@article {2580, title = {Dual-Objective Item Selection Criteria in Cognitive Diagnostic Computerized Adaptive Testing}, journal = {Journal of Educational Measurement}, volume = {54}, number = {2}, year = {2017}, pages = {165{\textendash}183}, abstract = {The development of cognitive diagnostic-computerized adaptive testing (CD-CAT) has provided a new perspective for gaining information about examinees{\textquoteright} 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{\textquoteright} 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.}, issn = {1745-3984}, doi = {10.1111/jedm.12139}, url = {http://dx.doi.org/10.1111/jedm.12139}, author = {Kang, Hyeon-Ah and Zhang, Susu and Chang, Hua-Hua} } @article {2506, title = {High-Efficiency Response Distribution{\textendash}Based Item Selection Algorithms for Short-Length Cognitive Diagnostic Computerized Adaptive Testing}, journal = {Applied Psychological Measurement}, volume = {40}, number = {8}, year = {2016}, pages = {608-624}, abstract = {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{\textendash}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.}, doi = {10.1177/0146621616665196}, url = {http://apm.sagepub.com/content/40/8/608.abstract}, author = {Zheng, Chanjin and Chang, Hua-Hua} } @article {2462, title = {Hybrid Computerized Adaptive Testing: From Group Sequential Design to Fully Sequential Design}, journal = {Journal of Educational Measurement}, volume = {53}, number = {1}, year = {2016}, pages = {45{\textendash}62}, abstract = {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.}, issn = {1745-3984}, doi = {10.1111/jedm.12100}, url = {http://dx.doi.org/10.1111/jedm.12100}, author = {Wang, Shiyu and Lin, Haiyan and Chang, Hua-Hua and Douglas, Jeff} } @article {2505, title = {Parameter Drift Detection in Multidimensional Computerized Adaptive Testing Based on Informational Distance/Divergence Measures}, journal = {Applied Psychological Measurement}, volume = {40}, number = {7}, year = {2016}, pages = {534-550}, abstract = {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.}, doi = {10.1177/0146621616663676}, url = {http://apm.sagepub.com/content/40/7/534.abstract}, author = {Kang, Hyeon-Ah and Chang, Hua-Hua} } @article {2354, title = {On-the-Fly Assembled Multistage Adaptive Testing}, journal = {Applied Psychological Measurement}, volume = {39}, number = {2}, year = {2015}, pages = {104-118}, abstract = {

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.

}, doi = {10.1177/0146621614544519}, url = {http://apm.sagepub.com/content/39/2/104.abstract}, author = {Zheng, Yi and Chang, Hua-Hua} } @article {2349, title = {An Enhanced Approach to Combine Item Response Theory With Cognitive Diagnosis in Adaptive Testing}, journal = {Journal of Educational Measurement}, volume = {51}, number = {4}, year = {2014}, pages = {358{\textendash}380}, abstract = {

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.

}, issn = {1745-3984}, doi = {10.1111/jedm.12057}, url = {http://dx.doi.org/10.1111/jedm.12057}, author = {Wang, Chun and Zheng, Chanjin and Chang, Hua-Hua} } @article {2281, title = {Deriving Stopping Rules for Multidimensional Computerized Adaptive Testing}, journal = {Applied Psychological Measurement}, volume = {37}, number = {2}, year = {2013}, pages = {99-122}, abstract = {

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.

}, doi = {10.1177/0146621612463422}, url = {http://apm.sagepub.com/content/37/2/99.abstract}, author = {Wang, Chun and Chang, Hua-Hua and Boughton, Keith A.} } @article {2312, title = {A Semiparametric Model for Jointly Analyzing Response Times and Accuracy in Computerized Testing}, journal = {Journal of Educational and Behavioral Statistics}, volume = {38}, number = {4}, year = {2013}, pages = {381-417}, abstract = {

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.

}, doi = {10.3102/1076998612461831}, url = {http://jeb.sagepub.com/cgi/content/abstract/38/4/381}, author = {Wang, Chun and Fan, Zhewen and Chang, Hua-Hua and Douglas, Jeffrey A.} } @conference {2077, title = {Building Affordable CD-CAT Systems for Schools To Address Today{\textquoteright}s Challenges In Assessment}, booktitle = {Annual Conference of the International Association for Computerized Adaptive Testing}, year = {2011}, keywords = {affordability, CAT, cost}, author = {Chang, Hua-Hua} } @article {2204, title = {Restrictive Stochastic Item Selection Methods in Cognitive Diagnostic Computerized Adaptive Testing}, journal = {Journal of Educational Measurement}, volume = {48}, number = {3}, year = {2011}, pages = {255{\textendash}273}, abstract = {

This paper proposes two new item selection methods for cognitive diagnostic computerized adaptive testing: the restrictive progressive method and the restrictive threshold method. They are built upon the posterior weighted Kullback-Leibler (KL) information index but include additional stochastic components either in the item selection index or in the item selection procedure. Simulation studies show that both methods are successful at simultaneously suppressing overexposed items and increasing the usage of underexposed items. Compared to item selection based upon (1) pure KL information and (2) the Sympson-Hetter method, the two new methods strike a better balance between item exposure control and measurement accuracy. The two new methods are also compared with Barrada et al.\&$\#$39;s (2008) progressive method and proportional method.

