Data collection and research methodology represents a critical part of the research pipeline. On the one hand, it is important that we collect data in a way that maximises the validity of what we are measuring, which may involve the use of long scales with many items. On the other hand, collecting a large number of items across multiple scales results in participant fatigue, and expensive and time consuming data collection. It is therefore important that we use the available resources optimally. In this work, we consider how the representation of a theory as a causal/structural model can help us to streamline data collection and analysis procedures by not wasting time collecting data for variables which are not causally critical for
answering the research question. This not only saves time and enables us to redirect resources to attend to other variables which are more important, but also increases research transparency and the reliability of theory testing. To achieve this, we leverage structural models and the Markov conditional independency structures implicit in these models, to identify the substructures which are critical for a particular research question. To demonstrate the benefits of this streamlining we review the relevant concepts and present a number of didactic examples, including a real-world example.