The first few lines of this data set is shown below.Ĭount id outcome survrate prognos amttreat gsi avoid intrus Set and use these results for comparison with what we find with missing data.
SPSS CODE DEPENDENT CAT FULL
so we can begin with a logistic regression on a full data I did this simply to create a better example.
This left me with the original 66 cases and an additionalĦ6 pseudocases.
I doubled the sample size by randomly adding or subtracting random numbers to or from theĭata in the original set. Rating by the oncologist of the individual's expected survival time, Prognosis (aįour point scale), Amttreat (amount of treatment), GSI (the Global Symptom Index fromĭerogatis' Symptom Checklist 90), Avoid (a measure of avoidance behavior), and Intrus Versus not improved) as a function of several variables. I am going to use a set of data from a study that I was involved with some timeĪgo and published as Epping-Jordan, Compas, & Howell (1994). 20/.80, the results obtained from both will be In fact, if the outcome proportionsĪre no more extreme than about. Where the dependent variable is a dichotomy. Logistic regression is very similar to a standard multiple regression Reference point because it started out with complete data. The process is not a lot different,īut it gives me a chance to use another data set that provides a convenient I will focus on the process of "imputing" observations to replace missing Treatment of missing data, so I will not go over that ground here. That first page covers the basic issues in the
You can see these at ( Missing-Part-One.html and Missing data are a part of almost all research, and we all have to decide Repeated Measures Analysis of Variance Using R Logistic Regression With Missing Data David C.
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