}, issn = {1745-3984}, doi = {10.1111/j.1745-3984.2011.00145.x}, url = {http://dx.doi.org/10.1111/j.1745-3984.2011.00145.x}, author = {Wang, Chun and Chang, Hua-Hua and Huebner, Alan} } @article {2203, title = {Stratified and Maximum Information Item Selection Procedures in Computer Adaptive Testing}, journal = {Journal of Educational Measurement}, volume = {47}, number = {2}, year = {2010}, pages = {202{\textendash}226}, abstract = {

In this study we evaluated and compared three item selection procedures: the maximum Fisher information procedure (F), the a-stratified multistage computer adaptive testing (CAT) (STR), and a refined stratification procedure that allows more items to be selected from the high a strata and fewer items from the low a strata (USTR), along with completely random item selection (RAN). The comparisons were with respect to error variances, reliability of ability estimates and item usage through CATs simulated under nine test conditions of various practical constraints and item selection space. The results showed that F had an apparent precision advantage over STR and USTR under unconstrained item selection, but with very poor item usage. USTR reduced error variances for STR under various conditions, with small compromises in item usage. Compared to F, USTR enhanced item usage while achieving comparable precision in ability estimates; it achieved a precision level similar to F with improved item usage when items were selected under exposure control and with limited item selection space. The results provide implications for choosing an appropriate item selection procedure in applied settings.

}, issn = {1745-3984}, doi = {10.1111/j.1745-3984.2010.00109.x}, url = {http://dx.doi.org/10.1111/j.1745-3984.2010.00109.x}, author = {Deng, Hui and Ansley, Timothy and Chang, Hua-Hua} } @article {507, title = {Constraint-weighted a-stratification for computerized adaptive testing with nonstatistical constraints: Balancing measurement efficiency and exposure control}, journal = {Educational and Psychological Measurement}, volume = {69}, year = {2009}, pages = {35-49}, author = {Cheng, Y and Chang, Hua-Hua and Douglas, J. and Guo, F.} } @article {2182, title = {Constraint-Weighted a-Stratification for Computerized Adaptive Testing With Nonstatistical Constraints}, journal = {Educational and Psychological Measurement}, volume = {69}, number = {1}, year = {2009}, pages = {35-49}, abstract = {

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

}, doi = {10.1177/0013164408322030}, url = {http://epm.sagepub.com/content/69/1/35.abstract}, author = {Ying Cheng, and Chang, Hua-Hua and Douglas, Jeffrey and Fanmin Guo,} } @inbook {1941, title = {Kullback-Leibler information in multidimensional adaptive testing: theory and application}, year = {2009}, note = {{PDF file, 316 KB}}, address = {D. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.}, abstract = {Built on multidimensional item response theory (MIRT), multidimensional adaptive testing (MAT) can, in principle, provide a promising choice to ensuring efficient estimation of each ability dimension in a multidimensional vector. Currently, two item selection procedures have been developed for MAT, one based on Fisher information embedded within a Bayesian framework, and the other powered by Kullback-Leibler (KL) information. It is well-known that in unidimensional IRT that the second derivative of KL information (also termed {\textquotedblleft}global information{\textquotedblright}) is Fisher information evaluated atθ 0. This paper first generalizes the relationship between these two types of information in two ways{\textemdash}the analytical result is given as well as the graphical representation, to enhance interpretation and understanding. Second, a KL information index is constructed for MAT, which represents the integration of KL nformation over all of the ability dimensions. This paper further discusses how this index correlates with the item discrimination parameters. The analytical results would lay foundation for future development of item selection methods in MAT which can help equalize the item exposure rate. Finally, a simulation study is conducted to verify the above results. The connection between the item parameters, item KL information, and item exposure rate is demonstrated for empirical MAT delivered by an item bank calibrated under two-dimensional IRT.}, author = {Wang, C. and Chang, Hua-Hua} } @article {77, title = {The maximum priority index method for severely constrained item selection in computerized adaptive testing}, journal = {British Journal of Mathematical and Statistical Psychology}, volume = {62}, number = {2}, year = {2009}, note = {Cheng, YingChang, Hua-HuaResearch Support, Non-U.S. Gov{\textquoteright}tEnglandThe British journal of mathematical and statistical psychologyBr J Math Stat Psychol. 2009 May;62(Pt 2):369-83. Epub 2008 Jun 2.}, month = {May}, pages = {369-83}, edition = {2008/06/07}, abstract = {This paper introduces a new heuristic approach, the maximum priority index (MPI) method, for severely constrained item selection in computerized adaptive testing. Our simulation study shows that it is able to accommodate various non-statistical constraints simultaneously, such as content balancing, exposure control, answer key balancing, and so on. Compared with the weighted deviation modelling method, it leads to fewer constraint violations and better exposure control while maintaining the same level of measurement precision.}, keywords = {Aptitude Tests/*statistics \& numerical data, Diagnosis, Computer-Assisted/*statistics \& numerical data, Educational Measurement/*statistics \& numerical data, Humans, Mathematical Computing, Models, Statistical, Personality Tests/*statistics \& numerical data, Psychometrics/*statistics \& numerical data, Reproducibility of Results, Software}, isbn = {0007-1102 (Print)0007-1102 (Linking)}, author = {Cheng, Y and Chang, Hua-Hua} } @inbook {1942, title = {Obtaining reliable diagnostic information through constrained CAT}, year = {2009}, note = {{PDF File, 252 KB}}, address = {D. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.}, author = {Wang, C. and Chang, Hua-Hua and Douglas, J.} } @article {273, title = {Combining computer adaptive testing technology with cognitively diagnostic assessment}, journal = {Behavioral Research Methods }, volume = {40}, number = {3}, year = {2008}, note = {McGlohen, MeghanChang, Hua-HuaUnited StatesBehavior research methodsBehav Res Methods. 2008 Aug;40(3):808-21.}, month = {Aug}, pages = {808-21}, edition = {2008/08/14}, abstract = {A major advantage of computerized adaptive testing (CAT) is that it allows the test to home in on an examinee{\textquoteright}s ability level in an interactive manner. The aim of the new area of cognitive diagnosis is to provide information about specific content areas in which an examinee needs help. The goal of this study was to combine the benefit of specific feedback from cognitively diagnostic assessment with the advantages of CAT. In this study, three approaches to combining these were investigated: (1) item selection based on the traditional ability level estimate (theta), (2) item selection based on the attribute mastery feedback provided by cognitively diagnostic assessment (alpha), and (3) item selection based on both the traditional ability level estimate (theta) and the attribute mastery feedback provided by cognitively diagnostic assessment (alpha). The results from these three approaches were compared for theta estimation accuracy, attribute mastery estimation accuracy, and item exposure control. The theta- and alpha-based condition outperformed the alpha-based condition regarding theta estimation, attribute mastery pattern estimation, and item exposure control. Both the theta-based condition and the theta- and alpha-based condition performed similarly with regard to theta estimation, attribute mastery estimation, and item exposure control, but the theta- and alpha-based condition has an additional advantage in that it uses the shadow test method, which allows the administrator to incorporate additional constraints in the item selection process, such as content balancing, item type constraints, and so forth, and also to select items on the basis of both the current theta and alpha estimates, which can be built on top of existing 3PL testing programs.}, keywords = {*Cognition, *Computers, *Models, Statistical, *User-Computer Interface, Diagnosis, Computer-Assisted/*instrumentation, Humans}, isbn = {1554-351X (Print)}, author = {McGlohen, M. and Chang, Hua-Hua} } @article {2152, title = {Severity of Organized Item Theft in Computerized Adaptive Testing: A Simulation Study}, journal = {Applied Psychological Measurement}, volume = {32}, number = {7}, year = {2008}, pages = {543-558}, abstract = {

