 Measurement and Study DesignAmber R. Crowell and
Mark A. Fossett
Chapter Chapter 2 in Racial and Ethnic Residential Segregation Across the United States, 2023, pp 23-67 from Springer Abstract: Abstract A major feature of our study is that we use new methods for measuring residential segregation that make it possible for us to assess levels and trends in segregation with consistent accuracy and across a wider range of measurement circumstances. This includes combinations of group comparisons and community settings where trustworthy measurements of segregation previously have not been possible. More specifically, we measure the dimension of segregation known as evenness using refined versions of two familiar and widely used measures, the dissimilarity index (D) and the separation index (S). The versions of the measures we use are free of index bias, a problem that poses major challenges for measuring segregation in many situations, and thus yield index scores that are accurate and trustworthy in situations where scores obtained using conventional approaches to measuring segregation used in previous studies would be distorted by index bias. In the past, the problem of index bias forced researchers to choose between two undesirable options. One option is to measure segregation across a more comprehensive and representative range of circumstances but with an understanding that the index scores obtained are in many cases untrustworthy and potentially misleading because they are distorted by bias. The other option is to restrict the scope of the analysis to a much smaller and less representative set of combinations of group comparisons and community settings where index bias is likely to be negligible and scores for standard versions of segregation indices are trustworthy and can sustain close analysis of cases. The measurement methods we use make it possible for us to sidestep these difficult choices and avoid the undesirable consequences that accompany them. The benefit of using these new measurement methods is that we are able to obtain segregation index scores that are consistently accurate across a much broader range of measurement circumstances (e.g., combinations on group comparisons and community settings) than has been possible in previous research. The consistent accuracy of the unbiased measures enables us to draw conclusions about the levels and patterns of variation in segregation across group comparisons, across communities, and over time with greater confidence. Additionally, it allows us to selectively conduct close analysis of index scores for individual cases including, for example, tracking changes in segregation over time for a small subpopulation (e.g., Latino immigrants) in a small nonmetropolitan community, an analysis that cannot be sustained with conventional measurement practices used in past research. The task we seek to accomplish in this chapter is to first provide an overview of the conceptualization of residential segregation and the motivations for studying it and to then highlight the features of our study design that enable us to make new and important contributions to research on this topic. Date: 2023 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-3-031-38371-7_2 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-38371-7_2 Access Statistics for this chapter More chapters in The Springer Series on Demographic Methods and Population Analysis from Springer |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.