l91 - Ordered Logit Models - Overview This is adapted...

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Ordered Logit Models – Page 1 Ordered Logit Models - Overview This is adapted heavily from Menard’s Applied Logistic Regression analysis; also, Borooah’s Logit and Probit: Ordered and Multinomial Models; Also, Hamilton’s Statistics with Stata, Updated for Version 7. We have talked about the analysis of dependent variables that have only two possible values, e.g. lives or dies, wins or loses, gets an A or doesn’t get an A. Of course, many dependent variables of interest will have more than two possible categories. These categories might be unordered (doesn’t move, moves South, moves East) or ordered (high, medium, low; favors more immigration, thinks the level of immigration is about right, favors less immigration). We will briefly discuss techniques for handling each of these. Ordinal Regression As Menard notes, when dependent variables are measured on an ordinal scale, there are many options for their analysis. These include Treating the variable as though it were continuous. In this case, just use OLS regression or the other techniques we have discussed for continuous variables. Certainly, this is widely done, particularly when the DV has 5 or more categories. Since this is probably the easiest approach for readers to understand, sometimes the other approaches are tried just to confirm that the use of OLS does not seriously distort the findings. Ignoring the ordinality of the variable and treating it as nominal. i.e. use multinomial logit techniques like those we will discuss later. The key problem here is a loss of efficiency. By ignoring the fact that the categories are ordered, you fail to use some of the information available to you, and you may estimate many more parameters than is necessary. This increases the risk of getting insignificant results. But, your parameter estimates still should be unbiased. Treating the variable as though it were measured on a true ordinal scale. For example, the professorial ranks of Full Professor, Associate Professor, and Assistance Professor are ordered but you may or may not think they reflect crude measurement of some underlying continuous variable. Stereotype logistic regression models (estimated by slogit in Stata) might be used in such cases. Treating the variable as though it were measured on an ordinal scale, but the ordinal scale represented crude measurement of an underlying interval/ratio scale. For example, the categories “High, Medium, Low” might be rough measures for Socio-economic status or intelligence. Ordered logit models can be used in such cases, and they are the primary focus of this handout.
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Ordered Logit Models – Page 2 Menard cautions that choosing the correct option requires careful judgment. In other words, don’t just assume that because Stata has a routine called ologit , or that the SPSS pulldown menu for Ordinal Regression brings up PLUM, that these are necessarily the best way to go.
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This note was uploaded on 02/29/2012 for the course SOC 63993 taught by Professor Richardwilliams during the Spring '11 term at Notre Dame.

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l91 - Ordered Logit Models - Overview This is adapted...

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