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- W31175013 abstract "Hierarchical linear modeling (HLM) is a particular regression technique that is designed to take into account the hierarchical structure of educational data. Generally, regression analysis is often used in monitoring to examine the relationships among a dependent variable, such as academic achievement, and one or more independent variables, such as students' prior academic experience and their family socioeconomic status. HLM can be understood by thinking of the analysis as being conducted in two steps. In the first step, analyses are conducted separately for every school in the system, using student-level data. For example, students' test scores in mathematics could be regressed on a set of student-level predictor variables, such as the students' socioeconomic status, and a dichotomous variable indicating whether the student is male or female. In the second step, the regression parameters from the first step of the analyses become the outcome variables of interest. These are regressed on school-level data describing schooling processes. For example, one could specify a regression of the adjusted levels of performance on the average class size for the school, and a measure of the school's disciplinary climate. The basic idea underlying HLM is that there are separate analyses for each unit in a hierarchical structure. The simple two-level model described in this chapter can be applied to address a range of questions that policy makers might pose. There are more complex hierarchical linear models; indeed, the statistical analyses specified at each level are not limited to linear regressions, and the models can include three- or even four-level models." @default.
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- W31175013 date "1999-01-01" @default.
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- W31175013 title "Basic Concepts in Hierarchical Linear Modeling with Applications for Policy Analysis" @default.
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- W31175013 doi "https://doi.org/10.1016/b978-012174698-8/50046-1" @default.
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