Quality evaluation criteria
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Contents
Scope
What is a set of quality evaluation measures such that it fulfils the following criteria:
- The evaluation measures are applicable to all objects.
- The evaluation brings information about the properties of the goodness of the object.
Definition
Data
- NUSAP criteria could be used here?
Amount of data
Amount of data can be measured with this question: How many sets of independent observations are used as data for defining the object?
Typically, each individual scientific article is one set of observations. However, if several articles are derived from the same observations, they should be counted as one set. There are also intermediate situations. For example, several follow-ups may be published from the same cohort. They are clearly not independent, but a later follow-up clearly includes observations that are not included in the previous one. In this case, the discussion should be whether the previous follow-up has any additional merit given the later one. It might for example be better for describing the impacts of exposures occurring at early stages of the follow-up.
Dependencies
Result
Format of the result
This technical classification can be used for object results that are quantitative.
- 0. Unspecified.
- The result is only a placeholder to enable technical usage of the object in an assessment. It may e.g. contain the indices to-be-used, but it contains little or no information about the result itself.
- The result is a point estimate without any uncertainty information.
- The result defines the result domain, i.e. the range in which all plausible values fall. It does not contain probabilistic information except e.g. a uniform distribution across the whole range. This does not imply that all values are equally likely but that any value is possible within the result domain, in a similar sense as in P-Box[1][2] approach.
- The result is defined as a marginal probability distribution with no explication of correlations with other objects.
- The result is defined as a marginal probability distribution with rank correlations (vines[3]) to objects that are causally linked or correlated with it.
- The result is defined as a full joint distribution with objects that are causally linked or correlated with it.
Amount of data
Amount of data is a quantitative measure with the following result domain:
- Number of independent sources of information. This doesn't need to be an integer, if sources are only partly independent.
- 0 means a "guesstimate" where the object result is based on general knowledge without any citable sources of information.
- -1 means a place holder that contains little or no information about the topic; it is just used to make a technically complete object that can be used in testing the usage of the object in e.g. an assessment model.
