Statistical Power and the Testing of Null Hypotheses: A Review of Contemporary Management Research and Recommendations for Future Studies

By Luke H. Cashen, Scott W. Geiger. 2004

125 citations recorded by the source corpus.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fed452785f2172e2ad0f38828acab9a460443f27

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In Management Science and Operations Research

Citations in the corpus

The Chrysalis Effect (2017, background): “A review of the literature suggests that as a field, we are winning the game despite management research often being statistically underpowered with relatively small samples and effect sizes (Cashen & Geiger, 2004; J. Cohen, 1990; Marszalek, Barber, Kohlhart, & Holmes, 2011).”
Effect sizes and the interpretation of research results in international business (2010, background): “88 – that is, close to one standard deviation, and a large difference by Cohen’s (1988) standards.”
STUDY QUALITY IN SLA (2013, background): “…signifi cance reported effect sizes, and only two studies reported a power analysis (see comparably bleak fi ndings with respect to the use and perception of statistical power in organizational research in Cashen & Geiger, 2004 ; Mone, Mueller, & Mauland, 1996 ; and Sedlmeier & Gigerenzer, 1989 ).”
Best-Practice Recommendations for Producers, Evaluators, and Users of Methodological Literature Reviews (2020, methodology): “4 Employ graphical tools to present recommendations? Cashen and Geiger (2004), Certo et al. (2016), Hill et al.”
Customer-Centric Science: Reporting Significant Research Results With Rigor, Relevance, and Practical Impact in Mind (2010, methodology): “…in the conventional way statistical significance has been used and reported for hypothesis testing have been discussed not only in management (e.g., Cashen & Geiger, 2004; Cortina & Folger, 1998; Sauley & Bedeian, 1989) but also in several other related fields including finance (e.g.,…”
Answers to 18 Questions About Open Science Practices (2018, unknown): “…results are reported in our top journals, little attention tends to be paid to sample size requirements prior to data collection (Bakker, van Dijk, & Wicherts, 2012; Cashen & Geiger, 2004; Maxwell, 2004), as is now required by some funding agencies (e.g., the National Institutes of Health).”
Effect Size, Confidence Intervals and Statistical Power in Psychological Research (2015, background): “…of NHST in research, stated that “Researchers who obtain (statistically) significant results have previously been satisfied when reaching their goal, without realizing that they have not achieved an improvement, in any way, to the understanding of the phenomenon being studied” (Clark, 2004, p.293).”
On the adoption of partial least squares in psychological research: Caveat emptor (2015, unknown): “If this probability falls below a pre-defined threshold (typically, 0.05), we can then infer that there is strong evidence against the null hypothesis (Cashen & Geiger, 2004;).”
Sample size planning for statistical power and accuracy in parameter estimation. (2008, background): “Despite the obvious nature of this statement, literature reviews continue to show that underpowered studies persist, not just in psychology but also in other disciplines (Bezeau & Graves 2001, Cashen & Geiger 2004, Chan & Altman 2005, Maggard et al. 2003).”
Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results (2016, background): “…a key recommendation of our work is that in order to make better statistical inferences, which is one cornerstone of (European) management research (Cashen & Geiger, 2004; Thietart, 2001), constructing bias-corrected percentile bootstrap confidence intervals based on a large number of bootstrap…”
Should Entrepreneurially Oriented Firms Have Narcissistic CEOs? (2016, methodology): “Further, we elaborated on issues of statistical power by conducting a statistical test for multiple regression (post hoc for given alpha, sample size, and effect size) (Cashen & Geiger, 2004).”