Estimation of Ability Using Globally Optimal Scoring Weights

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Estimation of Ability Using Globally Optimal Scoring Weights

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Estimation of Ability Using Globally Optimal Scoring Weights

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Shin-ichi Mayekawa

Graduate School of Decision Science and Technology

Tokyo Institute of Technology

- Review of existing methods
- Globally Optimal Weight: a set of weights that maximizes the Expected Test Information
- Intrinsic Category Weights
- Examples
- Conclusions

- Estimation of IRT ability q on the basis of simple and weighted summed score X.
- Conditional distribution of X given qas the distribution of the weighted sum of the Scored Multinomial Distribution.
- Posterior Distribution of q given X.
h(q|x) @ f(x|q) h(q )

- Posterior Mean(EAP) of q given X.
- Posterior Standard Deiation(PSD)

We must choose w to calculate X.

IRF

We must choose w and v to calculate X.

ICRF

- Binary items
- Conditional distribution of summed score X.
- Simple sum: Walsh(1955), Lord(1969)
- Weighted sum: Mayekawa(2003)

- Conditional distribution of summed score X.
- Polytomous items
- Conditional distribution of summed score X.
- Simple sum: Hanson(1994), Thissen et.al.(1995)
- With Item weight and Category weight: Mayekawa & Arai(2007)

- Conditional distribution of summed score X.

- Eight Graded Response Model items 3 categories for each item.

- Example: Mayekawa and Arai (2008)
- small posterior variance good weight.
- Large Test Information (TI) good weight

- Test Information Function is proportional to the slope of the conditional expectation of X given q, (TCC), and inversely proportional the squared width of the confidence interval (CI) of q given X.
- Width of CI
- Inversely proportional to the conditionalstandard deviation of X given q.

ICRF

- Category weighted Item Scoreand the Item Response Function

- Test Information

- First Derivative

- A set of weights that maximizethe Expected Test Informationwith some reference distribution of q .
It does NOT depend on q .

NABCT A B1 B2 GO GOINT A AINT

Q1 1.0 -2.0 -1.0 7.144 7 8.333 8

Q2 1.0 -1.0 0.0 7.102 7 8.333 8

Q3 1.0 0.0 1.0 7.166 7 8.333 8

Q4 1.0 1.0 2.0 7.316 7 8.333 8

Q5 2.0 -2.0 -1.0 17.720 18 16.667 17

Q6 2.0 -1.0 0.0 17.619 18 16.667 17

Q7 2.0 0.0 1.0 17.773 18 16.667 17

Q8 2.0 1.0 2.0 18.160 18 16.667 17

LOx LO GO GOINT A AINT CONST

7.4743 7.2993 7.2928 7.2905 7.2210 7.2564 5.9795

- Absorb the item weight in category weights.

- Test Information
- Linear transformation of the categoryweights does NOT affect the information.

- First Derivative

- Locally Optimal Weight

- Weights that maximizethe Expected Test Informationwith some reference distribution of q .

- A set of weights which maximizes:
- Since the category weights can belinearly transformed, we set v0=0, ….. vmax=maximum item score.

- h(q)=N(-0.5, 1): v0=0, v1=*, v2=2

- h(q)=N(0.5, 1): v0=0, v1=*, v2=2

- h(q)=N(1, 1 ): v0=0, v1=*, v2=2

- It does NOT depend on q, butdepends on the reference distributionof q: h(q) as follows.
- For the 3 category GRM, we found that
- For those items with high discriminationparameter, the intrinsic weights tendto become equally spaced: v0=0, v1=1, v2=2
- The Globally Optimal Weight isnot identical to the Intrinsic Weights.

- For the 3 category GRM, we found that
- The mid-category weight v1 increases according to the location of the peak ofICRF. That is:
The more easy the category is,

the higher the weight .

- v1 is affected by the relative location ofother two category ICRFs.

- The mid-category weight v1 increases according to the location of the peak ofICRF. That is:

- For the 3 category GRM, we found that
- The mid-category weight v1 decreases according to the location of the reference distribution of q: h(q).
- If the location of h(q) is high, the mostdifficult category gets relatively high weight,and vice versa.
- When the peak of the 2nd categorymatches the mean of h(q), we haveeqaully spaced category weights:
v0=0, v1=1, v2=2

LOx LO GO GOINT CONST

30.5320 30.1109 30.0948 29.5385 24.8868

(1/0.18)^2 = 30.864

- Polytomous item has the Intrinsic Weight.
- By maximizing the Expected Test Information with respect to either Item or Category weights, we can calculate the Globally Optimal Weights which do not depend on q.
- Use of the Globally Optimal Weights when evaluating the EAP of q given X reduces the posterior variance.

ご静聴ありがとうございました。Thank you.