validity-reliability

What problem does it solve?

It helps researchers evaluate whether a survey/scale instrument produces consistent (reliable) and meaningful (valid) measurements, reducing the risk of using poor-quality measurement tools in social science research.

Core Features & Use Cases

• Reliability analysis: Compute internal consistency (Cronbach’s Alpha), split-half reliability, test-retest reliability, and inter-rater agreement to judge measurement stability and consistency.
• Validity analysis: Estimate content validity, construct validity, criterion validity, and convergent/discriminant validity to verify whether the instrument measures the intended constructs.
• Measurement quality assessment: Provide item-level diagnostics (e.g., item-total relatedness), factor analysis support, and model-based reporting guidance for improving instrument design.

Use case: You developed a new Likert scale for organizational research and need a defensible report covering internal consistency, expert-judged content validity, and evidence from construct/criterion relationships before publishing.

Quick Start

Use the validity-reliability skill to analyze your dataset for reliability (Cronbach’s Alpha, split-half, and test-retest/inter-rater when applicable) and validity (content, construct, criterion, and convergent/discriminant) and return a structured JSON report plus an interpretation-ready summary.