Technical Content Validation

MIS Quarterly has now made our methods article available online in advance, and I’m excited to share it: “AI-Augmented Content Validation in Behavioral Research: Development and Evaluation of the RATER System” (with Jean-Charles Pillet, David Dobolyi, Magno Queiroz, Abram Handler, Jan Ketil Arnulf, and Rajeev Sharma). Content validation is arguably the most foundational validity check in psychometrics because it establishes whether items actually match their intended construct. Yet it has become surprisingly uncommon in published work, likely because it is so hard to do. In information systems, prior appraisals suggest content validity assessments appeared in about 26% of studies two decades ago, and about 19% more recently. Our goal with RATER is to shift that cost–benefit equation. We built a free, web-based system that makes two fine-tuned, high-quality content validation models available in a fast, easy-to-use workflow: RATERC (a highly efficient classifier model) and RATERD (a distribution model that emulates the classic item-rating procedure using “synthetic raters”). The models are trained and evaluated using a large, cross-disciplinary dataset drawn from 2,443 journal articles spanning eight disciplines, and the site returns results in a simple downloadable spreadsheet. If you develop, adapt, or review measurement instruments, I hope RATER makes rigorous content validation easier to do, repeat, and report. #psychometrics #measurement #contentvalidity #methodology #designscience

Can your idea actually be built with current technology? Sounds like a simple question. It's not. Here's what technical feasibility really means: ▪️ Material Science: Do the required materials exist and are they accessible? ▪️ Manufacturing Processes: Can current production methods achieve your specifications? ▪️ Tolerances & Precision: Are your design requirements technically achievable? ▪️ Integration Complexity: How many systems need to work together perfectly? ▪️ Cost Reality: Can it be built at a price point that allows for profitable sales? We recently evaluated an idea for a new medical device. Brilliant concept. Huge market need. One problem: the precision required was beyond current manufacturing capabilities at affordable costs. Instead of letting the inventor spend $200,000 discovering this, our $900 Innovation Overview identified the technical barriers upfront. The inventor pivoted to a modified design that was actually buildable. Now in prototype phase. That's the power of technical validation before you build. Your idea might be brilliant. But is it technically feasible? Let's find out. #TechnicalValidation #ProductDevelopment #Engineering

What is (CSV) ? CSV is the documented process of verifying that a computer-based system performs its intended function accurately and consistently while meeting regulatory standards, such as FDA's 21 CFR Part 11 or EMA Annex 11. It involves assessing systems used in activities like production, quality assurance, clinical trials, and more. Why is CSV Important? 1. Regulatory Compliance 2. Data Integrity 3. Risk Mitigation 4. Operational Efficiency Examples of Computerized Systems Requiring Validation 1. Laboratory Information Management System (LIMS) Tracks samples and associated data during quality control testing. Validation ensures data traceability and accuracy. 2. Enterprise Resource Planning (ERP) Systems Manages manufacturing, inventory, and supply chain. Validation ensures system-generated reports are reliable and accurate. 3. Electronic Batch Records (EBR) Automates batch manufacturing documentation. Validation guarantees proper tracking of deviations, approvals, and compliance. 4. SCADA Systems (Supervisory Control and Data Acquisition) Monitors and controls pharmaceutical production processes. Validation ensures accurate real-time data capture and alarm handling. 5. Environmental Monitoring Systems Tracks conditions like temperature, humidity, and particle counts in cleanrooms. Validation ensures reliable data critical for product quality. Steps in CSV :- 1. Risk Assessment Identify the system's impact on product quality and compliance. 2. Validation Planning Develop a Validation Master Plan (VMP) outlining scope, timelines, and responsibilities. 3. Testing (IQ, OQ, PQ) IQ: Verifies system installation. OQ: Confirms system operation within specified limits. PQ: Ensures performance under actual conditions. 4. Documentation Maintain thorough records, including protocols, test scripts, and deviation reports. 5. Periodic Review Revalidate systems regularly or after significant changes to ensure continued compliance.

Today I’m sharing key insights from my work with Analytical Method Validation through the Institute of Validation Technology (IVT). Method validation is not a regulatory formality. It protects data integrity. It protects patient safety. And it protects the credibility of every laboratory result. When you validate an analytical method you are answering a simple question. Can this method produce reliable results every time it is used? Strong validation focuses on core parameters. • Accuracy, how close the result is to the true value • Precision, repeatability across runs and analysts • Specificity, the ability to measure the analyte without interference • Linearity, reliable results across the working range • Range, the interval where the method performs well • Detection and quantitation limits, how little the method can measure • Robustness, stability when conditions slightly change A well-validated method builds confidence across the entire lifecycle of a pharmaceutical product. Yet many teams treat validation as a checklist activity. That mindset creates risk. Better practice focuses on understanding method behavior early, using statistical evaluation, and designing validation studies that truly challenge the method. Questions worth asking in every lab: • Are we validating methods or just documenting them? • Do our validation experiments reflect real routine conditions? • Are we using statistics correctly to interpret performance? Continuous learning matters. Resources from the Institute of Validation Technology help professionals strengthen validation strategy and stay aligned with regulatory expectations. More resources https://www.ivtnetwork.com https://lnkd.in/guVJYk2q https://lnkd.in/gyrvaeBD #AnalyticalChemistry #MethodValidation #PharmaceuticalQuality #DataIntegrity #QualityAssurance #GMP #PharmaIndustry #IVT #Validation #LaboratoryScience

Practice Note SC CR 3 Validation of scientific methods The term 'validation' refers to the process by which a scientific method is tested to demonstrate that it produces repeatable, reproducible and accurate results under conditions consistent with how it will be applied in an actual case. A method is only scientifically valid if it can reliably perform in practice, not just in theory. 📄 Practice Note SC CR 3 in Victoria This Practice Note (introduced in June 2025) anticipates that parties calling expert evidence must make appropriate enquiries to ensure that validity can be established (or that limitations are disclosed) so that a trial judge, if asked, can be informed whether the evidence is scientifically valid or not. 🧩 What this means in practice for criminal practitioners When instructing or challenging expert witnesses, you should now expect a declaration relating to validation in every scientific/technical expert report — or a reasoned explanation if validation is not feasible. If an expert fails to provide adequate detail on validation (or the court is left in the dark about error rates, reproducibility, domain of applicability), that may be a ground for challenging admissibility or weight.