This book provides an up-to-date review of commonly undertaken
methodological and statistical practices that are sustained, in
part, upon sound rationale and justification and, in part, upon
unfounded lore. Some examples of these "methodological urban
legends," as we refer to them in this book, are characterized by
manuscript critiques such as: (a) "your self-report measures suffer
from common method bias"; (b) "your item-to-subject ratios are too
low"; (c) "you can t generalize these findings to the real world";
or (d) "your effect sizes are too low."
Historically, there is a kernel of truth to most of these
legends, but in many cases that truth has been long forgotten,
ignored or embellished beyond recognition. This book examines
several such legends. Each chapter is organized to address: (a)
what the legend is that "we (almost) all know to be true"; (b) what
the "kernel of truth" is to each legend; (c) what the myths are
that have developed around this kernel of truth; and (d) what the
state of the practice should be. This book meets an important need
for the accumulation and integration of these methodological and
statistical practices.
General
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