August brings into sharp focus and a furious boil everything I've been listening to in the late spring and summer--Henry Rollins
| This summer, faced with my youngest spending 3 weeks participating in an academic program at University of Chicago, Steve (husband) and I decided to take advantage of a long weekend in the lovely blue ridge mountains. The pace of summers vacillate between frenetic and this new normal--kids with new found independence and opportunities beyond the familiar. Fewer ferry rides to the beach and more sojourns with friends--pausing only to grab a set of keys and hoping for a little cash (just in case). I have observed an increased efficiency in my work as I evolved from a hired gun to simply write what I was asked to now being responsible for the data that informs the narrative. Don't get me wrong. I always did my due diligence with research and probing evidence-based medical articles but let me be frank. I only looked where the client was pointing. |  |
Perhaps I can blame it on fewer distractions but I do my best reading when faced with long stretches of quiet and solitude. I have a stack of books to get through, several from a course I am teaching in the fall at a local university--Understanding Data.
Maya Angelou is attributed to saying something like, "When you know better, you do better." I do a fair share of pro bono work teaching the basics of survey design and how to clean data upstream from the fancy dashboard visualizations that everyone is clamoring for. And I spend a non-trivial amount of time learning how to think about analysis.
When you have been working in a field like medical writing or healthcare consulting you realize the secret sauce is scalability. Do you want to be a data mechanic constantly repairing and fixing poorly designed questions or do you crave higher level collaborations? I have noticed many colleagues falling in with the status quo and giving their clients what they have asked for. Poorly designed or articulated outcomes questions from multiple choice surveys or a data question only interested in probing or interrogating a single source of data.
You can dance around to the music provided to you like a little monkey--or become curious. Is there a better way? Am I measuring what I think I am measuring or just grabbing low hanging bananas?
Maya Angelou is attributed to saying something like, "When you know better, you do better." I do a fair share of pro bono work teaching the basics of survey design and how to clean data upstream from the fancy dashboard visualizations that everyone is clamoring for. And I spend a non-trivial amount of time learning how to think about analysis.
When you have been working in a field like medical writing or healthcare consulting you realize the secret sauce is scalability. Do you want to be a data mechanic constantly repairing and fixing poorly designed questions or do you crave higher level collaborations? I have noticed many colleagues falling in with the status quo and giving their clients what they have asked for. Poorly designed or articulated outcomes questions from multiple choice surveys or a data question only interested in probing or interrogating a single source of data.
You can dance around to the music provided to you like a little monkey--or become curious. Is there a better way? Am I measuring what I think I am measuring or just grabbing low hanging bananas?
 | Although my toolbox has expanded over the years to include Python, SQL, and R--I use them with intense focus and deliberation. When you evolve skills into exploring large data sets you innately comprehend why certain types of questions are higher value than others. For example, I sat through a Tableau session on how to analyze Likert data--data gathered from those ubiquitous uni- (one attribute) or multi (contrasting opposites)-polar scales. You are asked how likely you might be to exhibit a certain behavior. |
The process, even with Tableau Prep can be quite laborious but is the juice worth the squeeze? I would say yes for prepping your data--but maybe not when formatting your survey data.
The perfect format for most analytics is ranking data. We want to be able to create a hierarchy of sentiment not only between questions but also between respondents. In Likert, a 5 or even a 7 response shouldn't be compared to a similar response on a different question. Just because a respondent selects Strongly Agree for example can we make assumptions that the degree of agreement here is the same as on a different question?
No. No we can't. But if we use probabilities like the ones we can generate from asking respondents to rank responses--we now know how they prioritized their behavior or sentiment.
And ranking or rating questions can be more straightforward to analyze.
Do you have questions regarding cleaning data and what tools are available?
Join us on August 14th for a Healthcare Tableau User Group meeting. You can register below...
No. No we can't. But if we use probabilities like the ones we can generate from asking respondents to rank responses--we now know how they prioritized their behavior or sentiment.
And ranking or rating questions can be more straightforward to analyze.
Do you have questions regarding cleaning data and what tools are available?
Join us on August 14th for a Healthcare Tableau User Group meeting. You can register below...
