For a general introduction, see Diagnosing Misfit
Responses to Items: |
Diagnosis of Pattern |
OUTFIT MnSq |
INFIT MnSq |
111¦0110110100¦000 |
Modelled/Ideal |
1.0 |
1.1 |
111¦1111100000¦000 |
Guttman/Deterministic |
0.3 |
0.5 |
000¦0000011111¦111 |
Miscode |
12.6 |
4.3 |
011¦1111110000¦000 |
Carelessness/Sleeping |
3.8 |
1.0 |
111¦1111000000¦001 |
Lucky Guessing |
3.8 |
1.0 |
101¦0101010101¦010 |
Response set/Miskey |
4.0 |
2.3 |
111¦1000011110¦000 |
Special knowledge |
0.9 |
1.3 |
111¦1010110010¦000 |
Imputed outliers * |
0.6 |
1.0 |
111¦0101010101¦000 |
Low discrimination |
1.5 |
1.6 |
111¦1110101000¦000 |
High discrimination |
0.5 |
0.7 |
111¦1111010000¦000 |
Very high discrimination |
0.3 |
0.5 |
Right¦Transition¦Wrong |
|
|
|
high - low - high |
OUTFIT sensitive to outlying observations |
>>1.0 unexpected outliers |
>>1.0 disturbed pattern |
low - high - low |
INFIT sensitive to pattern of inlying observations |
<<1.0 overly predictable outliers |
<<1.0 Guttman pattern |
* as when a tailored test (such as a Binet intelligence test) is scored by imputing all "right" responses to unadministered easier items and all "wrong" responses to unadministered harder items. The imputed responses are indicated by italics |
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The exact details of these computations have been lost, but the items appear to be uniformly distributed about 0.4 logits apart, extracted from Linacre, Wright (1994) Rasch Measurement Transactions 8:2 p. 360
The ZSTD Z-score standardized Student's t-statistic report, as unit normal deviates, how likely it is to observe the reported mean-square values, when the data fit the model. The term 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 value has been adjusted to a unit normal value.