﻿ Anchored estimation

# Anchored estimation

How many anchor items or anchor persons?

The percent of anchor items is less important than the number of anchor items. We need enough anchor items to be statistically certain that a large incorrect value of one anchor item will not distort the equating. In my experiments, 10 were needed in order to prevent large accidental deviation in one from distorting the measurement. Please remember that the choice of 10 anchor items is before cleaning. We expect the 10 anchor items to malfunction somewhat. If we did not, only one anchor item would be needed.

Suppose that we have 10 anchor items. Then we discover one anchor item has changed its difficulty in the new test administration. In our analysis of the new test administration, we unanchor that item, so that there now only 9 anchor items. Have we failed the 10-anchor-item criterion? No! The remaining 9 anchor items are now better than the original 10 anchor items.

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. if large displacements are shown for the anchored items or persons, try changing the setting of ANCESTIM=.

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.

Example: I have a study in which an instrument is administered serially over time to each participant.  I want to estimate the trait for subsequent occasions treating the baseline parameter estimates as fixed.

Here's an approach:

1.Format the so that each data row is a set of 7 items followed by a person label containing a time-point code (say, 01,02,03,04,..) in the first two columns and a participant number.

2.Analyze each time-point in a separate file or separately in a combined file using PSELECT=01 then  PSELECT=02, etc. to verify that the data for each time-point is correctly entered.

3.Analyze the time-point 01 file or separately in a combined file using PSELECT=01. Output IFILE=01if.txt SFILE=01sf.txt

4.Analyze all the data in separate files or combined files with no PSELECT= and IAFILE=01if.txt SAFILE=01sf.txt
Output PFILE= filename.txt or PFILE=filename.xls containing a measure for every time-point + participant using the time-point 01 parameter values.

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