test reliability are compared across the five MCAT procedures and between the two stopping rules. It is found that the two stopping rules are implemented successfully and that KL uses the least number of items to reach the same precision level, followed by Vm; Ag uses the largest number of items. On average, to reach a precision of SE = .35, 40, 55, 63, 63, and 82 items are needed for KL, Vm, V 1, V 2, and Ag, respectively, for the SE stopping rule. PSER yields 38, 45, 53, 58, and 68 items for KL, Vm, V 1, V 2, and Ag, respectively; PSER yields only slightly worse results than SE, but with much fewer items. Overall, KL is recommended for varying-length MCAT.

A Comparison of Computerized Classification Testing and Computerized Adaptive Testing in Clinical Psychology - Smits, N.; Finkelman, M. D. (Journal of Computerized Adaptive Testing, 2013)

A Comparison of Exposure Control Procedures in CAT Systems Based on Different Measurement Models for Testlets - Boyd, Aimee M.; Dodd, Barbara; Fitzpatrick, Steven (Applied Measurement in Education, 2013)

A Comparison of Exposure Control Procedures in CATs Using the 3PL Model - Leroux, Audrey J.; Lopez, Myriam; Hembry, Ian; Dodd, Barbara G. (Educational and Psychological Measurement, 2013)
This study compares the progressive-restricted standard error (PR-SE) exposure control procedure to three commonly used procedures in computerized adaptive testing, the randomesque, Sympson–Hetter (SH), and no exposure control methods. The performance of these four procedures is evaluated using the three-parameter logistic model under the manipulated conditions of item pool size (small vs. large) and stopping rules (fixed-length vs. variable-length). PR-SE provides the advantage of similar constraints to SH, without the need for a preceding simulation study to execute it. Overall for the large and small item banks, the PR-SE method administered almost all of the items from the item pool, whereas the other procedures administered about 52% or less of the large item bank and 80% or less of the small item bank. The PR-SE yielded the smallest amount of item overlap between tests across conditions and administered fewer items on average than SH. PR-SE obtained these results with similar, and acceptable, measurement precision compared to the other exposure control procedures while vastly improving on item pool usage.

A Comparison of Four Methods for Obtaining Information Functions for Scores From Computerized Adaptive Tests With Normally Distributed Item Difficulties and Discriminations - Ito, K.; Segall, D.O. (Journal of Computerized Adaptive Testing, 2013)

Deriving Stopping Rules for Multidimensional Computerized Adaptive Testing - Wang, Chun; Chang, Hua-Hua; Boughton, Keith A. (Applied Psychological Measurement, 2013)
Multidimensional computerized adaptive testing (MCAT) is able to provide a vector of ability estimates for each examinee, which could be used to provide a more informative profile of an examinee’s performance. The current literature on MCAT focuses on the fixed-length tests, which can generate less accurate results for those examinees whose abilities are quite different from the average difficulty level of the item bank when there are only a limited number of items in the item bank. Therefore, instead of stopping the test with a predetermined fixed test length, the authors use a more informative stopping criterion that is directly related to measurement accuracy. Specifically, this research derives four stopping rules that either quantify the measurement precision of the ability vector (i.e., minimum determinant rule [D-rule], minimum eigenvalue rule [E-rule], and maximum trace rule [T-rule]) or quantify the amount of available information carried by each item (i.e., maximum Kullback–Leibler divergence rule [K-rule]). The simulation results showed that all four stopping rules successfully terminated the test when the mean squared error of ability estimation is within a desired range, regardless of examinees’ true abilities. It was found that when using the D-, E-, or T-rule, examinees with extreme abilities tended to have tests that were twice as long as the tests received by examinees with moderate abilities. However, the test length difference with K-rule is not very dramatic, indicating that K-rule may not be very sensitive to measurement precision. In all cases, the cutoff value for each stopping rule needs to be adjusted on a case-by-case basis to find an optimal solution.

