Partial least squares path modeling (PLS) has seen increased use in the information systems research community. One of the stated key advantages of PLS is that it weights the indicator variables based on the strength of the relationship between the indicators and the underlying constructs, which presumably decreases the effect of measurement error in the analysis results. In this paper we argue that this assumption is not valid. While PLS indeed does weight the indicators to maximize the explained variance, it does this by including error variance in the model thus reducing construct validity. We use a simulation study of a simple PLS model to show that when compared to traditional sum scale approach, PLS estimates are actually often less valid. Although our study has its limitations, it hints that the use of PLS as a theory testing tool should be reevaluated and that more research testing the effectiveness of the PLS approach is in order.