By Michael Rinck – Vice President
The Oakland A’s brand of Billy Ball brought to light the use of data as a competitive advantage for baseball teams. But for any data to be useful, it needs to be clean. We’re pretty sure cleaning research data doesn’t conjure up thoughts of baseball. Probably not right off the bat, anyway (pun intended). However, a deeper dive into the methods we employ to clean data are not unlike the myriad strategies used in the game of baseball itself . We guard against stolen bases, throw curveballs, and strike out those who are less than honest with their responses. And yes, occasionally we consult the instant replay. How, you might ask? Allow us to throw out the first pitch and explain:
THROW THE BUM OUT
Cleaning data is the quality control art of removing bad data that simply cannot be trusted. Just as a manager will pull his pitcher if he can’t throw strikes or thinks he can’t, don’t be afraid to toss erratic data.
You’ve invested a lot of time and money in creating a survey. You built it, and hoped they would come (okay sorry, reaching here). And you’ve invested a lot of time in scoping out sample and making sure you are targeting the right respondents for your research objectives. You collect the data. Now it’s time to step up to the plate and clean it—that is, remove bad or incomplete records.
Good data cleansing is not just about eliminating data but also ensuring its consistency. Data cleaning leads to high quality data the way good pitching leads to strikeouts. When data is of excellent quality, it can be easily processed and analyzed, leading to insights that help the organization make better decisions.
If you’re a baseball fan (and even if you’re not) here are 4 easy-to-remember and important tips for cleaning data:
- Stealing Bases: Speeding is considered problematic survey behavior because respondents are not providing thoughtful, accurate answers. Consequently, the data they provide may be of poor quality, and in turn, may have to be discarded so survey estimates are not adversely affected. One rule is that the length of interview needs to be at least 40% of the average length of the survey. Thus, if the average length is 10 minutes, we might cut off a speeder at 4 minutes. Of course, there are exceptions to the rule. For example, we need to adjust time requirements for skip patterns, open-ends, loading time for images, etc.
- A Curve Ball for Straight-Liners: We can see if a respondent straight-lines a series of rating scale questions. For example, if there are 20 statements being rated, we will check to see how many of the statements were given the same code. We can run a distribution of the count of codes to calculate what percentage of straight-lining we will use to remove a respondent (i.e., terminate a respondent out if they straight-lined (same code) 18 of the 20 statements).
- Three Strikes and You’re Out: For example, in the screener we may ask: What brands have you consumed in the past 3 months? And later in the survey we may have a follow up question detailing when exactly did they last consume that brand (with a scale, Last Week/Last Month/2-3 Months ago/6-12 months ago/Over a year ago)? If the respondent answers longer than past 3 months, we will flag that respondent. Depending on the survey, we may have up to 10 flags programmed for various questions. A red herring question may also be included that would also count as a flag. An example of a red herring question may be to ask about past 3-month consumption for a fictitious brand. Any respondents indicating consumption of a nonexistent brand will earn a flag. During the soft launch phase of fielding, we can determine how many flags are occurring and looking at that distribution we can determine what level we should terminate a respondent (i.e., term a respondent if they have 3 flags, 2 flags, etc.).
- Going to Instant Replay—Checking Response in Verbatims Questions: We can review the open-ends to verify that the respondent is answering the survey in a thoughtful manner. Short answers or garbage responses will count as flags.
To summarize, make sure that that the data you are including in the final dataset is accurate and reflects thoughtful and engaged respondents. Why spend all the time and money upfront and include garbage data points? Garbage in = Garbage out.
Utilize these top tips and you’ll avoid any foul balls in your research? Let Merrill Research help you pick up your game!
_________________________________________________________ George Will, Men at Work: The Craft of Baseball (1990)