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Abstract
There has been a growing awareness among educational researchers of the consequences of using data-analytic models that fail to account for the inherent clustered or hierarchical sampling structure of the data typically obtained. Such clustering poses special analytic problems related to levels of analysis, aggregation bias, heterogeneity of regression and parameter mis-estimation, with important implications for the correct interpretation of effects. This paper compares the results obtained from fitting single-level and multilevel models to two hierarchically structured data sets designed to explain variation in student achievement. Emphasis is given to the crucial importance of fitting models commensurate with the sampling structure of the data to which they are applied.
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