HESA

Higher Education Strategy Associates

Tag Archives: Survey Methodology

April 05

Student/Graduate Survey Data

This is my last thought on data for awhile, I promise.  But I want to talk a little bit today about what we’re doing wrong with the increasing misuse of student and graduate surveys.

Back about 15 years ago, the relevant technology for email surveys became sufficiently cheap and ubiquitous that everyone started using them.  I mean, everyone.  So what has happened over the last decade and a half has been a proliferation of surveys and with it – surprise, surprise – a steady decline in survey response rates.  We know that these low-participation surveys (nearly all are below 50%, and most are below 35%) are reliable, in the sense that they give us similar results year after year.  But we have no idea whether they are accurate, because we have no way of dealing with response bias.

Now, every once in awhile you get someone with the cockamamie idea that the way to deal with low response rates is to expand the sample.  Remember how we all laughed at Tony Clement when he claimed  the (voluntary) National Household Survey would be better than the (mandatory) Long-Form Census because the sample size would be larger?  Fun times.  But this is effectively what governments do when they decide – as the Ontario government did in the case of its sexual assault survey  – to carry out what amounts to a (voluntary) student census.

So we have a problem: even as we want to make policy on a more data-informed basis, we face the problem that the quality of student data is decreasing (this also goes for graduate surveys, but I’ll come back to those in a second).  Fortunately, there is an answer to this problem: interview fewer students, but pay them.

What every institution should do – and frankly what every government should do as well – is create a balanced, stratified panel of about 1000 students.   And it should pay them maybe $10/survey to complete surveys throughout the year.  That way, you’d have good response rates from a panel that actually represented the student body well, as opposed to the crapshoot which currently reigns.  Want accurate data on student satisfaction, library/IT usage, incidence of sexual assault/harassment?  This is the way to do it.  And you’d also be doing the rest of your student body a favour by not spamming them with questionnaires they don’t want.

(Costly?  Yes.  Good data ain’t free.  Institutions that care about good data will suck it up).

It’s a slightly different story for graduate surveys.  Here, you also have a problem of response rates, but with the caveat that at least as far as employment and income data is concerned, we aren’t going to have that problem for much longer.  You may be aware of Ross Finnie’s work  linking student data to tax data to work out long-term income paths.  An increasing number of institutions are now doing this, as indeed is Statistic Canada for future versions of its National Graduate Survey (I give Statscan hell, deservedly, but for this they deserve kudos).

So now that we’re going to have excellent, up-to-date data about employment and income data we can re-orient our whole approach to graduate surveys.  We can move away from attempted censuses with a couple of not totally convincing questions about employment and re-shape them into what they should be: much more qualitative explorations of graduate pathways.  Give me a stratified sample of 2000 graduates explaining in detail how they went from being a student to having a career (or not) three years later rather than asking 50,000 students a closed-ended question about whether their job is “related” to their education every day of the week.  The latter is a boring box-checking exercise: the former offers the potential for real understanding and improvement.

(And yeah, again: pay your survey respondents for their time.  The American Department of Education does it on their surveys and they get great data.)

Bottom line: We need to get serious about ending the Tony Clement-icization of student/graduate data. That means getting serious about constructing better samples, incentivizing participation, and asking better questions (particularly of graduates).  And there’s no time like the present. If anyone wants to get serious about this discussion, let me know: I’d be overjoyed to help.

February 23

Garbage Data on Sexual Assaults

I am going to do something today which I expect will not put me in good stead with one of my biggest clients.  But the Government of Ontario is considering something unwise and I feel it best to speak up.

As many of you know, the current Liberal government is very concerned about sexual harassment and sexual assault on campus, and has devoted no small amount of time and political capital to getting institutions to adopt new rules and regulations around said issues.  One can doubt the likely effectiveness of such policies, but not the sincerity of the motive behind them.

One of the tools the Government of Ontario wishes to use in this fight is more public disclosure about sexual assault.  I imagine they have been influenced with how the US federal government collects and publishes statistics on campus crime, including statistics on sexual assaults.  If you want to hold institutions accountable for making campuses safer, you want to be able to measure incidents and show change over time, right?

Well, sort of.  This is tricky stuff.

Let’s assume you had perfect data on sexual assaults by campus.  What would that show?  It would depend in part on the definitions used.  Are we counting sexual assaults/harassment which occur on campus?  Or are we talking about sexual assaults/harassment experiences by students?  Those are two completely different figures.  If the purpose of these figures is accountability and giving prospective students the “right to know” (personal safety is after all a significant concern for prospective students), how useful is that first number?  To what extent does it make sense for institutions to be held accountable for things which do not occur on their property?

And that’s assuming perfect data, which really doesn’t exist.  The problems multiply exponentially when you decided to rely on sub-standard data.  And according to a recent Request for Proposals placed on the government tenders website MERX, the Government of Ontario is planning to rely on some truly awful data for its future work on this file.

Here’s the scoop: the Ministry of Advanced Education and Skills Development is planning to do two surveys: one in 2018 and one in 2024.  They plan on getting contact lists of emails of every single student in the system – at all 20 public universities, 24 colleges and 417 private institutions and handing them over to a contractor so they can do a survey. (This is insane from a privacy perspective – the much safer way to do this is to get institutions to send out an email to students with a link to a survey so the contractor never sees the names without students’ consent).  Then they are going to send out an email to all those students – close to 700,000 in total – offering $5/per head to answer a survey.

