Analyzing dynamic data: A tutorial

William Revelle; Joshua Wilt

doi:10.1016/j.paid.2017.08.020

Analyzing dynamic data: A tutorial

William Revelle^*, Joshua Wilt

^*Corresponding author for this work

Psychology

Research output: Contribution to journal › Article › peer-review

25 Scopus citations

Abstract

Modern data collection techniques allow for intensive measurement within subjects. Analyzing this type of data requires analyzing data at the within subject as well as between subject level. Although sometimes conclusions will be the same at both levels, it is frequently the case that examining within subject data will show much more complex patterns of results than when they are simply aggregated. This tutorial is a simple introduction to the kind of data analytic strategies that are possible using the open source statistical language, R.

Original language	English (US)
Pages (from-to)	38-51
Number of pages	14
Journal	Personality and Individual Differences
Volume	136
DOIs	https://doi.org/10.1016/j.paid.2017.08.020
State	Published - Jan 1 2019

Keywords

Dynamic data
Open source
R
Repeated measures

ASJC Scopus subject areas

Psychology(all)

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10.1016/j.paid.2017.08.020

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author = "William Revelle and Joshua Wilt",

note = "Funding Information: Preparation of this manuscript was funded in part by grant SMA-1419324 from the National Science Foundation to WR. This is the authors' version as submitted to PAID. We gratefully acknowledge Aaron Fisher for making his data set publicly available. Publisher Copyright: {\textcopyright} 2017",

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AB - Modern data collection techniques allow for intensive measurement within subjects. Analyzing this type of data requires analyzing data at the within subject as well as between subject level. Although sometimes conclusions will be the same at both levels, it is frequently the case that examining within subject data will show much more complex patterns of results than when they are simply aggregated. This tutorial is a simple introduction to the kind of data analytic strategies that are possible using the open source statistical language, R.

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KW - R

KW - Repeated measures

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JF - Personality and Individual Differences

ER -

Analyzing dynamic data: A tutorial

Abstract

Keywords

ASJC Scopus subject areas

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