[ Note: later work has indicated that this is not an effective indicator of unidimensionality. ]
If you are so disinterested in your data that you are willing to reduce its state of dimensionality to a single index, (rather than studying where and how it departs from your intended dimension), then here is a Rasch-based procedure of assessing unidimensionality.
For all persons, on all items,
1. compute R(model), the person separation reliability using model (asymptotic) standard errors. This treats the data as unidimensional. All fluctuations away from stochastic unidimensionality are regarded as due to expected local stochastic variation in stochasticity.
2. compute R(real), the person separation reliability using real (misfit-inflated) standard errors. This treats the data as though it might be multidimensional. All fluctuations away from stochastic unidimensionality are regarded as multidimensionalities of whatever cause. Since the misfit-inflated standard errors are larger than the model errors, R(real) is always less than R(model).
3. compute R(unidimensional), a "reliability" of item unidimensionality:
This reliability coefficient can be interpreted in the same way as a conventional reliability coefficient. Values above 0.9 indicate a clearly unidimensional variable. Values below 0.5 might be cause for alarm.
Unidimensionality coefficient. Wright BD. Rasch Measurement Transactions, 1994, 8:3 p.385
Please help with Standard Dataset 4: Andrich Rating Scale Model
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