Effectiveness of Valid Mean Substitution in Treating Missing Data in Attitude Assessment

Missing values are problems in data evaluation. Missing values analysis can resolve the problem of incomplete data that is not stored properly. The missing data can reduce the precision of calculation, since the amount of information is incomplete. The purpose of this study is to implement missing values handling method for systems/components maintenance historical data evaluation in RSG GAS. Statistical methods, such as listwise deletion and mean substitution, and machine learning (KNNI), were used to determine the missing data that correspond to the systems/components maintenance historical data. Mean substitution and KNNI methods were chosen since those methods do not require the formation of predictive models for each item which is experiencing missing data. Implementation of missing data analysis on systems/components maintenance data using KNNI method results in the smallest RMSE value. The result shows that KNNI method is the best method to handle missing value compared with listwise deletion or mean substitution.Keywords: missing value, data evaluation, alghorithm, implementation IMPLEMENTASI METODE PENANGANAN DATA HILANG UNTUK MENGEVALUASI DATA SEJARAH PERAWATAN SISTEM/KOMPONEN. Data hilang merupakan masalah dalam melakukan evaluasi data. Analisis data hilang dapat menyelesaikan permasalahan ketidaklengkapan data yang tidak tersimpan dengan baik. Data yang hilang akan memperkecil presisi dari perhitungan, dikarenakan jumlah informasi yang tidak lengkap. Tujuan dari penelitian ini adalah implementasi metode penanganan data hilang untuk evaluasi data sejarah perawatan sistem/komponen RSG GAS. Metodologi yang digunakan untuk menentukan data hilang yang berhubungan dengan data sejarah perawatan sistem/komponen adalah statistics, listwise deletion dan mean substitution, dan machine learning (KNNI). Metode mean substitution dan KNNI dipilih karena metode ini tidak memerlukan informasi untuk pembentukan model prediksi untuk setiap item yang mengandung data hilang. Implementasi analisis data hilang pada data perawatan sistem/komponen menggunakan metode KNNI menghasilkan nilai RMSE terkecil. Hasil ini menunjukan bahwa metode KNNI merupakan metode terbaik untuk menangani data hilang dibanding dengan listwise deletion atau mean substitution.Kata kunci: data hilang, evaluasi data, algoritma, implementasi

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Imputing Cross-Sectional Missing Data: Comparison of Common Techniques

Australian & New Zealand Journal of Psychiatry ◽

10.1080/j.1440-1614.2005.01630.x ◽

2005 ◽

Vol 39 (7) ◽

pp. 583-590 ◽

Cited By ~ 150

Author(s):

Graeme Hawthorne ◽

Peter Elliott

Keyword(s):

Missing Data ◽

Cross Sectional ◽

Single Item ◽

Hot Deck Imputation ◽

Listwise Deletion ◽

Regression Imputation ◽

Software Packages ◽

Complete Dataset ◽

Mean Substitution ◽

T Values

Objective: Increasing awareness of how missing data affects the analysis of clinical and public health interventions has led to increasing numbers of missing data procedures. There is little advice regarding which procedures should be selected under different circumstances. This paper compares six popular procedures: listwise deletion, item mean substitution, person mean substitution at two levels, regression imputation and hot deck imputation. Method: Using a complete dataset, each was examined under a variety of sample sizes and differing levels ofmissing data. The criteria were the true t-values for the entire sample. Results: The results suggest important differences. Ifmissing data are from a scale where about half the items are present, hot deck imputation or person mean substitution are best. Because person mean substitution is computationally simpler, similar in its efficiency, advocated by other researchers and more likely to be an option on statistical software packages, it is the method of choice. If the missing data are from a scale where more than half the items are missing, or with single-item measures, then hot deck imputation is recommended. The findings also showed that listwise deletion and item mean substitution performed poorly. Conclusions: Person mean and hot deck imputation are preferred. Since listwise deletion and item mean substitution performed poorly, yet are the most widely reported methods, the findings have broad implications.

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