Criteria had been proposed for assessing the severity of possible test security violations for computerized tests with high-stakes outcomes. However, these criteria resulted from theoretical derivations that assumed uniformly randomized item selection. This study investigated potential damage caused by organized item theft in computerized adaptive testing (CAT) for two realistic item selection methods, maximum item information and a-stratified with content blocking, using the randomized method as a baseline for comparison. Damage caused by organized item theft was evaluated by the number of compromised items each examinee could encounter and the impact of the compromised items on examinees\&$\#$39; ability estimates. Severity of test security violation was assessed under self-organized and organized item theft simulation scenarios. Results indicated that though item theft could cause severe damage to CAT with either item selection method, the maximum item information method was more vulnerable to the organized item theft simulation than was the a-stratified method.

}, doi = {10.1177/0146621607311336}, url = {http://apm.sagepub.com/content/32/7/543.abstract}, author = {Qing Yi, and Jinming Zhang, and Chang, Hua-Hua} } @inbook {1745, title = {The modified maximum global discrimination index method for cognitive diagnostic computerized adaptive testing}, year = {2007}, note = {{PDF file, 172 KB}}, address = {~ D. J. Weiss (Ed.). ~Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.}, author = {Cheng, Y and Chang, Hua-Hua} } @article {508, title = {Two-phase item selection procedure for flexible content balancing in CAT}, journal = {Applied Psychological. Measurement}, volume = {3}, year = {2007}, pages = {467{\textendash}482}, author = {Cheng, Y and Chang, Hua-Hua and Yi, Q.} } @article {2149, title = {Two-Phase Item Selection Procedure for Flexible Content Balancing in CAT}, journal = {Applied Psychological Measurement}, volume = {31}, number = {6}, year = {2007}, pages = {467-482}, abstract = {

Content balancing is an important issue in the design and implementation of computerized adaptive testing (CAT). Content-balancing techniques that have been applied in fixed content balancing, where the number of items from each content area is fixed, include constrained CAT (CCAT), the modified multinomial model (MMM), modified constrained CAT (MCCAT), and others. In this article, four methods are proposed to address the flexible content-balancing issue with the a-stratification design, named STR_C. The four methods are MMM+, an extension of MMM; MCCAT+, an extension of MCCAT; the TPM method, a two-phase content-balancing method using MMM in both phases; and the TPF method, a two-phase content-balancing method using MMM in the first phase and MCCAT in the second. Simulation results show that all of the methods work well in content balancing, and TPF performs the best in item exposure control and item pool utilization while maintaining measurement precision.

