Conceptualizing rating scales and partial-credit response structures for communication can be challenging. Rasch measurement provides several approaches. Choose the one that is most meaningful for you.

 

Look at this excerpt of Table 3.2:

 

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|      DATA            |  QUALITY CONTROL  |   STEP      |  EXPECTATION  |.5 Cumul.|

| Category Counts  Cum.| Avge  Exp.  OUTFIT|CALIBRATIONS |  Measure at   |Probabil.|

|Score   Used   %    % | Meas  Meas   MnSq |Measure  S.E.|Category  -0.5 |    at   |

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|  0      891   6%   6%|  -.04   -.07  1.3 |             |( -1.12)       |   low   |

|  1      383   3%   9%|   .47    .51  1.3 |  1.07    .04|   -.31    -.74|   -.25  |

|  2     1017   7%  15%|  1.07   1.17   .8 | -.15    .04|    .30   -.01| -.02 |

|  3    12683  85% 100%|  2.15   2.14  1.0 |  -.91    .03|(  1.13)    .73|    .25  |

------------------------------------------------------------(Mean)---------(Median)-

 

Here at three ways of conceptualizing and communicating the transition, threshold, boundary between category 1 and category 2:

 

(1) Rasch-half-point thresholds. Someone at the boundary between "1" and "2" would have an expected rating of 1.5, or 1000 persons at the boundary between "1" and "2" would have an average rating of 1.5. This boundary is the "EXPECTATION Measure at 2 -0.5" which is -.01 logits, the Rasch-half-point threshold. To illustrate this, use the model item characteristic curve. The expected score ogive / model ICC (Table 21.2 - second on list in Graphs menu). The CAT+.25, CAT-0.5, AT CAT, and CAT-.25 columns in the ISFILE= plot points on this ogive. The expected score ogive relates most directly to the estimation of the Rasch parameters. Since it is only one line, it is also convenient for summarizing performance at any point on the latent variable by one number. Crucial points are the points on the variable corresponding to the lower category value + 0.5, i..e, more than the higher adjacent category value - 0.5. These Rasch-half-point thresholds are "average score thresholds" or "Rasch-ICC thresholds".

 

(2) Rasch-Thurstone thresholds. Someone at the boundary between "1" and "2" would have a 50% chance of being rated 1 or below, and a 50% chance of being rated 2 or above. This is the Rasch-Thurstone threshold of -.02 logits. To illustrate this, use the cumulative probability curves. The cumulative probability curves (Table 21.3 - and third on list in Graphs menu). The 50%PRB columns in the ISFILE= are the crucial points on these curves. and are the Rasch-Thurstone thresholds, useful for identifying whether a person is most likely to respond below, at or above a certain category.

 

(3) Rasch-Andrich thresholds. Someone at the boundary between "1" and "2" would have an equal chance of being rated 1 or 2. This is the Rasch-Step Calibration (Rasch-Andrich Threshold) of -.15 logits. To illustrate this, use the category probability curves. The probability curves (Table 21.1 - and top of list in Graphs menu). The Structure MEASURE in the ISFILE= gives the point of equal probability between adjacent categories. The points of highest probability of intermediate categories are given by the AT CAT values. These probability curves relate most directly to the Rasch parameter values, also called Rasch-Andrich thresholds. They are at the intersection of adjacent probability curves, and indicate when the probability of being observed in the higher category starts to exceed that of being observed in the adjacent lower one. This considers the categories two at a time, but can lead to misinference if there is Rasch-Andrich threshold disordering.

 

d) Empirical average measures. For any particular sample, there is the average ability of the people who scored in any particular category of any particular item. This is the "Average Measure" reported in Table 3.2. This is entirely sample-dependent. It is not reported in ISFILE=