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DATA CLEANING - ACAPS

+ data CLEANING April 2016 Dealing with messy data Table of Contents Introduction .. 0 A. The data CLEANING Process .. 0 B. Sources of Error .. 1 C. First Things First .. 2 D. Screening data .. 2 E. Diagnosing data .. 4 F. Treatment of data .. 4 G. Missing Values .. 5 H. Documenting Changes .. 6 I. Adapt Process .. 7 J. Recoding Variables .. 7 K. Quality Control Procedures .. 9 L. data Integration .. 10 M. Key Principles for data CLEANING .. 10 N. Tools and Tutorials for data CLEANING .. 11 O. Sources and Background Readings .. 11 Annex 1 Checklist for data CLEANING .. 13 Annex 2 Sample Job Description .. 15 Introduction No matter how data are collected (in face-to-face interviews, telephone interviews, self-administered questionnaires, etc.), there will be some level of error. Messy data refers to data that is riddled with inconsistencies.

Similarly, and under time pressure, consider the diminishing marginal utility of cleaning more and more compared to other demanding tasks such as analysis, visual display and interpretation. Understand when and how errors are produced during the data collection and workflow. Resources for data cleaning are limited.

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