}, doi = {10.1177/0146621606292933}, url = {http://apm.sagepub.com/content/31/6/467.abstract}, author = {Ying Cheng, and Chang, Hua-Hua and Qing Yi,} } @article {820, title = {Assessing CAT Test Security Severity}, journal = {Applied Psychological Measurement}, volume = {30(1)}, year = {2006}, pages = {62{\textendash}63}, author = {Yi, Q., Zhang, J. and Chang, Hua-Hua} } @conference {884, title = {Constraints-weighted information method for item selection of severely constrained computerized adaptive testing}, booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education}, year = {2006}, address = {San Francisco}, author = {Cheng, Y and Chang, Hua-Hua and Wang, X. B.} } @article {249, title = {Computerized adaptive testing: a mixture item selection approach for constrained situations}, journal = {British Journal of Mathematical and Statistical Psychology}, volume = {58}, number = {2}, year = {2005}, note = {Leung, Chi-KeungChang, Hua-HuaHau, Kit-TaiEnglandBr J Math Stat Psychol. 2005 Nov;58(Pt 2):239-57.}, month = {Nov}, pages = {239-57}, edition = {2005/11/19}, abstract = {In computerized adaptive testing (CAT), traditionally the most discriminating items are selected to provide the maximum information so as to attain the highest efficiency in trait (theta) estimation. The maximum information (MI) approach typically results in unbalanced item exposure and hence high item-overlap rates across examinees. Recently, Yi and Chang (2003) proposed the multiple stratification (MS) method to remedy the shortcomings of MI. In MS, items are first sorted according to content, then difficulty and finally discrimination parameters. As discriminating items are used strategically, MS offers a better utilization of the entire item pool. However, for testing with imposed non-statistical constraints, this new stratification approach may not maintain its high efficiency. Through a series of simulation studies, this research explored the possible benefits of a mixture item selection approach (MS-MI), integrating the MS and MI approaches, in testing with non-statistical constraints. In all simulation conditions, MS consistently outperformed the other two competing approaches in item pool utilization, while the MS-MI and the MI approaches yielded higher measurement efficiency and offered better conformity to the constraints. Furthermore, the MS-MI approach was shown to perform better than MI on all evaluation criteria when control of item exposure was imposed.}, keywords = {*Computer-Aided Design, *Educational Measurement/methods, *Models, Psychological, Humans, Psychometrics/methods}, isbn = {0007-1102 (Print)0007-1102 (Linking)}, author = {Leung, C. K. and Chang, Hua-Hua and Hau, K. T.} } @conference {1285, title = {Identifying practical indices for enhancing item pool security}, booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education (NCME)}, year = {2005}, address = {Montreal, Canada}, author = {Yi, Q. and Zhang, J. and Chang, Hua-Hua} } @booklet {1534, title = {Implementing content constraints in alpha-stratified adaptive testing using a shadow test approach}, year = {2005}, address = {Law School Admission Council, Computerized Testing Report 01-09}, author = {van der Linden, W. J. and Chang, Hua-Hua} } @conference {1058, title = {Combining computer adaptive testing technology with cognitively diagnostic assessment}, booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education}, year = {2004}, note = {{PDF file, 782 KB}}, address = {San Diego CA}, author = {McGlohen, MK and Chang, Hua-Hua and Wills, J. T.} } @inbook {1742, title = {Understanding computerized adaptive testing: From Robbins-Munro to Lord and beyond}, year = {2004}, address = {D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 117-133). New York: Sage.}, author = {Chang, Hua-Hua} } @article {744, title = {Alpha-stratified adaptive testing with large numbers of content constraints}, journal = {Applied Psychological Measurement}, volume = {27}, year = {2003}, pages = {107-120}, author = {van der Linden, W. J. and Chang, Hua-Hua} } @conference {876, title = {Assessing CAT security breaches by the item pooling index}, booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education}, year = {2003}, address = {Chicago IL}, author = {Chang, Hua-Hua and Zhang, J.} } @article {251, title = {Computerized adaptive testing: A comparison of three content balancing methods}, journal = {The Journal of Technology, Learning and Assessment}, volume = {2}, number = {5}, year = {2003}, pages = {1-15}, abstract = {Content balancing is often a practical consideration in the design of computerized adaptive testing (CAT). This study compared three content balancing methods, namely, the constrained CAT (CCAT), the modified constrained CAT (MCCAT), and the modified multinomial model (MMM), under various conditions of test length and target maximum exposure rate. Results of a series of simulation studies indicate that there is no systematic effect of content balancing method in measurement efficiency and pool utilization. However, among the three methods, the MMM appears to consistently over-expose fewer items.}, author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.} } @conference {1003, title = {Computerized adaptive testing: A comparison of three content balancing methods}, booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education}, year = {2003}, note = {{PDF file, 227 KB}}, address = {Chicago IL}, author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T. and Wen. Z.} } @article {745, title = {Implementing content constraints in alpha-stratified adaptive testing using a shadow test approach}, journal = {Applied Psychological Measurement}, volume = {27}, year = {2003}, pages = {107-120}, author = {van der Linden, W. J. and Chang, Hua-Hua} } @article {250, title = {Incorporation of Content Balancing Requirements in Stratification Designs for Computerized Adaptive Testing}, journal = {Educational and Psychological Measurement}, volume = {63}, number = {2}, year = {2003}, pages = {257-70}, abstract = {Studied three stratification designs for computerized adaptive testing in conjunction with three well-developed content balancing methods. Simulation study results show substantial differences in item overlap rate and pool utilization among different methods. Recommends an optimal combination of stratification design and content balancing method. (SLD)}, keywords = {computerized adaptive testing}, author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.} } @article {2169, title = {Incorporation Of Content Balancing Requirements In Stratification Designs For Computerized Adaptive Testing}, journal = {Educational and Psychological Measurement}, volume = {63}, number = {2}, year = {2003}, pages = {257-270}, abstract = {

