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In today’s public debates—on cities, housing, identity or development—there is growing faith in numbers. Dashboards, indices and predictive models dominate conversations, often giving the impression that social realities can be neatly captured through data alone. But anyone who has spent time in the field knows that numbers may inform policy, yet they rarely explain people. My research across urban neighbourhoods, housing markets, and spaces of cultural and religious life has repeatedly shown that method matters. Understanding society requires not a choice between stories and statistics, but a careful integration of both.
Learning From Being There
Before surveys and models come presence. Ethnography and participant observation remain indispensable because they allow researchers to understand how people inhabit space, assign meaning to places, and negotiate everyday life. Sitting in neighbourhood parks, observing interactions in housing societies, or participating in religious and cultural gatherings reveals dimensions of social life that formal instruments often miss.
In my recent work examining places of worship as sites of identity and memory, extended participant observation played a central role (Digital Narratives of Belonging and Places of Worship as Cultural Anchors in Girmitya Communities of Africa, African Journal of Religion Philosophy & Culture, 2025). Time spent within these spaces—observing rituals, informal conversations, digital engagements and commemorative practices—helped reveal how belonging is produced and performed, both offline and online. Such insights cannot be extracted from datasets alone; they require the researcher to be present, attentive, and embedded.
Housing, Infrastructure and Lived Experience
The same principle applies to urban housing and infrastructure. In studies of gated residential societies and the real estate sector in urban India, field engagement revealed how infrastructure is not merely functional but deeply emotional. Roads, security arrangements, shared spaces and neighbourhood networks shape whether residents feel rooted or transient, secure or alienated. These insights, emerging from observation and interaction, informed subsequent empirical work on housing satisfaction, place attachment and urban governance. They also highlighted a recurring gap between policy assumptions and lived realities—something only fieldwork can uncover.
From Field Insights to Evidence
Yet, lived experience must also be tested systematically. This is where sample surveys become crucial. Carefully designed questionnaires help translate qualitative insights into measurable patterns, allowing researchers to examine how widespread certain perceptions are and how different factors interact. Survey data enables comparison across households, neighbourhoods and social groups—helping move from individual stories to broader social trends without losing contextual depth.
When Models Respect Complexity
Social outcomes are rarely linear. Satisfaction, attachment or belonging are shaped by multiple, overlapping influences. To account for this, my work has used non-linear quantitative techniques, particularly logistic regression, to model outcomes that are categorical rather than continuous (Evaluating urban infrastructure and place attachment among residents of outskirt gated societies of Delhi, NCR, Transactions, 2022). Such methods help estimate probabilities instead of assuming certainties. They allow researchers to ask nuanced questions: What increases the likelihood of place attachment? Which factors matter more, and which matter less, once others are accounted for? When grounded in field-informed hypotheses, these models add clarity rather than abstraction.
Why Mixed Methods Matter Now
Whether studying housing markets, urban infrastructure, environmental heritage or religious spaces, one lesson remains constant: context gives meaning to data, and data disciplines context. Participant observation uncovers meanings, surveys establish patterns, and statistical models test relationships. At a time when policy-making is increasingly described as “data-driven,” there is a risk of mistaking precision for understanding. Numbers can tell us what is happening, but only grounded research explains why. Good research, therefore, does not rush to the spreadsheet. It begins on the street, in neighbourhoods, homes and shared spaces—before returning to data with sharper questions and greater humility.
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