Define and Conquer

For Episode 5, we invited Louise Poppe to introduce us to causal models, with a particular focus on Directed Acyclic Graphs (DAGs). Together, we discuss why researchers in epidemiology — and, in fact, across many scientific disciplines — can benefit from integrating DAGs into their study workflow. We talk about how to get started with causal thinking, common pitfalls and challenges when building DAGs, and what insights these models can provide for study design, analysis, and interpretation.
This episode offers an accessible introduction to causal thinking with DAGs and is a great opportunity to expand your methodological research toolbox.
If this episode sparked your interest and you would like to explore the topic further, here are some helpful resources
Poppe, L., Steen, J., Loh, W. W., Crombez, G., De Block, F., Jacobs, N., ... & Paepe, A. L. D. (2025). How to develop causal directed acyclic graphs for observational health research: a scoping review. https://doi.org/10.1080/17437199.2024.2402809
Poppe, L., De Paepe, A. L., Deforche, B., Van Dyck, D., Loeys, T., & Van Cauwenberg, J. (2025). Experience sampling method studies in physical activity research: the relevance of causal reasoning. https://doi.org/10.1186/s12966-025-01723-w 
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. https://doi.org/10.1177/251524591774562 
Online Harvard EdX course by Hernán: https://harvardonline.harvard.edu/course/causal-diagrams-draw-your-assumptions-your-conclusions
Hernán MA, Robins JM (2020). Causal Inference: What If. Causal Inference: What If (the book) — Miguel Hernán 
Tennant, P. W., Murray, E. J., Arnold, K. F., Berrie, L., Fox, M. P., Gadd, S. C., ... & Ellison, G. T. (2021). Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. https://doi.org/10.1093/ije/dyaa213

In our fourth episode, we speak with Dr Paulina Schenk about formal ontologies and what they can offer for health psychology and behavioural science. We discuss, in simple terms, what ontologies are, how they differ from taxonomies, and why relating concepts to one another matters for research, evidence synthesis, and practice. The episode also explores how ontologies are developed, how they can accommodate nuance and context, and how they connect to topics such as causal models and AI. 

Here is a small glossary of the important terms used in this episode (since, admittedly, there are quite a few when talking about ontologies).
Class: Ontology classes are representations of groupings or types of things in the world (Arp et al., 2015; Michie et al., 2025).
Ontology: A classification framework that includes representations of things (classes), their relationships and labels, definitions and unique computer-readable alphanumeric IDs for these (Arp et al., 2015; Michie et al., 2025).
Annotation: Process of coding selected parts of documents or other resources to identify the
presence of ontological entities (Arp et al., 2015; Michie et al., 2017).

If you are interested in a clearer understanding of what ontologies are and why they may become increasingly useful in behavioural research, this episode is for you!

Ressources:
Mechanism of Action Ontology part of the BCIO: Schenk, P. M., Wright, A. J., West, R., Hastings, J., Lorencatto, F., Moore, C., ... & Michie, S. (2024). An ontology of mechanisms of action in behaviour change interventions. Wellcome Open Research, 8, 337. https://doi.org/10.12688/wellcomeopenres.19489.2
Upper-level Behaviour Change Intervention Ontology: Michie, S., West, R., Finnerty, A. N., Norris, E., Wright, A. J., Marques, M. M., ... & Hastings, J. (2021). Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology. Wellcome open research, 5, 123. https://doi.org/10.12688/wellcomeopenres.15902.2
COPPER Ontology: Braun, M., Carlier, S., De Paepe, A., De Backere, F., De Turck, F., & Crombez, G. (2025). Development and evaluation of the COntextualised and Personalised Physical activity and Exercise Recommendations (COPPER) Ontology. International Journal of Behavioral Nutrition and Physical Activity, 22(1), 52. https://doi.org/10.1186/s12966-025-01744-5

In our third episode, we have two guests on the show: Maya Braun and Yong Zhang. Both are experts in measuring constructs that change over time and the challenges that come with that. We cover ambulatory assessment approaches for such constructs, how to deal with missing data and how to make your intensive longitudinal studies more engaging for participants.
If you want to better understand measurement of evolving constructs, this episode is for you! 

