Senior Project Advisor

Noguchi, Kimihiro

Document Type

Project

Publication Date

Spring 2020

Keywords

Repeated measures, multiple comparisons, contrasts, effect size, nonparametric.

Abstract

Many experiments in psychology, biology, medicine, etc., result in repeated measures data, i.e., multiple dependent observations over time. Researchers in these fields are often interested in reporting effect sizes; however, there currently is not a one-step procedure to deal with such a scenario. We achieve this through an application of the multivariate delta method, which enables us to derive an effect size generalization of the General Parametric Model (GPM) of Hothorn et al. (2008) which we refer to as the General Parametric Model with Effect Size (GPM-ES). We then utilize the GPM-ES framework to develop a one-step multiple contrast test procedure (MCTP). We demonstrate these methods by working out a real-world example with boys' dental growth data, and discuss how this framework can be applied to the nonparametric multiple comparisons -- extending the work of Noguchi et al. (2020) to the case of repeated measures data.

Department

Mathematics

Genre/Form

student projects; term papers

Type

Text

Rights

Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.

Language

English

Format

application/pdf

Included in

Mathematics Commons

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