{smcl} {* February 25th 2022}{...} {hline} {cmd:srtbayes} {hline 2} Bayesian Effect Size calculation for Simple Randomised Trials {hline} {marker syntax}{...} {title:Syntax} {cmd:srtbayes} {varlist}{cmd:,} {opt int:ervention(interv_var)} [{it:options}] {synoptset 20 tabbed}{...} {synopthdr: main} {synoptline} {synopt :{opt int:ervention()}}requires a factor variable identifying the intervention (arms) of the trial.{p_end} {synoptline} {synoptset 20 tabbed}{...} {synopthdr} {synoptline} {syntab:Model} {synopt :{opt *}}additional Bayesian arguments to be passed to the command. Stata defaults apply.{p_end} {syntab:Reporting} {synopt :{opt thr:eshold(#)}}a real scalar or vector of threshold(s) for estimating Bayesian posterior probability.{p_end} {synopt :{opt sepch:ains}}stores summary statistics for each chain.{p_end} {synopt :{opt diag:nostics}}generates convergence diagnostic graphs.{p_end} {synopt :{opt noi:sily}}displays regression output.{p_end} {synopt :{opt save}}saves the simulation output.{p_end} {synopt :{opt c:ond()}}wrapper for bayesian arguments specified only for the conditional model.{p_end} {synopt :{opt unc:ond()}}wrapper for bayesian arguments specified only for the unconditional model.{p_end} {synoptline} {phang} {it:varlist} and {cmd:intervention()} may contain factor-variable operators; see {help fvvarlist}.{p_end} {marker description}{...} {title:Description} {pstd} {cmd:srtbayes} performs {cmd:Effect Size} (ES) calculation of educational trials under the assumption of independent errors among pupils under a Bayesian framework; this can also be used with schools as fixed effects. This analysis produces ES estimates for both conditional and unconditional model specifications. {marker options}{...} {title:Options} {dlgtab:Model} {phang} {opt *} Additional Bayesian arguments to be passed to the command such as {cmd:mcmcsize(#) burnin(#) rseed(#) nchains(#)} and custom priors. Stata defaults of Bayesian regression models apply; see {opt bayes} prefix ({manhelp bayes BAYES}). {dlgtab:Reporting} {phang} {opt threshold(#)} A real scalar or vector for estimating Bayesian posterior probability such that the observed effect size is greater than or equal to the threshold(s).{p_end} {phang} {opt sepchains} Stores summary statistics for each number of chains specified in {cmd:nchains(#)}. {phang} {opt diagnostics} Generates convergence diagnostic graphs for each number of chains specified in {cmd:nchains(#)}. {phang} {opt noisily} Displays regression output for both conditional and unconditional models.{p_end} {phang} {opt save} Saves simulation output in two datasets {cmd:(mcmcCond.dta, mcmcUnc.dta)} for the conditional and unconditional models respectively. {phang} {opt cond()} Bayesian arguments are passed only to the conditional model; default is bayesian arguments are included in both models.{p_end} {phang} {opt uncond()} Bayesian arguments are passed only to the unconditional model; default is bayesian arguments are included in both models.{p_end} {marker Examples}{...} {title:Examples} {hline} {pstd}Setup:{p_end} {phang2}{cmd:. use mstData.dta, clear}{p_end} {pstd}Simple model:{p_end} {phang2}{cmd:. srtbayes Posttest Prettest, int(Intervention)}{p_end} {pstd}Model using custom simulation options and all diagnostic options with base level change:{p_end} {phang2}{cmd:. srtbayes Posttest Prettest, int(ib1.Intervention) thr(0.1) mcmcsize(50000) burnin(50000) rseed(1234) nchains(4) sepch diag save}{p_end} {pstd}Model using custom simulation options with three-arm intervention variable, a vector of thresholds and custom priors:{p_end} {phang2}{cmd:. srtbayes Posttest Prettest, int(Intervention2) thr(0.3 0.4 0.5) mcmcsize(50000) burnin(50000) rseed(1234) nchains(4) normalprior(10) igammaprior(1 2)}{p_end} {marker results}{...} {title:Stored results} {pstd} {cmd:srtbayes} stores the following in {cmd:r()}: {synoptset 20 tabbed}{...} {p2col 5 20 24 2: Matrices}{p_end} {synopt:{cmd:r(CondES)}}conditional Hedgesâ€™ g effect size and its 95% credible intervals for the trial arm(s) in {it:interv_var}.{p_end} {synopt:{cmd:r(UncondES)}}unconditional effect size for the trial arm(s) in {it:interv_var}, obtained based on variance from the unconditional model (model with only the intercept as a fixed effect).{p_end} {synopt:{cmd:r(Beta)}}estimates and credible intervals for variables specified in the model.{p_end} {synopt:{cmd:r(Sigma2)}}residual variance for conditional and unconditional models.{p_end} {synopt:{cmd:r(ProbES#)}}a matrix of Bayesian Posterior Probabilities such that the observed effect size is greater than or equal to a pre-specified threshold(s) for arm #.{p_end} {synopt:{cmd:r(sepchains_#)}}stores summary statistics for # number of chains separately.{p_end}