English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Data collection and research methodology represents a critical part of theresearch pipeline. On the one hand, it is important that we collect data in away that maximises the validity of what we are measuring, which may involve theuse of long scales with many items. On the other hand, collecting a largenumber of items across multiple scales results in participant fatigue, andexpensive and time consuming data collection. It is therefore important that weuse the available resources optimally. In this work, we consider how aconsideration for theory and the associated causal/structural model can help usto streamline data collection procedures by not wasting time collecting datafor variables which are not causally critical for subsequent analysis. This notonly saves time and enables us to redirect resources to attend to othervariables which are more important, but also increases research transparencyand the reliability of theory testing. In order to achieve this streamlineddata collection, we leverage structural models, and Markov conditionalindependency structures implicit in these models to identify the substructureswhich are critical for answering a particular research question. In this work,we review the relevant concepts and present a number of didactic examples withthe hope that psychologists can use these techniques to streamline their datacollection process without invalidating the subsequent analysis. We provide anumber of simulation results to demonstrate the limited analytical impact ofthis streamlining.

Prespecification of Structure for Optimizing Data Collection and Research Transparency by Leveraging Conditional Independencies