Inference in downstream analysis using individual-level posterior means from mixed logit models

Mixed logit models are widely used to recover individual-level preferences for secondary analysis, yet both first-stage sampling uncertainty and variability in conditional distributions are often overlooked. This technical note illustrates the implications of ignoring these sources of uncertainty and provides reproducible R code, compatible with the apollo package, to better approximate the empirical sampling distribution and improve the reliability of second-stage inference.