One of the products that Company W makes is snack foods. The research and develo
ID: 1215267 • Letter: O
Question
One of the products that Company W makes is snack foods. The research and development department of Company W has developed a new formula for one type of snack food that is cheaper to make than the current formula. They want to test the new formula with consumers. They want to see if consumers can tell the difference between the old and new formulas. You propose to conduct this test and analyze the results in line with WidgeCorp's approach to gathering statistical data and use it to form business intelligence decisions. You first need to explain the process to senior management at Company W. You advocate using an unbiased sample of consumers in their test, so that Company W is randomly selecting people based on the following: age; whether or not they have kids; where they live; how often they buy snack foods; and whether or not they already buy the current product.
Prepare a presentation that addresses the following:
List at least 3 qualitative attributes of the snack food about which they might want to ask consumers. Make sure at least 1 of them is nominal.
For each attribute that is ordinal, assign names for the endpoints of a 5 point rating scale.
Explain the difference between nominal and ordinal data.
Explain how nominal and ordinal data relate to a rating scale.
List at least 2 quantitative attributes of snack food that the scientists might want to measure.
Explain the difference between interval and ratio data.
Include in your presentation the concept of business intelligence.
Include in your presentation the difference between a population and a sample.
Note why it is important to avoid bias when conducting research (see note below).
Note: Give 2 examples of possible populations for this test.
Explanation / Answer
Ans
a an b
Rate new product from 0 to 5 scale
Rate old product from 0 to five scale
Which one you like more?
B
Categorical
A categorical variable (sometimes called a nominal variable) is one that has two or more categories, but there is no intrinsic ordering to the categories. For example, gender is a categorical variable having two categories (male and female) and there is no intrinsic ordering to the categories. Hair color is also a categorical variable having a number of categories (blonde, brown, brunette, red, etc.) and again, there is no agreed way to order these from highest to lowest. A purely categorical variable is one that simply allows you to assign categories but you cannot clearly order the variables. If the variable has a clear ordering, then that variable would be an ordinal variable, as described below.
Ordinal
An ordinal variable is similar to a categorical variable. The difference between the two is that there is a clear ordering of the variables. For example, suppose you have a variable, economic status, with three categories (low, medium and high). In addition to being able to classify people into these three categories, you can order the categories as low, medium and high. Now consider a variable like educational experience (with values such as elementary school graduate, high school graduate, some college and college graduate). These also can be ordered as elementary school, high school, some college, and college graduate. Even though we can order these from lowest to highest, the spacing between the values may not be the same across the levels of the variables. Say we assign scores 1, 2, 3 and 4 to these four levels of educational experience and we compare the difference in education between categories one and two with the difference in educational experience between categories two and three, or the difference between categories three and four. The difference between categories one and two (elementary and high school) is probably much bigger than the difference between categories two and three (high school and some college). In this example, we can order the people in level of educational experience but the size of the difference between categories is inconsistent (because the spacing between categories one and two is bigger than categories two and three). If these categories were equally spaced, then the variable would be an interval variable
D
Nominal scales
When measuring using a nominal scale, one simply names or categorizes responses. Gender, handedness, favorite color, and religion are examples of variables measured on a nominal scale. The essential point about nominal scales is that they do not imply any ordering among the responses. For example, when classifying people according to their favorite color, there is no sense in which green is placed "ahead of" blue. Responses are merely categorized. Nominal scales embody the lowest level of measurement.
Ordinal scales
A researcher wishing to measure consumers' satisfaction with their microwave ovens might ask them to specify their feelings as either "very dissatisfied," "somewhat dissatisfied," "somewhat satisfied," or "very satisfied." The items in this scale are ordered, ranging from least to most satisfied. This is what distinguishes ordinal from nominal scales. Unlike nominal scales, ordinal scales allow comparisons of the degree to which two subjects possess the dependent variable. For example, our satisfaction ordering makes it meaningful to assert that one person is more satisfied than another with their microwave ovens. Such an assertion reflects the first person's use of a verbal label that comes later in the list than the label chosen by the second person.
On the other hand, ordinal scales fail to capture important information that will be present in the other scales we examine. In particular, the difference between two levels of an ordinal scale cannot be assumed to be the same as the difference between two other levels. In our satisfaction scale, for example, the difference between the responses "very dissatisfied" and "somewhat dissatisfied" is probably not equivalent to the difference between "somewhat dissatisfied" and "somewhat satisfied." Nothing in our measurement procedure allows us to determine whether the two differences reflect the same difference in psychological satisfaction. Statisticians express this point by saying that the differences between adjacent scale values do not necessarily represent equal intervals on the underlying scale giving rise to the measurements. (In our case, the underlying scale is the true feeling of satisfaction, which we are trying to measure.)
What if the researcher had measured satisfaction by asking consumers to indicate their level of satisfaction by choosing a number from one to four? Would the difference between the responses of one and two necessarily reflect the same difference in satisfaction as the difference between the responses two and three? The answer is No. Changing the response format to numbers does not change the meaning of the scale
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