Quantifying and addressing the impact of measurement error in network models.

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 mu

SUBMITTER: de Ron J 

PROVIDER: S-EPMC10786122 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature