Abstract
Throughout the last 15 or 20 years, social scientists have seen a substantial body of literature published in their journals on the subject of analyzing categorical or qualitative data. Many of these articles begin by bemoaning the fact that most of the multivariate statistical tools that social scientists have at their disposal, i.e., the tools that they are trained to understand and use in their research, are not appropriate for the categorical kinds of data they often use. Much time is spent in research design devising scales that will measure a concept at an interval or ratio level, so that operationalized concept can be used in a regression model or factor analysis. Frequently the model assumptions would be violated less if variables were treated as qualitative. For instance, it does not make much sense to ask female victims of sexual assault how much guilt they feel, when we are really interested in whether or not they feel “guilty” at all, and how this state of guilt-feeling is related to other relevant variables.
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© 1992 Springer Science+Business Media New York
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Halli, S.S., Rao, K.V. (1992). Log-Linear Models. In: Advanced Techniques of Population Analysis. The Plenum Series on Demographic Methods and Population Analysis. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9030-6_6
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DOI: https://doi.org/10.1007/978-1-4757-9030-6_6
Publisher Name: Springer, Boston, MA
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