Table 10.1 Item statistics in misfit order |
(controlled by FITI=, LOCAL=, OUTFIT=, TOTAL=)
ITEMS STATISTICS: MISFIT ORDER
PERSON: REAL SEP.: 1.55 REL.: .70 ... ITEM: REAL SEP.: 3.73 REL.: .93
Above the Table are shown the "real" separation coefficient and reliability (separation index) coefficients from Table 3.
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|ENTRY TOTAL TOTAL MODEL| INFIT | OUTFIT |PTMEASUR-AL|EXACT MATCH|ESTIM| ASYMPTOTE | P- | | | | |
|NUMBER SCORE COUNT MEASURE S.E. |MNSQ ZSTD|MNSQ ZSTD|CORR. EXP.| OBS% EXP%|DISCR|LOWER UPPER|VALUE| RMSR|WEIGH|DISPLACE| TAP G |
|------------------------------------+----------+----------+-----------+-----------+-----+-----------+-----+-----------+--------+-----------------|
| 1 35 35 -6.59A 1.85|1.00 .00|1.00 .04|A .00 .00|100.0 100.0| 1.00| .00 1.00| 1.00| .000| .50| -.52| 1-4 1 |
| 5 31 35 -3.83 .70|1.04 .19| .52 .12|B .55 .55| 88.2 91.7| 1.01| .05 1.00| .89| .248| 1.00| -.87| 2-1-4 2 |
| 6 30 35 -3.38 .64| MAXIMUM MEASURE | .53 .58|100.0 100.0| | | .86| .289| 1.00| -.64| 3-4-1 1 |
| 7 31 35 -3.83 .70| MINIMUM MEASURE | .40 .55|100.0 100.0| | | .89| .281| 1.00| .57| 1-4-3-2 2 |
| 8 27 35 | DROPPED | | | | | | | | | 1-4-2-3 1 |
| 10 0 35 | INESTIMABLE: HIGH | | | | | | | | | 2-4-3-1 3 |
| 11 35 35 | INESTIMABLE: LOW | | | | | | | | | 1-3-1-2-4 4 |
| 9 DELETED | | | | | | | | | | | 1-3-2-4 2 |
| 12 DESELECTED | | | | | | | | | | | 1-3-2-4-3 1 |
....
|------------------------------------+----------+----------+-----------+-----------+-----+-----------+-----+-----+-----+--------+-----------------|
| MEAN 18.5 35.0 -.59 .94| .96 .04| .68 -.11| | 89.9 90.0| | | | | | .32| |
| P.SD 13.9 .0 4.21 .49| .28 .71| .58 .53| | 6.3 5.3| | | | | | .27| |
--------------------------------------------------------------------------------------------------------------------------------+------------------
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|ENTRY TOTAL JMLE MODEL| INFIT | OUTFIT | CMLE | CMLE INFIT | CMLE OUTFIT |
|NUMBER SCORE MEASURE S.E. |MNSQ ZSTD|MNSQ ZSTD| MEASURE S.E. | MNSQ ZSTD | MNSQ ZSTD |
|----------------------------+----------+----------+---------------+---------------+---------------+
| 5 37 2.42 .22|2.30 5.61|3.62 7.27| 2.32 .22| 2.31 5.65| 3.56 7.26|
| 23 42 2.18 .21|2.41 6.29|4.11 8.97| 2.09 .22| 2.43 6.35| 4.06 8.98|
| 20 50 1.83 .20|1.33 2.00|1.82 3.73| 1.76 .20| 1.34 2.06| 1.81 3.74|
Column |
Description |
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ENTRY NUMBER |
the sequence number of the person, or item, in your data, and is the reference number used for deletion or anchoring. |
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TOTAL SCORE TOTAL COUNT
|
Totalscore=Yes -. This is the score when reading in the data.
|
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NON-EXTREME SCORE NON-EXTREME COUNT |
Totalscore=No - the raw score and count of response by a person on the test, or the sum of the scored responses to an item by the persons, omitting responses in extreme and inestimable scores. This is the score when estimating person abilities and item difficulties. Scored responses are transformed (re-counted) so that the lowest response is zero. |
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MEASURE |
the estimate (or calibration) of the person ability (theta, B, beta, etc.), or the item difficulty (b, D, delta, etc.). Values are reported in logits with two decimal places, unless rescaled by USCALE=, UIMEAN=, UPMEAN=, UDECIM=.
