Quantifying and addressing the impact of measurement error in network models.
Behav Res Ther
; 157: 104163, 2022 10.
Article
em En
| MEDLINE
| ID: mdl-36030733
ABSTRACT
Network psychometric models are often estimated using a single indicator for each node in the network, thus failing to consider potential measurement error. In this study, we investigate the impact of measurement error on cross-sectional network models. First, we conduct a simulation study to evaluate the performance of models based on single indicators as well as models that utilize information from multiple indicators per node, including average scores, factor scores, and latent variables. Our results demonstrate that measurement error impairs the reliability and performance of network models, especially when using single indicators. The reliability and performance of network models improves substantially with increasing sample size and when using methods that combine information from multiple indicators per node. Second, we use empirical data from the STAR*D trial (n = 3,731) to further evaluate the impact of measurement error. In the STAR*D trial, depression symptoms were assessed via three questionnaires, providing multiple indicators per symptom. Consistent with our simulation results, we find that when using sub-samples of this dataset, the discrepancy between the three single-indicator networks (one network per questionnaire) diminishes with increasing sample size. Together, our simulated and empirical findings provide evidence that measurement error can hinder network estimation when working with smaller samples and offers guidance on methods to mitigate measurement error.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Reprodutibilidade dos Testes
Tipo de estudo:
Observational_studies
/
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Behav Res Ther
Ano de publicação:
2022
Tipo de documento:
Article