How many samples do you need for logistic regression?

How many samples do you need for logistic regression?

Dear researchers, in real world, a “reasonable” sample size for a logistic regression model is: at least 10 events (not 10 samples) per independent variable.

How many independent variables can you have in a logistic regression?

There must be two or more independent variables, or predictors, for a logistic regression. The IVs, or predictors, can be continuous (interval/ratio) or categorical (ordinal/nominal).

How can I compare two logistic regression models?

I have two logistic regression models, using the same data set and same dependent binary variable but with different sample sizes due to different IV’s. How would I go about comparing the two models aside from using a classification matrix?

When to use sample size in logistic regression?

King and Zeng have an interesting paper on “Logistic Regression in Rare Events Data”. When talking about rare events they have situations in mind where one group makes up 1% or less in a sample. On the one hand King and Zeng propose estimation techniques to overcome this problem. There is software implementing these techniques.

When to use logistic regression in an observational study?

In observational studies, logistic regression is commonly used to determine the associated factors with or without controlling for specific variables and also for predictive modelling (1–4). Since the purpose of most of statistical analyses is for inference, determination of sample size requirement is necessary before the analysis is conducted.

What does complete separation mean in logistic regression?

This implies that it requires an even larger sample size than ordinal or binary logistic regression. Complete or quasi-complete separation: Complete separation implies that the outcome variable separates a predictor variable completely, leading to perfect prediction by the predictor variable.