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Asking Questions - The Definitive Guide To Questionnaire Design ...

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ASKING QUESTIONS ABOUT ATTITUDES AND BEHAVIORS 131<br />

ioral intention questions are yes-no questions and likelihood questions.<br />

Consider the following two examples.<br />

1. Will you buy a new car within the next 12 months?<br />

Yes No<br />

2. How likely are you to buy a new car within the next 12 months?<br />

Not Very Likely 1— 2 — 3 — 4 — 5 — 6 — 7 — 8 — 9 Very Likely<br />

In telephone and electronic surveys, yes-no questions are frequently<br />

asked. This type of question is primarily useful as a screening<br />

question. <strong>The</strong>se yes-no behavioral intention questions provide<br />

very specific answers that can be translated into hard data estimates<br />

(“21% will buy a car within the next year”). Unfortunately, these<br />

percentages are likely to be in error. Because yes-no questions force<br />

all undecided people to choose either yes or no, the data are noisy<br />

and often inaccurate.<br />

Likelihood questions avoid this problem. With likelihood questions,<br />

respondents who are somewhere in the middle can answer<br />

somewhere in the middle. For academic researchers, likelihood<br />

questions have the advantage of providing reasonably high levels of<br />

discrimination. For practitioners, they have the disadvantage of not<br />

being able to readily transform the responses into percentages or<br />

projections. That is, it is much more satisfying (and seemingly<br />

more definite) to say “21% of the respondents will buy new cars in<br />

the next twelve months” than to say “on a likelihood scale of 1 to<br />

9, the average reported likelihood of buying a new car within the<br />

next twelve months is 4.1.”<br />

One solution to this dilemma is to ask respondents the yes-no<br />

question and then ask them a likelihood question. <strong>The</strong> yes-no question<br />

can be used in summary measures and the likelihood question<br />

can be used to weight the estimates or statistically adjust the data<br />

to improve the estimates. For instance, people can be asked (yes or<br />

no) if they think they will eat at a fast-food (quick service) restaurant<br />

in the next week. <strong>The</strong>y can then be asked to estimate how<br />

likely they think they are to eat there. Two people can answer “yes”

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