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

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Author(s): Swati Aggarwal (NSIT, India)and Shambeel Azim (Vidyadaan Institute of Technology and Management, India)
Copyright: 2017
Pages: 15
Source title: Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making
Source Author(s)/Editor(s): Arun Kumar Sangaiah (VIT University, India), Xiao-Zhi Gao (University of Eastern Finland, Finland)and Ajith Abraham (Machine Intelligence Research Labs, USA)
DOI: 10.4018/978-1-5225-1008-6.ch014

Keywords: Business & Management / Business and Organizational Research / Business Science Reference / Decision Support Systems

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

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