Multi-level covariance structure analysis of intra-familial substance use.
Conventional covariance structure analysis, such as factor analysis, is often applied to data that are obtained in a hierarchical fashion, such as parents and siblings observed within families. However, multivariate modeling of such data are most frequently done as if the data were obtained as a simple random sample from a single population. An alternative specification is presented which explicitly models the within-level and between-level covariance matrices in familial substance use. Results demonstrate homogeneity in substance use within families but heterogeneity across families which could be accounted for by family-level variables of marital status, economic status, and biological relationships. It is shown that conventional covariance structure software can be easily adapted to handle hierarchical models, providing a large set of new analysis possibilities for multi-level data.
Duncan
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TE
Oregon Research Institute, Eugene 97403, USA. terryd@ori.org
Duncan
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SC
Hops
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Alpert
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A
D000293:Adolescent; D000328:Adult; D000704:Analysis of Variance; D002648:Child; D016241:Child of Impaired Parents / Q000523:psychology*; D005260:Female; D006801:Humans; D013287:Illicit Drugs; D008297:Male; D017533:Marital Status; D015233:Models, Statistical; D015205:Northwestern United States / Q000453:epidemiology; D011619:Psychotropic Drugs; D012306:Risk; D012801:Sibling Relations; D012931:Social Environment; D012959:Socioeconomic Factors; D019966:Substance-Related Disorders / Q000453:epidemiology / Q000235:genetics* / Q000523:psychology; D014505:Urban Population / Q000706:statistics & numerical data
D013287:Illicit Drugs; D011619:Psychotropic Drugs