By Charles E. Lance, Charles E Lance, Robert J Vandenberg

This e-book presents an up to date assessment of quite often undertaken methodological and statistical practices which are sustained, partly, upon sound purpose and justification and, partially, upon unfounded lore. a few examples of those "methodological city legends", as we confer with them during this booklet, are characterised by means of manuscript evaluations corresponding to: (a) "your self-report measures be afflicted by universal approach bias"; (b) "your item-to-subject ratios are too low"; (c) "you can’t generalize those findings to the true world"; or (d) "your impact sizes are too low". traditionally, there's a kernel of fact to every one of these legends, yet in lots of situations that fact has been lengthy forgotten, overlooked or adorned past reputation. This ebook examines a number of such legends. every one bankruptcy is prepared to deal with: (a) what the legend is that "we (almost) all recognize to be true"; (b) what the "kernel of fact" is to every legend; (c) what the myths are that experience constructed round this kernel of fact; and (d) what the nation of the perform could be. This e-book meets a huge want for the buildup and integration of those methodological and statistical practices.

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Extra resources for Statistical and Methodological Myths and Urban Legends: Doctrine, Verity and Fable in the Organizational and Social Sciences

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The Partial Revival of a Dead Horse? 39 ἀ ere have been many different attempts to explain the concept of a true score.

1 assumed dmiss was orthogonal to the response rate, which may or may not hold up under empirical scrutiny. Missing Data Techniques A final advantage of considering SNPs is that these parameters indicate the extent to which popular missing data techniques (listwise and pairwise deletion) will result in biased estimates (low external validity). In specific, listwise and pairwise deletion are appropri2 = 0). , where dmiss = 0 and f miss the inferiority of listwise and pairwise deletion can be empirically demonstrated by looking at the SNPs.

Stated differently, nonresponse behavior is related to many and f miss social and psychological variables. 3). ἀ e reason missing data can bias results of research studies is that the concepts being studied are related to individual survey response decisions. 2 and assumed nonresponse is related equally to all constructs. 1—SNPs (a) vary across constructs being studied and (b) directly determine the extent of nonresponse bias. ἀ e above facts are useful in debunking two popular missing data legends, as explained below.

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