2021
DOI: 10.47836/pjst.29.2.15
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Comparison of Single and MICE Imputation Methods for Missing Values: A Simulation Study

Abstract: High quality data is essential in every field of research for valid research findings. The presence of missing data in a dataset is common and occurs for a variety of reasons such as incomplete responses, equipment malfunction and data entry error. Single and multiple data imputation methods have been developed for data imputation of missing values. This study investigated the performance of single imputation using mean and multiple imputation method using Multivariate Imputation by Chained Equations (MICE) vi… Show more

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Cited by 4 publications
(1 citation statement)
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“…The combining of all-class-one network (ACON) with one-class-one network, (OCON) improved the recognition performance from 73.8% with ACON and 83.3% with OCON to with 87.1% (ACON+OCON). The results agreement with previous studies (Pham and Oztemel 1993, Pandya and Macy 1995, Simon 1999.…”
Section: Introductionsupporting
confidence: 93%
“…The combining of all-class-one network (ACON) with one-class-one network, (OCON) improved the recognition performance from 73.8% with ACON and 83.3% with OCON to with 87.1% (ACON+OCON). The results agreement with previous studies (Pham and Oztemel 1993, Pandya and Macy 1995, Simon 1999.…”
Section: Introductionsupporting
confidence: 93%