Checklist questions are one of the more common survey question types, and they are also used heavily in data collection forms, which are a form of survey. And for good reason. You can get specific, actionable answers to a question – if the question is written correctly. A poorly designed checklist question can hide problems and confuse interpretation.
Ranked Choice Voting (RCV) is a different voting process that is being pushed due to purported benefits. This article describes the concept along with its pros and its cons.
Surveys are conducted to learn how some group feels. If the survey questions are flawed, then we don’t learn and may be misled. Ambiguous questions — questions whose phrasing leads to multiple interpretations — are the single biggest mistake made by survey designers. And perhaps a fatal one.
Various survey question types can be used to measure something. The choices have trade-offs between analytical usefulness of the data and respondent burden.
A confluence of survey biases – response, interviewer & instrumentation – likely overwhelmed what the NY Times’ surveyors think they measured about people’s feelings about having a female presidential candidate.
I’m anxious about your reaction to this article.
Unclear what I mean by that? That’s exactly the point. When designing survey questions and response scales for interval rating questions, It is critical to have “clarity of meaning” and “lack of ambiguity.” Without those you won’t be capturing valid, useful data, data that don’t suffer from instrumentation bias. “Anxious” is an anchor that has multiple meanings and thus should not be used in political surveys. Yet it is.
Proper survey question wording is essential to generate valid, meaningful data for organizational decisions. A survey question wording bias will lead to misleading interpretations and bad decisions. Here we examine a Pew survey on use of mobile devices in public settings.
Good question phrasing is an art form, and even the pros can make mistakes. Here we’ll show a question wording bias example from a survey done by Pew Research Center. Ambiguity in question wording likely led to incorrect data and conclusions. It provides some useful lessons for all survey designers.
Each of the five survey question types has potential value in a survey research project. This article presents brief definitions of each question type, the analysis you can do, and some key concerns with each question type.
Summary: Mobile survey taking has shot up recently. When a survey invitation is sent via email with a link to a webform, up to a third of respondents take the survey on a smartphone or equivalent. Survey designers must consider this when designing their surveys since some questions will not display properly on a mobile device. This article presents the issue and some tips for good practice.
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If you do any kind of design work that involves information displays on webpages, you know the challenges of designing when you don’t know the screen size that will be used. It makes you long for the days of green screens and VT 100s when the mouse was something that your cat killed and a trackpad was somewhere you ran.
Screen sizes and resolution always seem to be in flux as we’ve moved from desktops to laptops, netbooks, smartphones, tablets, and phablets.
As part of rewriting my Survey Guidebook, I have been talking with survey software vendors, and the dramatic rise of the survey taking via smartphones is a big issue. Roughly around one quarter to one third of survey submissions are coming via smartphone devices. Ouch!
The issue is: how does your survey render onto a smartphone? Website designers have tackled this with responsive website design, and the same issues apply for surveyors. But the issue is perhaps more critical for surveyors. While the webforms might be responsive to the size of the screen on which the survey will be displayed, the fact is some survey questions simply will not display well on a small screen.
For example I love — or loved — to set up my questions in what’s called a “grid” format (sometimes called table or matrix format). It’s very space efficient and reduces respondent burden. However that question layout does not work well, if at all, on a phone screen, even a phablet.
Look at the nearby screen shot from a survey I took. Notice how the anchors — the written descriptions — for the four response options crowd together. Essentially any question where the response options are presented on horizontally may not display well.
The webform rendering may have implications for the validity of the data collected. If one person takes a survey with the questionnaire rendered for a 15 inch laptop screen while another person takes the “same” survey rendered for a 5-inch smartphone screen, are they taking the same survey?
We know that survey administration mode affects responses. Telephone surveys tend to have more scores towards the extremes of response scales. Will surveys taken by smartphones have some kind of response effects? I am not aware of any research analyzing this, but I will be on the lookout for it or conduct that research myself.
So what are the implications for questionnaire design from smartphone survey taking?
First, we have to rethink the question design that we use. We may have to present questions that display the response options vertically as opposed to horizontally. This is a major impact. If you are going to use a horizontal display for an interval rating question, then you should use endpoint anchoring as opposed to having verbal descriptors for each scale point. Endpoint anchoring may allow display of the response scale without cramped words. But make sure you have constant spacing or you’re compromising the scale’s interval properties!
