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Item Pool Information Curve
The item with the highest Fisher Information for the computed ability estimate is administered next.
The item with the closest difficulty (𝛽) parameter to the computed ability estimate is administered next.
A random item is selected from the pool of available items to administered next.
An extension of PFI method that adds a random component whose contribution is more prominent at the beginning of administration and diminishes over administration.
The a-stratified multistage algorithm divides the item pool into K strata based on sorted values of a, where a fixed number of items is administered per stratum starting with the lowest discriminating strata.
A refinement of the a-stratified algorithm where items are divided into blocks based on the b parameter.
A variation of the a-stratified algorithm where items are divided into blocks based on the content codes within the item pool.
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Summary of Item Exposure and Utilization
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Summary of Stop Criteria
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The figure above is the plot of the average final ability estimate across all simulated θ values. For any given simulated θ, the final ability estimates determined for all simulations at that θ is averaged.
The figure above is the plot of the average simulated θ across all determined final ability estimates. For any given final ability estimate, the simulated θ at each reporting of that estimate is averaged.
The figure above is the plot of the average standard error across all determined final ability estimates. For any given final ability estimate, the standard error at each reporting of that estimate is averaged.
The figure above is the plot of the average test information across all determined final ability estimates. For any given final ability estimate, the test information at each reporting of that estimate is averaged.
The figure above is the plot of the average number of items administered across all determined final ability estimates. For any given final ability estimate, the number of items administered at each reporting of that estimate is averaged.
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The figure above compares the desired target weight of administration for a given content code to the average observed percentage of item administration for the same content code across all simulations.
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