**
Good
information comes from goo surveys, May 18, 2008, 2D.**

Since
problem solving is one of the major elements of a manager’s
job, having access to timely and accurate business
information is critically important. Information can come
from formal and informal organizational communication and
from sales, financial, economic, and industry data.
Managers might also find it beneficial to collect and
analyze information about the attitudes of workers and
consumers using surveys.

When
properly constructed and analyzed, surveys can provide
managers with valuable information. There is a science
behind good survey construction. Error and threats to
validity can creep into the survey process at many different
points. Improperly worded or confusing questions, selection
of a non-representative sample, data entry errors, and
measurement errors can all affect the validity, or accuracy,
of the survey results. One common problem encountered by
novice survey writers is the over-use of questions using
nominal-level data.

Nominal
data, or named data, are numbers that represent different
categories or groups of data. Gender, which is a commonly
asked survey question, is an example of nominal-level data.
The numbers entered into the data set for gender are simply
codes for male and female. It is common to code variables
like gender using zeros and ones or ones and twos. However,
since the numbers are merely codes for the categories, any
numbers could be used—such as 1,000 for males and 3.14 for
females. Asking respondents to “check all that apply” and
“identify your favorite choice” typically result in nominal
data.

Nominal
data can also be created from higher-level data by breaking
a variable into groups. Annual income, for example, is
frequently asked in pay ranges on surveys. It is also
common to ask respondents to indicate the number of visits
or number of purchases made over a specified amount of time
using artificially developed groups. Less than five, five
to ten, or more than ten are examples of ways to break real
numbers into groups. However, those same variables could
also be divided in a multitude of other ways. Being nominal
data, codes have to be assigned to the categories and
entered into the data set.

Once
coded and recorded in the data set, the options for
describing and analyzing nominal data are fairly limited.
For example, it does not make sense to calculate averages of
the coded numbers for nominal data variables. Averages and
other higher-level descriptive statistics only make sense
for variables that are real numbers—such as the number of
products sold, employee pay, amount of inventory,
advertising expenditures, etc. The appropriate statistical
analyses for nominal data are ones that count and report the
numbers of observations in each group. Researchers can
statistically analyze differences in the counts or
proportions of observations in each group.

When
combined with variables that are real numbers, nominal data
can be used in more sophisticated statistical analyses—for
example, is there is difference in sales between male and
female salespeople? Good and useful information comes from
good surveys. Decision makers should invest time and
attention learning proper survey construction techniques.

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