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In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social science research. [1] It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct (or factor).
Validity is the main extent to which a concept, conclusion, or measurement is well-founded and likely corresponds accurately to the real world. [1] [2] The word "valid" is derived from the Latin validus, meaning strong.
Lilliefors test is a normality test based on the Kolmogorov–Smirnov test.It is used to test the null hypothesis that data come from a normally distributed population, when the null hypothesis does not specify which normal distribution; i.e., it does not specify the expected value and variance of the distribution. [1]
MIL-STD-105 D Quick reference Table, TABLE I and TABLE IIA. MIL-STD-105 was a United States defense standard that provided procedures and tables for sampling by attributes based on Walter A. Shewhart, Harry Romig, and Harold F. Dodge sampling inspection theories and mathematical formulas. Widely adopted outside of military procurement applications.
The BHS moderately correlates with the Beck Depression Inventory, although research shows that the BDI is better suited for predicting suicidal ideation behavior. [3] The internal reliability coefficients are reasonably high (Pearson r = 0.82 to 0.93 in seven norm groups), but the BHS test-retest reliability coefficients are modest (0.69 after one week and 0.66 after six weeks).
Convergent validity in the behavioral sciences refers to the degree to which two measures that theoretically should be related, are in fact related. [1] Convergent validity, along with discriminant validity, is a subtype of construct validity.
The Dunn index, introduced by Joseph C. Dunn in 1974, is a metric for evaluating clustering algorithms. [1] [2] This is part of a group of validity indices including the Davies–Bouldin index or Silhouette index, in that it is an internal evaluation scheme, where the result is based on the clustered data itself.
In statistics, particularly in hypothesis testing, the Hotelling's T-squared distribution (T 2), proposed by Harold Hotelling, [1] is a multivariate probability distribution that is tightly related to the F-distribution and is most notable for arising as the distribution of a set of sample statistics that are natural generalizations of the statistics underlying the Student's t-distribution.