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