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Interpreting Contingency Tables and Association Measures

Cross tabulation, or Crosstabs, is a foundational analysis technique in SPSS used to examine the relationship between two or more categorical variables (nominal or ordinal). This table, also known as a contingency table, displays the joint distribution of variables, where rows and columns represent distinct values and each cell shows the count of cases for that unique combination. In applied econometrics, it is particularly useful for comparing groups, such as analyzing consumer behavior based on gender or income levels. The execution process begins by selecting Analyze - Descriptive Statistics - Crosstabs from the menu. In the dialog box, users move variables into the Row(s) and Column(s) boxes. for deeper analysis with control variables, variables can be added to the Layer box. The Cells button allows for customization to display observed counts (Observed), expected counts (Expected), and row or column percentages to easily compare proportions between groups. To test the statistical hypothesis of whether two variables are independent, users click the Statistics button and select Chi-square. Additionally, measures of association strength such as Phi and Cramer’s V can be selected. The results in the Output Viewer provide the Chi-square value and the significance level (p-value); if p is smaller than 0.05, one can conclude that there is a statistically significant relationship between the research variables.