: The concept of reliability originates from Spearman’s early work with factor analysis and measurement errors over hundred years ago. However, the importance of the reliability of measurement scales has been partially obscured because of poor estimators, such as Cronbach’s alpha is widely applied despite the poor estimate of the measurement error variance used. Subsequently Cronbach’s alpha underestimates the reliability and may even give absurd, negative estimates, it remains to be the most widely applied estimator of reliability for reason of easiness; a quick method for practical needs—long before the era of computers. [Vehkalahti,Puntanen &Tarkkonen, 2006]The availability of hi-speed computing technology, easily available in our lap top will change such necessity in the name of accuracy and precision. Cronbach Alpha always exceeds the maximum reliability possible for the measures underlying for a given dataset. This misleads the test-user into believing a test has better measurement characteristics than it actually has. It overstates the reliability of the test-independent, generalizable measures the test is intended to imply. For inference beyond the test, Rasch reliability is more conservative and less misleading. [Linacre, 1997]

Nunally (1978) definition of Cronbach-alpha has been grossly misused just like Krejcie & Morgan (1970) for 'random' sampling size; but many keep on citing it NOT knowing the precision is NOT in place as compared to other more current methods as discussed above. Cohen (1992) Statistical.Power Analysis shows an alternative method of sampling size for smaller size depending on d stats. test to be employed; correlation or multiple regression. (see:http://www.ipbl.edu.my/bm/penyelidikan/jurnalpapers/jurnal2006/chua06.pdf) .

Linacre (1994) employs JMLE to determine d sample size and meet sufficient statistics principles. see:http://www.rasch.org/rmt/rmt74m.htm. The fundamentals has to be observed n CANNOT simply be breached for convenience of a given case. Thus, Rasch reliability, MNSQ, z-std, PMC, eigenvalue ratio, item indepencence etc. are stats. values dat must be in place before an item is considered a fit.
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