Column
|
Description
|
ENTRY NUMBER
|
the sequence number of the person, or item, in your data, and is the reference number used for deletion or anchoring.
|
TOTAL SCORE
TOTAL COUNT
|
Totalscore=Yes -. This is the score when reading in the data.
|
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.
|
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.
If unexpected results are reported, check whether TARGET= or CUTLO= or CUTHI= or ISGROUPS= are specified.
|
A after MEASURE, MAXIMUM, etc.
|
see STATUS Table below
|
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.
|
INFIT
|
an information-weighted statistic, which is more sensitive to unexpected behavior affecting responses to items near the person's measure level.
|
OUTFIT
|
an unweighted statistic, more sensitive to unexpected behavior by persons on items far from the person's measure level.
|
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.
Value
|
Meaning
|
>2.0
|
Off-variable noise is greater than useful information. Degrades measurement. Always remedy the large misfits first.
|
>1.5
|
Noticeable off-variable noise. Neither constructs nor degrades measurement
|
0.5 - 1.5
|
Productive of measurement
|
<0.5
|
Overly predictable. Misleads us into thinking we are measuring better than we really are. (Attenuation paradox.). Item misfits <1.0 are only of concern when shortening a test. Person misfits <1.0 are rarely of concern.
|
|
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.
Ben Wright advises: "ZSTD is only useful to salvage non-significant MNSQ>1.5, when sample size is small or test length is short."
|
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.
|
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.
|
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.
|
ASYMPTOTE LOWER
ASYMPTOTE UPPER
|
estimates of the upper and lower asymptotes for dichotomous items, see ASYMPTOTE=
|
P-VALUE
|
the observed proportion-correct (p-value) for 0/1 dichotomies, or observed average rating on the item, see PVALUE=.
|
RMSR
|
root-mean-square-residual of observations not in extreme scores, see RMSR=.
|
WMLE
|
Warm's Mean Likelihood Estimate, see WMLE=
|
QCMLE
|
Quasi-Conditional Maximum Likelihood Estimate, see QCMLE=
|
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.
|
CMLE MEASURE
|
CMLE item measure or CMLE-based AMLE person measure when CMLE=Yes
|
CMLE S.E.
|
CMLE measure S.E. for items and person when CMLE=Yes
|
CMLE INFIT MNSQ
|
CMLE Infit Mean-square fit computed from CMLE probabilities when CMLE=Yes
|
CMLE INFIT ZSTD
|
CMLE Infit Z-standardized fit computed from CMLE probabilities when CMLE=Yes
|
CMLE OUTFIT MNSQ
|
CMLE Outfitfit Mean-square fit computed from CMLE probabilities when CMLE=Yes
|
CMLE OUTFIT ZSTD
|
CMLE Outfit Z-standardized fit computed from CMLE probabilities when CMLE=Yes
|
CMLE WMLE
|
CMLE Warm's Weighted Mean Likelihood estimates when CMLE=Yes and WMLE=Yes
|
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.
+ logit displacement for a person ability indicates that the observed person score is higher (higher ability) than the expected person score based on the reported measure (anchor value).
+ logit displacement for an item difficulty indicates that the observed item score is lower (harder item) than the expected item score based on the reported measure (anchor value).
Unanchored measures: If small displacements are shown, tighten the convergence criteria, CONVERGE=, LCONV=, RCONV=
Anchored measures: we expect half the displacements to be negative and half to be positive, and for them to be normally distributed according to the standard errors of the measures.
|
PERSON
ITEM
|
the name of the list of persons (data rows) or items (data columns) reported here
|
G
|
the grouping code assigned with ISGROUPS=. Table 3.2, 3.3, etc. show the details of rating scales, etc., for each grouping code.
|
M
|
model code assigned with MODELS=
|
SUBSET:
|
the person or items are in incomparable subsets, see Subsets.
|
