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Peer reviewed Peer reviewed
Wang, Jianjun – Journal of Experimental Education, 1999
Uses examples of hierarchical linear modeling (HLM) at local and national levels to illustrate proper applications of HLM and dummy variable regression. Raises cautions about the circumstances under which hierarchical data do not need HLM. (SLD)
Descriptors: Mathematical Models, Regression (Statistics), Research Methodology
Peer reviewed Peer reviewed
Preece, Peter F. W. – Journal of Experimental Education, 1978
Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…
Descriptors: Mathematical Models, Multiple Regression Analysis, Regression (Statistics), Statistical Analysis
Peer reviewed Peer reviewed
Willett, John B.; Singer, Judith D. – Journal of Experimental Education, 1989
Problems of estimation and interpretation are discussed that arise when a statistical package, which does not incorporate a dedicated weighted least-squares (WLS) routine, performs WLS regression by misapplication of a case-weighting strategy. A strategy is offered for adjusting WLS regression estimates after a case weighting strategy has been…
Descriptors: Estimation (Mathematics), Least Squares Statistics, Mathematical Models, Regression (Statistics)
Peer reviewed Peer reviewed
Singer, Judith, D. – Journal of Experimental Education, 1987
A two-stage generalized least squares model is developed for estimating the linear regression of an individual outcome on a group characteristic in studies of multilevel data. Results of this model are compared to the results of analytic methods, and formulas are developed for assessing the accuracy of the traditional approaches. (Author/JAZ)
Descriptors: Error of Measurement, Least Squares Statistics, Mathematical Models, Regression (Statistics)