{smcl} {* 15May2009}{...} {hline} help for {cmd:imbalance} {hline} {cmd:Check covariate imbalance before and after matching} {cmd:---------------------------------------------------} {cmd:Syntax} {cmd:------} {cmd:imbalance} dataname varname treatname blockname savfile {cmd:Description} {cmd:-----------} {cmd:imbalance} calculates absolute standardized difference in covariate means (ASAM) before matching (dx) and after matching (dxm), as described by Haviland, Nagin, and Rosenbaum (2007). It allows the analyst to evaluate whether matching balances an observed covariate between treated and control observations. {cmd:dataname} specifies the name of data file containing covariates to be checked. {cmd:varname} is the name of a covariate on which the analyst wants to check its balance between treated and control observations. {cmd:treatname} specifies the name of the dichotomous variable identifying treatment conditions. For {cmd:imbalance} to run properly, {cmd:treatname} must be coded treatname = 1 if the observation receives treatment, and treatname = 0 if the observation is a control. {cmd:blockname} specifies the variable name that identifies matched sets. {cmd:Output and Return Values} {cmd:------------------------} After running {cmd:imbalance}, Stata returns {cmd:dx} ¨C the absolute standardized difference in covariate means before matching, {cmd:dxm} - the absolute standardized difference in covariate means after matching, and the {cmd:name} of the covariate along with the blockname. Both dx and dxm are similar to CohenĄ¯s d. After running imbalance, Stata saves the results in a file named {cmd:savfile}. The analyst can use {cmd: return list} immediately after running {cmd:imbalance} to see statistics saved for further analysis. {cmd:Examples} {cmd:--------} {cmd:. imbalance cds black kuse fm results} {cmd:. imbalance "D:\PSA\cds.dta" black kuse fm "C:\tmp\results"} {cmd:. return list} {cmd:. use "C:\tmp\results", clear} {cmd:. list} {cmd:References} {cmd:----------} Guo, S., & Fraser, M. (2009). Propensity score analysis: Statistical methods and applications. Thousand Oaks, CA: Sage Publications Inc. Haviland, A., Nagin, D. S., & Rosenbaum, P. R. (2007). Combining propensity score matching and group-based trajectory analysis in an observational study. Psychological Methods, 12, 247-267. {cmd:Author} {cmd:------} Shenyang Guo University of North Carolina at Chapel Hill sguo@email.unc.edu {cmd:Also see:} {cmd:---------} {psee}Online: help for {helpb hodgesl} if installed