A survey of methodologies for the treatment of missing values within datasets: limitations and benefits

Young, William A.; Weckman, Gary R.; Holland, Wade B.

doi:10.1080/14639220903470205

Cited by 86 publications

(45 citation statements)

References 52 publications

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“…There were many investigations and comparisons of the performance of missing data handling methods, both in general (Afifi & Elashoff, 1966;Graham, Hofer, MacKinnon, 1996;Haitovsky, 1968;Peng, Harwell, Liou, & Ehman, 2009;Peugh & Enders, 2004;Wayman, 2003;Young, Weckman, & Holland, 2011) and in context of specific factors such as proportion of missing data (Alosh, 2009;Knol et al, 2010;Rubin, 1987) and sample size (Alosh, 2009;Rubin, 1987). Because the current study is not a review of the literature, any comprehensive attempt to reproduce that discussion is beyond its immediate scope.…”

Section: Introductionmentioning

confidence: 99%

Some General Guidelines for Choosing Missing Data Handling Methods in Educational Research

Cheema

2014

J. Mod. App. Stat. Meth.

220

121

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The effect of a number of factors, such as the choice of analytical method, the handling method for missing data, sample size, and proportion of missing data, were examined to evaluate the effect of missing data treatment on accuracy of estimation. A methodological approach involving simulated data was adopted. One outcome of the statistical analyses undertaken in this study is the formulation of easy-to-implement guidelines for educational researchers that allows one to choose one of the following factors when all others are given: sample size, proportion of missing data in the sample, method of analysis, and missing data handling method.

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Section: Introductionmentioning

confidence: 99%

Some General Guidelines for Choosing Missing Data Handling Methods in Educational Research

Cheema

2014

J. Mod. App. Stat. Meth.

220

121

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“…A significant body of evidence has focused on comparing the performance of missing data handling methods, both in general [2][3][4] and in context of specific factors such as proportion of missing data and sample size [5][6][7]. More detailed technical aspects, and application of these methods in various fields can also be found in the works of Jones and Little [8,9].…”

Section: Dealing With Missing Datamentioning

confidence: 99%

Missing Data

Salgado

Azevedo

Proença

et al. 2016

Secondary Analysis of Electronic Health Records

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“…Several data cleaning techniques have been reported in the past [7][8][9][10][11] [16][17][18] to reconstruct the single missed value by multiple values with multiple times in incomplete dataset. This technique follows three phases to reconstruct the missed value in inconsistent object or instance in incomplete dataset.…”

Section: Related Workmentioning

confidence: 99%

A New Dynamic Data Cleaning Technique for Improving Incomplete Dataset Consistency

S¹

2017

IJITCS

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Abstract-This paper presents a new approach named Dynamic Data Cleaning (DDC) aims to improve incomplete dataset consistency by identifying, reconstructing and removing inconsistent data objects for future data analysis process. The proposed DDC approach consists of three methods: Identify Normal Object (INO), Reconstruct Normal Object (RNO) and Dataset Quality Measure (DQM). The first method INO divides the incomplete dataset into normal objects and abnormal objects (outliers) based on degree of missing attributes values in each individual object. Second, the (RNO) method reconstructs missed attributes values in the normal objects by the closest object based on a distance metric and removes inconsistent data objects (outliers) with higher missed data. Finally, the DQM method measures the consistency and inconsistency among the objects in improved dataset with and without outlier. Experimental results show that the proposed DDC approach is suitable to identify and reconstruct the incomplete data objects for improving dataset consistency from lower to higher level without user knowledge.

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A survey of methodologies for the treatment of missing values within datasets: limitations and benefits

Cited by 86 publications

References 52 publications

Some General Guidelines for Choosing Missing Data Handling Methods in Educational Research

Some General Guidelines for Choosing Missing Data Handling Methods in Educational Research

Missing Data

A New Dynamic Data Cleaning Technique for Improving Incomplete Dataset Consistency

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