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Generalizability theory, or G theory, is a statistical framework for conceptualizing, investigating, and designing reliable observations. It is used to determine the reliability (i.e., reproducibility) of measurements under specific conditions.
In other words, it is the extent to which the results of a study can generalize or transport to other situations, people, stimuli, and times. [2] [3] Generalizability refers to the applicability of a predefined sample to a broader population while transportability refers to the applicability of one sample to another target population. [2]
Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...
Re-administering the same test to the same group at some later time; Correlating the first set of scores with the second; The correlation between scores on the first test and the scores on the retest is used to estimate the reliability of the test using the Pearson product-moment correlation coefficient: see also item-total correlation. 2.
Finally, the test data set is a data set used to provide an unbiased evaluation of a final model fit on the training data set. [5] If the data in the test data set has never been used in training (for example in cross-validation), the test data set is also called a holdout data set. The term "validation set" is sometimes used instead of "test ...
Content validity evidence involves the degree to which the content of the test matches a content domain associated with the construct. For example, a test of the ability to add two numbers should include a range of combinations of digits. A test with only one-digit numbers, or only even numbers, would not have good coverage of the content domain.
Classical test theory is an influential theory of test scores in the social sciences. In psychometrics, the theory has been superseded by the more sophisticated models in item response theory (IRT) and generalizability theory (G-theory).
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