Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessing Measurement Invariance in Neighborhood Socioeconomic Environment Across Levels of Urbanicity

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月29日

查看全文
  • S. S. A. De Silva,
  • Melissa A. Meeker,
  • Tara P. McAlexander,
  • Yasemin Algur,
  • Victoria Ryan,
  • Leslie A. McClure

摘要

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.