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I have one question about using SPSS to calculate Cronbach's alpha of a measure that draws on two types of items: one yields ordinal data and the other dichotomous. The ordinal data are produced through 4-point rating items and the dichotomous data through true-and-false questions.

Is it still OK for me to use the reliability analysis option to calculate the Cronbach's alpha for the measure?

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  • $\begingroup$ I have tried to edit your question so as to clarify and to eliminate some opportunities for misunderstanding. If I have not kept your original meaning, please rollback my edits. $\endgroup$ – rolando2 Oct 7 '12 at 0:37
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Cronbach's $\alpha$ is only designed for measures that are essentially $\tau$-equivalent, which essentially means they contribute equally to the underlying construct. One way to test this is to see if they have the same factor loadings in a factor model. If your measures are not $\tau$-equivalent, then $\alpha$ will underestimate reliability, regardless if the data are continuous or dichotomous.

There are many indices of internal-consistency reliability. $\omega$ (omega) as identified by McDonald (1999) is one of the most flexible for unidimensional constructs, and it can be easily extended to a multidimensional construct. Here is a procedure I recommend taking to identify which measure of reliability to use:

1) First assess dimensionality. Do you have 1 construct or many? If there are many, then no measure of unidimensional reliability will be accurate. Do this with factor analysis, ideally confirmatory factor analysis (CFA), but if you don't have the knowledge or the software you can use exploratory factor analysis (EFA). If you have more than 1 factor that is substantive, then you have a multidimensional construct. If that's the case, look for a measure of multidimensional reliability (these exist for both $\alpha$ and $\omega$. See here. Alternatively, identify the items that don't fit your desired construct and remove them (though take caution here, there are a lot of other psychometric tests you should do as well).

2) Assess $\tau$-equivalence. Again doing this in a factor model may be easiest. Basically, you test to see if the loadings are all equal - in a CFA, you can constrain the loadings and test fit, in an EFA you just have to ballpark the loadings to see if they are reasonably close. If you have $\tau$ equivalence, go ahead and use $\alpha$. If not, use $\omega$.

From what I can tell, SPSS does not calculate $\omega$ (see here). In my view, R is one of the best packages out there for psychometrics because it has the flexibility to do all of this. If you don't know R and don't have the time/energy to learn it (it's a big leap from SPSS) then you can probably safely go with $\alpha$ if you construct is unidimensional, just keep in mind reliability will be higher than what $\alpha$ gives you.

Reference: McDonald, R. P. (1999). Test Theory: A Unified Treatment. New York: Psychology Press.

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I've seen instruments like yours evaluated with Cronbach's alpha going back about 50 years. An example is the Marlowe-Crowne Social Desirability Scale, which is based on all dichotomous items. See Psychlopedia.

That Cronbach's alpha can incorporate such types of data is confirmed by SPSS's Help files. Upon clicking Analyze...Scale...Reliability Analysis...Help...Reliability Analysis Data Considerations, I see: "Data can be dichotomous, ordinal, or interval, but they should be coded numerically."

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Although some recommend using the KR-20 Kuder-Richardson coefficient if the data are dichotomous, Cronbach's coefficient alpha and KR-20 yield the same value. So it can be used normally without problems SPSS

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  • $\begingroup$ yeah to run the KR20 in SPSS just do the same procedure for Cronbach's alpha. $\endgroup$ – user67121 Jan 21 '15 at 15:12

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