Exploring the Effects of Sampling Variability, Scale Variability, and Node Aggregation on the Consistency of...

Area of Science:

• Psychological Science
• Network Science
• Quantitative Psychology

Background:

• Replicability and generalizability of network models are increasingly debated.
• Methodological issues like single-item indicators and non-identical measures may cause inconsistencies.

Purpose of the Study:

• To disentangle sampling variability from scale variability in network replicability.
• To explore if aggregating more items for node scores improves network characteristic consistency.

Main Methods:

• Employed a resampling approach with empirical data.
• Assessed network properties using varying sample sizes and node aggregation levels.

Main Results:

• Scale variability introduced more network property discrepancies than sampling variability.
• Discrepancies reduced with larger samples and increased node aggregation.
• Multi-item indicators yielded denser networks, higher sensitivity, greater global strength, and more consistent network properties.

Conclusions:

• Poor measurement conditions can cause variability in network properties across samples.
• Variability may reflect true network structure or measurement instrument limitations.
• Item aggregation is crucial for robust and replicable network analyses.