{smcl} {* *! version 1.1.1 24jan2017}{...} {findalias asfradohelp}{...} {vieweralsosee "" "--"}{...} {vieweralsosee "[R] help" "help help"}{...} {viewerjumpto "Syntax" "mkonfoundhelpfile##syntax"}{...} {viewerjumpto "Description" "mkonfoundhelpfile##description"}{...} {viewerjumpto "Options" "mkonfoundhelpfile##options"}{...} {viewerjumpto "Remarks" "mkonfoundhelpfile##remarks"}{...} {viewerjumpto "Examples" "mkonfoundhelpfile##examples"}{...} {viewerjumpto "Authors" "mkonfoundhelpfile##authors"}{...} {viewerjumpto "References" "mkonfoundhelpfile##references"}{...} {title:Title} {phang} {bf:mkonfound} {hline 2} Beta version: For multiple studies, this command calculates (1) % bias necessary to invalidate/sustain an inference; (2) the impact threshold of an omitted confounding variable to invalidate/sustain an inference. {marker syntax}{...} {title:Syntax} {p 8 17 2} {cmdab:mkonfound} [{var1 var2}] [{cmd:,} {it:options}] {synoptset 20 tabbed}{...} {synopthdr} {synoptline} {syntab:Main} {synopt:{opt var1}}the observed t-ratio from each study{p_end} {synopt:{opt var2}}the degrees of freedom from each study{p_end} {synoptline} {marker description}{...} {title:Description} {phang} {cmd:mkonfound} calculates the impact of an omitted variable necessary to invalidate an inference of a regression coefficient for multiple studies. The command also assesses how strong an omitted variable must be correlated with the outcome and with the predictor of interest to invalidate/sustain the inference for each study. Users input two variables: the observed t-ratio and the degrees of freedom for each study. The command {cmd:mkonfound} produces four variables. The first variable is {it:itcv_}, indicating the impact of an omitted variable needed to invalidate/sustain the inference. The second variable is {it:r_cv_y}, indicating the correlation between the omitted variable and the outcome necessary to invalidate/sustain an inference, conditioning on other covariates. Third variable is {it:r_cv_x}, indicating the correlation between the omitted variable and the predictor of interest necessary to invalidate/sustain an inference, conditioning on other covariates. Fourth variable is {it:stat_sig_}, indicating if the original regression coefficient is statistically significant; 1 if yes and 0 otherwise. {p_end} {phang} {cmd:mkonfound} also calculates how much bias there must be in an estimate to invalidate (or to sustain) an inference for multiple studies. The % bias necessary to invalidate/sustain an inference is interpreted in terms of sample replacement. Users input two variables: the observed t-ratio and the degrees of freedom in each study. The command mkonfound produces two variables. The first variable is {it:percent_replace}, indicating what % of the original cases must be replaced to invalidate the inference; the second variable is {it:percent_sustain}, indicating what % of the original cases must be replaced to sustain an inference. {p_end} {marker options}{...} {title:Options} {phang} {opt sig(#)} Significance level of the test; default is 0.05 {cmd:sig(.05)}. To change the significance level to .10 use {cmd:sig(.1)} {phang} {opt nu(#)} The null hypothesis against which to test the estimate. Null hypothesis is defined as a correlation, ranging from -1 to 1. The default is 0 {cmd:nu(0)} {phang} {opt onetail(#)} One-tail or two-tail test; the default is two-tail {cmd:onetail(0)}; to change to one-tail use {cmd:onetail(1)} {phang} {opt rep_0(#)} For % bias, this controls the effect in the replacement cases; the default is the null effect (which may or may not be 0) {cmd:rep_0(0)}; to force replacing cases with effect of zero use {cmd:rep_0(1)} {phang} {opt z_tran(#)} Calculates the % bias based on Fisher's z-transformation (only apply to non-zero hypothesis testing); default calculation is based on the original test statistic {cmd:z_tran(0)}; to calculate based on Fisher's z use {cmd:z_tran(1)}. This option will produce two additional variables based on Fisher's z: {it:percent_replace_z} and {it:percent_sustain_z} {marker remarks}{...} {title:Remarks} {phang} For a graphical illustration of the impact of a confounding variable see {browse "https://msu.edu/~kenfrank/research.htm#impact_diagram"} {p_end} {phang} For additional details of the calculations in a spreadsheet format and other supporting materials see {browse "https://msu.edu/~kenfrank/research.htm#causal"}. {p_end} {phang} For a web-based version of konfound see {browse "http://konfound-it.com"}. {p_end} {marker examples}{...} {title:Examples} {phang}{cmd:. mkonfound var1 var2}{p_end} {phang}{cmd:. mkonfound var1 var2, sig(0.1) nu(.5) rep_0(1) onetail(1) }{p_end} {marker authors}{...} {title:Authors} {phang} Kenneth A. Frank {p_end} {phang} Michigan State University {p_end} {phang} Ran Xu {p_end} {phang} Michigan State University {p_end} {phang} Please email {bf:ranxu@msu.edu} if you observe any problems. {p_end} {marker references}{...} {title:References} {pstd} Frank, K.A. 2000. Impact of a Confounding Variable on the Inference of a Regression Coefficient. Sociological Methods and Research, 29(2), 147-194 {pstd} Pan, W., and Frank, K.A. 2004. An Approximation to the Distribution of the Product of Two Dependent Correlation Coefficients. Journal of Statistical Computation and Simulation, 74, 419-443 {pstd} Pan, W., and Frank, K.A., 2004. A probability index of the robustness of a causal inference. Journal of Educational and Behavioral Statistics, 28, 315-337. {pstd} *Frank, K. A. and Min, K. 2007. Indices of Robustness for Sample Representation. Sociological Methodology. Vol 37, 349-392. * co first authors. {pstd} Frank, K.A., Gary Sykes, Dorothea Anagnostopoulos, Marisa Cannata, Linda Chard, Ann Krause, Raven McCrory. 2008. Extended Influence: National Board Certified Teachers as Help Providers. Education, Evaluation, and Policy Analysis. Vol 30(1): 3-30. {pstd} Frank, K.A., Maroulis, S., Duong, M., and Kelcey, B. 2013. What would it take to Change an Inference?: Using Rubin’s Causal Model to Interpret the Robustness of Causal Inferences. Education, Evaluation and Policy Analysis. Vol 35: 437-460.