Some important things to consider in multivariate analysis include:

- Purpose of the modeling
- Background information and relevant theory
- Whether data are cross-sectional or longitudinal
- Are there different levels, e.g., household within city or region
- Which variables to include
- Measurement level for each variable (e.g., continuous, ordinal, nominal)
- Will continuous latent variables (aka factors) be included
- How the variables interrelate, e.g., hypothesized causal relationships
- Random intercepts, slopes
- Will exogenous variables be included, e.g., age affects factors
- Will categorical latent variables (classes) be included
- Will multiple categorical latent variables be needed
- If latent classes included, which parts of the model can vary by class
- Estimation, e.g., MLE or Bayes, numerous options within each

This may sound like gobbledygook to non-statisticians, but all of it can seriously impact decision-making!

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