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What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data

In recent years there has been a growing interest in the use of intensive longitudinal research designs to study within-person processes. Examples are studies that use experience sampling data and autoregressive modeling to investigate emotion dynamics and between-person differences therein. Such de...

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Autores principales: de Haan-Rietdijk, Silvia, Kuppens, Peter, Hamaker, Ellen L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4906027/
https://www.ncbi.nlm.nih.gov/pubmed/27378986
http://dx.doi.org/10.3389/fpsyg.2016.00891
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author de Haan-Rietdijk, Silvia
Kuppens, Peter
Hamaker, Ellen L.
author_facet de Haan-Rietdijk, Silvia
Kuppens, Peter
Hamaker, Ellen L.
author_sort de Haan-Rietdijk, Silvia
collection PubMed
description In recent years there has been a growing interest in the use of intensive longitudinal research designs to study within-person processes. Examples are studies that use experience sampling data and autoregressive modeling to investigate emotion dynamics and between-person differences therein. Such designs often involve multiple measurements per day and multiple days per person, and it is not clear how this nesting of the data should be accounted for: That is, should such data be considered as two-level data (which is common practice at this point), with occasions nested in persons, or as three-level data with beeps nested in days which are nested in persons. We show that a significance test of the day-level variance in an empty three-level model is not reliable when there is autocorrelation. Furthermore, we show that misspecifying the number of levels can lead to spurious or misleading findings, such as inflated variance or autoregression estimates. Throughout the paper we present instructions and R code for the implementation of the proposed models, which includes a novel three-level AR(1) model that estimates moment-to-moment inertia and day-to-day inertia. Based on our simulations we recommend model selection using autoregressive multilevel models in combination with the AIC. We illustrate this method using empirical emotion data from two independent samples, and discuss the implications and the relevance of the existence of a day level for the field.
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spelling pubmed-49060272016-07-04 What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data de Haan-Rietdijk, Silvia Kuppens, Peter Hamaker, Ellen L. Front Psychol Psychology In recent years there has been a growing interest in the use of intensive longitudinal research designs to study within-person processes. Examples are studies that use experience sampling data and autoregressive modeling to investigate emotion dynamics and between-person differences therein. Such designs often involve multiple measurements per day and multiple days per person, and it is not clear how this nesting of the data should be accounted for: That is, should such data be considered as two-level data (which is common practice at this point), with occasions nested in persons, or as three-level data with beeps nested in days which are nested in persons. We show that a significance test of the day-level variance in an empty three-level model is not reliable when there is autocorrelation. Furthermore, we show that misspecifying the number of levels can lead to spurious or misleading findings, such as inflated variance or autoregression estimates. Throughout the paper we present instructions and R code for the implementation of the proposed models, which includes a novel three-level AR(1) model that estimates moment-to-moment inertia and day-to-day inertia. Based on our simulations we recommend model selection using autoregressive multilevel models in combination with the AIC. We illustrate this method using empirical emotion data from two independent samples, and discuss the implications and the relevance of the existence of a day level for the field. Frontiers Media S.A. 2016-06-14 /pmc/articles/PMC4906027/ /pubmed/27378986 http://dx.doi.org/10.3389/fpsyg.2016.00891 Text en Copyright © 2016 de Haan-Rietdijk, Kuppens and Hamaker. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
de Haan-Rietdijk, Silvia
Kuppens, Peter
Hamaker, Ellen L.
What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data
title What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data
title_full What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data
title_fullStr What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data
title_full_unstemmed What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data
title_short What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data
title_sort what's in a day? a guide to decomposing the variance in intensive longitudinal data
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4906027/
https://www.ncbi.nlm.nih.gov/pubmed/27378986
http://dx.doi.org/10.3389/fpsyg.2016.00891
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