Cross tabulation is a tool that allows you compare the relationship between two variables.
Defining Cross Tabulation
We can do this by an example:
Suppose that you are hired by the local school to conduct a survey on attitudes toward environmental education. The district is planning to modify its current environmental education curriculum, but it needs additional data to help determine what to include in the curriculum. Some of the school board members think that environmental education should focus solely on awareness, while others believe that environmental education should be more comprehensive. You create a 3-item survey. The items are:
- Do you think that high school students should be provided with awareness only environmental education?
- Do you think that high schools should provide more comprehensive environmental education that includes a detailed to do list?
- Do you think that receiving environmental education in high school is important?
You decide to give your survey to 250 students, 250 parents and all of the 100 teachers in the school .
You decide to compare the responses of the students, parents and teachers to each other on each of the three items. The best way for you to conduct the comparisons is to use cross tabulation.
So, what is cross tabulation? Cross tabulation is a statistical tool that is used to analyze categorical data. Categorical data is data or variables that are separated into different categories that are mutually exclusive from one another. An example of categorical data is eye color. Your eye color can be divided into ‘categories’ (i.e., blue, brown, green), and it is impossible for eye color to belong to more than one category (i.e., color).
Examples of Cross Tabulation
Cross tabulation helps you understand how two different variables are related to each other. For example, suppose you wanted to see if there is a relationship between the gender of the survey responder and if environmental education in high school is important.
Using the survey data, you can count the number of males and females who said that environmental education is important, and the number of males and females who said that environmental education is not important. You then take this information and create a contingency table, which displays the frequency of each of the variables. Suppose that there are 300 females and 300 males who completed the survey. Here is what our cross tabulation looks like:
Is there a relationship between gender and if environmental education in high school is important? If you look at the responses, you can see that almost all of the males believe that environmental education in high school is important. Although the majority of females believe that environmental education is important, the difference is not as big as between the males. From this analysis, we can conclude that males are more likely than females to believe that environmental education in high school is important.
Benefits of Using Cross Tabulations in Survey Analysis
When conducting survey analysis, cross tabulations are a quantitative research method appropriate for analyzing the relationship between two or more variables. Cross tabulations provide a way of analyzing and comparing the results for one or more variables with the results of another (or others). The axes of the table may be specified as being just one variable or formed from a number of variables. The resulting table will have as many rows and columns as there are codes in the corresponding axis specification.
In many research reports, survey results are presented in aggregate only – meaning, the data tables are based on the entire group of survey respondents. Cross tabulations are simply data tables that present the results of the entire group of respondents as well as results from sub-groups of survey respondents. Cross tabulations enable you to examine relationships within the data that might not be readily apparent when analyzing total survey responses.
Watch this video about reading Cross Tabs
Cross Tabulation and Chi-Square
- Determine the counts or percentages for combinations of categories across two or more categorical variables.
- Investigate the relationship between variables.
A cross tabulation displays the joint frequency of data values based on two or more categorical variables. The joint frequency data can be analyzed with the chi-square statistic to evaluate whether the variables are associated or independent. Cross tabulation analysis is used for two-way tables and is also known as contingency table analysis.
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