The relation between point-biserial correlation discrimination estimates (rpbis) and Rasch fit statistics (RFS) is nearly monotonic apart from the effect of item-person targeting on point-biserial ceilings.
Maximal point-biserial or point-measure correlation is (Normal Ordinate)/square-root(P-Value * (1 - P-Value))
Figure corrected on 7/25/2011
The Figure shows the maximum possible point-biserial correlations with complete dichotomous data for items with different p-values relative to a normally-distributed sample of person abilities.
a. When item response residuals are noisy, disturbed by unexpected variation, they contradict the item's calibration. This makes the item rpbis smaller than typical of these data and the item RFS larger than expected statistically.
b. When item response residuals are muted, subdued by unexpected lack of variation, they indicate a contraction from the modelled level of independence among residuals and hence an underestimation of standard errors of measurement based on this model. This makes the item rpbis larger than typical of these data and the item RFS smaller than expected statistically.
There is an important difference in the utility of these alternative fit statistics. For the rpbis based on raw scores, the most we can say is "smaller (or larger) than we are used to". We don't know where the value we are observing is placed in the possible range. We don't know whether that value is acceptable, undesirably large or undesirably small. The rpbis is a misfit statistic but of unknown size and significance. All we know for certain, (and this is useful in detecting miscoded data), is that negative rpbis means that the observed responses to that item contradict the general meaning of the test.
For RFS based on a measurement system, we can say - "larger (or smaller) than expected statistically". The basis for expectation is a statistical model for a standard distribution of residuals. RFS give a size and a significance to misfit. The size enables us to identify misfit big enough to disturb measurement. The significance indicates what proportion of all possible misfit statistics would be better fitting than this one.
One author writes: "Ideally, it is recommended that items have point-biserials ranging from 0.30 to 0.70
(Allen, M. J. and Yen, W. M. (1979). Introduction to Measurement Theory. Waveland Press, Inc. Prospect Heights Il)".
A rule such as this cuts off the very easy and very hard items, and may even eliminate good-fitting on-target items.
Point-biserials and item fits. Wright BD. Rasch Measurement Transactions, 1992, 5:4 p.174
Please help with Standard Dataset 4: Andrich Rating Scale Model
|Rasch Measurement Transactions (free, online)||Rasch Measurement research papers (free, online)||Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch||Applying the Rasch Model 3rd. Ed., Bond & Fox||Best Test Design, Wright & Stone|
|Rating Scale Analysis, Wright & Masters||Introduction to Rasch Measurement, E. Smith & R. Smith||Introduction to Many-Facet Rasch Measurement, Thomas Eckes||Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr.||Statistical Analyses for Language Testers, Rita Green|
|Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar||Journal of Applied Measurement||Rasch models for measurement, David Andrich||Constructing Measures, Mark Wilson||Rasch Analysis in the Human Sciences, Boone, Stave, Yale|
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