My project assessed different poverty indicators and their correlates in Laos. I was interested in poverty-related variables on the household level (such as household assets and health) and village level (such as infrastructure and population). Therefore, I chose a multilevel model that estimates connections on both these levels simultaneously. Multilevel modeling was necessary also because the sampling was clustered and hence, the observations (households) were not independent of each other. As I searched for quantitative methods that could flexibly integrate multilevel modeling with ordinal or non-normally distributed data, I encountered the idea of latent variable modeling, often referred to as structural equation modeling. In practice, structural equation modeling can be complex but also rewarding because it provides a vast range of options for model building and comprehensive tools for evaluating models as a whole. Structural equation modeling usually features latent variables or factors, meaning that observed variables are not assumed to be complete, but they contain measurement error. In this research methods case, I briefly describe the main issues I encountered while conducting a multilevel structural equation modeling analysis for multidimensional poverty and its correlates. I also provide some useful resources for additional information on these methods.