Data-driven and theory-driven approaches represent two distinct perspectives on how to conduct research and implement practices. A data-driven approach aims to identify patterns, associations, and outcomes through statistical or algorithmic analyses of existing data. This perspective does not necessarily rely on a pre-established theoretical framework or hypothesis. Researchers or practitioners focus on what the data itself reveals, seeking trends and structures that emerge directly from empirical evidence. In this context, machine learning, artificial intelligence, and statistical modeling play a central role, as they are designed to uncover complex relationships among numerous variables and provide predictive insights. In the current era marked by the proliferation of data and advanced computational capacity, data-driven methodologies have gained substantial popularity.
On the other hand, the theory-driven approach emphasizes the importance of a conceptual framework, theoretical model, or predefined hypothesis that guides the research or application. In this approach, the researcher starts with existing theories or models and seeks to test specific relationships within that framework. The aim of the analysis is to answer the question: “Which empirical findings support or refute the theoretically predicted hypotheses?” Methods such as structural equation modeling (SEM), causal modeling, and traditional statistical tests are inherently theory-driven, as they are used to examine predefined relationships, directional effects, and theoretical mechanisms such as mediation or moderation. This approach strengthens the link between theory and practice and allows for assessing the extent to which empirical findings align with existing theoretical propositions.
Although these two approaches are often portrayed as opposing ends of a methodological spectrum, they are frequently employed in a complementary manner within both academic and applied domains. Findings derived from data-driven modeling can inform the development of new theoretical hypotheses. Conversely, theory-driven approaches can guide data-driven analyses by indicating which variables and methods should be prioritized. In the age of big data, it is increasingly common to use data-driven methods to test theory-based hypotheses or to interpret empirical results within a theoretical framework.
Each approach offers distinct advantages. Data-driven methods are valuable for uncovering unexpected patterns due to their flexible structure, while theory-driven approaches provide depth by offering meaningful and causal explanations. Therefore, in designing a research study or developing an intervention, it is crucial to evaluate both perspectives in a balanced way. The nature of the research question, the structure and volume of the available data, and the presence of well-established theories in the literature are key determinants of which approach should take precedence. Ultimately, the integration of data-driven and theory-driven approaches is one of the keys to generating more robust and insightful results in contemporary scientific research and practical applications.
Note: This text is inspired by the methodological sections of the following academic publications.
References:
- Schafer, K. M., Kennedy, G., Gallyer, A., & Resnik, P. (2021). A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis. PloS one, 16(4), e0249833.
- Cox, C. R., Moscardini, E. H., Cohen, A. S., & Tucker, R. P. (2020). Machine learning for suicidology: A practical review of exploratory and hypothesis-driven approaches. Clinical Psychology Review, 82, 101940.
- Karvelis, P., Charlton, C. E., Allohverdi, S. G., Bedford, P., Hauke, D. J., & Diaconescu, A. O. (2022). Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review. Network Neuroscience, 6(4), 1066-1103.
- Kuhn, M., Steinberger, D. C., Bendezú, J. J., Ironside, M., Kang, M. S., Null, K. E., … & Pizzagalli, D. A. (2025). Psychobiological stress response profiles in current and remitted depression: A person-centered, multisystem approach. Biological Psychiatry Global Open Science, 5(1), 100400.
- Blekic, W., D’Hondt, F., Shalev, A. Y., & Schultebraucks, K. (2025). A systematic review of machine learning findings in PTSD and their relationships with theoretical models. Nature Mental Health, 1-20.
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