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Using these assumptions, Lawshe developed a formula termed the content validity ratio: = (/) / (/) where = content validity ratio, = number of SME panelists indicating "essential", = total number of SME panelists. This formula yields values which range from +1 to -1; positive values indicate that at least half the SMEs rated the item as essential.
E.g. a scale that is 5 pounds off is reliable but not valid. A test cannot be valid unless it is reliable. Validity is also dependent on the measurement measuring what it was designed to measure, and not something else instead. [6] Validity (similar to reliability) is a relative concept; validity is not an all-or-nothing idea.
The Dunn index (DI) (introduced by J. 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.
The phenomenon where validity is sacrificed to increase reliability is known as the attenuation paradox. [35] [36] A high value of reliability can conflict with content validity. To achieve high content validity, each item should comprehensively represent the content to be measured.
In statistics and research design, an index is a composite statistic – a measure of changes in a representative group of individual data points, or in other words, a compound measure that aggregates multiple indicators. [1] [2] Indices – also known as indexes and composite indicators – summarize and rank specific observations. [2]
L-index: L-index combines the number of citations, the number of coauthors, the age of publications into a single value, which is independent of the number of publications and conveniently ranges from 0.0 to 9.9. [32] With c as number of citations, a as number of authors and y as number of years, L-index is defined by the formula:
The comparative fit index (CFI) analyzes the model fit by examining the discrepancy between the data and the hypothesized model, while adjusting for the issues of sample size inherent in the chi-squared test of model fit, [21] and the normed fit index. [37] CFI values range from 0 to 1, with larger values indicating better fit.
Many psychologists and education researchers saw "predictive, concurrent, and content validities as essentially ad hoc, construct validity was the whole of validity from a scientific point of view" [15] In the 1974 version of The Standards for Educational and Psychological Testing the inter-relatedness of the three different aspects of validity ...