Table 20.1 Complete score-to-measure table on test of all items
Table 20.2 Person score and measure distribution
Table 20.3 Complete score-to-calibration table for tests based on whole sample
TABLE OF SAMPLE NORMS (500/100) AND FREQUENCIES CORRESPONDING TO COMPLETE TEST
+---------------------------------------------------------------------------+
| SCORE MEASURE S.E.|NORMED S.E. FREQUENCY % CUM.FREQ. % PERCENTILE|
|------------------------+--------------------------------------------------|
| 0 -6.17E 1.83| 147 107 0 .0 0 .0 0 |
| 1 -4.86 1.08| 225 63 0 .0 0 .0 0 |
| 2 -3.94 .85| 278 50 1 2.9 1 2.9 1 |
| 3 -3.27 .79| 318 46 2 5.9 3 8.8 6 |
| 4 -2.64 .78| 355 46 2 5.9 5 14.7 12 |
| 5 -1.97 .83| 394 49 2 5.9 7 20.6 18 |
| 6 -1.19 .92| 440 54 3 8.8 10 29.4 25 |
| 7 -.23 1.00| 496 59 12 35.3 22 64.7 47 |
| 8 .80 .97| 557 57 5 14.7 27 79.4 72 |
| 9 1.72 .92| 610 54 4 11.8 31 91.2 85 |
| 10 2.55 .89| 660 52 1 2.9 32 94.1 93 |
| 11 3.37 .89| 707 52 2 5.9 34 100.0 97 |
| 12 4.21 .93| 756 54 0 .0 34 100.0 100 |
| 13 5.23 1.12| 817 66 0 .0 34 100.0 100 |
| 14 6.60E 1.84| 897 108 0 .0 34 100.0 100 |
+---------------------------------------------------------------------------+
THE NORMED SCALE IS EQUIVALENT TO UIMEAN= 516.7919 USCALE= 45.0404
The columns in the Table of Sample Norms and Frequencies are:
Measures on the Complete Test:
|
SCORE
|
raw score on a complete test containing all calibrated items: TOTALSCORE=Yes includes extreme items. TOTALSCORE=No excludes extreme items (if any)
|
MEASURE
|
measure corresponding to score
|
|
If a person did not take all items or items are weighted, then that person is stratified with the measure on the complete test nearest the person's estimated measure (as reported in Table 18), regardless of that person's observed score.
|
S.E.
|
standard error of the measure (model). The statistical Test Information is (USCALE/S.E.)²
|
Statistics for this sample:
|
NORMED
|
measures linearly locally-rescaled so that the mean person measure for this sample is 500 and the population standard deviation is 100. Equivalent to UPMEAN=500, USCALE=100/(Person P.SD)
|
S.E.
|
standard error of the normed measure (for a score based on all the items)
|
FREQUENCY
|
count of sample with measures at or near (for missing data) the complete test measure
|
%
|
percentage of sample included in FREQUENCY
|
CUM.FREQ.
|
count of sample with measures near or below the test measure, the cumulative frequency.
|
%
|
percentage of sample include in CUM. FREQ.
|
PERCENTILE
|
The percent of the sample below the current measure.There are several definitions of percentile. Winsteps uses this one: Percentile Rank: The percentile is the cumulative frequency percent for the score below + half the frequency percent for the current score, half-rounded, and constrained to the range 1-99 for non-zero frequencies: . Percentiles for other definitions can be computed from the CUM. FREQ column. See, for instance, how-to-calculate-percentile
If the data are complete, then the raw data and the Rasch measures produce the same percentiles. If the data are incomplete, then the Rasch measures are used because they are robust against missing data.
|
NORMED SCALE
|
This shows the UIMEAN= and USCALE= values in order to report the person MEASURES at their NORMED (500/100) values.
|
Logit measures support direct probabilistic inferences about relative performances between persons and absolute performances relative to items. Normed measures support descriptions about the location of subjects within a sample (and maybe a population). Report the measures which are most relevant to your audience.
To paste this Table into Excel, set BOXSHOW=No to remove the vertical | and then copy-and-paste this table into Excel. Use Excel's "data", "text to columns" feature to put the scores and measures into columns.
Example: I want to stratify my sample into low, medium, high ability groups.
The separation index is based on the statistical fiction that your data accord exactly with a normal distribution and that the average measurement error (RMSE) precisely summarizes the precision of your data. In practice, these assumptions are only met approximately.
If your data are "complete" (everyone responds to every item), then a convenient places to start is Table 20.2. Starting at the lowest score, look down the scores until you find the score that best characterizes (as a first guess) your "low group". Then mentally multiply its S.E. by 3, and add it to the measure for the low group. This will take you to the measure for the middle group, which will be approximately statistically significantly different (p≤.05) from the low group. Do the same again for the middle group, and it will take you to the high group. Same again may take you to an even higher group, or up into the outliers at the top of the test. The cut-points will be half-way between the group centers that you have identified.
Do the same process from the top score downwards for another version of the stratification.
Then synthesize the two stratifications by adjusting the group by moving their center scores further apart (not closer together).
