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.