Types of validation
|Types of validation|
There are various types of validation':
- process validation (prospective, retrospective, revalidation)
- equipment validation (certification, measurement, quality)
- analytical method validation (mean error of results)
- computer system validation (security, reliability)
- validation of data entered in IT system
- validation of medical procedures
- validation of prediction models
Validation is one of two major way for achieving. It means how good it reflects reality. Validation includes face validity in which specialist estimate structure of the model, establishment and result confirmation or internal conformity . It check precision of coding.
Cross validity parallel effect with different models which resolving the same problems.
External validity is paralleling model effect to real results on world. Predictive validity is paralleling model effect with observed incident.
External validity and predictive validity are the most powerful type of validation(D.M. Eddy and others 2012, p. 733).
Validation “subjecting it to tests, such as comparing model results with events observed in reality”(D.M. Eddy and others 2012, p. 734).
Validation could be useful for definition the effects applicability. It can be useful by decision makers. Is one good way for recipients establish how well model does it(D.M. Eddy and others 2012, p. 736).
It is not practicable to write criteria which model need to have. Model could contains a lot of standards of validity for alternative applications. Validation need to fit to special applications, not to fit to the one model. There could be fragility about some issue of the model regardless of the quantity of validations are done(D.M. Eddy and others 2012, p. 736).
Validation relate step of theory and data support the results of test for which validation was be used. The permanent action of validation provides basis interpretation of test results. Validation is used for right interpretation and it helps take an advantage of results and use it to improve the studied behavior. There should not be any negative consequence after proceed the results. Validation should use evidence, result of interpretation of evidence. Test effect needs to be supported by correct validation process. It s need to use knowledge but except that the most important is thinking process and connection skills with our knowledge. It is useful for whole process which need to fulfill state standards. Capacity to resolve problems is equally important for validation process(M. Kovalenko 2018, p. 65).
One of two part development process is validation. “Model validation is defined as the ‘substantiation that a model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model’”. Validity need to be concentrate of the specific model purpose(R.G. Sargent 2013, p.12).
Model validation often does not include data validity. It is hard to do. It is costly to make accurate data. Problems with data are time consuming. These problems are very often cause of model fail(R.G. Sargent 2013, p. 17).
- Eddy, D. M., Hollingworth, W., Caro, J. and others (2012). Model Transparency and Validation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–7. Medical Decision Making (pp. 733-734, 736).
- Klügl, F. (2008, March). A validation methodology for agent-based simulations. In Proceedings of the 2008 ACM symposium on Applied computing (pp. 39-43). ACM.
- Kovalenko, M. (2018). The Validation Process in the IELTS Reading Component:Reading Requirements for Preparing International Students. Journal of Language & Education Volume 4, Issue 1 (pp. 65).
- Sargent R.G. (2013). Verification and validation of simulation models. Journal of Simulation (2013) 7,12–24 (pp.12,17).
Author: Jolanta Guz