This paper discusses the issues in estimating the effects of marketing variables with linear models. When the variables are not directly observable, it is well known that direct regression yields biased estimates. Several researchers have recently suggested reverse regression as an alternative procedure. However, it is shown that the reverse regression approach also fails to provide unbiased estimates in general, except for some special cases. It is proposed that covariance structure analysis with an appropriate measurement model can ensure the unbiasedness of estimated effects. These issues are examined in the context of assessing market pioneer advantages.