Estimating Measurement Precision in Reduced-Length Multi-Stage Adaptive Testing - Crotts, K.M.; Zenisky, A. L.; Sireci, S.G.; Li, X. (Journal of Computerized Adaptive Testing, 2013)

The Influence of Item Calibration Error on Variable-Length Computerized Adaptive Testing - Patton, Jeffrey M.; Ying Cheng,; Yuan, Ke-Hai; Diao, Qi (Applied Psychological Measurement, 2013)
Variable-length computerized adaptive testing (VL-CAT) allows both items and test length to be “tailored” to examinees, thereby achieving the measurement goal (e.g., scoring precision or classification) with as few items as possible. Several popular test termination rules depend on the standard error of the ability estimate, which in turn depends on the item parameter values. However, items are chosen on the basis of their parameter estimates, and capitalization on chance may occur. In this article, the authors investigated the effects of capitalization on chance on test length and classification accuracy in several VL-CAT simulations. The results confirm that capitalization on chance occurs in VL-CAT and has complex effects on test length, ability estimation, and classification accuracy. These results have important implications for the design and implementation of VL-CATs.

Integrating Test-Form Formatting Into Automated Test Assembly - Diao, Qi; van der Linden, Wim J. (Applied Psychological Measurement, 2013)
Automated test assembly uses the methodology of mixed integer programming to select an optimal set of items from an item bank. Automated test-form generation uses the same methodology to optimally order the items and format the test form. From an optimization point of view, production of fully formatted test forms directly from the item pool using a simultaneous optimization model is more attractive than any of the current, more time-consuming two-stage processes. The goal of this study was to provide such simultaneous models both for computer-delivered and paper forms, as well as explore their performances relative to two-stage optimization. Empirical examples are presented to show that it is possible to automatically produce fully formatted optimal test forms directly from item pools up to some 2,000 items on a regular PC in realistic times.

Item Ordering in Stochastically Curtailed Health Questionnaires With an Observable Outcome - Finkelman, M. D.; Kim, W.; He, Y.; Lai, A.M. (Journal of Computerized Adaptive Testing, 2013)

Item Pocket Method to Allow Response Review and Change in Computerized Adaptive Testing - Han, Kyung T. (Applied Psychological Measurement, 2013)
Most computerized adaptive testing (CAT) programs do not allow test takers to review and change their responses because it could seriously deteriorate the efficiency of measurement and make tests vulnerable to manipulative test-taking strategies. Several modified testing methods have been developed that provide restricted review options while limiting the trade-off in CAT efficiency. The extent to which these methods provided test takers with options to review test items, however, still was quite limited. This study proposes the item pocket (IP) method, a new testing approach that allows test takers greater flexibility in changing their responses by eliminating restrictions that prevent them from moving across test sections to review their answers. A series of simulations were conducted to evaluate the robustness of the IP method against various manipulative test-taking strategies. Findings and implications of the study suggest that the IP method may be an effective solution for many CAT programs when the IP size and test time limit are properly set.

Longitudinal Multistage Testing - Pohl, Steffi (Journal of Educational Measurement, 2013)
This article introduces longitudinal multistage testing (lMST), a special form of multistage testing (MST), as a method for adaptive testing in longitudinal large-scale studies. In lMST designs, test forms of different difficulty levels are used, whereas the values on a pretest determine the routing to these test forms. Since lMST allows for testing in paper and pencil mode, lMST may represent an alternative to conventional testing (CT) in assessments for which other adaptive testing designs are not applicable. In this article the performance of lMST is compared to CT in terms of test targeting as well as bias and efficiency of ability and change estimates. Using a simulation study, the effect of the stability of ability across waves, the difficulty level of the different test forms, and the number of link items between the test forms were investigated.

