We argue that reporting control variable results strengthens the transparency and credibility required for programmatic knowledge building in regression-based empirical research. Control variables are not just technical adjustments; their results provide diagnostic information that can reveal otherwise hidden biases and model misspecification. We present a practical multistep approach that employs control variable coefficients to uncover harmful combinations of multicollinearity, omitted variable biases and correlated measurement error. These harmful combinations, in turn, may generate type 1 errors (false positives) among variables of theoretical interest. We also show how to distinguish true suppressor effects from artifacts of poor model specification. Full reporting supports programmatic research by enabling scholars to compare results across studies, build on prior findings, and refine theory over time. Based on these benefits, we challenge a recent call to omit control variable results from manuscripts. Instead, we recommend that journals and reviewers require their inclusion in all published results tables.
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