In computerized adaptive testing, the multistage a-stratified design advocates a new philosophy on pool management and item selection in which, contradictory to common practice, less discriminating items are used first. The method is effective in reducing item-overlap rate and enhancing pool utilization. This stratification method has been extended in different ways to deal with the practical issues of content constraints and the positive correlation between item difficulty and discrimination. Nevertheless, these modified designs on their own do not automatically satisfy content requirements. In this study, three stratification designs were examined in conjunction with three well developed content balancing methods. The performance of each of these nine combinational methods was evaluated in terms of their item security, measurement efficiency, and pool utilization. Results showed substantial differences in item-overlap rate and pool utilization among different methods. An optimal combination of stratification design and content balancing method is recommended.

}, doi = {10.1177/0013164403251326}, url = {http://epm.sagepub.com/content/63/2/257.abstract}, author = {Leung, Chi-Keung and Chang, Hua-Hua and Hau, Kit-Tai} } @article {57, title = {Optimal stratification of item pools in α-stratified computerized adaptive testing}, journal = {Applied Psychological Measurement}, volume = {27}, number = {4}, year = {2003}, pages = {262-274}, abstract = {A method based on 0-1 linear programming (LP) is presented to stratify an item pool optimally for use in α-stratified adaptive testing. Because the 0-1 LP model belongs to the subclass of models with a network flow structure, efficient solutions are possible. The method is applied to a previous item pool from the computerized adaptive testing (CAT) version of the Graduate Record Exams (GRE) Quantitative Test. The results indicate that the new method performs well in practical situations. It improves item exposure control, reduces the mean squared error in the θ estimates, and increases test reliability. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)}, keywords = {Adaptive Testing, Computer Assisted Testing, Item Content (Test), Item Response Theory, Mathematical Modeling, Test Construction computerized adaptive testing}, author = {Chang, Hua-Hua and van der Linden, W. J.} } @conference {1275, title = {A simulation study to compare CAT strategies for cognitive diagnosis}, booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education}, year = {2003}, note = {{PDF file, 250 KB}}, address = {Chicago IL}, author = {Xu, X. and Chang, Hua-Hua and Douglas, J.} } @conference {874, title = {Test-score comparability, ability estimation, and item-exposure control in computerized adaptive testing}, booktitle = {Paper presented at the Annual meeting of the National Council on Measurement in Education}, year = {2003}, address = {Chicago IL}, author = {Chang, Hua-Hua and Ying, Z.} } @conference {1002, title = {Comparing three item selection approaches for computerized adaptive testing with content balancing requirement}, booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education}, year = {2002}, note = {{PDF file, 226 KB}}, address = {New Orleans LA}, author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.} } @article {308, title = {A comparison of item selection techniques and exposure control mechanisms in CATs using the generalized partial credit model}, journal = {Applied Psychological Measurement}, volume = {26}, number = {2}, year = {2002}, pages = {147-163}, abstract = {The use of more performance items in large-scale testing has led to an increase in the research investigating the use of polytomously scored items in computer adaptive testing (CAT). Because this research has to be complemented with information pertaining to exposure control, the present research investigated the impact of using five different exposure control algorithms in two sized item pools calibrated using the generalized partial credit model. The results of the simulation study indicated that the a-stratified design, in comparison to a no-exposure control condition, could be used to reduce item exposure and overlap, increase pool utilization, and only minorly degrade measurement precision. Use of the more restrictive exposure control algorithms, such as the Sympson-Hetter and conditional Sympson-Hetter, controlled exposure to a greater extent but at the cost of measurement precision. Because convergence of the exposure control parameters was problematic for some of the more restrictive exposure control algorithms, use of the more simplistic exposure control mechanisms, particularly when the test length to item pool size ratio is large, is recommended. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)}, keywords = {(Statistical), Adaptive Testing, Algorithms computerized adaptive testing, Computer Assisted Testing, Item Analysis, Item Response Theory, Mathematical Modeling}, author = {Pastor, D. A. and Dodd, B. G. and Chang, Hua-Hua} } @article {60, title = {Hypergeometric family and item overlap rates in computerized adaptive testing}, journal = {Psychometrika}, volume = {67}, number = {3}, year = {2002}, pages = {387-398}, abstract = {A computerized adaptive test (CAT) is usually administered to small groups of examinees at frequent time intervals. It is often the case that examinees who take the test earlier share information with examinees who will take the test later, thus increasing the risk that many items may become known. Item overlap rate for a group of examinees refers to the number of overlapping items encountered by these examinees divided by the test length. For a specific item pool, different item selection algorithms may yield different item overlap rates. An important issue in designing a good CAT item selection algorithm is to keep item overlap rate below a preset level. In doing so, it is important to investigate what the lowest rate could be for all possible item selection algorithms. In this paper we rigorously prove that if every item had an equal possibility to be selected from the pool in a fixed-length CAT, the number of overlapping item among any α randomly sampled examinees follows the hypergeometric distribution family for α >= 1. Thus, the expected values of the number of overlapping items among any randomly sampled α examinee can be calculated precisely. These values may serve as benchmarks in controlling item overlap rates for fixed-length adaptive tests. (PsycINFO Database Record (c) 2005 APA )}, keywords = {Adaptive Testing, Algorithms, Computer Assisted Testing, Taking, Test, Time On Task computerized adaptive testing}, author = {Chang, Hua-Hua and Zhang, J.