Its not clear what Ontario plans to do with this data.  But the fact that they are insistent that *every* student at *every* institution be sent the survey suggests to me that they want the option to be able to analyze and perhaps publish the data from this anonymous voluntary survey on a campus by campus basis.

Yes, really.

Now, one might argue: so what?  Pretty much every student survey works this way.  You send out a message to as many students as you can, offer an inducement and hope for the best in terms of response rate.  Absent institutional follow-up emails, this approach probably gets you a response rate between 10 and 15% (a $5 incentive won’t move that many students)  Serious methodologists grind their teeth over those kinds of low numbers, but increasingly this is the way of the world.  Phone polls don’t get much better than this.  The surveys we used to do for the Globe and Mail’s Canadian University Report were in that range.  The Canadian University Survey Consortium does a bit better than that because of multiple follow-ups and strong institutional engagement.  But hell, even StatsCan is down to a 50% response rate on the National Graduates Survey.

Is there non-response bias?  Sure.  And we have no idea what it is.  No one’s ever checked.  But these surveys are super-reliable even if they’re not completely valid.  Year after year we see stable patterns of responses, and there’s no reason to suspect that the non-response bias is different across institutions.  So if we see differences in satisfaction of ten or fifteen percent from one institution from another, most of us in the field are content to accept that finding.

So why is the Ministry’s approach so crazy when it’s just using the same one as every one else?  First of all, the stakes are completely different.  It’s one thing to be named an institution with low levels of student satisfaction.  It’s something completely different to be called the sexual assault capital of Ontario.  So accuracy matters a lot more.

Second, the differences between institutions are likely to be tiny.  We have no reason to believe a priori that rates differ much by institutions.  Therefore small biases in response patterns might alter the league table (and let’s be honest, even if Ontario doesn’t publish this as a league table, it will take the Star and the Globe about 30 seconds to turn it into one).  But we have no idea what the response biases might be and the government’s methodology makes no attempt to work that out.

Might people who have been assaulted be more likely to answer than those who did not?  If so, you’re going to get inflated numbers.  Might people have reasons to distort the results?  Might a Men’s Rights group encourage all its members to indicate they’d been assaulted to show that assault isn’t really a women’s issue?  With low response rates, it wouldn’t take many respondents to get that tactic to work.

The Government is never going to get accurate overall response rates from this approach.  They might, after repeated tries, start to see patterns in the data: sexual assault is more prevalent in institutions in large communities than in small ones, maybe; or it might happen more often to students in certain fields of study than others.  That might be valuable.  But if the first time the data is published all that makes the papers is a rank order of places where students are assaulted, we will have absolutely no way to contextualize the data, no way to assess its reliability or validity.

At best, if the data is reported system-wide, the data will be weak.  A better alternative would be to go with a smaller random sample and better incentives so as to obtain higher  response rates.  But if it remains a voluntary survey *and* there is some intention to publish on a campus-by campus basis, then it will be garbage.  And garbage data is a terrible way to support good policy objectives.

Someone – preferably with a better understanding of survey methodology – needs to put a stop to this idea.  Now.

February 29

The Heinous Difficulty in Understanding What Works

The empirical consensus on the question of barriers to access in Canadian education is pretty clear: and among those few secondary school graduates who don’t go on to post-secondary education, affordability is very much a secondary issue (not non-existent, but secondary). The primary issue is that most of these young people don’t feel very motivated by the idea of spending more years in a classroom. It’s a vicious circle: these students don’t identify with education, so they don’t work at it, so they receive poor grades and become even more demotivated.

The problem is that it is easier to identify problems than solutions. Big national datasets like the Youth in Transition Survey (YITS) can help identify relationships between inputs and outputs factors, but are useless at examining the effects of interventions because they simply don’t capture the necessary information. What is needed is more small-scale experimentation with various types of interventions, along with rigorously-designed research to help understand their impacts.

This, by the way, is the tragedy of Pathways to Education. It ought to work because it ticks nearly all the boxes that the literature suggests should ameliorate access. But for some reason there has yet to be any serious attempt to evaluate its outcomes (my bet is that Pathways board members prefer anecdotes to data for fundraising purposes – and given their fundraising success to date, it’s hard to blame them). That’s a shame, because if they are on to something it would be useful to know what it is so that it can be replicated.

Now, one shouldn’t pretend that these evaluations are easy. In the United States, a top-notch research company’s multi-year, multi-million-dollar evaluation of the Upward Bound program is currently the subject of intense controversy because of a dispute regarding how data from different intervention sites was weighted. Do it one way (as the evaluators did) and there’s no significant result, do it another and a significant effect appears.

The Upward Bound controversy is a shame because of its likely chilling effect on research in this area. Governments might well question the point of funding research if the results are so inconclusive. But the nature of social interventions is such that there are hundreds of factors that can affect outcomes and hence research is always going to be somewhat tentative.

So what’s the way forward? Research can’t be abandoned, but probably needs to go small-scale. Having lots of small experimental results aggregated through meta-analysis will in the end probably yield far better results than will mega-experiments or more large-scale surveys. It might take a little longer, but it’s both more financially feasible and more likely to deliver durable results.