Regression Models for Ordinal and Nominal Dependent Variables Using SAS, Stata, LIMDEP, and SPSS
dc.altmetrics.display | false | |
dc.contributor.author | Park, Hun Myoung | |
dc.date.accessioned | 2015-03-25T20:02:21Z | |
dc.date.available | 2015-03-25T20:02:21Z | |
dc.description.abstract | A categorical variable here refers to a variable that is binary, ordinal, or nominal. Event count data are discrete (categorical) but often treated as continuous variables. When a dependent variable is categorical, the ordinary least squares (OLS) method can no longer produce the best linear unbiased estimator (BLUE); that is, OLS is biased and inefficient. Consequently, researchers have developed various regression models for categorical dependent variables. The nonlinearity of categorical dependent variable models makes it difficult to fit the models and interpret their results. | |
dc.identifier.uri | https://hdl.handle.net/2022/19741 | |
dc.rights | Copyright 2015 by the Trustees of Indiana University. This content is released under the Creative Commons Attribution 3.0 Unported license (http://creativecommons.org/licenses/by/3.0/). | |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/ | |
dc.subject | Regression Models, Ordinal and Nominal Dependent Variables.SAS, Stata, LIMDEP, SPSS | |
dc.title | Regression Models for Ordinal and Nominal Dependent Variables Using SAS, Stata, LIMDEP, and SPSS |
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