By Charles E. Lance, Robert J. Vandenberg
This ebook offers an updated overview of generally undertaken methodological and statistical practices which are established partly in sound clinical cause and partly in unfounded lore. a few examples of those “methodological city legends” are characterised via manuscript reviews similar to: (a) “your self-report measures be afflicted by universal technique bias”; (b) “your item-to-subject ratios are too low”; (c) “you can’t generalize those findings to the true world”; or (d) “your impression sizes are too low.”
What do those reviews suggest, and what's their old foundation? More Statistical and Methodological Myths and concrete Legends catalogs a number of of those quirky practices and descriptions right study concepts. subject matters lined comprise pattern dimension requisites, lacking info bias in correlation matrices, damaging wording in survey examine, and lots more and plenty more.
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Extra resources for More Statistical and Methodological Myths and Urban Legends
The possibilities are endless, and yet our ﬁeld remains almost entirely skeptical. Some of our skepticism may be warranted, but why would we be so reluctant to pursue research on what we know to be the source of all of the phenomena in which we are interested? Is Ours a Hard Science (and Do We Care)? 27 We offer this bit of anecdotal yet shocking evidence of the reluctance of our ﬁeld to consider brain research. One of the authors of this chapter recently had a review paper on the physiology of sleepiness and its effects on workplace outcomes rejected.
If gatekeepers were more inclined to publish papers with difﬁcult but messy designs, we would use those designs. • If gatekeepers encouraged alternative measurement strategies, we would apply them. • If gatekeepers insisted on the most up-to-date analytic tests, we would conduct them. • If gatekeepers insist on appropriate application of analytic tests, we would apply them properly. Is Ours a Hard Science (and Do We Care)? 31 Whether our ﬁeld is viewed by others as a hard or soft science is, ultimately, beyond our direct control.
Cortina Complex Causal Models As the complexity of our conceptual models has increased, so have the requirements that we place upon our data. As a result, our data analytic techniques have had to advance rapidly. For example, a model that has become quite popular is the moderated mediation model. Holland, Shore, and Cortina (2014) found 68 different papers from two years of Journal of Applied Psychology (JAP) and Academy of Management Journal (AMJ) that tested moderated mediation models. Edwards and Lambert (2007) offered in-depth guidance regarding the testing of a variety of moderated mediation models, of which there are many.