Adjusting Experimental Data
Randomization in experiments allows researchers to assume that the treatment and control groups are balanced with respect to all characteristics except the treatment. Randomization, however, only makes balance probable, and accidental covariate imbalance can occur for any specific randomization. As such, statistical adjustments for accidental imbalance are common with experimental data. The most common method of adjustment for accidental imbalance is to use least squares to estimate the analysis of covariance (ANCOVA) model. ANCOVA, however, is a poor choice for the adjustment of experimental data. It has a strong functional form assumption, and the least squares estimator is notably biased in sample sizes of less than 500 when applied to the analysis of treatment effects. We evaluate alternative methods of adjusting experimental data. We compare ANCOVA to two different techniques. The first technique is a modified version of ANCOVA that relaxes the strong functional form assumption of this model. The second technique is matching, and we test the differences between two matching methods. For the first, we match subjects and then randomize treatment across pairs. For the second, we randomize the treatment and match prior to the estimation of treatment effects. We use all three techniques with data from a series of experiments on racial priming. We find that matching substantially increases the efficiency of experimental designs.
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