Do you need to standardise data before running MCA?

I have both ordinal and categorical data and am attempting to perform a Multiple correspondence analysis. My question is if a dataset contains a presence/absence variable (0 or 1), ranked data (1-3) as well as (1-4) and categorical data, do I need to standardise the data in some way? I am aware that PCA requires standardisation if variables are in different units, but am unsure if the MCA process would give more weight to a rank of 4 than a presence (1). I am relatively new to this technique, and cant find the answer to this question despite much googling, because most standardisation articles involve PCA. Thanks in advance and apologies if this is the incorrect forum.
I have grouped my variables eg. all demographic variables analysed in one MCA, attitudinal variables in another etc.


TS Contributor
while I know something about Correspondence Analysis, I never happened to use MCA.
What I can suggest is to read Greenacre's books (I seem to recall that one of his book was on MCA) and Husson et al's books (see references HERE).
Prof. Husson put a tutorial on YT on the use of the R package FactoMineR for MCA (HERE).

I hope this could point you in a right direction.