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. 2025 Oct 29.
doi: 10.1007/s11524-025-01020-8. Online ahead of print.

Assessing Measurement Invariance in Neighborhood Socioeconomic Environment Across Levels of Urbanicity

S S A De Silva  1   2 , Melissa A Meeker  1   3 , Tara P McAlexander  1   4 , Yasemin Algur  1 , Victoria Ryan  1 , Leslie A McClure  5   6
Affiliations

Assessing Measurement Invariance in Neighborhood Socioeconomic Environment Across Levels of Urbanicity

S S A De Silva et al. J Urban Health. .

Abstract

While neighborhood socioeconomic status (NSES) significantly impacts health outcomes, its measurement often assumes that the social and economic mechanisms that drive NSES operate similarly across the urban-rural continuum. This study aimed to develop a census-tract-level NSES measure that accounts for differences across community type and assesses its measurement invariance. Using data from 71,908 census tracts in the contiguous United States, we employed exploratory and confirmatory factor analyses to derive a one-factor NSES construct consisting of five variables: percent below the poverty line, households receiving public assistance, population unemployed, households without cars, and population with less than a high school education. Measurement invariance analysis revealed that while the overall NSES structure is consistent across urban and rural community types, factor loadings varied significantly. The percentage of the population living below poverty was the most reflective indicator across all community types, while other indicators, such as car access and unemployment, exhibited context-specific variability. These findings highlight the importance of incorporating urbanicity into NSES measures in health disparity research and to improve the effectiveness of public health interventions.

Keywords: Factor analysis; Measurement invariance; Neighborhood socioeconomic status; Rural health; Urban health.

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Conflict of interest statement

Declarations. Declaration of AI and AI-Assisted Technologies in the Writing Process: During the preparation of this work, the authors did not use any AI or AI-assisted technologies. Conflict of interest: The authors declare no competing interests.

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