Data validation is a process that checks the quality and accuracy of the data source before using, importing, or processing the data. It is a process that cleanses data after its collection. It is performed to ensure that data from various sources conform to the intended objectives and is not corrupted as a result of inconsistencies in context or type. Data validation ensures that a certain level of quality of the final data is achieved. Data validation gets rid of any falsification and covers areas of fraud, screening, procedure, completeness, and courtesy (Belouafa et al., 2017). Common errors that may arise during the data collection process include difficulties in defining and classifying statistical units, variations in interpreting questions, data recording errors, and other types of errors.
Data editing refers to the activity that detects and corrects errors (logical inconsistencies) in any given data. It is the process of reviewing and adjusting collected data with the aim of controlling its quality based on desired criteria (Chalmer, 2020). Errors made by the researcher and the respondents are eliminated using the data editing process
Data coding is the activity that transforms collected data into a set of cohesive and meaningful subcategories. Data coding provides summaries and re-presents data with the aim of providing a systematic account of the phenomena observed or recorded. Coding allows large amounts of data to be reduced into forms that can be more easily handled. Data analysis is made possible by data coding (Adu, 2019). It involves verbal data being converted into variables and categories of variables using numbers to facilitate entering the data into a computer program for purposes of analysis.
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