help for mlmsens

Title

mlmsens -- Robustness diagnostics for cross-level MLM interactions

Syntax

mlmsens , pred(string) modx(string) cluster(varname)
[options]

pred(string) focal predictor name

modx(string) moderator name

cluster(varname) level-2 clustering variable

alpha(#) significance threshold; default 0.05

iccrange(# #) ICC range to evaluate; default 0.01 0.40

iccgrid(#) number of ICC grid points; default 40

noloco skip leave-one-cluster-out analysis

verbose print progress during LOCO refitting

plot draw ICC-shift sensitivity plot

saving(filename) save plot to file

Description

mlmsens provides two MLM-appropriate robustness diagnostics for a
cross-level interaction:

ICC-shift: how does the interaction SE change if the intraclass
correlation were different? Reports a robustness index: the proportion of
the ICC range where the interaction remains statistically significant.

LOCO (leave-one-cluster-out): refits the model dropping one cluster
at a time and reports how the interaction coefficient changes. Flags
influential clusters.

Important: These are robustness diagnostics, not a full causal
sensitivity analysis. They do not quantify the strength of unmeasured
confounding needed to explain away the interaction.

Must be run immediately after mixed. LOCO refitting can be slow
for large datasets; use noloco to skip it.

Stored results

r(b_int) interaction coefficient

r(se_int) SE of interaction

r(icc_obs) observed ICC

r(rob_index) robustness index (0-1)

r(n_clusters) number of clusters

Example

. mixed math c.ses_c##c.climate_c gender || school:, reml

. mlmsens, pred(ses_c) modx(climate_c) cluster(school)

. mlmsens, pred(ses_c) modx(climate_c) cluster(school) noloco plot

Author

Subir Hait, Michigan State University.

Also see

mlmprobe, mlmjn, mlmsummary, mlmvdecomp