Detecting Interaction Effects in Moderated Multiple Regression With Continuous Variables Power and Sample Size Considerations
By Gwowen Shieh. 2009
195 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/822957ed831b024d61753a521e1bdd171bf2e798
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- R. Gottfredson
- Alvaro Cuervo‐Cazurra
- Marcin Zajenkowski
- John E. Mathieu
- Bo Bernhard Nielsen
- Ulf R. Andersson
- Bradley L Kirkman
- Ning Li
In Statistics and Probability
- Herman Aguinis
- R. Gottfredson
- Alvaro Cuervo‐Cazurra
- Marcin Zajenkowski
- T. Brad Harris
- John E. Mathieu
- Bo Bernhard Nielsen
- Richard G. Gardner
Citations in the corpus
- Other-rated personality and academic performance: Evidence and implications (2014, background): “The strength of these moderating effects is unusually large given “the notorious difficulty of detecting moderator effects” in correlational studies (Shieh, 2009, p. 511).”
- Best-practice recommendations for estimating interaction effects using moderated multiple regression (2010, methodology): “…subgroup sample sizes for categorical moderators) using computer programs available in the public domain (Aguinis, 2004 for categorical moderators; Shieh, 2009 for continuous moderators) (3) Make design choices considering practical constraints but also based on the statistical power analysis…”
- From the Editors: Explaining interaction effects within and across levels of analysis (2014, methodology): “We go beyond statistical explanations of interaction effects and their detection (see, e.g., Aguinis & Gottfredson, 2010; Jaccard & Turrisi, 2003; Shieh, 2009),”
- Understanding “It Depends” in Organizational Research (2017, background): “Several quality works provide summaries and best-practices for determining an appropriate sample size, including Aguinis et al. (2005), Dawson (2014), and Shieh (2009).”
- The Replication Recipe: What Makes for a Convincing Replication? (2014, unknown): “…on statistical power and sample size planning when designing replication attempts (see, e.g., Aberson, 2010; Cohen, 1992; Faul, Erdfelder, Lang, & Buchner, 2007; Maxwell, Kelley, & Rausch, 2008; Scherbaum & Ferreter, 2009; Shieh, 2009; Zhang & Wang, 2009 for useful information on power analysis).”
- The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data (2020, methodology): “Additionally, the Holling and Preckel's (2005) investigation was based on a sample of only 88 high school students, a sample size insufficiently powerful to detect typically reported moderator effects in the behavioural sciences (Shieh, 2009).”