Invalid measurement of constructs in survey research often remains undetected and can lead to false conclusions. An important determinant of a construct’s measurement validity is how it is modeled. A construct can often be modeled in different ways, such as the sum of its parts or the cause of its effects. Since each of these models is associated with a unique set of errors, the common practice of specifying only a single model undermines validity. Current guidelines on measurement have not focused on how better validity can be achieved by comparing and combining multiple models. In this paper we provide a framework for the development and use of multiple models. This, we hope, would lead researchers view their construct of interest from different perspectives and thus measure it more validly.