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Cronbach's alpha (Cronbach's ), also known as tau-equivalent reliability or coefficient alpha (coefficient ), is a reliability coefficient and a measure of the internal consistency of tests and measures. [1] [2] [3] It was named after the American psychologist Lee Cronbach.
A quantity similar (but not mathematically equivalent) to congeneric reliability first appears in the appendix to McDonald's 1970 paper on factor analysis, labeled . [2] In McDonald's work, the new quantity is primarily a mathematical convenience: a well-behaved intermediate that separates two values.
Alpha is also a function of the number of items, so shorter scales will often have lower reliability estimates yet still be preferable in many situations because they are lower burden. An alternative way of thinking about internal consistency is that it is the extent to which all of the items of a test measure the same latent variable. The ...
The name of this formula stems from the fact that is the twentieth formula discussed in Kuder and Richardson's seminal paper on test reliability. [1] It is a special case of Cronbach's α, computed for dichotomous scores. [2] [3] It is often claimed that a high KR-20 coefficient (e.g., > 0.90) indicates a homogeneous test. However, like ...
For the reliability of a two-item test, the formula is more appropriate than Cronbach's alpha (used in this way, the Spearman-Brown formula is also called "standardized Cronbach's alpha", as it is the same as Cronbach's alpha computed using the average item intercorrelation and unit-item variance, rather than the average item covariance and ...
Internal consistency (Cronbach's alpha, split half, etc.) Adequate A meta-analysis showed that nine studies contained Cronbach's alpha coefficients greater than or equal to 0.70. [4] Inter-rater reliability: TBD The PSQI is a relatively new assessment. Not enough research has been conducted on inter-rater reliability to give a comprehensive rating.
This software provides a comprehensive set of capabilities including frequencies, cross-tabs comparison of means (t-tests and one-way ANOVA), linear regression, logistic regression, reliability (Cronbach's alpha, not failure or Weibull), and re-ordering data, non-parametric tests, factor analysis, cluster analysis, principal components analysis, chi-square analysis and more.
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