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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