College Teacher Digital Competence Survey Data for Factor Analysis

Survey data from college teachers assesses digital competence and is structured for factor analysis. The dataset includes raw responses and prepared subsamples for Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Zhongyuan Liao published this data in April 2026.

Use Cases

• Perform Exploratory Factor Analysis (EFA) on the 38-item raw data to identify latent constructs of digital competence.
• Validate a factor structure using Confirmatory Factor Analysis (CFA) on the prepared 34-item CFA subsample.
• Compare model fit between EFA and CFA results using the separate split-sample groups.
• Analyze raw survey responses from the 'First complete sample' for descriptive statistics on teacher self-reported digital skills.
• Use the 'Second complete sample' data to test a full EFA+CFA workflow on a digital competence scale.

Strengths

• Data is explicitly structured for psychometric validation, with separate files for EFA and CFA procedures.
• Includes multiple validation samples: a first sample for scale development and a second sample for scale testing.
• Released under a permissive CC-BY-4.0 license, allowing for wide reuse and sharing.

Limitations

• The dataset is very small at 123.2 KB, indicating limited scope and likely a small number of survey respondents.
• Specific sample sizes (row counts) and demographic columns are unknown, limiting reproducibility of analyses.
• Data appears focused on a specific population (college teachers in an unspecified region), limiting generalizability.

Provenance

Source

figshare

Collection Method

Survey data collected from college teachers, likely via self-reported questionnaires.

Freshness

Last updated in April 2026.