Importance of Crosstabs

A crosstab is a frequency table that shows data for more than one variable. This view of the data shows how groups of respondents differ in how they answer survey questions, which can help identify patterns in the data and isolate the key takeaways from a study.
Crosstab

 

Examples:

One easy way to analyze data is to have columns for demographics in crosstabs. This allows the comparison of differences in answers by gender, age, income, employment status, etc.

Here are two charts to illustrate what can be done with crosstabs:

  • Crosstab
  • Crosstab

These two questions about cities came from a table in a crosstab, and two columns in the crosstab were filtered by male and female respondents. The percentages in the separate columns were tested against each other for statistical significance, which allows for more unique data points to stand out. Following these data points can help identify patterns in the data and use them to craft a larger story. In this example, men are consistently more excited about Austin, while women are especially attracted to New York City when it comes to nightlife and culture.

 

Applications

Due to their ability to quickly and clearly show relationships between variables, crosstabs can be invaluable in any type of research. For example:

  • In a survey about children’s snacks, crosstabs could be used to discern differences in snack preferences by the age of the children, the number of children in the family, and the family’s total household income.
  • For a study about hair care products, crosstabs could help understand how needs for core features differ by age, gender, hair length, and hair type.
  • In a research project conducted by an office furniture company, crosstabs could help the company understand how the amount and types of furniture purchased change based on company size, region, industry, and type of office space.

 

Additional Resources

There are limitless connections and stories within the data, so more columns are always helpful. However, it’s important to consider sample size — the smaller the sample size, the larger the margin of error. When sample size is too small, a large difference in percentages can often be explained away by the margin of error.