} } @conference {875, title = {Identify the lower bounds for item sharing and item pooling in computerized adaptive testing}, booktitle = {Paper presented at the annual meeting of the American Educational Research Association}, year = {2002}, address = {New Orleans LA}, author = {Chang, Hua-Hua and Zhang, J.} } @article {612, title = {Item selection in computerized adaptive testing: Improving the a-stratified design with the Sympson-Hetter algorithm}, journal = {Applied Psychological Measurement}, volume = {26}, year = {2002}, pages = {376-392}, author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.} } @article {248, title = {Item selection in computerized adaptive testing: Improving the a-stratified design with the Sympson-Hetter algorithm}, journal = {Applied Psychological Measurement}, volume = {26}, number = {4}, year = {2002}, pages = {376-392}, isbn = {0146-6216}, author = {Leung, C. K. and Chang, Hua-Hua and Hau, K. T.} } @conference {1259, title = {Optimum number of strata in the a-stratified adaptive testing design}, booktitle = {Paper presented at the annual meeting of the American Educational Research Association}, year = {2002}, note = {{PDF file, 114 KB}}, address = {New Orleans LA}, author = {Wen, J.-B. and Chang, Hua-Hua and Hau, K-T.} } @conference {873, title = {To weight or not to weight {\textendash} balancing influence of initial and later items in CAT}, booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education}, year = {2002}, note = {{PDF file, 252 KB}}, address = {New Orleans LA}, author = {Chang, Hua-Hua and Ying, Z.} } @conference {1258, title = {Adaptation of a-stratified method in variable length computerized adaptive testing}, booktitle = {Paper presented at the annual meeting of the American Educational Research Association}, year = {2001}, note = {{PDF file, 384 KB}}, address = {Seattle WA}, author = {Wen, J.-B. and Chang, Hua-Hua and Hau, K.-T.~} } @conference {1278, title = {a-stratified CAT design with content-blocking}, booktitle = {Paper presented at the Annual Meeting of the Psychometric Society}, year = {2001}, note = {{PDF file, 410 KB}}, address = {King of Prussia, PA}, author = {Yi, Q. and Chang, Hua-Hua} } @conference {905, title = {a-stratified computerized adaptive testing with unequal item exposure across strata}, booktitle = {Paper presented at the annual meeting of the American Educational Research Association}, year = {2001}, note = {$\#$DE01-01}, address = {Seattle WA}, author = {Deng, H. and Chang, Hua-Hua} } @article {56, title = {a-Stratified multistage computerized adaptive testing with b blocking}, journal = {Applied Psychological Measurement}, volume = {25}, number = {4}, year = {2001}, pages = {333-341}, abstract = {Chang \& Ying{\textquoteright}s (1999) computerized adaptive testing item-selection procedure stratifies the item bank according to a parameter values and requires b parameter values to be evenly distributed across all strata. Thus, a and b parameter values must be incorporated into how strata are formed. A refinement is proposed, based on Weiss{\textquoteright} (1973) stratification of items according to b values. Simulation studies using a retired item bank of a Graduate Record Examination test indicate that the new approach improved control of item exposure rates and reduced mean squared errors. }, isbn = {0146-6216}, author = {Chang, Hua-Hua and Qian, J. and Ying, Z.} } @article {55, title = {a-stratified multistage computerized adaptive testing with b blocking}, journal = {Applied Psychological Measurement}, volume = {25}, number = {4}, year = {2001}, pages = {333-41}, abstract = {Proposed a refinement, based on the stratification of items developed by D. Weiss (1973), of the computerized adaptive testing item selection procedure of H. Chang and Z. Ying (1999). Simulation studies using an item bank from the Graduate Record Examination show the benefits of the new procedure. (SLD)}, keywords = {computerized adaptive testing}, author = {Chang, Hua-Hua and Qian, J. and Yang, Z.} } @conference {930, title = {Deriving a stopping rule for sequential adaptive tests}, booktitle = {Paper presented at the annual meeting of the American Educational Research Association}, year = {2001}, note = {{PDF file, 111 KB}}, address = {Seattle WA}, author = {Grabovsky, I. and Chang, Hua-Hua and Ying, Z.} } @conference {1000, title = {An examination of item selection rules by stratified CAT designs integrated with content balancing methods}, booktitle = {Paper presented at the Annual Meeting of the American Educational Research Association}, year = {2001}, note = {{PDF file, 296 KB}}, address = {Seattle WA}, author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.} } @booklet {1345, title = {Implementing content constraints in a-stratified adaptive testing using a shadow test approach (Research Report 01-001)}, year = {2001}, address = {University of Twente, Department of Educational Measurement and Data Analysis}, author = {Chang, Hua-Hua and van der Linden, W. J.