Impact of Missing Data on Data Quality in Social Research

Missing data is a common issue in quantitative social research that negatively affects the data quality. This article explores the consequences of missing data, outlining the potential issues it may pose and emphasizing the importance of properly addressing the missingness. It outlines the patterns of missing data, with a focus on the need to distinguish data that's Missing at Random and data that's Missing Not at Random, explaining how these patterns may affect the choice of handling methods. The article illustrates various approaches to managing missing data through a combination of hypothetical scenarios and case studies from actual research in order to showcase the application and effectiveness of various methods. It showcases the traditional methods of handling missing data, such as complete case analysis and simple imputation methods, and their limitations. Emphasizing the importance of advanced statistical techniques, the article advocates for the use of multiple imputation as a main method of choice when dealing with missing data. By providing a methodological comparison and a strategic framework for social scientists facing missing data challenges, this work provides a strategy to be employed by social scientists when dealing with missing data in order to ensure the proper data quality.