Medical educators attempt to create reliable and valid tests and questionnaires in
order to enhance the accuracy of their assessment and evaluations. Validity and reliability
are two fundamental elements in the evaluation of a measurement instrument. Instruments
can be conventional knowledge, skill or attitude tests, clinical simulations or survey
questionnaires. Instruments can measure concepts, psychomotor skills or affective
values. Validity is concerned with the extent to which an instrument measures what
it is intended to measure. Reliability is concerned with the ability of an instrument
to measure consistently.
It should be noted that the reliability of an instrument is closely associated with
its validity. An instrument cannot be valid unless it is reliable. However, the reliability
of an instrument does not depend on its validity.
It is possible to objectively measure the reliability of an instrument and in this
paper we explain the meaning of Cronbach’s alpha, the most widely used objective measure
Calculating alpha has become common practice in medical education research when multiple-item
measures of a concept or construct are employed. This is because it is easier to use
in comparison to other estimates (e.g. test-retest reliability estimates)
as it only requires one test administration. However, in spite of the widespread use
of alpha in the literature the meaning, proper use and interpretation of alpha is
not clearly understood.
We feel it is important, therefore, to further explain the underlying assumptions
behind alpha in order to promote its more effective use. It should be emphasised that
the purpose of this brief overview is just to focus on Cronbach’s alpha as an index
of reliability. Alternative methods of measuring reliability based on other psychometric
methods, such as generalisability theory or item-response theory, can be used for
monitoring and improving the quality of OSCE examinations
, but will not be discussed here.
What is Cronbach alpha?
Alpha was developed by Lee Cronbach in 1951
to provide a measure of the internal consistency of a test or scale; it is expressed
as a number between 0 and 1. Internal consistency describes the extent to which all
the items in a test measure the same concept or construct and hence it is connected
to the inter-relatedness of the items within the test. Internal consistency should
be determined before a test can be employed for research or examination purposes to
ensure validity. In addition, reliability estimates show the amount of measurement
error in a test. Put simply, this interpretation of reliability is the correlation
of test with itself. Squaring this correlation and subtracting from 1.00 produces
the index of measurement error. For example, if a test has a reliability of 0.80,
there is 0.36 error variance (random error) in the scores (0.80×0.80 = 0.64; 1.00
– 0.64 = 0.36).
As the estimate of reliability increases, the fraction of a test score that is attributable
to error will decrease.
It is of note that the reliability of a test reveals the effect of measurement error
on the observed score of a student cohort rather than on an individual student. To
calculate the effect of measurement error on the observed score of an individual student,
the standard error of measurement must be calculated (SEM).
If the items in a test are correlated to each other, the value of alpha is increased.
However, a high coefficient alpha does not always mean a high degree of internal consistency.
This is because alpha is also affected by the length of the test. If the test length
is too short, the value of alpha is reduced.
Thus, to increase alpha, more related items testing the same concept should be added
to the test. It is also important to note that alpha is a property of the scores on
a test from a specific sample of testees. Therefore investigators should not rely
on published alpha estimates and should measure alpha each time the test is administered.
Use of Cronbach’s alpha
Improper use of alpha can lead to situations in which either a test or scale is wrongly
discarded or the test is criticised for not generating trustworthy results. To avoid
this situation an understanding of the associated concepts of internal consistency,
homogeneity or unidimensionality can help to improve the use of alpha. Internal consistency
is concerned with the interrelatedness of a sample of test items, whereas homogeneity
refers to unidimensionality. A measure is said to be unidimensional if its items measure
a single latent trait or construct. Internal consistency is a necessary but not sufficient
condition for measuring homogeneity or unidimensionality in a sample of test items.
Fundamentally, the concept of reliability assumes that unidimensionality exists in
a sample of test items
and if this assumption is violated it does cause a major underestimate of reliability.
It has been well documented that a multidimensional test does not necessary have a
lower alpha than a unidimensional test. Thus a more rigorous view of alpha is that
it cannot simply be interpreted as an index for the internal consistency of a test.
Factor Analysis can be used to identify the dimensions of a test.
Other reliable techniques have been used and we encourage the reader to consult the
paper “Applied Dimensionality and Test Structure Assessment with the START-M Mathematics
Test” and to compare methods for assessing the dimensionality and underlying structure
of a test.
Alpha, therefore, does not simply measure the unidimensionality of a set of items,
but can be used to confirm whether or not a sample of items is actually unidimensional.
On the other hand if a test has more than one concept or construct, it may not make
sense to report alpha for the test as a whole as the larger number of questions will
inevitable inflate the value of alpha. In principle therefore, alpha should be calculated
for each of the concepts rather than for the entire test or scale.
The implication for a summative examination containing heterogeneous, case-based questions
is that alpha should be calculated for each case.
More importantly, alpha is grounded in the ‘tau equivalent model’ which assumes that
each test item measures the same latent trait on the same scale. Therefore, if multiple
factors/traits underlie the items on a scale, as revealed by Factor Analysis, this
assumption is violated and alpha underestimates the reliability of the test.
If the number of test items is too small it will also violate the assumption of tau-equivalence
and will underestimate reliability.
When test items meet the assumptions of the tau-equivalent model, alpha approaches
a better estimate of reliability. In practice, Cronbach’s alpha is a lower-bound estimate
of reliability because heterogeneous test items would violate the assumptions of the
If the calculation of “standardised item alpha” in SPSS is higher than “Cronbach’s
alpha”, a further examination of the tau-equivalent measurement in the data may be
Numerical values of alpha
As pointed out earlier, the number of test items, item inter-relatedness and dimensionality
affect the value of alpha.
There are different reports about the acceptable values of alpha, ranging from 0.70
A low value of alpha could be due to a low number of questions, poor inter-relatedness
between items or heterogeneous constructs. For example if a low alpha is due to poor
correlation between items then some should be revised or discarded. The easiest method
to find them is to compute the correlation of each test item with the total score
test; items with low correlations (approaching zero) are deleted. If alpha is too
high it may suggest that some items are redundant as they are testing the same question
but in a different guise. A maximum alpha value of 0.90 has been recommended.
High quality tests are important to evaluate the reliability of data supplied in an
examination or a research study. Alpha is a commonly employed index of test reliability.
Alpha is affected by the test length and dimensionality. Alpha as an index of reliability
should follow the assumptions of the essentially tau-equivalent approach. A low alpha
appears if these assumptions are not meet. Alpha does not simply measure test homogeneity
or unidimensionality as test reliability is a function of test length. A longer test
increases the reliability of a test regardless of whether the test is homogenous or
not. A high value of alpha (> 0.90) may suggest redundancies and show that the test
length should be shortened.
Alpha is an important concept in the evaluation of assessments and questionnaires.
It is mandatory that assessors and researchers should estimate this quantity to add
validity and accuracy to the interpretation of their data. Nevertheless alpha has
frequently been reported in an uncritical way and without adequate understanding and
interpretation. In this editorial we have attempted to explain the assumptions underlying
the calculation of alpha, the factors influencing its magnitude and the ways in which
its value can be interpreted. We hope that investigators in future will be more critical
when reporting values of alpha in their studies.