What Are Some Data Collection Challenges and How Do You Overcome Them? (Part 3)

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Getting it right or as close to right as possible is critical when collecting data.  Failure to properly collect data include the inability to answer your research questions, inability to repeat and validate the study, distorted findings, wasted resources, misleading recommendations and decisions, and harm to participants.  

There are many things that could go wrong when collecting data, which may ultimately compromise the trustworthiness of your findings.  To help you think through potential challenges and how to address them, here are some challenges and advice to consider as a nonprofit organization.  

This article focuses on the challenges presented After Data Collection.

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  • Challenge: Disorganized databases.

Consideration: Take every effort to create clean and organized databases before the data entry has begun (do not wait until you need the database).  Provide step-by-step instructions and have trial runs with those entering the data.  To minimize data errors, include drop-down options.  In addition to actual survey data, be sure to include: survey respondent’s ID, data enterer’s ID, and date of entry. 

  • Challenge: Lost or overwhelming amount of data.

Consideration: To stay on top of incoming data, hire or assign a separate data entry team who daily enter data.  Each person should be assigned specific survey number ranges to complete daily.  The person who entered data should be noted in the database. 

  • Challenge: Invalid data entry.

Consideration: When creating the database, be sure to “lock” fields by providing drop-down options for data entry selections.  For every person working on data entry on a given day, randomly check 10% of data being entered.  If any part is entered incorrectly, STOP and recheck all the data that was entered that day.  

  • Challenge: Duplicate or missing data. Inevitably, it happens with every data set.  If duplicate or missing data is left unattended, it will skew your results. 

Consideration: Learn how to identify and remedy these issues within the database/analysis programs you are working with. 

Take every effort to set up appropriate measures before data collection begins (quality assurance) and then to follow the protocols during and after data collection (quality control). 

Be diligent in the process.  It is the only way you will be able to trust your findings.