} } @conference {1001, title = {Integrating stratification and information approaches for multiple constrained CAT}, booktitle = {Paper presented at the Annual Meeting of the National Council on Measurement in Education}, year = {2001}, note = {{PDF file, 322 KB}}, address = {Seattle WA}, author = {Leung, C.-I. and Chang, Hua-Hua and Hau, K-T.} } @article {188, title = {Item selection in computerized adaptive testing: Should more discriminating items be used first?}, journal = {Journal of Educational Measurement}, volume = {38}, number = {3}, year = {2001}, pages = {249-266}, abstract = {During computerized adaptive testing (CAT), items are selected continuously according to the test-taker{\textquoteright}s estimated ability. Test security has become a problem because high-discrimination items are more likely to be selected and become overexposed. So, there seems to be a tradeoff between high efficiency in ability estimations and balanced usage of items. This series of four studies addressed the dilemma by focusing on the notion of whether more or less discriminating items should be used first in CAT. The first study demonstrated that the common maximum information method with J. B. Sympson and R. D. Hetter (1985) control resulted in the use of more discriminating items first. The remaining studies showed that using items in the reverse order, as described in H. Chang and Z. Yings (1999) stratified method had potential advantages: (a) a more balanced item usage and (b) a relatively stable resultant item pool structure with easy and inexpensive management. This stratified method may have ability-estimation efficiency better than or close to that of other methods. It is argued that the judicious selection of items, as in the stratified method, is a more active control of item exposure. (PsycINFO Database Record (c) 2005 APA )}, keywords = {ability, Adaptive Testing, Computer Assisted Testing, Estimation, Statistical, Test Items computerized adaptive testing}, author = {Hau, Kit-Tai and Chang, Hua-Hua} } @article {482, title = {On maximizing item information and matching difficulty with ability}, journal = {Psychometrika}, volume = {66}, year = {2001}, pages = {69-77}, author = {Bickel, P. and Buyske, S. and Chang, Hua-Hua and Ying, Z.} } @article {70, title = {A comparison of item selection rules at the early stages of computerized adaptive testing}, journal = {Applied Psychological Measurement}, volume = {24}, number = {3}, year = {2000}, pages = {241-255}, abstract = {The effects of 5 item selection rules--Fisher information (FI), Fisher interval information (FII), Fisher information with a posterior distribution (FIP), Kullback-Leibler information (KL), and Kullback-Leibler information with a posterior distribution (KLP)--were compared with respect to the efficiency and precision of trait (θ) estimation at the early stages of computerized adaptive testing (CAT). FII, FIP, KL, and KLP performed marginally better than FI at the early stages of CAT for θ=-3 and -2. For tests longer than 10 items, there appeared to be no precision advantage for any of the selection rules. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)}, keywords = {Adaptive Testing, Computer Assisted Testing, Item Analysis (Test), Statistical Estimation computerized adaptive testing}, author = {Chen, S-Y. and Ankenmann, R. D. and Chang, Hua-Hua} } @article {502, title = {A comparison of item selection rules at the early stages of computerized adaptive testing}, journal = {Applied Psychological Measurement}, volume = {24}, year = {2000}, pages = {241-255}, author = {Chen, S.Y. and Ankenmann, R. D. and Chang, Hua-Hua} } @conference {998, title = {Content balancing in stratified computerized adaptive testing designs}, booktitle = {Paper presented at the annual meeting of the American Educational Research Association}, year = {2000}, note = {{PDF file, 427 KB}}, address = {New Orleans, LA}, author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.} } @article {646, title = {Does adaptive testing violate local independence?}, journal = {Psychometrika}, volume = {65}, year = {2000}, pages = {149-156}, author = {Mislevy, R. J. and Chang, Hua-Hua} } @booklet {1584, title = {Multiple stratification CAT designs with content control}, year = {2000}, address = {Unpublished manuscript}, author = {Yi, Q. and Chang, Hua-Hua} } @conference {877, title = {Performance of item exposure control methods in computerized adaptive testing: Further explorations}, booktitle = {Paper presented at the Annual Meeting of the American Educational Research Association}, year = {2000}, address = {New Orleans , LA}, author = {Chang, Hua-Hua and Chang, S. and Ansley} } @conference {999, title = {Solving complex constraints in a-stratified computerized adaptive testing designs}, booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education}, year = {2000}, note = {PDF file, 384 K}, address = {New Orleans, USA}, author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.} } @article {59, title = {a-stratified multistage computerized adaptive testing}, journal = {Applied Psychological Measurement}, volume = {23}, number = {3}, year = {1999}, note = {Sage Publications, US}, pages = {211-222}, abstract = {For computerized adaptive tests (CAT) based on the three-parameter logistic mode it was found that administering items with low discrimination parameter (a) values early in the test and administering those with high a values later was advantageous; the skewness of item exposure distributions was reduced while efficiency was maintain in trait level estimation. Thus, a new multistage adaptive testing approach is proposed that factors a into the item selection process. In this approach, the items in the item bank are stratified into a number of levels based on their a values. The early stages of a test use items with lower as and later stages use items with higher as. At each stage, items are selected according to an optimization criterion from the corresponding level. Simulation studies were performed to compare a-stratified CATs with CATs based on the Sympson-Hetter method for controlling item exposure. Results indicated that this new strategy led to tests that were well-balanced, with respect to item exposure, and efficient. The a-stratified CATs achieved a lower average exposure rate than CATs based on Bayesian or information-based item selection and the Sympson-Hetter method. (PsycINFO Database Record (c) 2003 APA, all rights reserved).