Validating qualitative research
Therefore, the qualitative test validation goal is to confirm, based on data, that the requirements for its use have been fulfilled.
These specifications should be intended to assure a nonsignificant risk of false results. Reverend Thomas Bayes (1702-1761) developed a probabilistic model for defining the likelihood that an element would be a member of a specific class.
It is the proportion of variance in observed scores (i.e.
scores on the test) attributable to true scores (the theoretical “real” score that a person would get if a perfect test existed).
This classification is referred as diagnostic sensitivity Se[%] when “the percentage (number fraction multiplied by 100) of subjects with the target condition (as determined by the diagnostic accuracy criteria) whose test values are positive”, and diagnostic specificity Sp[%] when “the percentage (number fraction multiplied by 100) of subjects without the target condition (as determined by the diagnostic accuracy criteria) whose test values are negative” (5.3 of ).
The comparison of methods can be determined primarily when the comparator is the diagnostic accuracy criteria, or it can be determined secondarily when the comparator is other than the diagnostic accuracy criteria.
ISO defines verification as the “confirmation, through the provision of objective evidence, which specified requirements had been fulfilled” (3.8.12 of ).
Validation is defined as the “confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled” (3.8.13 of ).
One specific type is parallel forms reliability, where two equivalent tests are given to students a short time apart.If the forms are parallel, then the tests produce the same observed results.A reliability coefficient is a measure of how well a test measures achievement.In contrast to the verification explanation, validation is directly related to the interested parties requirements, such as the accuracy of clinical decision required by the patients.Erroneous binary results, i.e., false results, affect the clinical decision directly.