When to use a multilevel logistic regression model?
In this paper simulation studies based on multilevel logistic regression models are used to assess the impact of varying sample size at both the individual and group level on the accuracy of the estimates of the parameters and their corresponding variance components.
How are simulation studies used in logistic regression?
In this paper simulation studies are used to assess the effect of varying sample size at both the individual and group level on the accuracy of the estimates of the parameters and variance components of multilevel logistic regression models.
Can you do diagnostics with multinomial logistic regression?
Diagnostics and model fit: Unlike logistic regression where there are many statistics for performing model diagnostics, it is not as straightforward to do diagnostics with multinomial logistic regression models.
How big should the sample size be for a logistic regression?
Results suggest that low prevalent events require larger sample sizes with at least a minimum of 100 groups and 50 individuals per group. We recommend using a minimum group size of 50 with at least 50 groups to produce valid estimates for multi-level logistic regression models.
Educational researchers, psychologists, social, epidemiological and medical scientists are often dealing with multilevel data. Sometimes, the response variable in multilevel data is categorical in nature and needs to be analyzed through Multilevel Logistic Regression Models.
What should the group size be in logistic regression?
We recommend using a minimum group size of 50 with at least 50 groups to produce valid estimates for multi-level logistic regression models. Group size should be adjusted under conditions where the prevalence of events is low such that the expected number of events in each group should be greater than one.
How are model validation techniques used in machine learning?
The below validation techniques do not restrict to logistic regression only. It can be used for other classification techniques such as decision tree, random forest, gradient boosting and other machine learning techniques.