Sulistyanto, Aldi Dwi (2019) Studi Simulasi: Metode Zero Imputation Untuk Mengatasi Missing Data Untuk Respons Polytomous Pada Person-Trait Estimation. Sarjana thesis, Universitas Brawijaya.
Abstract
Penelitian ini bertujuan untuk mengetahui keefektifan metode zero imputation untuk mengatasi missing data dalam mengestimasi person-trait. Penelitian ini merupakan penelitian studi simulasi dengan menggunakan pendekatan Items Response Theory (IRT) dengan model penskoran Graded Response Model (GRM) serta ilustrasi pada real data. Studi simulasi dilakukan dengan dua kondisi jumlah butir yang berbeda yaitu 20 butir dan 40 butir dengan format respons polytomous. Setiap kondisi butir yang berbeda tersebut akan dikondisikan dengan presentase missing data sebesar 10%, 20% dan 50% dengan jumlah responden sebanyak 1000 responden. Kemudian estimasi person-trait dilakukan dengan expected a posteriori (EAP) berdasarkan model GRM dan analisis RMSE. Hasil estimasi person-trait akan dikorelasikan menggunakan pearson product moment. Hasil penelitian menunjukan bahwa metode zero imputation lebih efektif digunakan bila presentase missing data 50%. Selain itu, semakin banyak butir yang digunakan dalam sebuah tes, akan memberikan informasi lebih banyak mengenai trait yang diukur.
English Abstract
This study aims to determine the effectiveness of the zero imputation method to overcome missing data in estimating person-trait. This research is a simulation study using the Items Response Theory (IRT) approach with a Graded Response Model (GRM) scoring model and an illustration on real data. Simulation studies were carried out with two conditions, the number of different items, namely 20 items and 40 items in the format of the polytomous response. Each condition of the different items will be conditioned with 10%, 20% and 50% of missing data with the number of respondents as many as 1000 respondents. The person-trait estimation is done with the expected a posteriori (EAP) based on the GRM model and RMSE analysis. The person-trait estimation results will be correlated using Pearson product moment. The results showed that the zero imputation method was more effectively used when the percentage of missing data was 50%. In addition, the more items used in a tes, the more information about the trait measured will be provided.
| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | SKR/FISIP/2019/452/051904140 |
| Uncontrolled Keywords: | Graded Response Model, Items Response Theory, missing data, zero imputation Graded Response Model, Items Response Theory, missing data, zero imputation |
| Subjects: | 500 Natural sciences and mathematics > 511 General principles of mathematics > 511.8 Mathematical models (Mathematical simulation) |
| Divisions: | Fakultas Ilmu Sosial dan Ilmu Politik > Psikologi |
| Depositing User: | Budi Wahyono Wahyono |
| Date Deposited: | 20 Jul 2020 07:36 |
| Last Modified: | 24 Sep 2020 04:08 |
| URI: | http://repository.ub.ac.id/id/eprint/170357 |
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