methodological triangulation
Definition
Methodological triangulation refers to the deliberate use of multiple methods, data sources, and theoretical frameworks to arrive at more defensible conclusions than any single approach could support. The strategy is proposed as one solution to the mutual-internal-validity problem, in which theories explaining only within-paradigm phenomena gradually lose touch with outcomes beyond the paradigm when the same designs are used both to test and to develop theory. Combining self-report, behavioral, and physiological measures, alongside perspectives drawn from disciplines such as economics and political science, helps address idiosyncratic artifacts including sample bias, measurement error, and context-specific influences. In misinformation and conspiracy research specifically, clearer specification of causal quantities under a counterfactual framework is presented as a means of enabling such triangulation in a systematic rather than ad hoc manner.
Sources: Tay et al. (2024)
Related Terms
Applications
Methodological Triangulation and Causal Inference
Triangulation of methods and theories is explicitly linked to the project of drawing credible causal conclusions in misinformation research. Formally defining target quantities as counterfactual comparisons frees researchers from reliance on any single empirical strategy, whether laboratory vignettes or observational regression modelling, and thereby creates the conditions under which multiple approaches can be coordinated toward estimating the same theoretical quantity.
Sources: Tay et al. (2024)
Methodological Triangulation and Natural Experiments
Natural experiment designs, including instrumental variable analysis, regression discontinuity design, difference-in-differences, and synthetic control, are presented as part of the expanded methodological toolkit that triangulation requires. Because laboratory experiments are constrained by ethical and feasibility considerations, these approaches allow researchers to test causal claims with real-world data, extending inquiry beyond what within-paradigm designs alone can support.
Sources: Tay et al. (2024)
