Ordinal interaction refers to a specific type of interaction effect in statistical modeling where the relationship between a predictor and an outcome variable varies depending on the levels of another predictor, particularly when dealing with ordinal variables. This concept is crucial for understanding how different factors can influence each other and the outcome in a model, helping researchers identify more complex relationships rather than assuming direct or linear associations.
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