The statistical procedure EI-R, in which point estimates produced by the King (1997) ecological inference technique are used as dependent variables in a linear regression, can be logically inconsistent insofar as the assumptions necessary to support EI-R's first stage (ecological inference via King's technique) can be incompatible with the assumptions supporting its second stage (linear regression). In light of this problem, we develop a specification test for logical consistency of EI-R and describe options available to a researcher who confronts test rejection. We then apply our test to the implementation of EI-R in Burden and Kimball's (1998) study of ticket splitting and find that this implementation is logically inconsistent. In correcting for this problem we show that Burden and Kimball's substantive results are artifacts of a self-contradictory statistical technique. I t is becoming increasingly common for researchers to use point estimates produced by the King (1997) ecological inference technique as dependent variables in second-stage linear regressions. This two-stage statistical procedure, which Herron and Shotts (2003b) call EI-R, was initially proposed by King (1997), who has said that "[EI-R] has enormous potential for uncovering new information" (1997, 279). Burden and Kimball (1998), for example, use King-based estimates of district-level ticket-splitting rates as dependent variables in secondstage regressions which examine why individuals cast split-ticket ballots. Other examples of EI-R and variants of this two-stage statistical technique can be found in logically inconsistent insofar as the assumptions necessary to support the procedure's first stage (ecological inference via King's method) can be incompatible with the assumptions supporting its second stage (linear regression). In contrast, we say that an application of EI-R is logically consistent when its first-stage and second-stage assumptions are not mutually contradictory.The matter of EI-R's being logically consistent merits attention because EI-R has two stages, unlike a regular regression that has only a single stage. Whenever a researcher employs a two-stage statistical procedure, she must simultaneously adopt assumptions that support both stages. Previous work has not sought to determine whether this is possible in the case of EI-R. Indeed, to the best of our knowledge all published and working-paper implementations of EI-R assume that the procedure's two stages are inherently compatible.Nonetheless, we show that the assumptions necessary to support EI-R can be self-contradictory. This result follows from the fact that standard implementations of King's technique to an ecological dataset (the first stage of EI-R) assume that there is no aggregation