Introduction

Almost everyone in the organizational and social sciences can recite a number of research-related “truisms” that we learned in our graduate training, while conducting research, in our experience publishing, while reviewing grant proposals, and so on. For example, nearly everyone could probably recite (a) some rule of thumb as to what constitutes an acceptably large factor loading, (b) how many subjects it takes to conduct a/n XXX (regression, factor, item analysis-pick one), and (c) good reasons why samples with low response rates (e.g., 15%–30%) cannot be trusted. These truisms have been referred to as “received doctrines” (Barrett, 1972, p. 1) and “statistical and methodological myths and urban legends” (Vandenberg, 2006, p. 194). Beliefs in such “urban legends” (ULs) seem to be based, in part, on some kernel of truth(s) that can often be identified in relevant literature and, in part, on myth that has developed around their application and invocation. The purpose of this book is to provide a set of up-to-date reviews of the origin, development, pervasiveness, and present status of several of these ULs. These ULs reinforce a number of methodological and statistical beliefs and practices that are based, in part, on sound rationale and justification and, in part, on unfounded lore. The beliefs and practices themselves are not necessarily intrinsically faulty, but the rationale for them often is questionable. The chapters in this book examine several such beliefs and practices, illustrated anecdotally by the following statements:

Each of these statements represents a chapter in this volume. We asked contributing authors to address the following points regarding statements such as these in each chapter: (a) What is the legend that “we (almost) all know to be true”? (b) What is the “kernel of truth” to each legend? (c) What are the myths that have developed around this kernel of truth? and (d) What should the state of the practice be? As editors, we sought to work with the authors to reveal the truth, the lore, and the recommended best practice associated with their own legend. In the end, our goal was to provide researchers with a set of guidelines for sounder research practice.