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Validity [5] of an assessment is the degree to which it measures what it is supposed to measure. This is not the same as reliability, which is the extent to which a measurement gives results that are very consistent. Within validity, the measurement does not always have to be similar, as it does in reliability.
Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]
There are many subcategories of members checks, including: narrative accuracy checks, interpretive validity, descriptive validity, theoretical validity, and evaluative validity. In many member checks, the interpretation and report (or a portion of it) is given to members of the sample (informants) in order to check the authenticity of the work.
Reliability does not imply validity. That is, a reliable measure that is measuring something consistently is not necessarily measuring what is supposed to be measured. For example, while there are many reliable tests of specific abilities, not all of them would be valid for predicting, say, job performance.
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
Data type validation is customarily carried out on one or more simple data fields. The simplest kind of data type validation verifies that the individual characters provided through user input are consistent with the expected characters of one or more known primitive data types as defined in a programming language or data storage and retrieval ...
Statistical conclusion validity is the degree to which conclusions about the relationship among variables based on the data are correct or "reasonable". This began as being solely about whether the statistical conclusion about the relationship of the variables was correct, but now there is a movement towards moving to "reasonable" conclusions that use: quantitative, statistical, and ...
Response biases can have a large impact on the validity of questionnaires or surveys. [1] [2] Response bias can be induced or caused by numerous factors, all relating to the idea that human subjects do not respond passively to stimuli, but rather actively integrate multiple sources of information to generate a response in a given situation. [3]