Learn more about the Conceptualization, Formalization and Measurement interest group here: https://cfm.opens.science/
Here are some resources for your further study:
Paper: So you want to do ESM? https://doi.org/10.1177/25152459241267912 

Sebastian Castro Alvarez: reliability of time-series measurements https://psycnet.apa.org/doi/10.1037/met0000778

Maya’s linktree: https://linktr.ee/mayabraun 
A recent EMA paper on physical activity: https://doi.org/10.1080/08870446.2025.2582467 

Yong’s website: https://www.rug.nl/staff/yong.zhang/?lang=en
Paper on quality check of temporal network models https://doi.org/10.1177/25152459251372116
Paper on model selection in nonstationary time series analysis: https://doi.org/10.1111/bmsp.70012

Apps for running your own EMA: https://sema3.com (free) https://m-path.io/landing/pricing.html (free for “piloting” which can be using it with yourself)

In our second episode, we continue our conversation on Constructs with Gjalt-Jorn, GJ, Peters. We then move on to the measurement of these constructs and the difficulties in assessing the validity of our measurements. GJ introduces us to the idea of Narrative Response Models as a tool to link constructs to measurement instruments and assess their validity.

If you are unsatisfied with current ways to assess validity and want to think more deeply about the connection between what we want to measure and how we measure it, this episode is for you! 

Learn more about the Conceptualization, Formalization and Measurement interest group here: https://cfm.opens.science/

Here are some resources for your further study:
GJ’s website: https://www.behaviorchange.eu/

GJ’s preprint on Narrative Response Models: https://doi.org/px2z 

DCT website: https://open.lnu.se/index.php/metapsychology/article/view/3638

Denny Borsboom, Angelique Cramer, Rogier Kievit, Annemarie Zand Scholten, Sanja Franic (2009). The End of Construct Validity. https://dennyborsboom.com/wp-content/uploads/2017/11/borsboomendofcv2009.pdf 

Michael Kane (2013). The argument-based approach to validation. https://doi.org/gjk8mr

Eronen, Markus and Bringmann, Laura (2025). On worms, mirror neurons and explaining human behavior. https://philsci-archive.pitt.edu/26718/

In our first guest episode, we talk with Annick De Paepe about why vague construct definitions stall science and how to fix this.
Topics include jingle–jangle pitfalls, pragmatic realism (and why multiple context-specific definitions can coexist), the Decentralised Construct Taxonomy, and a practical 4-step method for crafting definitions—illustrated with HAPA constructs (action planning, coping planning, self-efficacy, action control).
We hope that you leave with concrete steps to write and cite clearer definitions and how to link them to measurement.

Learn more about the Conceptualization, Formalization and Measurement interest group here: https://cfm.opens.science/

Here are some resources for your further study:
De Paepe, A., Perski, O., Tack, E., Van Alboom, M., De Bruecker, M., Van Overbeke, M., … Braun, M. (2025, August 5). Towards clear construct definitions: Applying an iterative approach to constructs of the Health Action Process Approach  (HAPA). https://doi.org/10.31234/osf.io/wbcn3_v1  
Podsakoff, P.M., MacKenzie, S.B. & Podsakoff, N.P. (2016). Recommendations for creating better concept definitions in the organizational, behavioral and social sciences. Organizational Rsearch Methods, 19(2):159-203. https://doi/10.1177/1094428115624965

Bringmann, L.F., Elmer, T. & Eronen, M.I. Back to basics: The importance of conceptual clarification in psychological science. Current Directions in Psychological Science, 31(4):340-346. https://https://doi.org/10.1177/0963721422109648 
MacKenzie, S.B., Podsakoff, P.M. & Podsakoff, N.P. Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques. MIS Quarterly, 35(2):293-334. https://doi.org/10.2307/23044045 
Peters, G.-J.& Crutzen, R. (2024). Knowing what we’re talking about: Facilitating Decentralized Unequivocal Publication of and Reference to Psychological Construct definitions and instructions. Meta-Psychology, 8. https://doi.org/10.15626/MP.2022.3638