}, keywords = {computerized adaptive testing}, author = {Chang, Hua-Hua and Ying, Z.} } @article {497, title = {a-stratified multistage computerized adaptive testing}, journal = {Applied Psychological Measurement}, volume = {23}, year = {1999}, pages = {211-222}, author = {Chang, Hua-Hua and Ying, Z.} } @article {495, title = {a-stratified multistage computerized adaptive testing}, journal = {Applied Psychological Measurement}, volume = {23}, year = {1999}, pages = {211{\textendash}222}, author = {Chang, Hua-Hua and Ying, Z.} } @conference {996, title = {An enhanced stratified computerized adaptive testing design}, booktitle = {Paper presented at the annual meeting of the American Educational Research Association}, year = {1999}, note = {{PDF file, 478 KB}}, address = {Montreal, Canada}, author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.} } @conference {997, title = {Item selection in computerized adaptive testing: improving the a-stratified design with the Sympson-Hetter algorithm}, booktitle = {Paper presented at the Annual Meeting of the American Educational Research Association}, year = {1999}, address = {Montreal, CA}, author = {Leung, C-K.. and Chang, Hua-Hua and Hau, K-T.} } @conference {1195, title = {A comparison of two methods of controlling item exposure in computerized adaptive testing}, booktitle = {Paper presented at the meeting of the American Educational Research Association. San Diego CA.}, year = {1998}, author = {Tang, L. and Jiang, H. and Chang, Hua-Hua} } @booklet {1478, title = {Does adaptive testing violate local independence? (Research Report 98-33)}, year = {1998}, address = {Princeton NJ: Educational Testing Service}, author = {Mislevy, R. J. and Chang, Hua-Hua} } @conference {946, title = {Item selection in computerized adaptive testing: Should more discriminating items be used first? Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA}, year = {1998}, author = {Hau, K. T. and Chang, Hua-Hua} } @article {567, title = {Item selection in computerized adaptive testing: Should more discriminating items be used first?}, journal = {Journal of Educational Measurement}, volume = {38}, year = {1998}, pages = {249-266}, author = {Hau, K. T. and Chang, Hua-Hua} } @conference {872, title = {Multi-stage CAT with stratified design}, booktitle = {Paper presented at the annual meeting of the Psychometric Society. Gatlinburg TN.}, year = {1997}, author = {Chang, Hua-Hua and Ying, Z.} } @article {496, title = {Nonlinear sequential designs for logistic item response theory models with applications to computerized adaptive tests}, journal = {The Annals of Statistics.}, year = {1997}, author = {Chang, Hua-Hua and Ying, Z.} } @conference {871, title = {Building a statistical foundation for computerized adaptive testing}, booktitle = {Paper presented at the annual meeting of the Psychometric Society}, year = {1996}, address = {Banff, Alberta, Canada}, author = {Chang, Hua-Hua and Ying, Z.} } @article {58, title = {A global information approach to computerized adaptive testing}, journal = {Applied Psychological Measurement}, volume = {20}, number = {3}, year = {1996}, pages = {213-229}, abstract = {based on Fisher information (or item information). At each stage, an item is selected to maximize the Fisher information at the currently estimated trait level (\&thetas;). However, this application of Fisher information could be much less efficient than assumed if the estimators are not close to the true \&thetas;, especially at early stages of an adaptive test when the test length (number of items) is too short to provide an accurate estimate for true \&thetas;. It is argued here that selection procedures based on global information should be used, at least at early stages of a test when \&thetas; estimates are not likely to be close to the true \&thetas;. For this purpose, an item selection procedure based on average global information is proposed. Results from pilot simulation studies comparing the usual maximum item information item selection with the proposed global information approach are reported, indicating that the new method leads to improvement in terms of bias and mean squared error reduction under many circumstances. Index terms: computerized adaptive testing, Fisher information, global information, information surface, item information, item response theory, Kullback-Leibler information, local information, test information.}, isbn = {0146-6216}, author = {Chang, Hua-Hua and Ying, Z.} } @conference {870, title = {A model for score maximization within a computerized adaptive testing environment}, booktitle = {Paper presented at the annual meeting of the NMCE}, year = {1996}, address = {New York NY}, author = {Chang, Hua-Hua} } @booklet {1346, title = {Recursive maximum likelihood estimation, sequential design, and computerized adaptive testing}, year = {1996}, address = {Princeton NJ: Educational Testing Service}, author = {Chang, Hua-Hua and Ying, Z.} } @conference {869, title = {A global information approach to computerized adaptive testing}, booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education}, year = {1995}, address = {San Francisco CA}, author = {Chang, Hua-Hua} } @conference {1286, title = {Recursive maximum likelihood estimation, sequential designs, and computerized adaptive testing}, booktitle = {Paper presented at the Eleventh Workshop on Item Response Theory}, year = {1995}, address = {University of Twente, the Netherlands}, author = {Ying, Z. and Chang, Hua-Hua} }