Mutual Information Item Selection Method in Cognitive Diagnostic Computerized Adaptive Testing With Short Test Length - Wang, Chun (Educational and Psychological Measurement, 2013)
Cognitive diagnostic computerized adaptive testing (CD-CAT) purports to combine the strengths of both CAT and cognitive diagnosis. Cognitive diagnosis models aim at classifying examinees into the correct mastery profile group so as to pinpoint the strengths and weakness of each examinee whereas CAT algorithms choose items to determine those strengths and weakness as efficiently as possible. Most of the existing CD-CAT item selection algorithms are evaluated when test length is relatively long whereas several applications of CD-CAT, such as in interim assessment, require an item selection algorithm that is able to accurately recover examinees’ mastery profile with short test length. In this article, we introduce the mutual information item selection method in the context of CD-CAT and then provide a computationally easier formula to make the method more amenable in real time. Mutual information is then evaluated against common item selection methods, such as Kullback–Leibler information, posterior weighted Kullback–Leibler information, and Shannon entropy. Based on our simulations, mutual information consistently results in nearly the highest attribute and pattern recovery rate in more than half of the conditions. We conclude by discussing how the number of attributes, Q-matrix structure, correlations among the attributes, and item quality affect estimation accuracy.

The Philosophical Aspects of IRT Equating: Modeling Drift to Evaluate Cohort Growth in Large-Scale Assessments - Taherbhai, Husein; Seo, Daeryong (Educational Measurement: Issues and Practice, 2013)
Keywords: cohort growth; construct-relevant drift; evaluation of scale drift; philosophical aspects of IRT equating

The Random-Threshold Generalized Unfolding Model and Its Application of Computerized Adaptive Testing - Wang, Wen-Chung; Liu, Chen-Wei; Wu, Shiu-Lien (Applied Psychological Measurement, 2013)
The random-threshold generalized unfolding model (RTGUM) was developed by treating the thresholds in the generalized unfolding model as random effects rather than fixed effects to account for the subjective nature of the selection of categories in Likert items. The parameters of the new model can be estimated with the JAGS (Just Another Gibbs Sampler) freeware, which adopts a Bayesian approach for estimation. A series of simulations was conducted to evaluate the parameter recovery of the new model and the consequences of ignoring the randomness in thresholds. The results showed that the parameters of RTGUM were recovered fairly well and that ignoring the randomness in thresholds led to biased estimates. Computerized adaptive testing was also implemented on RTGUM, where the Fisher information criterion was used for item selection and the maximum a posteriori method was used for ability estimation. The simulation study showed that the longer the test length, the smaller the randomness in thresholds, and the more categories in an item, the more precise the ability estimates would be.

Reporting differentiated literacy results in PISA by using multidimensional adaptive testing. - Frey, A.; Seitz, N-N.; Kröhne, U. (Research on PISA., 2013)

A Semiparametric Model for Jointly Analyzing Response Times and Accuracy in Computerized Testing - Wang, Chun; Fan, Zhewen; Chang, Hua-Hua; Douglas, Jeffrey A. (Journal of Educational and Behavioral Statistics, 2013)
The item response times (RTs) collected from computerized testing represent an underutilized type of information about items and examinees. In addition to knowing the examinees’ responses to each item, we can investigate the amount of time examinees spend on each item. Current models for RTs mainly focus on parametric models, which have the advantage of conciseness, but may suffer from reduced flexibility to fit real data. We propose a semiparametric approach, specifically, the Cox proportional hazards model with a latent speed covariate to model the RTs, embedded within the hierarchical framework proposed by van der Linden to model the RTs and response accuracy simultaneously. This semiparametric approach combines the flexibility of nonparametric modeling and the brevity and interpretability of the parametric modeling. A Markov chain Monte Carlo method for parameter estimation is given and may be used with sparse data obtained by computerized adaptive testing. Both simulation studies and real data analysis are carried out to demonstrate the applicability of the new model.

