Kreuzklassifikationsmethoden

Kreuzklassifikation, or cross-classification, is a statistical method used to analyze the relationship between two or more categorical variables by organizing observed frequencies into a contingency table, also known as a cross-tabulation. The table's rows and columns represent the categories of the variables; each cell shows the number of observations that fall into that combination of categories. This structure makes it possible to detect patterns, assess whether variables are associated, and test for independence.

Common statistical procedures applied to Kreuzklassifikation include tests of association and independence. The chi-square test for independence evaluates whether the observed frequencies differ significantly from what would be expected if the variables were independent. For 2x2 tables, the phi coefficient provides a simple measure of association; for larger tables, Cramér's V is often used. When sample sizes are small and expected counts are low, alternatives such as Fisher's exact test may be appropriate.

Example: A 2x2 Kreuzklassifikation of gender (male, female) and product preference (A, B) might yield counts: male-A 28, male-B 32, female-A 12, female-B 28. The chi-square test can determine whether product preference differs by gender.

Kreuzklassifikation is widely used in survey research, market analytics, epidemiology, and social sciences to summarize relationships between categorical variables. Limitations include its focus on descriptive association rather than causation and the need for sufficient sample sizes to ensure reliable cell counts.