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[3] Criterion validity is typically assessed by comparison with a gold standard test. [4] An example of concurrent validity is a comparison of the scores of the CLEP College Algebra exam with course grades in college algebra to determine the degree to which scores on the CLEP are related to performance in a college algebra class. [5]
In statistics, canonical analysis (from Ancient Greek: κανων bar, measuring rod, ruler) belongs to the family of regression methods for data analysis. Regression analysis quantifies a relationship between a predictor variable and a criterion variable by the coefficient of correlation r, coefficient of determination r 2, and the standard regression coefficient β.
A variable is considered dependent if it depends on an independent variable. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of ...
In psychometrics, predictive validity is the extent to which a score on a scale or test predicts scores on some criterion measure. [1] [2]For example, the validity of a cognitive test for job performance is the correlation between test scores and, for example, supervisor performance ratings.
Criterion validity evidence involves the correlation between the test and a criterion variable (or variables) taken as representative of the construct. In other words, it compares the test with other measures or outcomes (the criteria) already held to be valid.
The prediction is obtained by adding these products along with a constant. When the weights are chosen to give the best prediction by some criterion, the model referred to as a proper linear model. Therefore, multiple regression is a proper linear model. By contrast, unit-weighted regression is called an improper linear model.
The assumptions of discriminant analysis are the same as those for MANOVA. The analysis is quite sensitive to outliers and the size of the smallest group must be larger than the number of predictor variables. [8] Multivariate normality: Independent variables are normal for each level of the grouping variable. [10] [8]
Principal component regression (PCR) [7] [8] is used when the number of predictor variables is large, or when strong correlations exist among the predictor variables. This two-stage procedure first reduces the predictor variables using principal component analysis, and then uses the reduced variables in an OLS regression fit. While it often ...