Speededness and Adaptive Testing - van der Linden, Wim J.; Xiong, Xinhui (Journal of Educational and Behavioral Statistics, 2013)
Two simple constraints on the item parameters in a response–time model are proposed to control the speededness of an adaptive test. As the constraints are additive, they can easily be included in the constraint set for a shadow-test approach (STA) to adaptive testing. Alternatively, a simple heuristic is presented to control speededness in plain adaptive testing without any constraints. Both types of control are easy to implement and do not require any other real-time parameter estimation during the test than the regular update of the test taker’s ability estimate. Evaluation of the two approaches using simulated adaptive testing showed that the STA was especially effective. It guaranteed testing times that differed less than 10 seconds from a reference test across a variety of conditions.

Uncertainties in the Item Parameter Estimates and Robust Automated Test Assembly - Veldkamp, Bernard P.; Matteucci, Mariagiulia; de Jong, Martijn G. (Applied Psychological Measurement, 2013)
Item response theory parameters have to be estimated, and because of the estimation process, they do have uncertainty in them. In most large-scale testing programs, the parameters are stored in item banks, and automated test assembly algorithms are applied to assemble operational test forms. These algorithms treat item parameters as fixed values, and uncertainty is not taken into account. As a consequence, resulting tests might be off target or less informative than expected. In this article, the process of parameter estimation is described to provide insight into the causes of uncertainty in the item parameters. The consequences of uncertainty are studied. Besides, an alternative automated test assembly algorithm is presented that is robust against uncertainties in the data. Several numerical examples demonstrate the performance of the robust test assembly algorithm, and illustrate the consequences of not taking this uncertainty into account. Finally, some recommendations about the use of robust test assembly and some directions for further research are given.

Variable-Length Computerized Adaptive Testing Based on Cognitive Diagnosis Models - Hsu, Chia-Ling; Wang, Wen-Chung; Chen, Shu-Ying (Applied Psychological Measurement, 2013)
Interest in developing computerized adaptive testing (CAT) under cognitive diagnosis models (CDMs) has increased recently. CAT algorithms that use a fixed-length termination rule frequently lead to different degrees of measurement precision for different examinees. Fixed precision, in which the examinees receive the same degree of measurement precision, is a major advantage of CAT over nonadaptive testing. In addition to the precision issue, test security is another important issue in practical CAT programs. In this study, the authors implemented two termination criteria for the fixed-precision rule and evaluated their performance under two popular CDMs using simulations. The results showed that using the two criteria with the posterior-weighted Kullback–Leibler information procedure for selecting items could achieve the prespecified measurement precision. A control procedure was developed to control item exposure and test overlap simultaneously among examinees. The simulation results indicated that in contrast to no method of controlling exposure, the control procedure developed in this study could maintain item exposure and test overlap at the prespecified level at the expense of only a few more items.

Adaptives Testen [Adaptive testing]. - Frey, A. (Testtheorie und Fragebogenkonstruktion, 2012)

Balancing Flexible Constraints and Measurement Precision in Computerized Adaptive Testing - Moyer, Eric L.; Galindo, Jennifer L.; Dodd, Barbara G. (Educational and Psychological Measurement, 2012)
Managing test specifications—both multiple nonstatistical constraints and flexibly defined constraints—has become an important part of designing item selection procedures for computerized adaptive tests (CATs) in achievement testing. This study compared the effectiveness of three procedures: constrained CAT, flexible modified constrained CAT, and the weighted penalty model in balancing multiple flexible constraints and maximizing measurement precision in a fixed-length CAT. The study also addressed the effect of two different test lengths—25 items and 50 items—and of including or excluding the randomesque item exposure control procedure with the three methods, all of which were found effective in selecting items that met flexible test constraints when used in the item selection process for longer tests. When the randomesque method was included to control for item exposure, the weighted penalty model and the flexible modified constrained CAT models performed better than did the constrained CAT procedure in maintaining measurement precision. When no item exposure control method was used in the item selection process, no practical difference was found in the measurement precision of each balancing method.

