A speededness item response model for associating ability and speededness parameters
Creators: name: Williams, Immanuel James
Publisher: name: No Publisher Supplied
Year: 2017

description: Test speededness is defined as the failure to attempt all items on an assessment  within a specified time frame. The presence of speededness is an issue known to  undermine assessments (Bejar, 1985). Therefore, researchers have developed several approaches to reduce test speededness, including non-statistical methods (e.g.,  Evans &amp; Reilly, 1972), as well as probabilistic models, such as augmented item  response theory (IRT) models (e.g., Cao &amp; Stokes, 2008). However, an assumption  about speededness that is often not discussed in the literature is the relationship  between speededness and ability of the examinee in the context of IRT modeling.  Previous studies have used modified IRT models to reduce test speededness, but  none have evaluated the effect of neglecting the association between speededness  and ability. In the same regard, only one model has addressed this association.  This study will address four different purposes regarding the relationship be-  tween speededness and ability. The first purpose is to propose a new IRT model  that associates ability with speededness and to develop an estimation algorithm  for the proposed model, which is evaluated in terms of the recovery of model  parameters by manipulating certain hyperparameters in the model. The second  purpose of this study is to determine the robustness of the proposed IRT model  when speededness is not present, and to show the inefficiency of the traditional  IRT model when speededness is associated with ability. The third purpose is to  examine the impact of ignoring the association between ability and speededness  on parameter estimation and to investigate the robustness of the proposed model  under conditions when speededness and ability are independent. Lastly, data are  generated from an existing model that associated ability and speededness in a  different manner to determine how robust the proposed model is under a different  speededness schema. These four purposes are used to thoroughly understand the  proposed model and its contribution to the research of test speededness.  The Markov Chain Monte Carlo (MCMC) Metropolis Hastings algorithm was  implemented to estimate model parameters using C++ and R, an object-oriented  language and a statistical software, respectively. The results showed that the pro-  posed model was able to recover the model parameters accurately under various  conditions of known hyperparameters. The proposed model was also able to es-  timate model parameters well when ability was not associated with speededness  when there were a large amount of respondents and items. In addition, the pro-  posed model was also able to estimate model parameters well when speededness  was not present when the sample size and the number of items were large. Lastly,  when speededness and ability were generated under a different method, the pro-  posed model was unable to estimate the model parameters well. In summary, this  work allows researchers to further understand the impact of speededness and its  association with ability in a variety of conditions.