Ch.12. A. This Code Is Vague --

Ch.12. a. This code is vague -- "depends" encompasses a very broad range of responses. B. There is no middle ground with this survey, just two positive and two negative. C. This is okay, but some respondents might be fuzzy on the distinction between "average" and "fair." D. I like this scale, and see it a lot, except that "uncertain" might be better worded as "do not know" or "does not apply." It is best to get people to respond, with their gut feeling if need be.

There are a number of different factors that affect the value of a survey in management decision making. The purpose is very important because the survey needs to be relevant to the managerial problem at hand. If it is not relevant, it has no value. The scope is important because scope is complementary to purpose. The survey might be similar in purpose to the managerial problem, but the scope of the survey could be all wrong. For example, a study of tipping behavior in the U.S.

will not help a manager figure out how to tip in China. They have the same objective, but the difference in geographic scope makes the survey useless. Authority is only somewhat important in terms of value. This is simple, in that the appeal to authority is a logical fallacy (Constitution.org, 2014). The strength of the argument itself is what matters in terms of study value.

However, it is worth noting that those with expert authority are far more likely to create material that is relevant, free from logical errors and is therefore valuable. It is just not guaranteed that the authority figure will do so. Audience is important to the extent that it frames the purpose and scope. Otherwise, audience is not very important in determining value. It is actually a valuable skill to transfer knowledge across disciplines. Format only determines value to the extent that the source can be understood.

Format is typically framed by the purpose and scope. As long as the reader can understand it, format is not relevant. It is worth considering that many managers, for example, have trouble penetrating academic journals about management -- the format reduces accessibility and therefore value. State Farm: 1. This study was designed to measure the most dangerous intersections in the U.S. The study looked at number of accidents and accident severity as key variables, and was constructed from State Farm's nationwide data. 2.

A hypothesis that could drive this research is that there are common traits among the most dangerous intersections. If such traits can be identified and remedied, these intersections can be made safer. 3. State Farm's methodology at first was fairly basic and did not give great results. They had the data, but didn't control for intersection volume, or severity of accident when they operationalized danger, though both could reasonably have been included. The revised methodology was more robust in nature. The data set was always sufficiently large. 4.

I would work with transportation engineers on this one. Their biggest concern comes when they don't have budgets to do anything, but the spotlight helps them to make their case. Unless their city is bankrupt, they should be able to leverage that spotlight to get the funding they need. 5. There is merit to using traffic counts as part of the study, but with or without yields two different and equally valid conclusions.

Raw numbers of accidents are obviously important to the insurance company, and to drivers who need to know when to be aware. An intersection with.