[1][2][3] It was named after the American psychologist Lee Cronbach.
Numerous studies warn against using Cronbach's alpha unconditionally.
Statisticians regard reliability coefficients based on structural equation modeling (SEM) or generalizability theory as superior alternatives in many situations.
[10] Coefficient alpha had been used implicitly in previous studies,[11][12][13][14] but his interpretation was thought to be more intuitively attractive relative to previous studies and it became quite popular.
[15] To use Cronbach's alpha as a reliability coefficient, the following conditions must be met:[17][18] Cronbach's alpha is calculated by taking a score from each scale item and correlating it with the total score for each observation.
The resulting correlations are then compared with the variance for all individual item scores.
Cronbach's alpha is best understood as a function of the number of questions or items in a measure, the average covariance between pairs of items, and the overall variance of the total measured score.
where: Alternatively, it can be calculated through the following formula:[20] where: Application of Cronbach's alpha is not always straightforward and can give rise to common misconceptions, some of which are detailed here.
can occur for reasons such as negative discrimination or mistakes in processing reversely scored items.
, but Cronbach (1951)[10] did not comment on this problem in his article that otherwise discussed potentially problematic issues related
This misconception stems from the inaccurate explanation of Cronbach (1951)[10] that high
[3] The term "internal consistency" is commonly used in the reliability literature, but its meaning is not clearly defined.
Removing an item using "alpha if item deleted"[clarification needed] may result in 'alpha inflation,' where sample-level reliability is reported to be higher than population-level reliability.
[29] Nunnally's book[31][32] is often mentioned as the primary source for determining the appropriate level of dependability coefficients.
However, his proposals contradict his aims as he suggests that different criteria should be used depending on the goal or stage of the investigation.
Rather than 0.7, Nunnally's applied research criterion of 0.8 is more suited for most empirical studies.
If a criterion means a cutoff point, it is important whether or not it is met, but it is unimportant how much it is over or under.
[7] For example, a person who takes the test with a reliability of one will either receive a perfect score or a zero score, because if they answer one item correctly or incorrectly, they will answer all other items in the same manner.
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.
However, a strategy of repeatedly measuring essentially the same question in different ways is often used solely to increase reliability.
However, the increase in the number of items hinders the efficiency of measurements.
[3] However, simulation studies comparing the accuracy of several reliability coefficients have led to the common result that
[7] The majority opinion is to use structural equation modeling or SEM-based reliability coefficients as an alternative to
[3][7][46][5][47][8][6][48] However, there is no consensus on which of the several SEM-based reliability coefficients (e.g., uni-dimensional or multidimensional models) is the best to use.
General-purpose statistical software such as SPSS and SAS include a function to calculate
SEM software such as AMOS, LISREL, and MPLUS does not have a function to calculate SEM-based reliability coefficients.
Users need to calculate the result by inputting it to the formula.
To avoid this inconvenience and possible error, even studies reporting the use of SEM rely on
[3] There are a few alternatives to automatically calculate SEM-based reliability coefficients.