For the Love of Counting: The Response Rate Rat Race

2015-03-14_OhNoLogo22-abby3I’m in the midst of our annual summer experiences survey – my office’s push to understand what do students do over the summer? And is it meaningful? We know that getting ALL students to respond to our 3-12* question survey would be near impossible, but, as the assessment person on the team, it’s my job to always chase that dream (let’s be real, it’s my obsession to chase that dream!). And at a small institution like where I work getting a response rate of 100% (~1500 students) is seemingly an attainable goal. But this raises so many questions for me.

A little bit of context about the survey. Students do many valuable things over the summer that add meaning to their college experience; the particular subsection of this data that chiefly interests me (as I rep the Career Center) is the number of students who intern.

Common statistical wisdom would tell me that if I am indeed going to report on how many students intern over the summer then I need a certain response rate in order to make an accurate, broader statement about what percentage of Carleton students intern. This stats wisdom is based on a few factors: my population size, my sample size, and the margin of error with which I’m comfortable (I know, I know…ugh, statistic terms. Or maybe some of you are saying YAY! Statistic terms! Don’t let me stereotype you):

Population size = 1500 (all the upperclass students)

Sample size = 1275 (well…this is the goal…which is an 85% response rate…but do I need this # to be accurate and broad?? Hmm…better look at what margin of error I’m comfortable with…)

Margin of error = um…no error??? Baaaahhhh statistics! Sorry readers, I’m not a stats maven. But that’s ok, because SurveyMonkey greatly helped me to determine this

Margin of Error

Ok, so if I want to be SUPER confident (99%) then my goal of 1,275 students (or an 85% response rate) will get me a VERY small margin of error (read: this is good). But, turns out if I look at this from the angle of sample size, I could have the same small margin of error if I only had 1,103 students respond (74% response rate).

Sample Size

So, at this point, I could ask: Why the heck am I busting my butt to get those extra 11% of respondents??? YARG! And statistically, that would be a valid question.

But I don’t ask that question. I know I chase the 85% and 100% response rate dream because I aim to serve ALL students. And even if statistically all the students after 1,103 respond consistently, there is likely an outlier…one or a few student stories that tell me something that the first 1,103 couldn’t that shape a better student experience for all.

So to all of you regardless of if you have a relatively small population size (like me) or a much larger one (hint, Mark, Michigan Engineering, hint), I say keep up the good work trying to reach and understand the stories of 100% of your students. It may be an impossible dream but that doesn’t make it any less worthy a pursuit.

*3-12 question survey based on what the student did over the summer - skip logic, woot woot!

3 thoughts on “For the Love of Counting: The Response Rate Rat Race

  1. Great post! And yes the outliers matter!!! Your last line “It may be an impossible dream but that doesn’t make it any less worthy a pursuit.” AMEN! Keep up the great! I love reading y’all blog!

    Liked by 1 person

  2. Pingback: Assessment Takes a Village: The Power of Collaboration |

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