Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models

Latent change score (LCS) models within a Continuous-Time State-Space Modeling framework (CT-SSM) provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the context of accelerated longitudinal designs (ALDs). ALDs...

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Detalles Bibliográficos
Autores: Martínez Huertas, José Ángel, Estrada, Eduardo, Olmos, Ricardo
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/22784
Acceso en línea:https://hdl.handle.net/20.500.14468/22784
Access Level:acceso abierto
Palabra clave:61 Psicología
latent change score models
state-space modeling
continuous-time modeling
Kalman scores
missing data imputation
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spelling Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space modelsMartínez Huertas, José ÁngelEstrada, EduardoOlmos, Ricardo61 Psicologíalatent change score modelsstate-space modelingcontinuous-time modelingKalman scoresmissing data imputationLatent change score (LCS) models within a Continuous-Time State-Space Modeling framework (CT-SSM) provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the context of accelerated longitudinal designs (ALDs). ALDs are especially interesting because they imply a very high rate of planned data missingness. Additionally, most longitudinal studies present unexpected participant attrition leading to unplanned missing data. Therefore, in ALDs, both sources of data missingness are combined. Previous research has shown that ALDs for developmental research allow recovering the population generating process. However, it is unknown how participant attrition impacts the model estimates. We have three goals: (1) to evaluate the robustness of the group-level parameter estimates in scenarios with empirically plausible unplanned data missingness; (2) to evaluate the performance of Kalman scores (KS) imputations for individual data points that were expected but unobserved; and (3) to evaluate the performance of KS imputations for individual data points that were outside the age ranged observed for each case (i.e., to estimate the individual trajectories for the complete age range under study). In general, results showed lack of bias in the simulated conditions. The variability of the estimates increased with lower sample sizes and higher missingness severity. Similarly, we found very accurate estimates of individual scores for both planned and unplanned missing data points. These results are very important for applied practitioners in terms of forecasting and making individual-level decisions. R code is provided to facilitate its implementation by applied researchersAmerican Psychological Associationhttps://orcid.org/0000-0003-0899-4057https://orcid.org/0000-0002-1298-6861e-Spacio UNED20242024-07-0220242024-01-0120242024-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/22784reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/227842026-06-06T12:38:31Z
dc.title.none.fl_str_mv Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
title Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
spellingShingle Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
Martínez Huertas, José Ángel
61 Psicología
latent change score models
state-space modeling
continuous-time modeling
Kalman scores
missing data imputation
title_short Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
title_full Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
title_fullStr Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
title_full_unstemmed Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
title_sort Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models
dc.creator.none.fl_str_mv Martínez Huertas, José Ángel
Estrada, Eduardo
Olmos, Ricardo
author Martínez Huertas, José Ángel
author_facet Martínez Huertas, José Ángel
Estrada, Eduardo
Olmos, Ricardo
author_role author
author2 Estrada, Eduardo
Olmos, Ricardo
author2_role author
author
dc.contributor.none.fl_str_mv https://orcid.org/0000-0003-0899-4057
https://orcid.org/0000-0002-1298-6861
e-Spacio UNED
dc.subject.none.fl_str_mv 61 Psicología
latent change score models
state-space modeling
continuous-time modeling
Kalman scores
missing data imputation
topic 61 Psicología
latent change score models
state-space modeling
continuous-time modeling
Kalman scores
missing data imputation
description Latent change score (LCS) models within a Continuous-Time State-Space Modeling framework (CT-SSM) provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the context of accelerated longitudinal designs (ALDs). ALDs are especially interesting because they imply a very high rate of planned data missingness. Additionally, most longitudinal studies present unexpected participant attrition leading to unplanned missing data. Therefore, in ALDs, both sources of data missingness are combined. Previous research has shown that ALDs for developmental research allow recovering the population generating process. However, it is unknown how participant attrition impacts the model estimates. We have three goals: (1) to evaluate the robustness of the group-level parameter estimates in scenarios with empirically plausible unplanned data missingness; (2) to evaluate the performance of Kalman scores (KS) imputations for individual data points that were expected but unobserved; and (3) to evaluate the performance of KS imputations for individual data points that were outside the age ranged observed for each case (i.e., to estimate the individual trajectories for the complete age range under study). In general, results showed lack of bias in the simulated conditions. The variability of the estimates increased with lower sample sizes and higher missingness severity. Similarly, we found very accurate estimates of individual scores for both planned and unplanned missing data points. These results are very important for applied practitioners in terms of forecasting and making individual-level decisions. R code is provided to facilitate its implementation by applied researchers
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-07-02
2024
2024-01-01
2024
2024-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/22784
url https://hdl.handle.net/20.500.14468/22784
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Psychological Association
publisher.none.fl_str_mv American Psychological Association
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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