Minitab Statistical Software makes it easy to analyze survey data you’ve
collected and answer questions that can affect your business or
This article highlights several basic tools in Minitab that will help you
interpret your survey data accurately.
Surveys are an important tool for market research. Businesses use them to
systematically and objectively gather information from respondents to discover
what people want and identify market needs. Surveys also have many applications
beyond market research, and students in statistics (and many other types of
classes) can learn a great deal from gathering and analyzing survey data.
You can use Minitab’s Power and Sample Size tools to make sure you survey
enough people to conduct a reliable analysis, while avoiding wasting resources
by collecting more data than you need. Minitab’s Power and Sample Size tools
help you balance your need for statistical power with the expense of gathering
data by answering this question: How much data do you need? For example, Minitab
can quickly tell you how many people should you survey to be 95% confident that
the proportion of people supporting a candidate is within 5% of its true
Now that you’ve gathered and prepared your survey data, what next? A good
first step in your analysis is to conveniently summarize the data by counting
the responses for each level of a given variable. These counts, or frequencies,
are called the frequency distribution and are commonly accompanied by the
percentages and cumulative percentages as well.
A frequency distribution can quickly reveal:
Suppose a pet adoption and rescue agency wants to find out whether dogs or
cats are more popular in a certain location. To answer this question, we survey
a random sample of 100 local pet owners to find out if dogs are more popular
than cats, or vice versa.
The frequency distribution reveals that the percentages for both cats and
dogs are nearly 50%, indicating that there may not be a strong local preference
for one type of pet over another.
A frequency distribution can tell you about a single variable, but it does
not provide information about how two or more variables relate to one another.
To understand the association between multiple variables, we can use cross
Let’s say we want to see if a gender preference exists for dogs versus cats.
Are men more likely to want a dog than a cat compared to women, or vice
To summarize data from both variables at the same time, we need to construct
a cross-tabulation table, also known as a contingency table. This table lets us
evaluate the counts and percents, just like a frequency distribution. But while
a frequency distribution provides information for each level of one variable,
cross tabulation shows results for all level combinations of both variables.
This cross tabulation shows that women prefer cats 70 to 30%, and men
prefer dogs 76 to 24%. Based on these percentages, we can conclude that males
are more likely to own a dog while females are more likely to own a cat.
But what if there is third variable to consider, such as marital status? We
could then create a similar cross tabulation, but break it down into two tables:
one for married people, and another for those who are single.
Frequency distributions and cross tabulation are great starting points for
survey analysis, but they may not be sufficient for a comprehensive
analysis.To get a fuller understanding of your data, we need to include
hypothesis testing. For our pet survey, we want to make sure the difference we
see between gender and pet preference is due to a true association, and not
random chance. A hypothesis test can tell us if the difference we see in the
percentages is statistically significant, and whether the pet preference and
gender variables are independent or not.
To evaluate the statistical significance of cross tabulation results, we use
a hypothesis test called the chi-square test. This test compares the counts
observed in the data we’ve collected to the counts we would expect if there is
no relationship between the variables.
Minitab makes it easy to evaluate several variations of the
Chi-Square test. In this analysis, we displayed the Pearson, likelihood ratio
and Fisher’s exact test. The p-value computed for each was less than α=0.05,
which means the difference is statistically significant. Therefore, we can
reject the null hypothesis that the variables are independent and conclude that
a statistically significant relationship exists between gender and pet
Based on these results, we would focus on marketing a local dog
rescue drive to male residents and a local cat rescue in a way that appeals to
A chi-square test was the tool we needed in this case, but there
are other hypothesis tests commonly used for survey data, including t-tests and
proportion tests. These types of tests can be used to compare averages or
proportions to a target value, or to compare averages or proportions to each
These hypothesis tests can answer questions such as:
From frequency distributions to cross tabulation to hypothesis
testing to more sophisticated types of analysis, Minitab has what you need to
analyze survey data and make sound conclusions about markets, customers, or
whatever you’re trying to assess. For more information and additional examples
detailing how to use these and other useful tools, Minitab offers an extensive
Help system and free Technical Support.
Michelle ParetProduct Marketing Manager, Minitab, LLC.
Eston MartzSenior Creative Services Specialist, Minitab
Visit www.minitab.com for more information about
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