1.Outfit/Infit MNSQ

2.Summary Table

3.Overall Fit Stat.

4.ANOVA

5.Wright Map

6.Wright Map(Groups)

7.KIDMAP

8.ICC_cat

9.Person fit plot(Outfit)

10.Person fit plot(Infit)

11.Measure/Outfit

12.Measure/Infit

13.Rawscore/Measure

14.Simulation data

15.DIF:Table

16.DIF:Graph

17.Copy & Paste

Category number=2(i.e., 0 and 1): maximum=1, and minimum =0. The type of model is based on RSM(rating scale model).

Model residuals are ended at the deviation between previous and after residual smaller than a criterion of 0.05. The step threshold =1(i.e. categories of 0 and 1). The step difficulty=0.

By dividing strata into five strata by logit scores (e.g., >2,0.5,-0.5,2), there are one stratum only in this dataset with a summation score of 88. The model data fit cannot be computed due to df=0(see the reference at https://jsdajournal.springeropen.com/articles/10.1186/s40488-020-00108-7 with Equation 4). The counts in the stratum of C_1 are 175, which is computed by df*k, where k is the item length mentioned at the link above.

On the bottom, we see the model data for item# 1 which is assigned at the previous conformation step. The model data fit is obtained via the method shown in the table.

Notes that the strata are computed and assigned the labels as programs below:

kidat=round((6-person(jk))*2,0) ‘ person(jk)=person measure

if kidat<0 then kidat=0

if kidat>25 then kidat=25

ranking=Fix(kidat/5)+1

alpheta="ABCDEFGJIJK"

remainer=kidat mod 5

rk=mid("ABCDEFGJIJK",ranking,1) ' & right("0" & (remainer+1),2)

personrk(jk)=rk ‘assigning the label the person

if instr(rktext, rk)=0 then

rktext=rktext & rk

end if cutting(ranking)=cutting(ranking) +round(person(jk),2)

‘ used to compute the mean measures in each stratum, which is used in drawing the ICC later.

Person estimations are listed above by series of number, group label, groups by measures in logit(i.e., C03 is around at Zero logit. Theta is person measure(e.g., all at zero logit). Model SE(standard error=sqrt(1/variance for person). Infit and Outfit MNSQs are followed. * indicates significantly higher than 2.0.

Chisqure is the results of summation of squared Zscore(=[(obs.-exp)/SD]^2) based on chiSQ test.

check the probability at https://www.socscistatistics.com/pvalues/fdistribution.aspx

https://www.real-statistics.com/correlation/kendalls-tau-correlation/

A03 means the ability with measures at the top, followed by B, C, and D grades.

That is, four strata from A to D are divided in this dataset.

(It is worth learning how to compute those values in the Table of person and item parameters. Any further information is to read relevant references in Rasch analysis or refers to Winsteps Manual for readers.

Four strata are in existence. The model data fit is expressed by chSQ=3.58 with df=30 and prob.=1.0, indicating the data fit the Rasch model fairly well. Note that if the fit statistics with Infit MNSQ are shown later in this manual, we can see that all items’ Infit MNSQs are within the criteria between 0.5 and 1.5.

For person 2, we can see that item 8 in red bubble is unexpected(Zscore <-2.0) due to the easy item with incorrectness at the left-bottom side.

For person 6, we can see that item 7 in red is harder but correct in the right-top side(Zscore>2.0)

Persons with higher ability are on the right-hand side. The bubble denoted by the vertical probability corresponds to the person ability.

Moving to the left on Google Maps, the ICC of item 1 is shown above. The red bubbles denote the observed responses by persons and the blue ones represent the expected responses by persons. The ChSQ statistics shown below, similar to the overall fit selected in the menu at the beginning.

Persons with higher ability are on the right-hand side. The bubble denoted by the vertical probability corresponds to the person ability.

To add lines linked to observed bubbles in ICC