Cross-tabulation

Cross-tabulation, also known as contingency table analysis, is a statistical technique used to analyze the relationship between two or more variables. It involves creating a table that shows the frequency distribution of one variable in relation to another variable.

The table is organized into rows and columns, with each row representing a category of one variable and each column representing a category of the other variable. The cells in the table represent the frequency or count of observations that fall into each category. Cross-tabulation can be used to explore the relationship between two categorical variables, or a categorical variable and a continuous variable that has been grouped into categories.

Cross-tabulation is commonly used in social sciences, business, and healthcare to explore relationships between variables and identify patterns in data. For example, in healthcare, cross-tabulation can be used to analyze the relationship between patient demographics and medical conditions, or to analyze the effectiveness of different treatments for different patient groups. In business, cross-tabulation can be used to analyze customer satisfaction data, or to explore the relationship between demographic variables and buying behavior.

To perform cross-tabulation in SPSS, you can use the Crosstabs procedure. This procedure allows you to select the variables you want to cross-tabulate and specify the order of the rows and columns in the table. You can also specify the type of statistics you want to compute, such as counts, percentages, or chi-square tests of independence. The output of the Crosstabs procedure includes the contingency table, as well as various statistics and graphical representations of the data.

Cross-tabulation examples

Here are some examples of cross-tabulation:

1. Gender and Income: A researcher wants to analyze the relationship between gender and income. They create a cross-tabulation table with rows for male and female and columns for income categories (e.g., <$30,000, $30,000-$50,000, >$50,000). The table shows the frequency or count of males and females in each income category. The researcher can use this table to explore whether there is a relationship between gender and income.
2. Product Preferences: A marketing team wants to analyze customer preferences for their products. They create a cross-tabulation table with rows for different products and columns for customer demographics (e.g., age, income, education). The table shows the frequency or count of customers who prefer each product in each demographic category. The marketing team can use this table to identify which products are most popular among different customer groups.
3. Student Performance: A teacher wants to analyze the relationship between student attendance and grades. They create a cross-tabulation table with rows for attendance categories (e.g., 0-25%, 25-50%, 50-75%, 75-100%) and columns for grade categories (e.g., A, B, C, D, F). The table shows the frequency or count of students in each attendance and grade category. The teacher can use this table to explore whether there is a relationship between attendance and grades.
4. Health Outcomes: A healthcare provider wants to analyze the relationship between patient demographics and health outcomes. They create a cross-tabulation table with rows for patient demographics (e.g., age, gender, race/ethnicity) and columns for health outcomes (e.g., mortality, hospital readmission, complications). The table shows the frequency or count of patients in each demographic and outcome category. The healthcare provider can use this table to identify which patient groups are at higher risk for poor health outcomes.