mfcurve: Visualizing results from multifactorial designs

Multifactorial designs are used to study the (joint) impact of two or more factors on an outcome. They typically occur in conjoint, choice, and factorial survey experiments but have recently gained increasing popularity in field experiments, too. Technically, they allow researchers to investigate moderation as an instance of treatment heterogeneity by crossing multiple treatments. Naturally, multifactorial designs quickly spawn a spiraling number of distinct treatment combinations: even a moderately complex design of two factors with three levels each yields 32 unique combinations. For more elaborate setups, full factorials can easily produce dozens of distinct combinations, rendering the visualization of results difficult. This presentation introduces the new Stata command mfcurve as a potential remedy. Mimicking the appearance of a specification curve, mfcurve produces a two-part chart: the graph’s upper panel displays average effects for all distinct treatment combinations; its lower panel indicates the presence or absence of any level given the respective treatment condition. Unlike existing visualization techniques, this enables researchers to plot and inspect results from multifactorial designs much more comprehensively. Highlighting potential applications, the presentation will demonstrate mfcurve’s most important features and options, which currently include replacing point estimates by box plots and testing results for statistical significance.