- small sample size: 116
- binary outcome variable
- long list of explanatory variables: 44
- explanatory variables did not come from the top of my head; their choice was based on the literature.
Statistical test chosen: logistic regression
I need to find the variables that best explain variations in the outcome variable (I am not interested in making predictions).
The problem: This question is a follow-up on the 2 questions listed below. From them, I got that performing automated stepwise regression has its downsides. Anyway, it seems that my sample size would be too small for that. It seems that my sample is also too small to enter all variables at once (using the SPSS 'Enter' method). This leaves me with my issue unresolved: how can I select a subset of variables from my original long list in order to perform multivariate logistic regression analysis?
UPDATE1: I am not an statistician, so I would appreciate if jargons can be reduced to the minimum. I am working with SPSS and am not familiar with other packages, so options that could be run with that software would be highly preferable.
UPDATE2: It seems that SPSS does not support LASSO for logistic regression. So following one of your suggestions, I am now struggling with R. I have passed through the basics, and managed to run a univariate logistic regression routine successfully using the glm code. But as I tried glmnet with the same dataset, I am receiving an error message. How could I fix it? Below is the code I used, followed by the error message:
data1 <- read.table("C:\\\data1.csv",header=TRUE,sep=";",na.string=99:9999) y <- data1[,1] x <- data1[,2:45] glmnet(x,y,family="binomial",alpha=1) **in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : (list) object cannot be coerced to type 'double'**
UPDATE3: I got another error message, now related to missing values. My question concerning that matter is here.