Validity
Validity is the degree to which data, measures, outputs, or evaluations satisfy the requirements and accurately represent the intended use or construct.
Effective Data Analysis
Validity is the degree to which a measure accurately represents or supports conclusions about the construct it is intended to measure.
- How it is used
- Tests whether a measure represents the intended concept before stakeholders use its results in decisions.
- How deep it goes
- Defines validity and distinguishes three assessment types, with qualitative guidance for content or face validity.
- Examples
- Uses job-satisfaction facets, employee well-being measures, and organizational goals translated into specific metrics.
- What you can do after
- Evaluate/understand whether proposed measures adequately represent their intended concepts and warrant use in stakeholder decisions.
Data Without Labels
Validity is the degree to which data conforms to the properties, characteristics, scope, and variations required by its use case.
- How it is used
- Uses validity to inspect whether data supports the scope and requirements of a machine-learning use case.
- How deep it goes
- Defines conformance and gives a scope-based example, but stops short of procedural validation checks or implementation details.
- Examples
- Uses a 2015–2019 study that incorrectly includes 2014 data to demonstrate invalid scope.
- What you can do after
- Evaluate whether observations and measurements conform to a use case's defined scope and required characteristics.
Related concepts
- data qualitypart of · Data quality is the degree to which data is accurate, complete, consistent, relevant, and suitable for its intended use.
- criterion validityincludes · Criterion validity is the degree to which a measure is associated with an external criterion representing an established measure or outcome of interest.
- content validityincludes · Content validity is the degree to which a measure comprehensively captures all relevant facets of the concept or process it is intended to measure.
- discriminant validityincludes · Discriminant validity is the degree to which a measure is not associated with measures of theoretically unrelated constructs.
- convergent validityincludes · Convergent validity is the degree to which a measure is associated with other measures that theoretically assess the same construct.
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