  It is well known that the standard F test is severely affected by
heteroskedasticity in unbalanced analysis of covariance (ANCOVA) models.
Currently available potential remedies for such a scenario are based on
heteroskedasticity-consistent covariance matrix estimation (HCCME). However,
the HCCME approach tends to be liberal in small samples. Therefore, in the
present manuscript, we propose a combination of HCCME and a wild bootstrap
technique, with the aim of improving the small-sample performance. We precisely
state a set of assumptions for the general ANCOVA model and discuss their
practical interpretation in detail, since this issue may have been somewhat
neglected in applied research so far. We prove that these assumptions are
sufficient to ensure the asymptotic validity of the combined HCCME-wild
bootstrap ANCOVA. The results of our simulation study indicate that our
proposed test remedies the problems of the ANCOVA F test and its
heteroskedasticity-consistent alternatives in small to moderate sample size
scenarios. Our test only requires very mild conditions, thus being applicable
in a broad range of real-life settings, as illustrated by the detailed
discussion of a dataset from preclinical research on spinal cord injury. Our
proposed method is ready-to-use and allows for valid hypothesis testing in
frequently encountered settings (e.g., comparing group means while adjusting
for baseline measurements in a randomized controlled clinical trial).
