College Teacher Digital Competence Survey Data for Factor Analysis

Survey data supports psychometric validation of a digital competence scale for college teachers. The dataset includes multiple samples for exploratory and confirmatory factor analysis, created by researcher Zhongyuan Liao and last updated in April 2026. It comprises raw and processed data files for 38-item and 34-item versions of the scale.

Use Cases

• Perform exploratory factor analysis (EFA) on the 38-item raw data to identify latent constructs of digital competence.
• Validate the factor structure using confirmatory factor analysis (CFA) on the 34-item data.
• Compare factor models between the first and second complete samples to test measurement invariance.
• Analyze the direct rotation matrix results to understand the relationships between survey items and underlying factors.

Strengths

• Data is structured for direct use in factor analysis with separate EFA and CFA samples.
• Includes multiple validation samples (first and second complete/split samples) for reliability testing.
• Released under a permissive CC-BY-4.0 license allowing for reuse and modification.

Limitations

• The dataset is small at 123.2 KB, indicating a limited sample size for robust statistical modeling.
• No column names or sample data are provided, making initial exploration and interpretation difficult.
• Geographic and institutional context for the surveyed college teachers is unknown, limiting generalizability.

Provenance

Source

Figshare repository, uploaded by author Zhongyuan Liao.

Collection Method

Likely collected via survey from college teachers for psychometric scale development.

Freshness

Last updated in April 2026, indicating the data is current.