Regression toward the mean is a complex statistical principle that plays a crucial role in any research involving the measurement of change. This primer is designed to help researchers more fully understand this phenomenon and avoid common errors in interpretation. The book presents new methods of graphing regression toward the mean, facilitating comprehension with a wealth of figures and diagrams. Special attention is given to applications related to program or treatment evaluation. Numerous concrete examples illustrate the ways researchers all too often attribute effects to an intervention or other causal variable without considering regression artifacts as an alternative explanation for change. Also discussed are instances when problems are actually created, instead of solved, by correction for regression toward the mean. Throughout, the authors strive to use nontechnical language and to keep simulations and formulas as accessible as possible.
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Donald T. Campbell, PhD, before his death in 1996 was University Professor of Social Relations, Psychology, and Education at Lehigh University. He had previously taught at Ohio State University, the University of Chicago, Northwestern University, and Syracuse University. He was a member of the National Academy of Sciences and a President of the American Psychological Association. He was the recipient of 9 honorary doctorates.
David A. Kenny, PhD, is Professor of Social Psychology at the University of Connecticut. He has been a visiting professor at Oxford University and Arizona State University, and was a Fellow at the Center for Advanced Study in the Behavioral Sciences.
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Buch. Zustand: Neu. Neuware - Regression toward the mean is a complex statistical principle that plays a crucial role in any research involving the measurement of change. This primer is designed to help researchers more fully understand this phenomenon and avoid common errors in interpretation. The book presents new methods of graphing regression toward the mean, facilitating comprehension with a wealth of figures and diagrams. Special attention is given to applications related to program or treatment evaluation. Numerous concrete examples illustrate the ways researchers all too often attribute effects to an intervention or other causal variable without considering regression artifacts as an alternative explanation for change. Also discussed are instances when problems are actually created, instead of solved, by 'correction' for regression toward the mean. Throughout, the authors strive to use nontechnical language and to keep simulations and formulas as accessible as possible. Artikel-Nr. 9781572304826
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