A High-Stakes Approach to Response Time Effort in Low-Stakes Assessment

<p style="text-align: justify;">Response times are one of the important sources that provide information about the performance of individuals during a test process. The main purpose of this study is to show that survival models can be used in educational data. Accordingly, data sets of items measuring literacy, numeracy and problem-solving skills of the countries participating in Round 3 of the Programme for the International Assessment of Adult Competencies were used. Accelerated failure time models have been analyzed for each country and domain. As a result of the analysis of the models in which various covariates are included as independent variables, and response time for giving correct answers is included as a dependent variable, it was found the associations between the covariates and response time for giving correct answers were concluded to vary from one domain to another or from one country to another. The results obtained from the present study have provided the educational stakeholders and practitioners with valuable information.</p>

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Further Increasing Fisher's Information for Parameters of Association in Accelerated Failure Time Models via Double Extreme Ranks

Pakistan Journal of Statistics and Operation Research ◽

10.18187/pjsor.v15i3.2605 ◽

2019 ◽

pp. 679-700

Author(s):

Hani Samawi ◽

Amal Helu ◽

Herash Rochani

Keyword(s):

Failure Time ◽

Auxiliary Variable ◽

Ranked Set Sampling ◽

Survival Models ◽

Simple Random Sample ◽

Accelerated Failure Time ◽

Simulation Studies ◽

Accelerated Failure Time Models ◽

Accelerated Failure ◽

Extreme Ranked Set Sampling

Double Extreme Ranked Set Sampling (DERSS) was first introduced by Samawi (2002) as a modification to the well-known Ranked Set Sampling (RSS) and Extreme Ranked Set Sampling (ERSS). In this article, we provide a modification to DERSS scheme with ranking based on an easy-to-rank baseline auxiliary variable known to be associated with survival time. We show that using the modified DERSS improves the performance of the Accelerated failure time (AFT) survival model and provides a more efficient estimator of the hazard ratio than that based on their counter parts simple random sample (SRS), RSS and ERSS, which results in reducing the sample size required and hence the total cost of the study. Our theoretical and simulation studies show the superiority of using the modified DERSS for AFT survival models compared with using SRS, RSS and ERSS.Â A numerical example based on Worcester Heart Attack Study is presented to illustrate the implementation of the DERSS.

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Effects of covariates on alternating recurrent events in accelerated failure time models

The International Journal of Biostatistics ◽

10.1515/ijb-2019-0099 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Moumita Chatterjee ◽

Sugata Sen Roy

Keyword(s):

Recurrent Events ◽

Failure Time ◽

Likelihood Method ◽

Model Parameters ◽

Accelerated Failure Time ◽

Survival Times ◽

Copula Functions ◽

Event Time ◽

Accelerated Failure Time Models ◽

Accelerated Failure

AbstractIn this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.

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