Abstract
The present study aims to investigate the effects of model based missing data methods on guessing parameter in case of ignorable missing data. For this purpose, data based on Item Response Theory with 3 parameters logistic model were created in sample sizes of 500, 1000 and 3000; and then, missing values at random and missing values at completely random were created in ratios of 2.00%, 5.00% and 10.00%. These missing values were completed using expectation'maximization (EM) algorithm and multiple imputation methods. It was concluded that the performance of EM algorithm and multiple imputation methods was efficient depending on the rate of missing values on the data sets with missing values completely at random. When the missing value rate was 2.00%, both methods performed well in all sample sizes; however, they moved away from reference point as the number of missing values increased. On the other hand, it was also found that when the sample size was 3000, the cuts were closer to reference point even when the number of missing values was high. As for missing values at random mechanism, it was observed that both methods performed efficiently on guessing parameter when the number of missing values was low. Yet, this performance deteriorated considerably as the number of missing values increased. Both EM algorithm and multiple imputation methods did not perform effectively on guessing parameter in missing values at random mechanism.
Highlights
Critical in assessment and testing theories in the fields of education and psychology, the ultimate aim is to interpret unobservable variables based on the observable ones
Since the aim of the present study is to identify the effects of multiple imputation and expectation– maximization (EM) algorithm methods used in data sets with ignorable missing values on guessing parameter (c) in one-dimension item response theory in three-parameter logistic model, the study is basic research (Karasar, 2007)
Figure.1 shows the effects of multiple imputation and imputation with expectation–maximization algorithm methods on guessing parameter in missing completely at random mechanism when the sample size is 500
Summary
Critical in assessment and testing theories in the fields of education and psychology, the ultimate aim is to interpret unobservable (latent) variables based on the observable ones. Estimation of item and test parameters depending on specific groups and the variation of estimations from one group to another is one of the reasons of criticism. When the respondent does not possess the desired behaviour and reaches the correct answer totally by luck, this is called “guessing success”. This is a possible phenomenon in all multiple choice items (Turgut & Baykul, 2010)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
