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Study Length, Change Process Separability, Parameter Estimation, and Model Evaluation in Hybrid Autoregressive-Latent Growth Structural Equation Models for Longitudinal Data

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Title: Study Length, Change Process Separability, Parameter Estimation, and Model Evaluation in Hybrid Autoregressive-Latent Growth Structural Equation Models for Longitudinal Data
Language: English
Authors: Clark, D. Angus (ORCID 0000-0002-8334-492X), Nuttall, Amy K. (ORCID 0000-0003-3382-3516), Bowles, Ryan P.
Source: International Journal of Behavioral Development. Sep 2021 45(5):440-452.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Peer Reviewed: Y
Page Count: 13
Publication Date: 2021
Sponsoring Agency: Institute of Education Sciences (ED)
National Institute on Alcohol Abuse and Alcoholism (NIAAA) (DHHS/NIH)
Contract Number: R305A110293
R324A150063
T32AA007477
Document Type: Journal Articles
Reports - Research
Descriptors: Structural Equation Models, Longitudinal Studies, Risk, Accuracy, Goodness of Fit, Monte Carlo Methods, Simulation, Robustness (Statistics), Change, Developmental Stages
DOI: 10.1177/01650254211022862
ISSN: 0165-0254
1464-0651
Abstract: Hybrid autoregressive-latent growth structural equation models for longitudinal data represent a synthesis of the autoregressive and latent growth modeling frameworks. Although these models are conceptually powerful, in practice they may struggle to separate autoregressive and growth-related processes during estimation. This confounding of change processes may, in turn, increase the risk of the models producing deceptively compelling results (i.e., models that fit excellently by conventional standards despite highly biased parameter estimates). Including additional time points provides models with more raw information about change, which could help improve process separability and the accuracy of parameter estimates to a degree. This study thus used Monte Carlo simulation methods to examine associations between change process separability, the number of time points in a model, and the consequences of misspecification, across three prominent hybrid autoregressive-latent growth models: the Latent Change Score model (LCS), the Autoregressive Latent Trajectory Model (ALT), and the Latent Growth Model with Structured Residuals (LGM-SR). Results showed that including more time points increased process separability and robustness to misspecification in the LCS and ALT, but typically not at a rate that would be practically feasible for most developmental researchers. Alternatively, regardless of how many time points were in the model process separability was high in the LGM-SR, as was robustness to misspecification. Overall, results suggest that the LGM-SR is the most effective of the three hybrid autoregressive-latent growth models considered here.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2021
Accession Number: EJ1311867
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1311867
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Study Length, Change Process Separability, Parameter Estimation, and Model Evaluation in Hybrid Autoregressive-Latent Growth Structural Equation Models for Longitudinal Data
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  Data: <searchLink fieldCode="AR" term="%22Clark%2C+D%2E+Angus%22">Clark, D. Angus</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8334-492X">0000-0002-8334-492X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nuttall%2C+Amy+K%2E%22">Nuttall, Amy K.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3382-3516">0000-0003-3382-3516</externalLink>)<br /><searchLink fieldCode="AR" term="%22Bowles%2C+Ryan+P%2E%22">Bowles, Ryan P.</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Behavioral+Development%22"><i>International Journal of Behavioral Development</i></searchLink>. Sep 2021 45(5):440-452.
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  Label: Availability
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
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  Data: 13
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  Data: Institute of Education Sciences (ED)<br />National Institute on Alcohol Abuse and Alcoholism (NIAAA) (DHHS/NIH)
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  Data: R305A110293<br />R324A150063<br />T32AA007477
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  Data: Journal Articles<br />Reports - Research
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  Data: 10.1177/01650254211022862
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0165-0254<br />1464-0651
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Hybrid autoregressive-latent growth structural equation models for longitudinal data represent a synthesis of the autoregressive and latent growth modeling frameworks. Although these models are conceptually powerful, in practice they may struggle to separate autoregressive and growth-related processes during estimation. This confounding of change processes may, in turn, increase the risk of the models producing deceptively compelling results (i.e., models that fit excellently by conventional standards despite highly biased parameter estimates). Including additional time points provides models with more raw information about change, which could help improve process separability and the accuracy of parameter estimates to a degree. This study thus used Monte Carlo simulation methods to examine associations between change process separability, the number of time points in a model, and the consequences of misspecification, across three prominent hybrid autoregressive-latent growth models: the Latent Change Score model (LCS), the Autoregressive Latent Trajectory Model (ALT), and the Latent Growth Model with Structured Residuals (LGM-SR). Results showed that including more time points increased process separability and robustness to misspecification in the LCS and ALT, but typically not at a rate that would be practically feasible for most developmental researchers. Alternatively, regardless of how many time points were in the model process separability was high in the LGM-SR, as was robustness to misspecification. Overall, results suggest that the LGM-SR is the most effective of the three hybrid autoregressive-latent growth models considered here.
– Name: AbstractInfo
  Label: Abstractor
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  Data: As Provided
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– Name: DateEntry
  Label: Entry Date
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  Data: 2021
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  Label: Accession Number
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  Data: EJ1311867
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1311867
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1177/01650254211022862
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 440
    Subjects:
      – SubjectFull: Structural Equation Models
        Type: general
      – SubjectFull: Longitudinal Studies
        Type: general
      – SubjectFull: Risk
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Goodness of Fit
        Type: general
      – SubjectFull: Monte Carlo Methods
        Type: general
      – SubjectFull: Simulation
        Type: general
      – SubjectFull: Robustness (Statistics)
        Type: general
      – SubjectFull: Change
        Type: general
      – SubjectFull: Developmental Stages
        Type: general
    Titles:
      – TitleFull: Study Length, Change Process Separability, Parameter Estimation, and Model Evaluation in Hybrid Autoregressive-Latent Growth Structural Equation Models for Longitudinal Data
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            NameFull: Clark, D. Angus
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            NameFull: Nuttall, Amy K.
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            NameFull: Bowles, Ryan P.
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              Type: published
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            – TitleFull: International Journal of Behavioral Development
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