Cross-lagged network models

Mijke Rhemtulla, Riet van Bork, A.O.J. Cramer

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Network models for psychological constructs are becoming increasingly popular as an alternative to latent variable models for representing psychological constructs. We show how these models may be combined with crosslagged panel models to reveal longitudinal processes that occur within and across constructs over time. The proposed crosslagged network model uses regularized regression estimation to discover autoregressive and crosslagged pathways that characterize the effects of observed components of psychological constructs on each other over time. After describing the model we demonstrate its application to longitudinal data on students’ commitment to school and selfesteem.
LanguageEnglish
JournalMultivariate Behavioral Research
Publication statusE-pub ahead of print - 2019

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Psychological Models
Network Model
Regression Estimation
Latent Variable Models
Longitudinal Data
Pathway
Model
Alternatives
Demonstrate
Psychological

Cite this

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title = "Cross-lagged network models",
abstract = "Network models for psychological constructs are becoming increasingly popular as an alternative to latent variable models for representing psychological constructs. We show how these models may be combined with crosslagged panel models to reveal longitudinal processes that occur within and across constructs over time. The proposed crosslagged network model uses regularized regression estimation to discover autoregressive and crosslagged pathways that characterize the effects of observed components of psychological constructs on each other over time. After describing the model we demonstrate its application to longitudinal data on students’ commitment to school and selfesteem.",
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Cross-lagged network models. / Rhemtulla, Mijke; van Bork, Riet; Cramer, A.O.J.

In: Multivariate Behavioral Research, 2019.

Research output: Contribution to journalArticleScientificpeer-review

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AB - Network models for psychological constructs are becoming increasingly popular as an alternative to latent variable models for representing psychological constructs. We show how these models may be combined with crosslagged panel models to reveal longitudinal processes that occur within and across constructs over time. The proposed crosslagged network model uses regularized regression estimation to discover autoregressive and crosslagged pathways that characterize the effects of observed components of psychological constructs on each other over time. After describing the model we demonstrate its application to longitudinal data on students’ commitment to school and selfesteem.

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JO - Multivariate Behavioral Research

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