Abstract
<p>In this study, it was aimed to investigate the impact of different missing data<br />handling methods on DINA model parameter estimation and classification<br />accuracy. In the study, simulated data were used and the data were generated<br />by manipulating the number of items and sample size. In the generated data,<br />two different missing data mechanisms (missing completely at random and<br />missing at random) were created according to three different amounts of<br />missing data. The generated missing data was completed by using methods<br />of treating missing data as incorrect, person mean imputation, two-way<br />imputation, and expectation-maximization algorithm imputation. As a result,<br />it was observed that both s and g parameter estimations and classification<br />accuracies were effected from, missing data rates, missing data handling<br />methods and missing data mechanisms.</p>
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