Use this if careless responses are evident. CUTHI= cuts off the top left-hand corner of the Scalogram in Table 22.
Eliminates (cuts off) observations where examinee ability measure is CUTHI= logits or more higher than item difficulty measure, so the examinee has a high probability of success. Removing off-target responses takes place after PROX has converged. After elimination, PROX is restarted, followed by JMLE estimation and fit calculation using only the reduced set of responses. This may mean that the original score-based ordering is changed.
Usually with CUTLO= and CUTHI=, misfitting items aren't deleted - but miskeys etc. must be corrected first. Setting CUTLO= and CUTHI= is a compromise between fit and missing data. If you loose too much data, then increase the values. If there is still considerable misfit or skewing of equating, then decrease the values.
Here are the usual effects of CUTLO= and CUTHI=
1. Fit to the Rasch model improves.
2. The count of observations for each person and item decreases.
3. The variance in the data explained by the measures decreases.
Polytomous items: CUTLO= and CUTHI= trim the data relative to the item difficulty, so they tend to remove data in high and low categories. You can adjust the item difficulty relative to the response structure using SAFILE=.
Example 1: Eliminate responses where examinee measure is 3 or more logits higher than item measure, to eliminate their worst careless wrong responses:
CUTHI= 3
This produces a scalogram with eliminated responses blanked out:: Table 22
RESPONSES SORTED BY MEASURE:
KID TAP
111111111
123745698013245678
------------------------
15 111 11100000 observations for extreme scores remain
14 111 1110000000
28 111 111010000000
30 1111 1111000000000
27 111111100000000000
Example 2. At www.rasch.org/rmt/rmt62a.htm the originator of this approach suggests CUTLO= -1 and CUTHI=2
