A solution to dependency: using multilevel analysis to accommodate nested data
By E. Aarts, M. Verhage, Jesse V. Veenvliet, Conor V. Dolan, Sophie Van der Sluis. Nature Neuroscience, 2014
588 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/cd12e4e62c5ef11f94cfc07bb428cc04998b52f8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
- Barbara S. Grave
- Robert A. I. Davies
- Tom Tyler
- Lotte Meteyard
- Christian Hundt
- Heiko Bergmann
- Mario Geissler
- Steven L. Blader
In Statistics and Probability
- Samuel J Lord
- Robert A. I. Davies
- L. Fritz-Laylin
- R. Mullins
- Tom Tyler
- Jonathan Z. Bakdash
- Lotte Meteyard
- Heiko Bergmann
Citations in the corpus
- Repeated Measures Correlation (2017, methodology): “A powerful and flexible method for handling different sources of variance simultaneously is multilevel (linear) modeling3 (Kreft and de Leeuw, 1998; Singer and Willett, 2003; Gelman and Hill, 2007; see Aarts et al., 2014).”
- If your P value looks too good to be true, it probably is: Communicating reproducibility and variability in cell biology (2019, methodology): “This simple approach might fail to detect small but real differences between groups, where more advanced techniques may prove to be more powerful (Galbraith et al., 2010; Aarts et al., 2014).”
- Best practice guidance for linear mixed-effects models in psychological science (2019, background): “, 2014; see also Clark 1973; Rietveld & van Hout, 2007) and the power of summary statistics to detect experimental effects is reduced (Aarts et al., 2014).”
- The Climate for Entrepreneurship at Higher Education Institutions (2018, background): “Finally, ignoring the multi-level structure is likely to result in artificially smaller standard errors which in turn leads to smaller observed p-values and thus to inflated Type I error rates (Aarts et al., 2014).”
- Justice from within: The relations between a procedurally just organizational climate and police organizational efficiency, endorsement of democratic policing, and officer well-being. (2016, background): “Nested data is problematic because it can violate statistical assumptions of independence in the outcomes of interest (Aarts et al., 2014).”