Linda Suskie

  A Common Sense Approach to Assessment & Accreditation

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Lessons from the Election for Assessment

Posted on November 21, 2016 at 2:45 PM

The results of the U.S. presidential election have lessons both for American higher education and for assessment. Here are the lessons I see for meaningful assessment; I’ll tackle implications for American higher education in my next blog post.

 

Lesson #1: Surveys are a difficult way to collect meaningful information in the 21st century. If your assessment plan includes telephone or online surveys of students, alumni, employers, or anyone else, know going in that it’s very hard to get a meaningful, representative sample.

 

A generation ago (when I wrote a monograph Questionnaire Survey Research: What Works for the Association of Institutional Research), most people had land line phones with listed numbers and without caller ID or voice mail. So it was easy to find their phone number, and they usually picked up the phone when it rang. Today many people don’t have land line phones; they have cell phones with unlisted numbers and caller ID. If the number calling is unfamiliar to them, they let the call go straight to voice mail. Online surveys have similar challenges, partly because databases of e-mail addresses aren’t as readily available as phone books and partly because browsing habits affect the validity of pop-up polls such as those conducted by Survey Monkey. And all survey formats are struggling with survey fatigue (how many surveys have you been asked to complete in the last month?).

 

Professional pollsters have ways of adjusting for all these factors, but those strategies are difficult and expensive and often beyond our capabilities.

 

Lesson #2: Small sample sizes may not yield meaningful evidence. Because of Lesson #1, many of the political polls we saw were based on only a few hundred respondents. A sample of 250 has an error margin of 6% (meaning that if, for example, you find that 82% of the student work you assessed meets your standard, the true percentage is probably somewhere between 76% and 88%). A sample of 200 has an error margin of 7%. And these error margins assume that the samples of student work you’re looking at are truly representative of all student work. Bottom line: We need to look at a lot of student work, from a broad variety of classes, in order to draw meaningful conclusions.

 

Lesson #3: Small differences aren’t meaningful. I was struck by how many reporters and pundits talked about Clinton having, say, a 1% or 2% point lead without mentioning that the error margin made these leads too close to call. I know everyone likes to have a single number—it’s easiest to grasp—but I wish we could move to the practice of reporting ranges of likely results, preferably in graphs that show overlaps and convey visually when differences aren’t really significant. That would help audiences understand, for example, whether students’ critical thinking skills really are worse than their written communication skills, or whether their information literacy skills really are better than those of their peers.

 

Lesson #4: Meaningful results are in the details. Clinton won the popular vote by well over a million votes but still lost enough states to lose the Electoral College. Similarly, while students at our college may be doing well overall in terms of their analytic reasoning skills, we should be concerned if students in a particular program or cohort aren’t doing that well. Most colleges and universities are so diverse in terms of their offerings and the students they serve that I’m not sure overall institution-wide results are all that helpful; the overall results can mask a great deal of important variation.

 

Lesson #5: We see what we want to see. With Clinton the odds-on favorite to win the race, it was easy to see Trump’s chances of winning (anywhere from 10-30%, depending on the analysis) as insignificant, when in fact these probabilities meant he had a realistic chance of winning. Just as it was important to take a balanced view of poll results, it’s important to bring a balanced view to our assessment results. Usually our assessment results are a mixed bag, with both reasons to cheer and reasons to reflect and try to improve. We need to make sure we see—and share—both the successes and the areas for concern.

 

 

Categories: Ideas

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