Anchoring or fixing parameter estimates (measures) is done with IAFILE= for items, PAFILE= for persons, and SAFILE= for response structures.

 

From the estimation perspective under JMLE, anchored and unanchored items appear exactly alike. The only difference is that anchored values are not changed at the end of each estimation iteration, but unanchored estimates are. JMLE converges when "observed raw score = expected raw score based on the estimates". For anchored values, this convergence criterion is never met, but the fit statistics etc. are computed and reported by Winsteps as though the anchor value is the "true" parameter value. Convergence of the overall analysis is based on the unanchored estimates.

 

Using pre-set "anchor" values to fix the measures of items (or persons) in order to equate the results of the current analysis to those of other analyses is a form of "common item" (or "common person") equating. Unlike common-item equating methods in which all datasets contribute to determining the difficulties of the linking items, the current anchored dataset has no influence on those values. Typically, the use of anchored items (or persons) does not require the computation of equating or linking constants. During an anchored analysis, the person measures are computed from the anchored item values. Those person measures are used to compute item difficulties for all non-anchored items. Then all non-anchored item and person measures are fine-tuned until the best possible overall set of measures is obtained. Discrepancies between the anchor values and the values that would have been estimated from the current data can be reported as displacements. The standard errors associated with the displacements can be used to compute approximate t-statistics to test the hypothesis that the displacements are merely due to measurement error.