The difficulty of an item is defined to be the point on the latent variable at which its high and low categories are equally probable. SAFILE= can be used to alter this definition. |
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A after MEASURE, MAXIMUM, etc. |
see STATUS Table below |
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MODEL S.E. REAL S.E. |
MODEL S.E. is the standard error of the estimate. REAL S.E. is the misfit-inflated standard error. These are commonly referred to as conditional standard errors of measurement (CSEM). Real S.E> while you are improving your results. This assumes misfit contradicts the Rasch model. Model S.E. when your results are as good as they can be. This assumes misfit is the randomness predicted by the Rasch model. |
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INFIT |
an information-weighted statistic, which is more sensitive to unexpected behavior affecting responses to items near the person's measure level. |
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OUTFIT |
an unweighted statistic, more sensitive to unexpected behavior by persons on items far from the person's measure level. |
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MNSQ |
a mean-square statistic computed for all scores responses, excluding responses in extreme total scores. This is a chi-square statistic divided by its degrees of freedom. Its expectation is 1.0. Values substantially less than 1.0 indicate overfit = dependency in your data. Values values substantially greater than 1.0 indicate underfit = unmodeled noise. See dichotomous and polytomous fit statistics.
|
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ZSTD ZEMP LOG PROB |
the INFIT or OUTFIT mean-square fit statistic t standardized to approximate a theoretical "unit normal", mean 0 and variance 1, distribution. ZSTD (standardized as a z-score) is used of a t-test result when either the t-test value has effectively infinite degrees of freedom (i.e., approximates a unit normal value) or the Student's t-statistic distribution value has been adjusted to a unit normal value. The standardization is shown on RSA, p.100-101. When LOCAL=Y, then ZEMP is shown, indicating a local {0,1} standardization. When LOCAL=LOG, then LOG is shown, and the natural logarithms of the mean-squares are reported. More exact values are shown in the Output Files. When LOCAL=PROB, the probability of the mean-square is shown. |
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PTMEASUR-AL CORR. PTMEASUR-AL EXP. |
an observed point-correlation: PTBISERL-AL, PTBISERL-EX, PTMEASURE-A, PTMEASUR-EX, see Correlations. Negative reported correlations suggest that the orientation of the scoring on the item, or by the person, may be opposite to the orientation of the latent variable. This may be caused by item miskeying, reverse scoring, person special knowledge, guessing, data entry errors, or the expected randomness in the data.
Correlations of 0.00 may be because the correlation cannot be calculated due to the structure of the data.
In FIT ORDER Tables 6.1 and 10.1, letters A, B, ... indicating the identity of persons or items appearing on the Infit and Outfit plots, Tables 4, 5, 8, 9, precede the correlations. If the point-correlation is inestimable because items have different numbers of categories, this column does not appear, or only the FIT letters display.
EXP. is the expected value of the point-correlation when the data fit the Rasch model with the estimated measures. See Correlations. |
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EXACT MATCH OBS% EXACT MATCH EXP% |
OBServed% is the percent of data points that are within 0.5 score points of their expected values, i.e., that match predictions. EXPected% is the percent of data points that are predicted to be within 0.5 score points of their expected values. |
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ESTIM DISCRIM |
an estimate of the 2-PL item discrimination, see DISCRIM= Negative discriminations are usually problematic and accompanied by negative point-biserial correlations. These indicate that the scoring on the item may be contradicting the overall latent variable. However, this is not a universal rule, so please look at the infit and outfit mean-squares to see whether they also indicate problems (values much greater than 1.0). Exceptions to the rule include very hard and very easy items, and situations where the person sample variance is very small. |
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ASYMPTOTE LOWER ASYMPTOTE UPPER |
estimates of the upper and lower asymptotes for dichotomous items, see ASYMPTOTE= |
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P-VALUE |
the observed proportion-correct (p-value) for 0/1 dichotomies, or observed average rating on the item, see PVALUE=. |
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RMSR |
root-mean-square-residual of observations not in extreme scores, see RMSR=. |
