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In the example above, predictive validity would be the best choice for validating an employment test, because using the employment test on existing employees may not be a strong analog for using the tests for selection. Reduced motivation and restriction of range are just two possible biasing effects for concurrent validity studies. [2] [3]
Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery". A stated confidence level generally applies only to each test considered individually, but often it is desirable to have a confidence level for the whole family of simultaneous tests. [4]
The Das–Naglieri cognitive assessment system (CAS) test is an individually administered test of cognitive functioning for children and adolescents ranging from 5 through 17 years of age that was designed to assess the planning, attention, simultaneous and successive cognitive processes as described in the PASS theory of intelligence.
The Kaufman Assessment Battery for Children (KABC) is a clinical instrument (psychological diagnostic test) for assessing cognitive development. Its construction incorporates several recent developments in both psychological theory and statistical methodology. The test was developed by Alan S. Kaufman and Nadeen L. Kaufman in
Although concurrent and predictive validity are similar, it is cautioned to keep the terms and findings separated. "Concurrent validity should not be used as a substitute for predictive validity without an appropriate supporting rationale." [3] Criterion validity is typically assessed by comparison with a gold standard test. [4]
The Sawilowsky I test, [5] [6] however, considers all of the data in the matrix with a distribution-free statistical test for trend. Example of a MTMM measurement model . The test is conducted by reducing the heterotrait-heteromethod and heterotrait-monomethod triangles, and the validity and reliability diagonals, into a matrix of four levels.
The Šidák correction is derived by assuming that the individual tests are independent.Let the significance threshold for each test be ; then the probability that at least one of the tests is significant under this threshold is (1 - the probability that none of them are significant).
In statistics, Dunnett's test is a multiple comparison procedure [1] developed by Canadian statistician Charles Dunnett [2] to compare each of a number of treatments with a single control. [ 3 ] [ 4 ] Multiple comparisons to a control are also referred to as many-to-one comparisons.