Second, we have to simplify our questionnaires. We need to make them shorter. A survey that may be perfectly reasonable to take from a time perspective on the laptop with a table display will almost certainly feel longer to complete on the phone because of the amount of scrolling required. While smartphone users are used to scrolling, there must be a limit to people’s tolerance. A 30-question survey on a laptop might take 3 to 6 screens but take 30 screen’s worth of scrolling on a smartphone. You might be tempted to put one question per screen to limit the scrolling. However, the time to load each screen, even on a 4G network, may tax the respondent’s patience.
Third, beyond question design we should reconsider the question types we use, such as forced ranking and fixed sum. Those are especially useful for trade-off analysis to judge what’s important to a respondent. However, they would be very challenging to conduct on a small screen. So, how do we conduct trade-off analysis on a smartphone? I’m not sure. Probably using a multiple choice question asking the respondent to choose the top two or three items.
Fourth, extraneous verbiage now becomes even more extraneous. In my survey workshops I stress to remove words that don’t add value. With smart phone rendering, it becomes absolutely essential. Introductions or instructions that would cover an entire screen on the laptop would simply drive away a smartphone respondent. Even the questions should be as brief as possible as well as the response options. The downside is that we may be so brief as to not be clear, introducing ambiguity.
Fifth, the data should be analyzed first by device on which the survey was taken. Are the scores the same? (There are statistical procedures for answering that question.) If not, the difference could be due to response effects caused by the device or a difference in the type of people who use the each device. Young people who have grown up with personal electronic devices are more likely to take surveys on a mobile device. So are differences in scores between devices a function of respondents’ age or a function of the device and how it displays the survey (response effects)? Without some truly scientific research, we won’t know the answer.
Not analyzing the data separately assumes the smartphone survey mode has no response effects. That could be a bad assumption. We made that same assumption about telephone surveys, and we now know that is wrong. Could organizations be making incorrect decisions based upon data collected via smart phones? We don’t know but it’s a good question to ask.
In summary, the smartphone medium of interaction is a less rich visual communication medium than a laptop, just as telephone interviews are less rich since they lack the visual presentation of information. If we’re allowing surveys to be taken on smartphones, we must write our surveys so that they “work” on mobile devices — basically the lowest common denominator. Ideally, the survey tool vendors will develop ways to better render a survey webform for smartphone users, but there are clearly limits and the above suggestions should be heeded.
“What’s the objective of your survey program?” is the first issue I suggest people should consider when they create a survey program. In fact, developing a Statement of Research Objectives is the first exercise in my Survey Design Workshops. The project step seems innocuous; the goal would be to capture information from customers, employees or some other stakeholder to create or improve products or services. Or is it? Is there some other goal or agenda — stated or unstated — that trumps that logical goal?
That real agenda may manifest itself in the survey questionnaire design. Disraeli said, “There are three types of lies: lies, damn lies, and statistics” and survey questionnaire design affords us the opportunity to lie through survey statistics it generates — or unwittingly be mislead as a result of decisions made during the survey questionnaire design process.
Let’s look at an example. Below is an image of the Ritz Carlton customer satisfaction survey — an event or transactional feedback survey — that you may have received if you stay with them in the early part of this century. The survey was professionally developed for them. (The survey shown has been abridged. That is, some questions have been omitted. Also, the formatting is very true to but not exactly as on the original survey. I took some minor liberties in translating from a paper form to a web page for readability’s sake.)
Ritz Carlton is the gold standard of customer service, and they are well known for their efforts to identify and correct any customer problems, though I had been personally disappointed in their service recovery efforts. One key purpose of transaction-driven surveys — surveys conducted after the conclusion of some event — is to identify specific customers in need of a service recovery act. A second purpose is to find aspects of the operation in need of improvement. Consider how well this survey serves those purposes. In a follow-up article, we’ll consider other flaws in the wording of the survey questions.
First, let’s look at the survey as a complaint identifier. Putting aside the issues of the scale design , the questions at the end of the survey capture how happy you were with the service recovery attempt. But what if you had a problem and you did NOT report it? Sure, you could use the Comments field, but no explicit interest is shown in your unreported problem. No suggestion is made that you should state the nature of the unreported problem so they could make amends in some way.
