Investigating the Impact of Uncertainty About Item Parameters on Ability Estimation

Psychometrika ◽  
2011 ◽  
Vol 76 (1) ◽  
pp. 97-118 ◽  
Author(s):  
Jinming Zhang ◽  
Minge Xie ◽  
Xiaolan Song ◽  
Ting Lu
2021 ◽  
pp. 107699862199436
Author(s):  
Yue Liu ◽  
Hongyun Liu

The prevalence and serious consequences of noneffortful responses from unmotivated examinees are well-known in educational measurement. In this study, we propose to apply an iterative purification process based on a response time residual method with fixed item parameter estimates to detect noneffortful responses. The proposed method is compared with the traditional residual method and noniterative method with fixed item parameters in two simulation studies in terms of noneffort detection accuracy and parameter recovery. The results show that when severity of noneffort is high, the proposed method leads to a much higher true positive rate with a small increase of false discovery rate. In addition, parameter estimation is significantly improved by the strategies of fixing item parameters and iteratively cleansing. These results suggest that the proposed method is a potential solution to reduce the impact of data contamination due to severe low test-taking effort and to obtain more accurate parameter estimates. An empirical study is also conducted to show the differences in the detection rate and parameter estimates among different approaches.


2021 ◽  
Author(s):  
Angély Loubert ◽  
Antoine Regnault ◽  
Véronique Sébille ◽  
Jean-Benoit Hardouin

Abstract BackgroundIn the analysis of clinical trial endpoints, calibration of patient-reported outcomes (PRO) instruments ensures that resulting “scores” represent the same quantity of the measured concept between applications. Rasch measurement theory (RMT) is a psychometric approach that guarantees algebraic separation of person and item parameter estimates, allowing formal calibration of PRO instruments. In the RMT framework, calibration is performed using the item parameter estimates obtained from a previous “calibration” study. But if calibration is based on poorly estimated item parameters (e.g., because the sample size of the calibration sample was low), this may hamper the ability to detect a treatment effect, and direct estimation of item parameters from the trial data (non-calibration) may then be preferred. The objective of this simulation study was to assess the impact of calibration on the comparison of PRO results between treatment groups, using different analysis methods.MethodsPRO results were simulated following a polytomous Rasch model, for a calibration and a trial sample. Scenarios included varying sample sizes, with instrument of varying number of items and modalities, and varying item parameters distributions. Different treatment effect sizes and distributions of the two patient samples were also explored. Comparison of treatment groups was performed using different methods based on a random effect Rasch model. Calibrated and non-calibrated approaches were compared based on type-I error, power, bias, and variance of the estimates for the difference between groups.Results There was no impact of the calibration approach on type-I error, power, bias, and dispersion of the estimates. Among other findings, mistargeting between the PRO instrument and patients from the trial sample (regarding the level of measured concept) resulted in a lower power and higher position bias than appropriate targeting. ConclusionsCalibration of PROs in clinical trials does not compromise the ability to accurately assess a treatment effect and is essential to properly interpret PRO results. Given its important added value, calibration should thus always be performed when a PRO instrument is used as an endpoint in a clinical trial, in the RMT framework.


2021 ◽  
pp. 107699862199456
Author(s):  
Yi-Hsuan Lee ◽  
Charles Lewis

In many educational assessments, items are reused in different administrations throughout the life of the assessments. Ideally, a reused item should perform relatively similarly over time. In reality, an item may become easier with exposure, especially when item preknowledge has occurred. This article presents a novel cumulative sum procedure for detecting item preknowledge in continuous testing where data for each reused item may be obtained from small and varying sample sizes across administrations. Its performance is evaluated with simulations and analytical work. The approach is effective in detecting item preknowledge quickly with group size at least 10 and is easy to implement with varying item parameters. In addition, it is robust to the ability estimation error introduced in the simulations.


2015 ◽  
Vol 1 (1) ◽  
pp. 55
Author(s):  
Abadyo Abadyo ◽  
Bastari Bastari

The main purpose of the study was to investigate the superiority of scoring by utilizing the combination of MCM/GPCM model in comparison to 3PLM/GRM model within a mixed-item format of Mathematics tests. To achieve the purpose, the impact of two scoring models was investigated based on the test length, the sample size, and the M-C item proportion within the mixed-item format test and the investigation was conducted on the aspects of: (1) estimation of ability and item parameters, (2) optimalization of TIF, (3) standard error rates, and (4) model fitness on the data. The investigation made use of simulated data that was generated based on fixed effects factorial design 2 x 3 x 3 x 3 and 5 replications resulting in 270 data sets. The data were analyzed by means of fixed effect MANOVA on Root Mean Square Error (RMSE) of the ability and RMSE and Root Mean Square Deviation (RNSD) of the itemparameters in order to identify the significant main effects at level of a = .05; on the other hand, the interaction effects were incorporated into the error term for statistical testing. The -2LL statistics were also used in order to evaluate the moel fitness on the data set. The results of the study show that the combination of MCM/GPCM model provide higher accurate estimation than that of 3PLM/GRM model. In addition, the test information given by the combination of MCM/GPCM model is three times hhigher than that of 3PLM/GRM model although the test information cannot offer a solid conclusion in relation to the sample size and the M-C item proportion on each test length which provides the optimal score of thest information. Finally, the differences of fit statistics between the two models of scoring determine the position of MCM/GPCM model rather than that of 3PLM/GRM model.


1962 ◽  
Vol 14 ◽  
pp. 415-418
Author(s):  
K. P. Stanyukovich ◽  
V. A. Bronshten

The phenomena accompanying the impact of large meteorites on the surface of the Moon or of the Earth can be examined on the basis of the theory of explosive phenomena if we assume that, instead of an exploding meteorite moving inside the rock, we have an explosive charge (equivalent in energy), situated at a certain distance under the surface.


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