categorical data analysis I've found very helpful:
- Generalized Linear Models and Extensions (Hardin and Hilbe)
- Categorical Data Analysis (Agresti)
- Analyzing Categorical Data (Simonoff)
- Regression Models for Categorical Dependent Variables (Long and Freese)
- Applied Logistic Regression (Hosmer and Lemeshow)
- Logistic Regression Models (Hilbe)
- Analysis of Ordinal Categorical Data (Agresti)
- Applied Ordinal Logistic Regression (Liu)
Surveys also collect data in the form of counts ("How many times have you...").
Analyzing count data with methods designed for continuous data is usually unwise, and here are a couple of excellent books on that topic:
- Modeling Count Data (Hilbe)
- Negative Binomial Regression (Hilbe)
There are numerous "machine learners" I have also used over the years but, in general, they tell us less about the Why, essential in consumer research. Though predictive analytics is not typically the purpose of consumer surveys, used competently, statistical methods are also highly competitive with machine learning in terms of predictive accuracy.
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@AI_Python_EN
