After estimating the measures, Facets checks to see if any Model= specifications include Bias specifiers, "B". If so, for each such model, the specified Bias interaction is estimated for all the data (not just the data matching that particular model). Bias can be due to any type of interaction including Differential Item Functioning DIF, Differential Person Functioning DPF, Differential Rater Functioning DRF.

This is done by iterating through the data again and, after convergence, doing one further iteration to calculate statistics.

 

Algebraically, first the Bn, Di, Cj, Fk are estimated using a Rasch model like:

 

       log ( Pnijk / Pnij(k-1)) = Bn - Di - Cj - Fk

 

Then the Bn, Di, Cj, Fk are anchored, and the bias/interaction terms, e.g., Cij, are estimated:

 

       log ( Pnijk / Pnij(k-1)) = ( Bn - Di - Cj - Fk ) - Cij

 

Thus the Cij are estimated from the residuals left over from the main analysis. The conversion from residual score to bias interaction size is non-linear. Bias sizes may not sum to zero.

 

Bias, (also called interaction, differential item function, differential person function, etc.,) estimation serves several purposes:

 

1) in diagnosing misfit:

The response residuals are partitioned by element, e.g., by judge-item pairs, and converted into a logit measure. Estimates of unexpected size and statistical significance flag systematic misfit, focussing the misfit investigation.

 

2) in investigating validity:

A systematic, but small, bias in an item or a judge, for or against any group of persons, may be overwhelmed by the general stochastic component in the responses. Consequently it may not be detected by the usual summary fit statistics. Specifying a bias analysis between elements of facets of particular importance provides a powerful means of investigating and verifying the fairness and functioning of a test.

 

3) in assessing the effect of bias:

Since bias terms have a measure and a standard error (precision), their size and significance (t-statistic) are reported. This permits the effect of bias to be expressed in the same frame of reference as the element measures. Thus each element measure can be adjusted for any bias which has affected its estimation, e.g., by adding the estimate of bias, which has adversely affected an element, to that element's logit measure. Then the practical implications of removing bias can be determined. Does adjustment for bias alter the pass-fail decision? Does adjustment for bias affect the relative performance of two groups in a meaningful way?

 

4) in partitioning unexplained "error" variance:

The bias logit sample standard deviation corrected for its measurement error, can be an estimate of the amount of systematic error in the error variance (RMSE).

e.g., for a bias analysis of judges,

       Bias logit S.D. = 0.47, mean bias S.E. = 0.32 (Table 13),

       so "true" bias S.D. = (0.47² - 0.32²) = 0.35 logits,

       but, this exceeds the RMSE for judges = 0.12 (Table 7).

Here, locally extreme judge-person scores cause an overestimation of systematic bias.