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WMLE |
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QCMLE |
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WEIGH |
the weight assigned by IWEIGHT= or PWEIGHT=. When WEIGHT = 0.0, the item or person is estimated, but does not influence the estimate of any other item or person. |
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CMLE MEASURE |
CMLE item measure or CMLE-based AMLE person measure when CMLE=Yes |
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CMLE S.E. |
CMLE measure S.E. for items and person when CMLE=Yes |
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CMLE INFIT MNSQ |
CMLE Infit Mean-square fit computed from CMLE probabilities when CMLE=Yes |
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CMLE INFIT ZSTD |
CMLE Infit Z-standardized fit computed from CMLE probabilities when CMLE=Yes |
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CMLE OUTFIT MNSQ |
CMLE Outfitfit Mean-square fit computed from CMLE probabilities when CMLE=Yes |
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CMLE OUTFIT ZSTD |
CMLE Outfit Z-standardized fit computed from CMLE probabilities when CMLE=Yes |
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CMLE WMLE |
CMLE Warm's Weighted Mean Likelihood estimates when CMLE=Yes and WMLE=Yes |
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DISPLACE |
the displacement of the reported MEASURE from its data-derived value. This should only be shown with anchored measures. The displacement values can be see in IFILE= and PFILE= output files. The displacement is an estimate of the amount to add to the MEASURE to make it conform with the data. |
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PERSON ITEM |
the name of the list of persons (data rows) or items (data columns) reported here |
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G |
the grouping code assigned with ISGROUPS=. Table 3.2, 3.3, etc. show the details of rating scales, etc., for each grouping code. |
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M |
model code assigned with MODELS= |
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SUBSET: |
the person or items are in incomparable subsets, see Subsets. |
Reported |
STATUS |
Measured? |
Reason |
A (after Measure) |
2 |
Yes |
Anchored (fixed) measure. The reported S.E. is that which would have been obtained if the value had been estimated. Values are reported in logits with two decimal places, unless rescaled by USCALE=, UDECIM= |
(MEASURE) |
1 |
Yes |
Estimated measure |
MINIMUM |
0 |
Yes |
Extreme minimum score. Measure estimated using EXTRSC= MINIMUM and MAXIMUM measures may occur because other MINIMUM and MAXIMUM measures have been dropped from the estimation. Inspect with TOTALSCORE=No. |
MAXIMUM |
-1 |
Yes |
Extreme maximum score. Measure estimated using EXTRSC= MINIMUM and MAXIMUM measures may occur because other MINIMUM and MAXIMUM measures have been dropped from the estimation. Inspect with TOTALSCORE=No. |
DROPPED |
-2 |
No |
No responses available for measurement. Perhaps due to CUTLO=, CUTHI=, CODES=, or deletion of other persons or items. Also check that anchored items. IAFILE=, has matching SAFILE= for all items. |
DELETED |
-3 |
No |
Deleted by user. PDELETE=, PDFILE=, IDELETE=, IDFILE=, PSELECT=, ISELECT= |
INESTIMABLE: HIGH |
-4 |
No |
For an item, this can be resolved using SAFILE= or grouping this item with a similar estimable item: SAFILE=* 23 0 0 ; item-number bottom-category 0 23 1 0 ; item-number top-category 0 * For a person: change the observation by this person on the hardest item into a lower category. |
INESTIMABLE: LOW |
-5 |
No |
The measure probably has a low value. For an item, this can be resolved using SAFILE= or grouping this item with a similar estimable item: SAFILE=* 23 0 0 ; item-number bottom-category 0 23 1 0 ; item-number top-category 0 * For a person: change the observation by this person on the easiest item into a higher category. |
DROPPED A (after Measure) |
-6 |
Yes |
Anchored (fixed) measure with no observed raw score |
DESELECTED |
-7 to -16 |
No |
Temporarily deselected by Specification box with iSELECT= , etc. (usual STATUS - 10) |
OMITTED |
-17 to -26 |
No |
Temporarily deleted by Specification box with iDELETE=, etc. (usual STATUS - 20) |
REMOVED |
-27 to -36 |
No |
Temporarily deselected and deleted by Specification box with iSELECT= , etc., and iDELETE=, etc. (usual STATUS - 30) |
Example: To eliminate misfitting items, output Table 10.1. Copy into Notepad++ (freeware) or other software capable of rectangular copy (alt+mouse). Then paste into your Winsteps control file
pasted entry numbers - put enough blank lines here first for all the numbers
*
Another approach is to screen out very unexpected answers, rather than whole people. Do this with CUTLO= -2
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