I am analyzing survey data (n=60, regarding their risk/return expectations, which is my dummy variable in the model, the problem is that 95% of the sample are located in one group of the dummy variable. I ran this multiple logit model, where i control for other risk/return expectations (ESG and Mrisk both dummy variables) as well as for gender (dummy), age (continuous) and education (dummy).

Now can I use these results for an interpretation? if not what can I do to control for the dependent variables?

Many thanks in advance!

Grisk5.i <- glm(Grisk~ESG + Mrisk + gender + age + edu_work.IS, data = Data_IS,binomial)

glm(formula = Grisk ~ ESG + Mrisk + gender + age + edu_work.IS, 
    family = binomial, data = Data_IS)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3480  -0.2356  -0.1851  -0.1515   2.5173  

                 Estimate Std. Error z value Pr(>|z|)  
(Intercept)     -23.07030 3556.12539  -0.006   0.9948  
ESGB              0.37620    1.42856   0.263   0.7923  
MriskB            3.11528    1.31539   2.368   0.0179 *
genderMale       17.34287 3556.12383   0.005   0.9961  
age               0.05564    0.07059   0.788   0.4306  
edu_work.ISlow   -0.37620    1.58109  -0.238   0.8119  
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 29.392  on 59  degrees of freedom
Residual deviance: 19.821  on 54  degrees of freedom
AIC: 31.821
Number of Fisher Scoring iterations: 18 ```


For a GLM your problem isn't the imbalance per se, but rather the low frequency of the less frequent outcome given your total # of observations.

5% of 60 is only 3 observations; a common rule of thumb for logistic regression is to have 10 observations of the least common outcome per predictor (others suggest more; there are several Q&A about this here including Sample size for logistic regression? ), so you don't have enough to meaningfully evaluate even one predictor variable.

| cite | improve this answer | |
  • $\begingroup$ thank you for your answer, do you, by chance know a different statistic method, where i can interpret the influence age, gender, etc has on my dependent variable (the ris/return expectation) $\endgroup$ – Antonio Thurnher May 4 at 21:36
  • $\begingroup$ @AntonioThurnher You don't have enough data for any statistical method. $\endgroup$ – Bryan Krause May 4 at 21:50
  • $\begingroup$ Hi @BryanKrause, another question, can i, however, use the variable with the very uneven groups (the dependent variable in the model above) as a predictor in a different model ? or will my results be again meaningless? $\endgroup$ – Antonio Thurnher May 5 at 11:53

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.