I thought I would just re-use the same dataset for my SAS class. There were no problems with missing data, sample size, quasi-complete separation, because like all data that has no quality issues, I had just completely made it up. I had run a logistic regression with SPSS with the dependent variable of marriage (0 = no, 1 = yes) and independent variable of career choice (computer science or French literature ). After I sobered up (don’t you know, all of my saddest stories begin this way), I realized it would be a heck of a lot easier to use data I already had lying around than go to any actual, you know, effort. While I was under the influence of alcohol/ drugs that caused me to hallucinate about having spare time during the current decade, I agreed to give a workshop on categorical data analysis at the WUSS conference this fall. There are actually a few cases where you will get different results and last week I ran across one of them. Your results are still going to be significant, or not, the explained variance is going to be the same.” It’s not as if you’re going to get a F-value of 67.24 with SAS and one of 2.08 with Stata. “Of course, whatever you choose is going to give you the same results.
#Spss logistic regression software#
When people ask me what type of statistical software to use, I run through the advantages and disadvantages, but always conclude,
The Independent_Dicotomicvariable, on the other hand, is not statistically significant in order to predict the model (0.125> 0.05).SAS and SPSS Give Different Results for Logistic Regression but not really The p-value associated with the explanatory variable ‘Independent_Continuous’ is statistically significant at 5% (0.019 0) inclines the probability of occurrence towards the value defined as 1 for the dependent variable of dichotomous response. The p-value associated with the explanatory variable ‘AGE’ is statistically significant (0.058 0) inclines the probability of occurrence towards the value defined as 1 for the dichotomous response dependent variable. With the Hosmer-Lemeshow test, the logistic model is considered potable (0.631> 0.05), and explains 53.9% of the variability from the Nagelkerke R2 value, with these 3 variables becoming part of the same. Recode into different variables with SPSS, variable with 6 categories is recoded into 5 dummies variables, taking as reference the first category or a concret category… We proceed to generate 2 new dummies variables, using the Transform: Recode into different variables. If the individual belongs to the Primary category, we give 0 value to each of these 2 dummies created. Then by having 3 categories in the variable level of studies, we generate 2 variables, Secondary and University. The coding scheme that is used to create, and therefore, recode into a dummy a categorical or ordinal variable with more than 2 categories ( example: Level of Studies), taking into account that the reference category is “Primary Studies”, so that it can become part of the binary logistic regression it is of the form, that is, 2 dummy variables are generated for 3 categories, 3 for 4 categories, the omitted category remaining as reference variable: To be able to introduce a nominal variable of this type, which has 3 categories (or more), in the model we must resort to the “dummy” categorization, which consists of the generation of dummy dichotomous variables for the different categories of the variable.
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