Monitoring Data Quality

Data quality monitoring involves both checks done in the field and high-frequency checks run on the data. The primary field activity for monitoring data quality is Back Checks. High-frequency data checks include: response quality checks, programming checks and enumerator checks.

Read First

• All surveys should do Back Checks for at least 10% of the sample, aiming to stratify the backcheck sample by enumerator
• CAPI and CAFE Surveys should also do data checks while the survey is in the field. Data checks may be run for PAPI Surveys once the data entry is complete to assess data quality, though it will be too late to make changes.
• The best time to design and code the data checks is in parallel to the Questionnaire Design and the Questionnaire Programming. Data quality checks are often completed too late to be relevant or omit important tests when not written in parallel to the questionnaire.
• Data quality checks should be run daily, as the enumerator will still remember the interview if we have questions, and she/he is likely to still be able to go back to the respondent in case we have questions. These checks serve as additional enumerator support mechanisms that allow team members and enumerators to notice data discrepancies as they arise and resolve the issue(s) immediately.

Guidelines

Back Checks

Back Checks, also known as Survey Audits, are a second visit to the household to confirm the interview was conducted and verify key pieces of information. Best practice is for back checks to be completed by an independent third party.

High Frequency Checks

There are many different best practices on what checks to do during data quality checks. There is no best practice that is best in every single case, but this section presents a general framework useful to anyone that are new to data quality checks, for three main types of checks.

Response Quality Checks

As we do not know the 'true' answer, it is challenging to check the quality of responses. However there are a few areas we should check:

1. Consistency of responses across the survey instrument.
   1. For example, if a respondent answered that no one in the household worked outside of the household in a labor module, but then in the income reported earnings from wage labor, that would be inconsistent. Most of these tests can and should be built into the questionnaire programming. However, some checks may be overly complex to program in the survey instrument, particularly when comparing responses across rosters or dealing with multiple units. Let's say that we ask about plot size and harvest size and allow the respondent to answer in the unit of his/her choice. In order to test if the harvest in terms of kilos per hectare is plausible, we need to convert harvest and plot size to kilos and hectares, which may be challenging to program. The rule of thumb is to program as many checks as possible into the survey instrument, and to include all the rest in a Stata .do file or R script which will be run against the data on a daily basis.

1. Reasonable ranges of responses
   1. While range checks should always be programmed into the survey instrument, the typical recommendation is to use 'soft' constraints in the survey instrument (i.e. enumerators are warned that the response is unusual but can continue). Data checks should include checks for extreme values and outliers, and confirm whether those make sense in context. In addition, it is important to check range for constructed indicators as multiplication or division can create outliers even when the numerator and denominator are reasonable. For example, say a household reported they had a plot that was 0.05 hectares (the low end of an acceptable range) and produced 1000kg of maize (within an acceptable range): the yield for the plot would be 20,000kg/ha (extreme outlier).

Programming Checks

This type of tests help us understand if we have designed and programmed the questionnaire properly. Most programming errors should be caught when testing the questionnaire, but it is impossible to test all possible outcomes before data collection. This is especially important when there are last-minute edits to the survey instrument.

Enumerators Checks

Enumerator checks help us determine if any individual enumerator record data that are different from other enumerators in the data sets or different from the mean of a given question.

The enumerators are the eyes and the ears of the project in the field, and it is absolutely necessary for high data quality that our enumerators are making their best effort. Enumerator checks are meant as a tool to support enumerators that need extra help.

Practical Examples of Data Quality Checks

Duplicates and Survey Logs

It is very important to do quality checks on data during the survey implementation as it is difficult to fix the problem/re-collect the data if the error is found after the completion of the survey.

• Testing for Duplicates - Since SurveyCTO/ODK data has a number of duplicates, the first thing you need to do is check for duplicates and remove the duplicates. Use the command ieduplicates
• Test that all data from the field is on the server - Survey data logs from the field can then be matched with the logs from the survey data logs on the server to see if the all the data from the field has been transferred to the server.
• Test for target number - Since surveys are submitted in daily waves, you should keep track of the numbers of surveys that are submitted and the target number of surveys needed for an area to be completed.

Tip: Verifying that the data is complete should be done the day of or the day after the survey. Since, the interviewer is most likely close by, it would be easy to re-interview and get missing data if significant chunks of data were missing.

To see how to remove duplicates and check that all the field data is on servers, please see the main article at Duplicates and Survey Logs.

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This article is part of the topic Field Management

Additional Resources

• Innovation for Poverty Action's template for high frequency checks