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|a Osborne, Jason W.
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|a The Advantages of Hierarchical Linear Modeling /
|c Jason W. Osborne.
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|a [Place of publication not identified] :
|b Distributed by ERIC Clearinghouse,
|c 2000.
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|a This digest introduces hierarchical data structure, describes how hierarchical models work, and presents three approaches to analyzing hierarchical data. Hierarchical, or nested data, present several problems for analysis. People or creatures that exist within hierarchies tend to be more similar to each other than people randomly sampled from the entire population; for this reason, observations based on these individuals are not fully independent. Hierarchical linear modeling can address problems caused by this situation. The basic concept is similar to that of ordinary least squares regression. On a base level (usually the individual), an outcome variable is predicted as a function of a linear combination of one or more level 1 variables. On subsequent levels, the level 1 slope (or slopes) and intercept become dependent variables being predicted from level 2 variables. Through this process, the effects of level 1 variables on the outcome are accurately modeled, and the effects of level 2 variables are also modeled on the outcome. Cross-level interactions can be modeled. Data from the National Education Longitudinal Survey of 1988 are used to illustrate disaggregated, aggregated, and hierarchical analyses. These analyses reveal the need for multilevel analysis of multilevel data. (SLD)
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|a Nested Data
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