Comparison Between Dichotomous and Polytomous Scoring of Innovative Items in a Large-Scale Computerized Adaptive Test - Jiao, H.; Liu, J.; Haynie, K.; Woo, A.; Gorham, J. (Educational and Psychological Measurement, 2012)
This study explored the impact of partial credit scoring of one type of innovative items (multiple-response items) in a computerized adaptive version of a large-scale licensure pretest and operational test settings. The impacts of partial credit scoring on the estimation of the ability parameters and classification decisions in operational test settings were explored in one real data analysis and two simulation studies when two different polytomous scoring algorithms, automated polytomous scoring and rater-generated polytomous scoring, were applied. For the real data analyses, the ability estimates from dichotomous and polytomous scoring were highly correlated; the classification consistency between different scoring algorithms was nearly perfect. Information distribution changed slightly in the operational item bank. In the two simulation studies comparing each polytomous scoring with dichotomous scoring, the ability estimates resulting from polytomous scoring had slightly higher measurement precision than those resulting from dichotomous scoring. The practical impact related to classification decision was minor because of the extremely small number of items that could be scored polytomously in this current study.

Comparison of Exposure Controls, Item Pool Characteristics, and Population Distributions for CAT Using the Partial Credit Model - Lee, HwaYoung; Dodd, Barbara G. (Educational and Psychological Measurement, 2012)
This study investigated item exposure control procedures under various combinations of item pool characteristics and ability distributions in computerized adaptive testing based on the partial credit model. Three variables were manipulated: item pool characteristics (120 items for each of easy, medium, and hard item pools), two ability distributions (normally distributed and negatively skewed data), and three exposure control procedures (randomesque procedure, progressive–restricted procedure, and maximum information procedure). A number of measurement precision indexes such as descriptive statistics, correlations between known and estimated ability levels, bias, root mean squared error, and average absolute difference, exposure rates, item usage, and item overlap were computed to assess the impact of matched or nonmatched item pool and ability distributions on the accuracy of ability estimation and the performance of exposure control procedures. As expected, the medium item pool produced better precision of measurement than both the easy and hard item pools. The progressive–restricted procedure performed better in terms of maximum exposure rates, item average overlap, and pool utilization than both the randomesque procedure and the maximum information procedure. The easy item pool with the negatively skewed data as a mismatched condition produced the worst performance.

Comparison of two Bayesian methods to detect mode effects between paper-based and computerized adaptive assessments: a preliminary Monte Carlo study. - Riley, Barth B; Carle, Adam C (BMC Med Res Methodol, 2012)
BACKGROUND: Computerized adaptive testing (CAT) is being applied to health outcome measures developed as paper-and-pencil (P&P) instruments. Differences in how respondents answer items administered by CAT vs. P&P can increase error in CAT-estimated measures if not identified and corrected. METHOD: Two methods for detecting item-level mode effects are proposed using Bayesian estimation of posterior distributions of item parameters: (1) a modified robust Z (RZ) test, and (2) 95% credible intervals (CrI) for the CAT-P&P difference in item difficulty. A simulation study was conducted under the following conditions: (1) data-generating model (one- vs. two-parameter IRT model); (2) moderate vs. large DIF sizes; (3) percentage of DIF items (10% vs. 30%), and (4) mean difference in θ estimates across modes of 0 vs. 1 logits. This resulted in a total of 16 conditions with 10 generated datasets per condition. RESULTS: Both methods evidenced good to excellent false positive control, with RZ providing better control of false positives and with slightly higher power for CrI, irrespective of measurement model. False positives increased when items were very easy to endorse and when there with mode differences in mean trait level. True positives were predicted by CAT item usage, absolute item difficulty and item discrimination. RZ outperformed CrI, due to better control of false positive DIF. CONCLUSIONS: Whereas false positives were well controlled, particularly for RZ, power to detect DIF was suboptimal. Research is needed to examine the robustness of these methods under varying prior assumptions concerning the distribution of item and person parameters and when data fail to conform to prior assumptions. False identification of DIF when items were very easy to endorse is a problem warranting additional investigation.
Keywords: Bayes Theorem; Data Interpretation, Statistical; Humans; Mathematical Computing; Monte Carlo Method; Outcome Assessment (Health Care)

