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In most surveys, one is confronted with missing or, more generally, coarse data. Many methods dealing with these data make strong, untestable assumptions, e.g. coarsening at random. But due to the potentially resulting severe bias, interest increases in approaches that only include tenable knowledge about the coarsening process, leading to imprecise, but credible results. We elaborate such cautious methods for regression analysis with a coarse categorical dependent variable and precisely observed categorical covariates. Our cautious results from the German panel study "Labour market and social security'' illustrate that traditional methods may even pretend specific signs of the regression estimates.