Making Sense of Belonging: Analytical Pathways, Bias Corrections, and What Statistics Can (and Cannot) Tell Us about Inclusion.

This methodological companion piece provides a practical and detailed account of the analytical choices underpinning my article Societal Factors Influencing the Sense of National Belonging: A Statistical Analysis of Residents in Canada (Keiff, 2025). Drawing on a nationally representative dataset, the guide reconstructs the step-by-step process of building a composite index of belonging using factor analysis, distinguishing between territorial and community anchors. It then presents the construction of an intersectional discrimination score via logistic regression, designed to capture the cumulative and intersecting nature of exclusion. It discusses how the analysis addresses selection bias, particularly the counterintuitive finding of lower belonging among highly educated individuals, through an instrumental variable approach. Throughout, the text explains not only the technical procedures but also the theoretical assumptions, limits, and interpretive challenges at each stage. It engages explicitly with methodological debates such as the “Table 2 fallacy” and the interpretation of statistical significance. Beyond the technical details, this guide demonstrates how quantitative models both reveal and obscure social structures and invites readers to consider the broader implications for public debate and inclusion policy. The aim is to provide a transparent, reproducible resource for those wishing to understand, critique, or extend the analytical pathway followed in the original article.