Design, measurement, and analysis considerations and evaluations in intensive longitudinal method

This dissertation examined the accuracy of a few selected statistical approaches in evaluating invariance in measurement and mediation with the presence of planned-missing data in the context of intensive longitudinal method (ILM). The planned-missing data design was implemented as a three-form de...

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Main Author: Lim, Jie Xin
Other Authors: Ho Moon-Ho Ringo
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/146267
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1462672023-03-05T15:53:17Z Design, measurement, and analysis considerations and evaluations in intensive longitudinal method Lim, Jie Xin Ho Moon-Ho Ringo School of Social Sciences HOmh@ntu.edu.sg Social sciences::Psychology This dissertation examined the accuracy of a few selected statistical approaches in evaluating invariance in measurement and mediation with the presence of planned-missing data in the context of intensive longitudinal method (ILM). The planned-missing data design was implemented as a three-form design where a portion of measurement scale items were selectively removed for individuals at each measurement occasion with the purpose of reducing participation burden and fatigue stemming from the burst of measurements in ILM ranging from 2 to 12 measurement occasions per day. Three simulation studies were conducted with the aim of providing insights and recommendations to applied researchers in the design, measurement, and analysis of intensive longitudinal data. Study 1 compared two methods for testing intensive longitudinal measurement invariance in their performance in detecting invariant and non-invariant measurement parameters. Study 2 and Study 3 evaluated the performance of the dynamic structural equation model (DSEM) framework in estimating the time-invariant and time-varying effects of longitudinal mediation models. Sample sizes (N), length of measurement occasions (T), percentage of planned-missing data (PMD), and effect sizes were manipulated in the simulation studies. The dissertation concluded with recommendations for applied researchers. Limitation and areas for future research were also discussed. Doctor of Philosophy 2021-02-04T07:58:38Z 2021-02-04T07:58:38Z 2021 Thesis-Doctor of Philosophy Lim, J. X. (2021). Design, measurement, and analysis considerations and evaluations in intensive longitudinal method. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/146267 10.32657/10356/146267 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Social sciences::Psychology
spellingShingle Social sciences::Psychology
Lim, Jie Xin
Design, measurement, and analysis considerations and evaluations in intensive longitudinal method
description This dissertation examined the accuracy of a few selected statistical approaches in evaluating invariance in measurement and mediation with the presence of planned-missing data in the context of intensive longitudinal method (ILM). The planned-missing data design was implemented as a three-form design where a portion of measurement scale items were selectively removed for individuals at each measurement occasion with the purpose of reducing participation burden and fatigue stemming from the burst of measurements in ILM ranging from 2 to 12 measurement occasions per day. Three simulation studies were conducted with the aim of providing insights and recommendations to applied researchers in the design, measurement, and analysis of intensive longitudinal data. Study 1 compared two methods for testing intensive longitudinal measurement invariance in their performance in detecting invariant and non-invariant measurement parameters. Study 2 and Study 3 evaluated the performance of the dynamic structural equation model (DSEM) framework in estimating the time-invariant and time-varying effects of longitudinal mediation models. Sample sizes (N), length of measurement occasions (T), percentage of planned-missing data (PMD), and effect sizes were manipulated in the simulation studies. The dissertation concluded with recommendations for applied researchers. Limitation and areas for future research were also discussed.
author2 Ho Moon-Ho Ringo
author_facet Ho Moon-Ho Ringo
Lim, Jie Xin
format Thesis-Doctor of Philosophy
author Lim, Jie Xin
author_sort Lim, Jie Xin
title Design, measurement, and analysis considerations and evaluations in intensive longitudinal method
title_short Design, measurement, and analysis considerations and evaluations in intensive longitudinal method
title_full Design, measurement, and analysis considerations and evaluations in intensive longitudinal method
title_fullStr Design, measurement, and analysis considerations and evaluations in intensive longitudinal method
title_full_unstemmed Design, measurement, and analysis considerations and evaluations in intensive longitudinal method
title_sort design, measurement, and analysis considerations and evaluations in intensive longitudinal method
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/146267
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