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

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dc.contributor.authorMartínez Huertas, José Ángel
dc.contributor.authorEstrada, Eduardo
dc.contributor.authorOlmos, Ricardo
dc.contributor.orcidhttps://orcid.org/0000-0003-0899-4057
dc.contributor.orcidhttps://orcid.org/0000-0002-1298-6861
dc.date.accessioned2024-07-02T16:13:33Z
dc.date.available2024-07-02T16:13:33Z
dc.date.issued2024
dc.descriptionThe registered version of this article, first published in Psychological Methods, is available online at the publisher's website: American Psychological Association, https://doi.org/10.1037/met0000664
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Psychological Methods, está disponible en línea en el sitio web del editor: American Psychological Association, https://doi.org/10.1037/met0000664
dc.description.abstractLatent 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 researchersen
dc.description.versionversión final
dc.identifier.citationMartínez-Huertas, J.A., Estrada, E., & Olmos, R. (2024). Estimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space modeling. Psychological Methods. https://doi.org/10.1037/met0000664.
dc.identifier.doihttps://doi.org/10.1037/met0000664
dc.identifier.issn1082-989X - eISSN 1939-1463
dc.identifier.urihttps://hdl.handle.net/20.500.14468/22784
dc.journal.titlePsychological Methods
dc.language.isoen
dc.publisherAmerican Psychological Association
dc.relation.centerFacultad de Psicología
dc.relation.departmentMetodología de las Ciencias del Comportamiento
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject61 Psicología
dc.subject.keywordslatent change score modelsen
dc.subject.keywordsstate-space modelingen
dc.subject.keywordscontinuous-time modelingen
dc.subject.keywordsKalman scoresen
dc.subject.keywordsmissing data imputationen
dc.titleEstimate of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space modelsen
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublicationca510876-0be8-438a-a565-ac5f8953fb78
relation.isAuthorOfPublication.latestForDiscoveryca510876-0be8-438a-a565-ac5f8953fb78
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