Microsoft Office Tutorials and References
In Depth Information
Exhibit 5.24 Calculation of average (avg) and weighted average (Wt-avg)
A casual analysis of the table suggests that Product 1 shows a 50% or greater
favorable rating for all categories. No other product can equal this favorable rating:
Product 1 is the top choice of all respondent categories except for 1
1 and it is
tied with Product 3 for category 1
2. Although this casual analysis suggests a clear
choice, we can now do more formal analyses to arrive at the selection of a single
First, we will calculate the average of all favorable ratings for each category
2) as a single composite score. This is a simple calculation and it
provides a straightforward method for TiendaMía.com to assess products. In Exhibit
5.24 the calculation of averages is found in F25:F28–0.6181 for Product 1 , 0.4965
for Product 2 ,etc. Product 1 has the highest average favorable rating. But there
are some questions that we might ask about the fairness of the calculated averages.
Should there be an approximately similar number of respondents in each category
for this approach to be fair? Stated differently, is it fair to count an individual cate-
gory average equally to others when the number of respondents in that category is
substantially less than other categories?
In TiendaMía.com’s study, there are different numbers of respondents in the vari-
ous categories. This can be signiﬁcant for the calculation of averages. The difference
in the numbers can be due to the particular sample that we selected. A random sam-
ple of this small size can lead to wide variation in the respondents selected. One way
to deal with this problem is to consciously sample customers to reﬂect the propor-
tion of category members that shop at TiendaMía.com. There are many techniques
and methods for formally sampling 7 data that we cannot study here.
7 Sampling theory is a rich science that should be carefully considered prior to initiating a study.