Kryssanalyse

Kryssanalyse, or cross-analysis, is a statistical method used to examine the relationship between two or more variables by summarizing data in cross-tabulations or contingency tables. It is commonly applied in social sciences, public health, and market research to identify associations such as between gender and voting behavior or between exposure and disease status.

The analysis typically follows several steps. First, researchers define the variables of interest and ensure consistent coding of categories. Second, a cross-tabulation is created, often starting with a two-way table and expanding to higher dimensions if the data permit. Third, statistical tests such as the chi-squared test for independence are used to assess whether observed associations are unlikely to occur by chance. Measures of association, including Cramér’s V, the Phi coefficient, and, for 2x2 tables, the odds ratio or relative risk, quantify the strength of the relationship.

Interpretation requires caution. A statistically significant association indicates a relationship between variables but does not establish causation. Effect size and confidence intervals provide insight into practical significance. Researchers should consider sample size, expected cell counts, potential confounding variables, and the issue of multiple testing, which can inflate the likelihood of false positives.

Software tools commonly used for kryssanalyse include R (functions for contingency tables and tests), Python with pandas and SciPy, SPSS, and SAS. Extensions such as log-linear analysis allow examination of multi-way interactions, while visualization tools like mosaic plots aid interpretation.