Using Logistic Regression to analyze qualitative data while conducting research specially on behavioral variables, don't you think output of Logistic Regression is extraordinarily difficult to explain?
Yes because it is using probability, as the dependent variables is binary. So, the independent variables need some modification to get the same interpretation as the normal regression with monotonic increasing measurement.
Yes because it is using probability, as the dependent variables is binary. So, the independent variables need some modification to get the same interpretation as the normal regression with monotonic increasing measurement.
Md Edrich Molla , you don't provide many details on the specifics of your analyses, but it seems worth clarifying a few things that Chung Tin Fah said. First, the dependent variable in a logistic regression may or may not be binary (i.e., Bernoulli). The usual assumption is the dependent variable is binomial (the sum of independent and identically distributed Bernoulli trials). For now, let's suppose your DV is binary. Second, the norm is to think about transforming the predicted values, not the independent variables. Given the link function of a logistic regression is the logit, the inverse of these is usually used (and some software gives you the option of whether to report the predicted values in terms of probabilities or not). Third, whether the relationship is monotonic with any of the x variables depends on if the model is monotonic. So if I have x^2 in the model--depending on the values of x--the relationship between x and the predicted values may or may not be monotonic.
- Badges
- Science topic
- Similar topics
- Biological Science
- Bioecology
- Systems Ecology
- Landscape Ecology
- Connectivity