Empirical SE for Each Condition Shown in Fig 5a

Simon Turner's dataset provides empirical standard error results from a simulation study on analytical validation for sensor-based digital health technologies. The 5.5 KB XLS file, last updated in May 2026, compares the Pearson Correlation Coefficient and confirmatory factor analysis for validating digital measures against clinical outcome assessments. The study demonstrates how multiple reference measures can improve the assessment of a target measure's validity.

Use Cases

• Comparing the stability and bias of Pearson Correlation Coefficient versus confirmatory factor analysis for measure validation based on the described simulation.
• Evaluating the use of multiple clinical outcome assessment reference measures to represent a target digital measure based on the regression models described.
• Assessing the performance of validation methods under small sample size conditions as highlighted in the findings.
• Developing a simulation toolkit for the analytical validation of novel digital measures as referenced in the description.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• File size is 5.5 KB, indicating a focused and manageable dataset.
• Description provides a detailed methodological context for the simulation study.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is simulated, not empirical, which may limit direct real-world applicability.

Provenance

Source

Simon Turner via figshare

Collection Method

Simulated data from a study on analytical validation methods.

Freshness

Last updated 2026-05-13 18:00:42; freshness should be verified.