A recurring theme over the past several years has been the discussion of regression models for dependent variables that do not conform to the requirements of the classic linear regression model. Besides individuals directly asking about a specific regression model, they have raised this issue indirectly. The linear regression model requires no assumptions regarding the measurement of the independent variables.
Independent variables can be dichotomous, nominal, ordinal, or continuous.
In contrast, the linear regression model requires that the dependent variable be continuous. In practice, the measurement of many outcome variables does not meet the assumptions of the linear regression model. The first two problems undermine one's ability to trust predicted values and the direction and size of estimated relations. The last two problems undermine one's ability to produce unbiased standard errors and to conduct tests of statistical significance. The first category of dependent variables presented in Table 1 is discrete outcomes.