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For the moment, let’s assume that the random sample has selected a proportion-
ally fair representation of respondents, and this is precisely what TiendaMía.com
desires. Thus, 28% (8
÷
29, 8 of 1
\
1 respondents out of 29) should be relatively
close to the population of all 1
1’s in TiendaMía.com’s customer population. If we
want to account for the difference in respondent category size in our analysis, then
we will want to calculate a weighted average of favorable ratings, which reflects the
relative size of the respondent categories. Note that the first average that we calcu-
lated is a special form of weighted average: one where all weights were assumed to
be equal. In range G25:G28 of Exhibit 5.24 we see the calculation of the weighted
average. Each average is multiplied by the fraction of respondents that it represents
of the total sample. 8 This approach provides a proportional emphasis on averages. If
a particular average is composed of many respondents, then it will receive a higher
weight; if an average is composed of fewer respondents, then it will receive a lower
weight.
So what do our respondent weighted averages (G25:G28) reveal about Products
compared to the equally weighted averages (F25:F28)? The results are approxi-
mately the same for Products 1 and 4 . The exceptions are Product 2 with a somewhat
stronger showing from 0.4965 to 0.5172 and Product 3 with a substantial drop in
score from 0.3073 to 0.2931. Still, there is no change in the ranking of the products;
it remains P-1, P-2, P-3, and P-4.
What has led to the increase in the Product 2 score? Categories 1
\
2
are the highest favorable ratings for Product 2 ; they also happen to be the largest
weighted categories (8/29
\
1 and 2
\
0.310). Larger weights applied to the
highest scores will of course yield a higher weighted average. If TiendaMía.com
wants to focus attention on these market segments, then a weighted average may
be appropriate. Market segmentation is in fact a very important element in their
marketing strategy.
There may be other ways to weight the favorable ratings. For example, there
may be categories that are more important than others due to their higher spending
per transaction or more frequent transactions at the site. So, as you can see, many
weighting schemes are possible.
=
0.276 and 9/29
=
5.6 Summary
Cross-tabulation analysis through the use PivotTables and PivotCharts is a simple
and effective way to analyze qualitative data, but to insure fair and accurate analysis
the data must be carefully examined and prepared. Rarely is a data set of significant
size exempt from errors. Although most errors are usually accidental, there may be
some that are intentional. Excel provides many logical cell functions to determine
if data have been accurately captured and fit the specifications that an analyst has
imposed.
8 (0.7500 8 + 0.5000 6 + 0.6667 6 + 0.5556 9) / 29
=
0.6207.
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