When dealing with ordinal data, many methods require you to assign a number or score to each level of a variable. For instance, if you ask people about their political orientation and whether it is very conservative, somewhat conservative, moderate, somewhat liberal or very liberal, you might assign these scores of 1, 2, 3, 4 and 5, respectively. But that is somewhat arbitrary.
One alternative was suggested by Bross (1958) and brought to my attention in reading Alan Agresti’s excellent book: Analysis of Ordinal Categorical Data . Continue reading 'Using ridits to assign scores to categories of ordinal scales'»
There are many books that teach you to use SAS or that teach you to use R. There is at least one book that teaches R to people who know SAS or SPSS (R for SAS and SPSS users by Robert Muenchen, and it’s very good).
Continue reading 'Book review: SAS and R by Ken Kleinman and Nicholas J. Horton'»
In a recent article in Sociological Methodology entitled “How to impute interactions, squares, and other transformed variables”, Paul T. von Hippel shows that, when y0u have missing data and are using interactions, squares, or other transformed variables in a regression, it is better to transform first, and then impute.
In multiple imputation, the problem of missing data is dealt with by imputing multiple sets of data, and then combining them. When there are no interactions or quadratics, the process is well-understood. But relatively little is known about the proper procedure when you do have transformations. von Hippel shows, using both mathematics and example data, that it is better to first transform the data that you do have, and then impute. Although this leads to the odd situation that, e.g. the imputed values X^2 are not equal to the square of the imputed values for X; doing it in the reverse order (that is, imputing and transforming) yields biased estimates of the regression coefficients.
This is so both for ordinary least squares regression and other regression models.
I found the article fascinating and accessible.
Description of concordant and discordant in SAS PROC LOGISTIC
Part of the default output from PROC LOGISTIC is a table that has entries including`percent concordant’ and `percent discordant’. To me, this implies the percent that would correctly be assigned, based on the results of the logistic regression. But that is not what it is. It looks at all possible pairs of observations. A pair is concordant if the observation with the larger value of X also has the larger value of Y. A pair is discordant if the observation with the larger value of X has the
smaller value of Y; here, X and Y are the predicted value and the actual value.
Continue reading 'PROC LOGISTIC: Concordant and discordant'»
I introduced this series the other day. Next up in the list is “getting help”. In both SAS and R, there are many sources of help. SAS has one that the usual R package does not have – technical support – although if you read the comments to the above article, you see that there are commercial versions of R that do have it. I won’t say much about this, because I think it’s bound up with the fact that R is free and SAS is not. I do find SAS technical support very helpful. Continue reading 'SAS v R: Getting help'»
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