  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 a
consideration for theory and the associated causal/structural model can help us
to streamline data collection procedures by not wasting time collecting data
for variables which are not causally critical for subsequent analysis. 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. In order to achieve this streamlined
data collection, we leverage structural models, and Markov conditional
independency structures implicit in these models to identify the substructures
which are critical for answering a particular research question. In this work,
we review the relevant concepts and present a number of didactic examples with
the hope that psychologists can use these techniques to streamline their data
collection process without invalidating the subsequent analysis. We provide a
number of simulation results to demonstrate the limited analytical impact of
this streamlining.