Next, let’s examine how well the survey instrument design captures those service attributes that had room for improvement. Notice the scale design that Ritz uses. The anchor for the highest point is Very Satisfied and the next highest point is Somewhat Satisfied, with mirror image on the lower end of the scale.
Consider your last stay at any hotel — or the Ritz if your budget has made you so inclined. Were your minimal expectations met for front-desk check-in, room cleanliness, etc.? My guess is that your expectations were probably met or close to met, unless you had one of those disastrous experiences. If your expectations were just met, then you would probably consider yourself “just satisfied.”
So, what point on the scale would you check? You were more than Somewhat Satisfied — after all, they did nothing wrong — therefore it’s very possible you’d choose the highest response option, Very Satisfied, despite the fact that you were really only satisfied, an option not on the scale. To summarize, if your expectations were essentially just met, the choices described by the anchors may well lead you to use the highest response option.
In conference keynotes I draw a two-sided arrow with a midpoint mark and ask people where on that spectrum they would place their feelings if their expectations were just met for some product or service. Almost universally, they place themselves in the center at the midpoint marker or just barely above it. In other words, most people view satisfaction — the point where expectations were just met — as a midpoint or neutral position. This is a positive position, but not an extremely positive position.
What if your expectations were greatly exceeded from an absolutely wonderful experience along one or more of these service attributes? What response option would you choose? Very Satisfied is the only real option. So consider this: customers who were just satisfied would likely choose the same option as those who were ecstatic. (By the way, the arguments described here for the high end of the scale apply equally to the low end of the scale.)
Here’s the issue: this scale lacks good dispersal properties. Put in Six Sigma and Pareto Analysis terminology, it does NOT “separate the critical few from the trivial many.” (For you engineering types, the noise-to-signal ratio is very low.) The scale design — either intentionally or unwittingly — drives respondents to the highest response option. Further, it’s a truncated scale since those with extreme feelings are lumped in with those with only moderately strong feelings. We really learn very little about the range of feelings that customers have.
Is Ritz Carlton the only company who uses this practice? Of course not. After a recent purchase at Staples, I took their transactional survey. Its error was even more extreme. The anchors were Extremely Satisfied, then Somewhat Satisfied. I was satisfied with the help I got from the store associates; they met my expectations. Which option should I choose? I was not ecstatic, but I was pleased.
A well-designed scale differentiates respondents along the spectrum of feelings described in the anchors. Learning how to choose good anchors is vitally important to get actionable data. How actionable do you expect the data from these questions would be? I’ve never seen their data, but I would bet they get 90% to 95% of scores in the top two response options, so called “Top Box scores.” I would not be surprised if they got 98% top box scores. If I were the Ritz, I would interpret any score other than a Very Satisfied to be a call to action. (Maybe they do that.) I would also bet that any hotel — a Motel 6 or Red Roof Inn — would also get 90%+ top box scores using this scale. The dispersal properties of this scale are just that poor.
A simple change would improve it. Make the highest option Extremely Satisfied and the next highest Satisfied. Or use a different scale. Have the midpoint be Expectations Just Met, which is still a positive statement, and the highest point be Greatly Exceeds Expectations. I have used that scale and found a dispersion of results that lends itself to actionable interpretation.
If you’re a cynic, then you might be asking what “damn lie” is really behind this questionnaire scale design. Here’s a theory: Public Relations or Inside Relations. Perhaps the “other goal” of the survey was to develop a set of statistics to show how much customers love Ritz Carlton. Or perhaps the goal is for one level of management to get kudos from senior management.
This questionnaire scale design issue is one reason why comparative benchmarking efforts within an industry are so fundamentally flawed. You may be familiar with these benchmarking data bases that collect data from companies and then share the average results with all who participate. Self-reported customer satisfaction survey scores are typically one of the data points, that is, the data submitted are not audited. Yet, if some companies use scale designs as shown here, how valid is the benchmark if you use a scale that truly differentiates? Your 50% top box score may reflect higher levels of customer satisfaction than the other company’s 90% top box score. Self-reported data for comparative benchmarking databases where there’s no standard practice for the data collected is suspect — to say the least.
The Disraeli quote at the opening is also attributed to Mark Twain. (Isn’t every good quote from Twain, Disraeli, or Churchill?) If Twain had lived in the days of customer satisfaction surveys, he would have augmented Disraeli’s quote thusly: “There are four types of lies: lies, damn lies, statistics, and survey statistics.”