An investigation into the effect of different missing data imputation methods on IRT-based differential item functioning

Fatma Ünal; Hakan Koğar

Article Details

An investigation into the effect of different missing data imputation methods on IRT-based differential item functioning

Authors:

Fatma Ünal | Hakan Koğar

Publisher:

İzzet KARA

Main Topics:

Measurement Theories and Applications in Education and Psychology

Journal:

International Journal of Assessment Tools in Education Vol.11 Issue 3, pp.445-462

Document Type:

Article

Publication Date:

9/2024

Document Language:

English

Document ID (Org No.):

2001319918

pISSN:

2148-7456

Keywords:

Abstract:

 The purpose of this study is to examine the effect of missing data imputation methods, namely regression imputation RI, multiple imputation MI and k-nearest neighbor kNN on differential item functioning DIF. In this regard, the datasets used in the research were created by deleting some of the data via the missing completely at random mechanism from the complete datasets obtained from 600 students in Türkiye, the United Kingdom, the USA, New Zealand and Australia, who answered booklets 14 and 15 from the PISA 2018 science literacy test. Data imputation was applied to the datasets through missing data using RI, MI and kNN methods and DIF analysis was performed on all datasets in terms of language and gender variables via Lords χ2 method, Rajus area measurement method and item response theory likelihood ratio method. DIF results from the complete datasets were taken as a reference and they were compared with the results from other datasets. As a result of the research, values close to 10 of accurate imputation were achieved in the RI method depending on language and gen-der variables. In MI and kNN methods, results closest to the complete datasets were obtained at a rate of 5 depending on the language variable. In the MI method, inaccurate results were obtained in all proportions in terms of the gender variable. For the gender variable, the kNN method gave accurate results at rates of 5 and 10.

 The purpose of this study is to examine the effect of missing data imputation methods, namely regression imputation RI, multiple imputation MI and k-nearest neighbor kNN on differential item functioning DIF. In this regard, the datasets used in the research were created by deleting some of the data via the missing completely at random mechanism from the complete datasets obtained from 600 students in Türkiye, the United Kingdom, the USA, New Zealand and Australia, who answered booklets 14 and 15 from the PISA 2018 science literacy test. Data imputation was applied to the datasets through missing data using RI, MI and kNN methods and DIF analysis was performed on all datasets in terms of language and gender variables via Lords χ2 method, Rajus area measurement method and item response theory likelihood ratio method. DIF results from the complete datasets were taken as a reference and they were compared with the results from other datasets. As a result of the research, values close to 10 of accurate imputation were achieved in the RI method depending on language and gen-der variables. In MI and kNN methods, results closest to the complete datasets were obtained at a rate of 5 depending on the language variable. In the MI method, inaccurate results were obtained in all proportions in terms of the gender variable. For the gender variable, the kNN method gave accurate results at rates of 5 and 10.

 The purpose of this study is to examine the effect of missing data imputation methods, namely regression imputation RI, multiple imputation MI and k-nearest neighbor kNN on differential item functioning DIF. In this regard, the datasets used in the research were created by deleting some of the data via the missing completely at random mechanism from the complete datasets obtained from 600 students in Türkiye, the United Kingdom, the USA, New Zealand and Australia, who answered booklets 14 and 15 from the PISA 2018 science literacy test. Data imputation was applied to the datasets through missing data using RI, MI and kNN methods and DIF analysis was performed on all datasets in terms of language and gender variables via Lords χ2 method, Rajus area measurement method and item response theory likelihood ratio method. DIF results from the complete datasets were taken as a reference and they were compared with the results from other datasets. As a result of the research, values close to 10 of accurate imputation were achieved in the RI method depending on language and gen-der variables. In MI and kNN methods, results closest to the complete datasets were obtained at a rate of 5 depending on the language variable. In the MI method, inaccurate results were obtained in all proportions in terms of the gender variable. For the gender variable, the kNN method gave accurate results at rates of 5 and 10.

 The purpose of this study is to examine the effect of missing data imputation methods, namely regression imputation RI, multiple imputation MI and k-nearest neighbor kNN on differential item functioning DIF. In this regard, the datasets used in the research were created by deleting some of the data via the missing completely at random mechanism from the complete datasets obtained from 600 students in Türkiye, the United Kingdom, the USA, New Zealand and Australia, who answered booklets 14 and 15 from the PISA 2018 science literacy test. Data imputation was applied to the datasets through missing data using RI, MI and kNN methods and DIF analysis was performed on all datasets in terms of language and gender variables via Lords χ2 method, Rajus area measurement method and item response theory likelihood ratio method. DIF results from the complete datasets were taken as a reference and they were compared with the results from other datasets. As a result of the research, values close to 10 of accurate imputation were achieved in the RI method depending on language and gen-der variables. In MI and kNN methods, results closest to the complete datasets were obtained at a rate of 5 depending on the language variable. In the MI method, inaccurate results were obtained in all proportions in terms of the gender variable. For the gender variable, the kNN method gave accurate results at rates of 5 and 10.

Article Details

An investigation into the effect of different missing data imputation methods on IRT-based differential item functioning

Authors:

Fatma Ünal | Hakan Koğar

Publisher:

İzzet KARA

Main Topics:

Measurement Theories and Applications in Education and Psychology

Journal:

International Journal of Assessment Tools in Education Vol.11 Issue 3, pp.445-462

Document Type:

Article

Publication Date:

9/2024

Document Language:

English

Document ID (Org No.):

2001319918

pISSN:

2148-7456

Differential item functioning

Missing data

Item response theory

Raju’s area measurement

Likelihood ratioDifferential item functioning

Missing data

Item response theory

Raju’s area measurement

Likelihood ratio

Issues

Article Details

Authors: Fatma Ünal | Hakan Koğar

Publisher: İzzet KARA

Main Topics: Measurement Theories and Applications in Education and Psychology

Journal: International Journal of Assessment Tools in Education Vol.11 Issue 3, pp.445-462

Document Type: Article

Publication Date: 9/2024

Document Language: English

Document ID (Org No.): 2001319918

pISSN: 2148-7456

Issues

Authors:

Fatma Ünal | Hakan Koğar

Publisher:

İzzet KARA

Main Topics:

Measurement Theories and Applications in Education and Psychology

Journal:

International Journal of Assessment Tools in Education Vol.11 Issue 3, pp.445-462

Document Type:

Article

Publication Date:

9/2024

Document Language:

English

Document ID (Org No.):

2001319918

pISSN:

2148-7456