The use of unidimensional item parameter estimates of multidimensional items in adaptive testing

Author(s):  
Terry Ackerman
2016 ◽  
Vol 77 (3) ◽  
pp. 389-414 ◽  
Author(s):  
Yin Lin ◽  
Anna Brown

A fundamental assumption in computerized adaptive testing is that item parameters are invariant with respect to context—items surrounding the administered item. This assumption, however, may not hold in forced-choice (FC) assessments, where explicit comparisons are made between items included in the same block. We empirically examined the influence of context on item parameters by comparing parameter estimates from two FC instruments. The first instrument was composed of blocks of three items, whereas in the second, the context was manipulated by adding one item to each block, resulting in blocks of four. The item parameter estimates were highly similar. However, a small number of significant deviations were observed, confirming the importance of context when designing adaptive FC assessments. Two patterns of such deviations were identified, and methods to reduce their occurrences in an FC computerized adaptive testing setting were proposed. It was shown that with a small proportion of violations of the parameter invariance assumption, score estimation remained stable.


2019 ◽  
Vol 44 (4) ◽  
pp. 296-310
Author(s):  
Yong He ◽  
Zhongmin Cui

Item parameter estimates of a common item on a new test form may change abnormally due to reasons such as item overexposure or change of curriculum. A common item, whose change does not fit the pattern implied by the normally behaved common items, is defined as an outlier. Although improving equating accuracy, detecting and eliminating of outliers may cause a content imbalance among common items. Robust scale transformation methods have recently been proposed to solve this problem when only one outlier is present in the data, although it is not uncommon to see multiple outliers in practice. In this simulation study, the authors examined the robust scale transformation methods under conditions where there were multiple outlying common items. Results indicated that the robust scale transformation methods could reduce the influences of multiple outliers on scale transformation and equating. The robust methods performed similarly to a traditional outlier detection and elimination method in terms of reducing the influence of outliers while keeping adequate content balance.


2021 ◽  
Author(s):  
Jan Steinfeld ◽  
Alexander Robitzsch

This article describes the conditional maximum likelihood-based item parameter estimation in probabilistic multistage designs. In probabilistic multistage designs, the routing is not solely based on a raw score j and a cut score c as well as a rule for routing into a module such as j < c or j ≤ c but is based on a probability p(j) for each raw score j. It can be shown that the use of a conventional conditional maximum likelihood parameter estimate in multistage designs leads to severely biased item parameter estimates. Zwitser and Maris (2013) were able to show that with deterministic routing, the integration of the design into the item parameter estimation leads to unbiased estimates. This article extends this approach to probabilistic routing and, at the same time, represents a generalization. In a simulation study, it is shown that the item parameter estimation in probabilistic designs leads to unbiased item parameter estimates.


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