Quantifying and addressing the impact of measurement error in network models

Area of Science:

• Psychometrics
• Network Analysis
• Statistics

Background:

• Network psychometric models commonly use single indicators per node.
• This approach overlooks potential measurement error.
• Measurement error can compromise the accuracy of network models.

Purpose of the Study:

• To investigate the impact of measurement error on cross-sectional network models.
• To compare the performance of single-indicator versus multiple-indicator network models.
• To evaluate methods for mitigating measurement error in network analysis.

Main Methods:

• A simulation study evaluating single-indicator and multiple-indicator (average, factor, latent variable) models.
• Analysis of empirical data from the STAR*D trial (n=3,731) with multiple indicators per symptom.
• Examination of network discrepancies across different sample sizes.

Main Results:

• Measurement error significantly impairs network model reliability and performance, particularly with single indicators.
• Network model reliability and performance improve with larger sample sizes.
• Methods incorporating multiple indicators per node enhance network estimation accuracy.
• Empirical findings align with simulation results, showing reduced network discrepancies with increased sample size.

Conclusions:

• Measurement error is a critical factor affecting network estimation, especially in smaller samples.
• Utilizing multiple indicators per node and increasing sample size are effective strategies to mitigate measurement error.
• The study provides practical guidance for improving the robustness of network psychometric models.