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Outliers, Missing Values, and Reliability: An Integrated Framework for Pre-Processing of Coding Data

Outliers, Missing Values, and Reliability: An Integrated Framework for Pre-Processing of Coding Data

Swati Aggarwal, Shambeel Azim
Copyright: © 2017 |Pages: 15
ISBN13: 9781522510086|ISBN10: 1522510087|EISBN13: 9781522510093
DOI: 10.4018/978-1-5225-1008-6.ch014
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MLA

Aggarwal, Swati, and Shambeel Azim. "Outliers, Missing Values, and Reliability: An Integrated Framework for Pre-Processing of Coding Data." Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making, edited by Arun Kumar Sangaiah, et al., IGI Global, 2017, pp. 316-330. https://doi.org/10.4018/978-1-5225-1008-6.ch014

APA

Aggarwal, S. & Azim, S. (2017). Outliers, Missing Values, and Reliability: An Integrated Framework for Pre-Processing of Coding Data. In A. Sangaiah, X. Gao, & A. Abraham (Eds.), Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making (pp. 316-330). IGI Global. https://doi.org/10.4018/978-1-5225-1008-6.ch014

Chicago

Aggarwal, Swati, and Shambeel Azim. "Outliers, Missing Values, and Reliability: An Integrated Framework for Pre-Processing of Coding Data." In Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making, edited by Arun Kumar Sangaiah, Xiao-Zhi Gao, and Ajith Abraham, 316-330. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1008-6.ch014

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Abstract

Reliability is a major concern in qualitative research. Most of the current research deals with finding the reliability of the data, but not much work is reported on how to improve the reliability of the unreliable data. This paper discusses three important aspects of the data pre-processing: how to detect the outliers, dealing with the missing values and finally increasing the reliability of the dataset. Here authors have suggested a framework for pre-processing of the inter-judged data which is incomplete and also contains erroneous values. The suggested framework integrates three approaches, Krippendorff's alpha for reliability computation, frequency based outlier detection method and a hybrid fuzzy c-means and multilayer perceptron based imputation technique. The proposed integrated approach results in an increase of reliability for the dataset which can be used to make strong conclusions.

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