Network analysis of smartphone addiction and sleep disorder symptoms in Chinese college st

Xiaonan Li's research dataset contains survey responses from 1,842 Chinese college students, collected via convenience sampling. The data was used to construct comorbidity networks between smartphone addiction and sleep disorder symptoms using the SAS-SV and PSQI scales. The dataset was last updated on April 28, 2026.

Use Cases

• Constructing Gaussian Graphical Models (GGM) based on the described comorbidity network analysis.
• Identifying core and bridge symptoms in behavioral health networks based on the reported symptoms PSQI2 and SA2.
• Exploring directional associations between symptoms using Bayesian network approaches as described in the methodology.
• Performing network comparison tests to examine gender differences in symptom clustering patterns.

Strengths

• Includes data from 1,842 participants, providing a substantive sample size.
• Network metrics are reported, including a connection density of 0.80 and a global strength of 9.39.
• Specific core and bridge symptoms are identified (e.g., PSQI2, SA2, SA5), offering clear analysis targets.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect geographic and demographic bias inherent to the convenience sampling of Chinese college students.

Provenance

Source

figshare

Collection Method

Convenience sampling of college students, assessed with the Smartphone Addiction Scale-Short Version (SAS-SV) and Pittsburgh Sleep Quality Index (PSQI).

Time Range

null

Freshness

Last updated 2026-04-28 01:04:36; freshness should be verified.

Geography

China