TY - JOUR
T1 - A solution to dependency: using multilevel analysis to accommodate nested data
AU - Aarts, E.
AU - Verhage, M.
AU - Veenvliet, J.V.
AU - Dolan, C.V.
AU - van der Sluis, S.
N1 - With supplementary information
PY - 2014
Y1 - 2014
N2 - In neuroscience, experimental designs in which multiple observations are collected from a single research object (for example, multiple neurons from one animal) are common: 53% of 314 reviewed papers from five renowned journals included this type of data. These so-called 'nested designs' yield data that cannot be considered to be independent, and so violate the independency assumption of conventional statistical methods such as the t test. Ignoring this dependency results in a probability of incorrectly concluding that an effect is statistically significant that is far higher (up to 80%) than the nominal level (usually set at 5%). We discuss the factors affecting the type I error rate and the statistical power in nested data, methods that accommodate dependency between observations and ways to determine the optimal study design when data are nested. Notably, optimization of experimental designs nearly always concerns collection of more truly independent observations, rather than more observations from one research object. © 2014 Nature America, Inc. All rights reserved.
AB - In neuroscience, experimental designs in which multiple observations are collected from a single research object (for example, multiple neurons from one animal) are common: 53% of 314 reviewed papers from five renowned journals included this type of data. These so-called 'nested designs' yield data that cannot be considered to be independent, and so violate the independency assumption of conventional statistical methods such as the t test. Ignoring this dependency results in a probability of incorrectly concluding that an effect is statistically significant that is far higher (up to 80%) than the nominal level (usually set at 5%). We discuss the factors affecting the type I error rate and the statistical power in nested data, methods that accommodate dependency between observations and ways to determine the optimal study design when data are nested. Notably, optimization of experimental designs nearly always concerns collection of more truly independent observations, rather than more observations from one research object. © 2014 Nature America, Inc. All rights reserved.
UR - https://pure.uva.nl/ws/files/2110835/164191_A_solution_to_dependency_suppl.pdf
UR - https://pure.uva.nl/ws/files/2110837/164192_A_solution_to_dependency_suppl._2.xls
U2 - https://doi.org/10.1038/nn.3648
DO - https://doi.org/10.1038/nn.3648
M3 - Article
C2 - 24671065
SN - 1097-6256
VL - 17
SP - 491
EP - 496
JO - Nature neuroscience
JF - Nature neuroscience
IS - 4
ER -