title: Assessing interchangeability at cluster levels with multiple-informant data.
abstract: Studies examining the relationship between neighborhood social disorder and health often rely on multiple informants. Such studies assume interchangeability of the latent constructs derived from multiple-informant data. Existing methods examining this assumption do not clearly delineate the uncertainty at individual levels from that at neighborhood levels. We propose a multilevel variance component factor model that allows this delineation. Data come from a survey of a representative sample of children born between 1983 and 1985 in the inner city of Detroit and nearby middle-class suburbs. Results indicate that the informant-level models tend to exaggerate the effect of places because of differences between persons. Our evaluations of different methodologies lead to the recommendation of the multilevel variance component factor model whenever multiple-informant reports can be aggregated at a neighborhood level.
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, U.S.A.
authors.forename: Zhehui
authors.initials: Z
authors.lastname: Luo
authors.forename: Joshua
authors.initials: J
authors.lastname: Breslau
authors.forename: Joseph C
authors.initials: JC
authors.lastname: Gardiner
authors.forename: Qiaoling
authors.initials: Q
authors.lastname: Chen
authors.forename: Naomi
authors.initials: N
authors.lastname: Breslau
mesh_terms: D000293:Adolescent; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008824:Michigan; D015233:Models, Statistical; D012111:Residence Characteristics; D012959:Socioeconomic Factors; D013383:Suburban Population; D011795:Surveys and Questionnaires; D014505:Urban Population
keywords: interchangeability; multilevel models; multiple-informant data; neighborhood effects
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