Comparison of item response theory ability and item parameters according to classical and Bayesian estimation methods

Eray Selçuk; Ergül Demir

Makale detayları

Comparison of item response theory ability and item parameters according to classical and Bayesian estimation methods

Yazarlar:

Eray Selçuk | Ergül Demir

Yayıncı:

İzzet KARA

Ana Başlıklar:

Studies on Education

Dergi:

International Journal of Assessment Tools in Education Vol.11 Issue 2, pp.213-248

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001312691

pISSN:

2148-7456

Anahtar kelimeler:

Özet:

 This research aims to compare the ability and item parameter estimations of Item Response Theory according to Maximum likelihood and Bayesian approaches in different Monte Carlo simulation conditions. For this purpose, depending on the changes in the priori distribution type, sample size, test length, and logistics model, the ability and item parameters estimated according to the maximum likelihood and Bayesian method and the differences in the RMSE of these parameters were examined. The priori distribution normal, left-skewed, right-skewed, leptokurtic, and platykurtic, test length 10, 20, 40, sample size 100, 500, 1000, logistics model 2PL, 3PL. The simulation conditions were performed with 100 replications. Mixed model ANOVA was performed to determine RMSE differentiations. The prior distribution type, test length, and estimation method in the differentiation of ability parameter and RMSE were estimated in 2PL models; the priori distribution type and test length were significant in the differences in the ability parameter and RMSE estimated in the 3PL model. While prior distribution type, sample size, and estimation method created a significant difference in the RMSE of the item discrimination parameter estimated in the 2PL model, none of the conditions created a significant difference in the RMSE of the item difficulty parameter. The priori distribution type, sample size, and estimation method in the item discrimination RMSE were estimated in the 3PL model; the a priori distribution and estimation method created significant differentiation in the RMSE of the lower asymptote parameter. However, none of the conditions significantly changed the RMSE of item difficulty parameters.

 This research aims to compare the ability and item parameter estimations of Item Response Theory according to Maximum likelihood and Bayesian approaches in different Monte Carlo simulation conditions. For this purpose, depending on the changes in the priori distribution type, sample size, test length, and logistics model, the ability and item parameters estimated according to the maximum likelihood and Bayesian method and the differences in the RMSE of these parameters were examined. The priori distribution normal, left-skewed, right-skewed, leptokurtic, and platykurtic, test length 10, 20, 40, sample size 100, 500, 1000, logistics model 2PL, 3PL. The simulation conditions were performed with 100 replications. Mixed model ANOVA was performed to determine RMSE differentiations. The prior distribution type, test length, and estimation method in the differentiation of ability parameter and RMSE were estimated in 2PL models; the priori distribution type and test length were significant in the differences in the ability parameter and RMSE estimated in the 3PL model. While prior distribution type, sample size, and estimation method created a significant difference in the RMSE of the item discrimination parameter estimated in the 2PL model, none of the conditions created a significant difference in the RMSE of the item difficulty parameter. The priori distribution type, sample size, and estimation method in the item discrimination RMSE were estimated in the 3PL model; the a priori distribution and estimation method created significant differentiation in the RMSE of the lower asymptote parameter. However, none of the conditions significantly changed the RMSE of item difficulty parameters.

 This research aims to compare the ability and item parameter estimations of Item Response Theory according to Maximum likelihood and Bayesian approaches in different Monte Carlo simulation conditions. For this purpose, depending on the changes in the priori distribution type, sample size, test length, and logistics model, the ability and item parameters estimated according to the maximum likelihood and Bayesian method and the differences in the RMSE of these parameters were examined. The priori distribution normal, left-skewed, right-skewed, leptokurtic, and platykurtic, test length 10, 20, 40, sample size 100, 500, 1000, logistics model 2PL, 3PL. The simulation conditions were performed with 100 replications. Mixed model ANOVA was performed to determine RMSE differentiations. The prior distribution type, test length, and estimation method in the differentiation of ability parameter and RMSE were estimated in 2PL models; the priori distribution type and test length were significant in the differences in the ability parameter and RMSE estimated in the 3PL model. While prior distribution type, sample size, and estimation method created a significant difference in the RMSE of the item discrimination parameter estimated in the 2PL model, none of the conditions created a significant difference in the RMSE of the item difficulty parameter. The priori distribution type, sample size, and estimation method in the item discrimination RMSE were estimated in the 3PL model; the a priori distribution and estimation method created significant differentiation in the RMSE of the lower asymptote parameter. However, none of the conditions significantly changed the RMSE of item difficulty parameters.

 This research aims to compare the ability and item parameter estimations of Item Response Theory according to Maximum likelihood and Bayesian approaches in different Monte Carlo simulation conditions. For this purpose, depending on the changes in the priori distribution type, sample size, test length, and logistics model, the ability and item parameters estimated according to the maximum likelihood and Bayesian method and the differences in the RMSE of these parameters were examined. The priori distribution normal, left-skewed, right-skewed, leptokurtic, and platykurtic, test length 10, 20, 40, sample size 100, 500, 1000, logistics model 2PL, 3PL. The simulation conditions were performed with 100 replications. Mixed model ANOVA was performed to determine RMSE differentiations. The prior distribution type, test length, and estimation method in the differentiation of ability parameter and RMSE were estimated in 2PL models; the priori distribution type and test length were significant in the differences in the ability parameter and RMSE estimated in the 3PL model. While prior distribution type, sample size, and estimation method created a significant difference in the RMSE of the item discrimination parameter estimated in the 2PL model, none of the conditions created a significant difference in the RMSE of the item difficulty parameter. The priori distribution type, sample size, and estimation method in the item discrimination RMSE were estimated in the 3PL model; the a priori distribution and estimation method created significant differentiation in the RMSE of the lower asymptote parameter. However, none of the conditions significantly changed the RMSE of item difficulty parameters.

Makale detayları

Comparison of item response theory ability and item parameters according to classical and Bayesian estimation methods

Yazarlar:

Eray Selçuk | Ergül Demir

Yayıncı:

İzzet KARA

Ana Başlıklar:

Studies on Education

Dergi:

International Journal of Assessment Tools in Education Vol.11 Issue 2, pp.213-248

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001312691

pISSN:

2148-7456

IRT parameter estimation

Maximum likelihood estimation

Bayesian estimation method

MCMC

RMSEIRT parameter estimation

Maximum likelihood estimation

Bayesian estimation method

MCMC

RMSE

Sayı

Makale detayları

Yazarlar: Eray Selçuk | Ergül Demir

Yayıncı: İzzet KARA

Ana Başlıklar: Studies on Education

Dergi: International Journal of Assessment Tools in Education Vol.11 Issue 2, pp.213-248

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001312691

pISSN: 2148-7456

Sayı

Yazarlar:

Eray Selçuk | Ergül Demir

Yayıncı:

İzzet KARA

Ana Başlıklar:

Studies on Education

Dergi:

International Journal of Assessment Tools in Education Vol.11 Issue 2, pp.213-248

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001312691

pISSN:

2148-7456