Robustheitschecks

Robustheitschecks, often called robustness checks, are a set of analyses used to assess whether empirical results remain valid under alternative assumptions, data definitions, or sample selections. They help researchers gauge the reliability of conclusions by testing their sensitivity to modeling choices rather than by proving truth.

Typical focuses include: specification choices (different functional forms, added or removed control variables, fixed effects vs random effects), sample definition (subsamples, exclusion of outliers or influential observations), data sources or measurement definitions, and time periods or outcome metrics.

Common methods include: estimating the main model under multiple specifications and comparing results; using robust standard errors or alternative estimators; resampling techniques such as bootstrapping or leave-one-out analyses; cross-validation in predictive settings; placebo or falsification tests to check for spurious effects; permutation tests to assess chance findings.

Interpretation: robustness checks are not proofs but indicators of whether findings hold under reasonable variations. They should be planned a priori where possible and reported transparently to avoid data-mining biases.

Limitations: even robust findings can be sensitive to unconsidered factors; multiple testing increases false positives; robustness does not establish causal identification.

Example: in a study of policy impact, authors report consistent direction and significance of estimated effects across several specifications, alternative control variables, and with and without outliers, and in different time windows.