The Uniform Prior for Bayesian Estimation of Ability in Item Response Theory Models

Tuğba Karadavut

doi:https://doi.org/10.21449/ijate.581314

Article Details

The Uniform Prior for Bayesian Estimation of Ability in Item Response Theory Models

Authors:

Tuğba Karadavut

Publisher:

İzzet KARA

Main Topics:

Education

Journal:

International Journal of Assessment Tools in Education 2019 Vol.6 Issue 4, pp.568-579

Document Type:

Article

Publication Date:

1/2020

Document Language:

English

Document ID (Org No.):

2000718649

Frequency:

Quarterly

eISSN:

2148-7456

DOI:

https://doi.org/10.21449/ijate.581314

Abstract:

 


Item
Response Theory (IRT) models traditionally assume a normal distribution for
ability. Although normality is often a reasonable assumption for ability, it is
rarely met for observed scores in educational and psychological measurement.
Assumptions regarding ability distribution were previously shown to have an
effect on IRT parameter estimation. In this study, the normal and uniform
distribution prior assumptions for ability were compared for IRT parameter
estimation when the actual distribution was either normal or uniform. A
simulation study that included a short test with a small sample size and a long
test with a large sample size was conducted for this purpose. The results
suggested using a uniform distribution prior for ability to achieve more accurate
estimates of the ability parameter in the 2PL and 3PL models when the true
distribution of ability is not known. For the Rasch model, an explicit pattern
that could be used to obtain more accurate item parameter estimates was not
found.

.

 


Item Response Theory (IRT) models traditionally assume a normal distribution for ability. Although normality is often a reasonable assumption for ability, it is rarely met for observed scores in educational and psychological measurement. Assumptions regarding ability distribution were previously shown to have an effect on IRT parameter estimation. In this study, the normal and uniform distribution prior assumptions for ability were compared for IRT parameter estimation when the actual distribution was either normal or uniform. A simulation study that included a short test with a small sample size and a long test with a large sample size was conducted for this purpose. The results suggested using a uniform distribution prior for ability to achieve more accurate estimates of the ability parameter in the 2PL and 3PL models when the true distribution of ability is not known. For the Rasch model, an explicit pattern that could be used to obtain more accurate item parameter estimates was not found.

.

Article Details

The Uniform Prior for Bayesian Estimation of Ability in Item Response Theory Models

Authors: Tuğba Karadavut

Publisher: İzzet KARA

Main Topics: Education

Journal: International Journal of Assessment Tools in Education 2019 Vol.6 Issue 4, pp.568-579

Document Type: Article

Publication Date: 1/2020

Document Language: English

Document ID (Org No.): 2000718649

Frequency: Quarterly

eISSN: 2148-7456

DOI: https://doi.org/10.21449/ijate.581314

Issues

Authors:

Tuğba Karadavut

Publisher:

İzzet KARA

Main Topics:

Education

Journal:

International Journal of Assessment Tools in Education 2019 Vol.6 Issue 4, pp.568-579

Document Type:

Article

Publication Date:

1/2020

Document Language:

English

Document ID (Org No.):

2000718649

Frequency:

Quarterly

eISSN:

2148-7456

DOI:

https://doi.org/10.21449/ijate.581314