Computerized Adaptive Testing for Student Selection to Higher Education - Kalender, I. (Journal of Higher Education, 2012)
The purpose of the present study is to discuss applicability of computerized adaptive testing format as an alternative for current student selection examinations to higher education in Turkey. In the study, first problems associated with current student selection system are given. These problems exerts pressure on students that results in test anxiety, produce measurement experiences that can be criticized, and lessen credibility of student selection system. Next, computerized adaptive test are introduced and advantages they provide are presented. Then results of a study that used two research designs (simulation and live testing) were presented. Results revealed that (i) computerized adaptive format provided a reduction up to 80% in the number of items given to students compared to paper and pencil format of student selection examination, (ii) ability estimations have high reliabilities. Correlations between ability estimations obtained from simulation and traditional format were higher than 0.80. At the end of the study solutions provided by computerized adaptive testing implementation to the current problems were discussed. Also some issues for application of CAT format for student selection examinations in Turkey are given.

Computerized adaptive testing in industrial and organizational psychology - Makransky, G. (University of Twente, 2012)

Computerized Adaptive Testing Using a Class of High-Order Item Response Theory Models - Huang, Hung-Yu; Chen, Po-Hsi; Wang, Wen-Chung (Applied Psychological Measurement, 2012)
In the human sciences, a common assumption is that latent traits have a hierarchical structure. Higher order item response theory models have been developed to account for this hierarchy. In this study, computerized adaptive testing (CAT) algorithms based on these kinds of models were implemented, and their performance under a variety of situations was examined using simulations. The results showed that the CAT algorithms were very effective. The progressive method for item selection, the Sympson and Hetter method with online and freeze procedure for item exposure control, and the multinomial model for content balancing can simultaneously maintain good measurement precision, item exposure control, content balance, test security, and pool usage.

Detecting Local Item Dependence in Polytomous Adaptive Data - Mislevy, Jessica L.; Rupp, André A.; Harring, Jeffrey R. (Journal of Educational Measurement, 2012)
A rapidly expanding arena for item response theory (IRT) is in attitudinal and health-outcomes survey applications, often with polytomous items. In particular, there is interest in computer adaptive testing (CAT). Meeting model assumptions is necessary to realize the benefits of IRT in this setting, however. Although initial investigations of local item dependence have been studied both for polytomous items in fixed-form settings and for dichotomous items in CAT settings, there have been no publications applying local item dependence detection methodology to polytomous items in CAT despite its central importance to these applications. The current research uses a simulation study to investigate the extension of widely used pairwise statistics, Yen's Q3 Statistic and Pearson's Statistic X2, in this context. The simulation design and results are contextualized throughout with a real item bank of this type from the Patient-Reported Outcomes Measurement Information System (PROMIS).

Development of a computerized adaptive test for depression - Robert D. Gibbons; David .J. Weiss; Paul A. Pilkonis; Ellen Frank; Tara Moore; Jong Bae Kim; David J. Kupfer (Archives of General Psychiatry, 2012)

An Efficiency Balanced Information Criterion for Item Selection in Computerized Adaptive Testing - Han, Kyung T. (Journal of Educational Measurement, 2012)
Successful administration of computerized adaptive testing (CAT) programs in educational settings requires that test security and item exposure control issues be taken seriously. Developing an item selection algorithm that strikes the right balance between test precision and level of item pool utilization is the key to successful implementation and long-term quality control of CAT. This study proposed a new item selection method using the “efficiency balanced information” criterion to address issues with the maximum Fisher information method and stratification methods. According to the simulation results, the new efficiency balanced information method had desirable advantages over the other studied item selection methods in terms of improving the optimality of CAT assembly and utilizing items with low a-values while eliminating the need for item pool stratification.