Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9188
Main Title: Powerful Statistical Inference for Nested Data Using Sufficient Summary Statistics
Author(s): Dowding, Irene
Haufe, Stefan
Type: Article
Language Code: en
Abstract: Hierarchically-organized data arise naturally in many psychology and neuroscience studies. As the standard assumption of independent and identically distributed samples does not hold for such data, two important problems are to accurately estimate group-level effect sizes, and to obtain powerful statistical tests against group-level null hypotheses. A common approach is to summarize subject-level data by a single quantity per subject, which is often the mean or the difference between class means, and treat these as samples in a group-level t-test. This “naive” approach is, however, suboptimal in terms of statistical power, as it ignores information about the intra-subject variance. To address this issue, we review several approaches to deal with nested data, with a focus on methods that are easy to implement. With what we call the sufficient-summary-statistic approach, we highlight a computationally efficient technique that can improve statistical power by taking into account within-subject variances, and we provide step-by-step instructions on how to apply this approach to a number of frequently-used measures of effect size. The properties of the reviewed approaches and the potential benefits over a group-level t-test are quantitatively assessed on simulated data and demonstrated on EEG data from a simulated-driving experiment.
URI: https://depositonce.tu-berlin.de/handle/11303/10226
http://dx.doi.org/10.14279/depositonce-9188
Issue Date: 19-Mar-2018
Date Available: 30-Oct-2019
DDC Class: 310 Sammlungen allgemeiner Statistiken
Subject(s): hierarchical inference
group-level effect size
significance test
statistical power
sufficient summary statistic
inverse-variance-weighting
Stouffer's method
event-related potentials
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Frontiers in Human Neuroscience
Publisher: Frontiers Media S.A.
Publisher Place: Lausanne
Volume: 12
Article Number: 103
Publisher DOI: 10.3389/fnhum.2018.00103
EISSN: 1662-5161
Appears in Collections:FG Maschinelles Lernen » Publications



This item is licensed under a Creative Commons License Creative Commons