Introduction to repest

1. Introduction to repest Francois Keslair

2. Different types of error • Measurementerror (non samplingerror) • Samplingerror Can impact: - point estimates - standard errors

3. Repest is a Stata routine (ado file), freely available at IDEAS, that: Is specially designed for complex survey designs: Accommodates final weights and uses replicate weights for the sampling variance; Allows analysis with multiply imputed variables: Accepts plausible values and incorporates imputation variance in the computation of total variance. By Francesco Avvisati and Francois Keslair (OECD)

4. Origins • One generictoolfor all OECD skillssurveysisbettersurveysthanseveralspecificones. • Making life easier for internalandexternalusers Program coreprinciple: Repestrunanyeclass command insideloops over plausible values and/or replicatedweights

5. Empirical distribution of the statistics Eachnumbercomesfrom a standard stata command

6. The variance for a statistic X* with plausible values is given by Sampling variance for each plausible value (80 replicates per PV) Imputation variance (variability of estimates across PVs) • : r-th estimate for plausible value p • : final estimate (i.e. with final weights) for plausible value p • : average of the plausible values • : variance factor (depends on replication method: BRR, jackknife-1, jk-2,…)

7. Table I.6.2A Use repest to compute simple means of variables repestPISA,estimate(means escs) by(cnt) • estimates correct sampling variance (accounting for clustering + stratification)

8. Figure I.7.4 Testing differences across subpopulations repest PISA, est(means pv@scie) over(immig,test) by(cnt)

9. repestsvyname [if] [in] , estimate(cmd [,cmd_options]) [options] Excerptfrom the help file (h repest): n_cmd: freq , means, summarize, corr

10. Whatrepestcan do: • Works withanyeclass command • Veryeasy to adaptrepest to new LSAS • Severalpossibilitiesto export results