Toward Measuring Geographical Belongingness in a Mobile, Hybrid Globe – Development of a Place-Belongingness Scale Among Immigrants

Abstract

Place-belongingness, defined as the subjective sense of belonging to a geographic location, influences both individual well-being and community resilience. Researchers have developed interventions to foster place-belongingness, particularly among immigrants at risk of socio-spatial exclusion. However, quantitative tools to assess these interventions remain scarce. Single-item indicators cannot capture the complexity of this multidimensional concept. To address this gap, we present a validated place-belongingness scale as an initial step toward a composite indicator. The scale draws on survey data from N = 270 immigrants worldwide and was examined using exploratory factor analysis. Findings show that place-belongingness can be assessed through four factors: feeling at home, feeling accepted, feeling empowered, and feeling secure. We situate these results within the broader literature to highlight the scale’s contribution. The tool can inform policy and placemaking practices that respond to challenges of globalization, migration, and digitization. Finally, we discuss the study’s limitations and provide guidance for administrators and respondents on using and scoring the scale.

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Citations
Methods
Paper Only

Study specs

Survey data from 270 immigrants worldwide analyzed using exploratory factor analysis.

Discipline
Social Science
Sample Size
N=270
Study Type
methodology
Year
2026
Human Data Platform
Prolific

Measured Outcomes

The subjective sense of place-belongingness, decomposed into four factors: feeling at home, feeling accepted, feeling empowered, and feeling secure.

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.

1 day ago

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