Survey Data Collection Part 1: 12 Pre-launch Steps to Quality Data

by | Aug 3, 2015 | Business Leadership, Social Science Business, Survey Methodology, Survey Operations

Ready to collect survey data?  It’s just a press of a button, right?

How I wish that were the case!  As with any science, care must be taken to ensure there is a consistent application.  Bias introduced by inconsistencies in the study implementation is not desirable.  Survey data researchers can minimize such bias by reducing the impact of unintended errors.

It is important to state that I believe that there is no perfectly implemented complex survey data collection.  We are humans studying humans, so there will always be unintended biases.  Our goal should be reduction, not the unrealistic ideal of perfection.

Today I would like to share the first six (of twelve) areas to consider prior to data collection launch.  Look for the remaining items in my next post.


Research is best done with collaborative working relationships that extend within and beyond research teams.  This collaboration is the basis for ensuring consistent and full understanding of the goals and trade-offs required to execute a quality study and collect quality survey data.  Share a common ground and understanding with all collaborators before proceeding.


Implementation of survey research is a logistical problem that calls for a logistic solution.  This means a detailed schedule that is built on a foundation of the work already done and provides a path for all efforts before, during, and after the data collection.  The best methodologies at work cannot contribute anything to the study if not implemented in a timely manner and in an appropriate context.


Survey methodologists are valuable (and can be expensive!) for a reason – they have an expertise in the conduct of surveys, and not only can help avoid quality problems, but can contribute effectively to the trade-off discussion (i.e. “I have $12,000 left in my study budget for incentives – how could we use that most effectively?”).  For the results of your data to be solid, the survey methodology must be sound, with trade-offs communicated and determined purposefully.


The best questionnaire fielded with the best data collection methodology is useless if the sample used for a study does not meet the study goals.  With the design documented, including limitations, a solid sampling plan and execution is critical for any study’s success.


Often overlooked by many researchers, thinking about the data early and frequently is critical to a successful study.  Discuss data needs before the questionnaire is even written, again before the survey is programmed or designed, and again after the survey is tested.  Check data frequently and critically in the early phases of data collection, and periodically after.  After the data collection is completed should not be the first time you look at your data.


The questions and answers included in a social science research study are the tools by which researchers measure many elements of human behavior.  However, the questionnaire does not stop at the questions—it includes any other mechanism by which we capture data from humans.  From mobile geolocation captures to innovative links with bluetooth bio-collection devices, a great questionnaire is a key to a great dataset.

Keep checking back to see items seven through twelve of our pre-launch steps to quality survey data!

About the Author

Scott D. Crawford

Scott D. Crawford is the Founder and Chief Vision Officer at SoundRocket. He is also often found practicing being a husband, father, entrepreneur, forever-learner, survey methodologist, science writer & advocate, and podcast lover. While he doesn’t believe in reincarnation, he’s certain he was a Great Dane (of the canine type) in a previous life.