Data Cleaning

Data cleaning is an essential step between data collection and data analysis. The aim is to (i) identify data errors, (ii) correct errors, and (iii) improve data collection process.

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It is really difficult to have a fully efficient data collection procedure in place that would generate error-free raw data. Any output of raw data needs some level of cleaning, either minor or major. Through the cleaning process, the research team can learn lessons and feed such information into next round's data collection, and to make the whole process more efficient.

Data cleaning becomes essential because without it any analytical work loses validity. Models used in research work assume data to be clean at the least.

Data cleaning is an important aspect of any impact evaluation project. Almost every research team keep research assistant(s) solely for the purpose of data cleaning, hence the additional costs.

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