Tverrklassifiserte

Tverrklassifiserte describes data or analytical designs in which units are grouped simultaneously according to two or more classification factors that are not nested within each other. In such data, the grouping factors cross, meaning an observation is associated with multiple classifications that do not form a simple hierarchical structure. This contrasts with hierarchical or nested structures, where units are organized in a clear parent–child sequence (for example, students within classes within schools).

In cross-classified designs, the response variable may be influenced by several random effects corresponding to each classification, along with fixed effects. The random effects capture variance attributable to each classification, and researchers may also investigate interactions between classifications. Cross-classified multilevel models, or cross-classified random effects models, are used to estimate these components and to obtain valid inferences when cross-classification is present.

Estimation typically relies on mixed-effects modeling approaches, such as linear or generalized linear mixed models, and can also be performed within Bayesian hierarchical frameworks. Software support exists in several statistical environments, including R, MLwiN, Stata, SAS, and Python libraries; the exact implementation depends on the type of outcome (continuous, binary, etc.) and the model specification.

Common examples include educational data where students are associated with both schools and teachers, health data where patients are linked to hospitals and physicians, or organizational data where employees participate in multiple projects and departments. The strengths of tverrklassifiserte analyses include more accurate attribution of variance to the correct sources and improved standard errors, while limitations involve increased model complexity, the need for sufficient data in each cross-classified cell, and potential identifiability challenges.