The possibility of using surrogate variables (e.g., school grades, other test scores, examinee background information) as replacements for common items predicting sample-selection bias between groups was investigated. The problem was specified as an incomplete data problem of comparability studies and was addressed using nonequivalent groups. A general model for estimating complete data (fitted) distributions through covariates is proposed (including common-item scores and surrogate variables as special cases). Model parameters are estimated using the EM algorithm. Standard errors of comparable scores are derived under the proposed model. Data from an empirical example examined the use of surrogate variables for establishing score comparability.
The evolution time of ADC is faster for TI than for WI. This difference, which likely originates from the different pathophysiologic and hemodynamic features of the two infarction types, might account for the relatively large range of ADC values reported for the time course of ischemic strokes.
We propose simplified formulas for computing the standard errors of equiper-centile equating for continuous and discrete test scores. The suggested formulas are conceptually simple and easily extended to more complicated equating designs such as chained equipercentile equating, smoothed equipercentile equating, and equating using the frequency estimation method. Results from an empirical study indicate that the derived formulas work reasonably well for samples with moderate sizes (e.g., 1,000 examinees).
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