I have a data frame comprising more than two dozen variables, all of which are binary (0/1) with <5% missing data. These variables can be classified into groups that pertain to different aspects of health. For one group, disease for example, 0/1 represents the answer to a yes/no question: do you have a given disease? Another group, is based on ordinal questions (ie. how many days of the week to you perform a given activity: 1,2,3,4 or 5+?), which are transformed into binary variables (ie. 0=answer 1-3; 1=answer 4-5+). A third group are based on physical measures, and are transformed into binary variables based on established cutpoints, or an arbitrary one (ie. within 1st quartile or not).
I would like to perform some exploratory analyses (ie. partial correlation analysis, factor analysis, etc.) to look at the relationships among these variables. My understanding is that a phi correlation would be more appropriate for the first group type described, while a tetrachoric correlation would be more appropriate for the latter two. For generating a correlation matrix on all of my variables, is one more appropriate to use over the other, or should I be considering a different approach. Preliminary partial correlation networks using a phi correlation matrix look much more expected (disease groups cluster together, biological similar variables are connected) as compared to what results from a tetrachoric correlation matrix (more of a hairball in which seemingly everything is connected).