(This question is an edited version of a question I previously posted which one user recommended would benefit from more focus).
I have 2000 questionnaires from respondents which ask 33 different questions about which issues are present in their lives - i.e. alcohol abuse, domestic violence, mental health, child abuse, learning difficulties etc.
Each question can only be answered yes/no (which I've re-coded as 1/0).
I'd like to use this dataset to start creating n profiles of respondents to define which variables naturally cluster together e.g. (alcohol abuse and domestic violence), (mental health, child abuse, domestic violence), (alcohol abuse, learning difficulties) across some/all of the 33 differnt variables.
A note I've read on-line indicates that hierarchical cluster analysis is not appropriate for a dataset of this scale/type due to sensitivity of the position of how data is sorted in the dataset, and recommends two-step cluster analysis instead.
Consequently, I'd be really interested in your input on whether hierarchical, two-step or other methods are most appropriate for exploring clusters of responses that natually associate together using a binary dataset.