Taking a Hatchet to Your Matrix

Not a newsflash: I hate matrixes. That being said, I acknowledge they’re sometimes going to be necessary. If you’ve got to use one, though, I think it’s in everyone’s interest to keep each one as small as possible, and to use as few of them as possible.

There’s often a point in web surveys where the respondent is asked whether or not he has heard of a number of different items – brands of orange juice, for instance, to use my favorite example. That’s followed by another question asking which of the brands the respondent has personally tried.

Then come the matrixes, where respondents are asked to rate each of the brands that they’ve heard of – not just the subset they’ve personally tried – across a number of rating criteria, each one likely being its own matrix on its own page. This is the point where the respondent suddenly regrets being so honest about the brands he’s seen in the grocery store or advertised on TV, because he suddenly realizes he’s going to be spending the next fifteen minutes of his life clicking “don’t know” or “not applicable” on matrix after matrix inquiring about the best flavor, the least pulp, the nicest packaging, and so on. I get, very clearly, that as researchers, this isn’t entirely a waste of time – we can give our clients a report that shows the attitudes crosstabbed by both active users and those who are just aware of each brand. It has the added “bonus” of letting us inflate the number of respondents — you get to tell your client that you asked the evaluation questions of significantly more people than you would have if you’d only included those who use the brands in question. (This is the product research version of asking unlikely voters how they’ll be voting.) And, of course, it’s possible that some respondents will have differing levels of familiarity with the products they don’t themselves use, and may actually be able to provide useful feedback nevertheless. But, still:

I’m writing this, actually, as I take a break from a piece of research I’m in the middle of taking. I think I’m on about the sixth matrix page. I’ve got 8 columns going across – 7 point Likert plus a “not sure” – and 10 rows of brands going down, only 1 of which is asking me about something I truly have knowledge of – the other 9 are things I’ve heard of, but have no ability to evaluate. I don’t want to go into specifics, but let’s pretend it’s about travel, and that it first asked me which foreign cities I’d ever considered traveling to, and then asked which ones I’d actually visited — and now it’s asking me about every city I’d considered going to, to rate the quality of its museums, central train station, hotels, safety, and so on. There might be the occasional question I can answer based on something a friend told me or based on something I vaguely remember reading on Wikipedia or in a Rough Guide, but in general, I’m just not able to comment on the friendliness of the Dublin populace, you know?

Not only is this frustrating, but I’m also (and this wouldn’t apply to an ordinary respondent) acutely aware that my speeding through page after page, clicking “not sure” for 9 of the 10 choices and then assigning an answer choice to the one thing I’m familiar with is probably going to result in my responses being discarded anyway.

I have a sense, based on the level of detail each matrix has gone into, that I’m going to have another 4 or 5 of these waiting for me, and honestly, I’m hoping I time out while I write this; if I do, I’m done.

Is an aggravated respondent really in anyone’s best interest?

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4 Comments

Filed under bad user experiences, data quality, Market Research, matrixes make me cry, web research

4 responses to “Taking a Hatchet to Your Matrix

  1. Giles

    I wholeheartedly agree about matrices, or batteries as I learnt to know them. I prefer the term batteries because of the pejorative connotation of battering the respondent, which is what they do. Particularly ones that ask you to rate something you don’t really know anything about. I can however vouch for the friendliness of Dubliners!
    I wonder if your break to write this post inflated your interview length to the point where it looked like you weren’t speeding?

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  3. Questionnairews which are overly dense, which force respondents through tiresome & time-consuming grids and which assume unnatural (to them) levels of knowledge or interest have always been the BANE of quant. research. This was bad enough with Telephone & Face-to-Face surveys in the good old days – but at least you had a human questioner to bring some semblance of control and to coax the respondent to give honest answers. On the web such questions are utterly self-defeating because the risk of low-engagement, unthinking answers is huge. Does this not occur to the clients who demand this stuff – or is it just a case of ‘out of sight, out of mind?’. Maybe all Marketing & Insight clients should be tied to a chair for a day and forced to answer a series of inane and infuriating web questionnaires before they get their wings? Jem Keen, UK

  4. I agree with Jem. Two points. First, worth remembering that a lot of these questions were used on the phones because other means (such as ranking or constant sum) were not practical on the phones. So they were a compromise…question is, why would we use them on the web?

    Second, always frustrating for me when we ask respondents about things that we know they know nothing about. Methods like constant sum help reduce the annoyance a bit, but I’d still prefer to limit questions to those who have something to say. In college I was a telephone interviewer and on one study I had to ask questions about “Weight Watchers Frozen Dinners” among anyone who had heard of “Weight Watchers”. In fact, after a battery of attributes I had to ask two open ends about “likes” and “dislikes” of the meal. Respondents gave bland answers to the first (“I guess they are low in calories”) and had no idea on the second. One respondent, however, grew increasingly annoyed and having explained countless times that she had heard of Weight Watchers but knew nothing about the meals, decided to have some fun. When asked what she didn’t like, she replied, “I don’t like it when I cook them and they blow up, leveling my entire neighborhood.” I always wondered if that got a laugh in coding.

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