Bayesian regression techniques for determining true measurements

• US 10,198,961 B2
• Filed: 06/20/2013
• Issued: 02/05/2019
• Est. Priority Date: 08/25/2006
• Status: Active Grant

• Current Assignee: Ronald Weitzman
• Original Assignee: Ronald Weitzman
• Inventors: Weitzman, Ronald
• Primary Examiner(s): Fletcher, Jerry-Daryl
• Application Number:
  US13/923,217
• Publication Number:
  US 20140038155A1
• Time in Patent Office: 2,056 Days
• Field of Search: 434353
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26

Abstract

Techniques for estimating a true measurement from a Bayesian regression on an observed measurement of received responses.

Claims

1. A non-transitory computer-readable medium having instructions stored thereon that, responsive to execution by an electronic system, cause said electronic system to perform operations to estimate difficulty or ability, said operations comprising:

receiving a correct or incorrect response for each item and each test-taker and a total score for each test-taker on a multi-item test taken by a plurality of test-takers,

wherein a correct response is identified by the numeral 1 and an incorrect response by the numeral 0;

estimating a probability of a correct response to each item by each test-taker from a Bayesian regression on the response of the test-taker to the item taking the form of a weighted average of the item response of the test-taker and an average response to the item over the plurality of test-takers, the respective weights being the square of a correlation between the item response and the total score of the test-takers over the plurality of test-takers and one minus the squared correlation;

determining a logit of the Bayesian-estimated probability of the correct response for each item and each test-taker;

estimating a difficulty of each item as minus an average of the logits over the plurality of test-takers;

determining an average of the item difficulties over the plurality of items;

estimating an ability of each test-taker as a sum of the average item difficulty and an average of the logits over the plurality of items; and

iteratively performing the actions of estimating the probability of the correct response, determining the logit, estimating the item difficulty, determining the average item difficulty, and estimating the ability, wherein the most recently estimated ability replaces the total test score on each iteration,

wherein the actions of estimating are iteratively performed until a change in each test-taker'"'"'s ability estimate is less than a predetermined amount,

wherein a reduced test length is required to achieve a given non-zero internal validity in comparison to estimating an ability of each test-taker by equally weighting all item responses.

2. The non-transitory computer-readable medium according to claim 1, further comprising:

estimating a new difficulty for each item using a Rasch model; and

substituting an average of the new Rasch difficulties for the average item difficulty to obtain a new ability estimate for each test-taker.

3. A non-transitory computer-readable medium having instructions stored thereon that, responsive to execution by an electronic system, cause said electronic system to perform operations comprising:

receiving a correct or incorrect response for each item and each test-taker and a total score for each test-taker on a multi-item test taken by a plurality of test-takers, wherein a correct response is identified by the numeral 1 and an incorrect response by the numeral 0;

estimating a probability of a correct response to each item by each test-taker from a Bayesian regression on the response of the test-taker to the item taking the form of a weighted average of the item response of the test-taker and an average response to the item over the plurality of test-takers, the respective weights being the square of a correlation between the item response and the total score of the test-taker over the plurality of test-takers and one minus the squared correlation;

determining a logit of the Bayesian-estimated probability of the correct response for each item and each test-taker;

estimating a difficulty of each item as minus an average of the logits over the plurality of test-takers;

determining an average of the item difficulties over the plurality of items;

estimating an ability of each test-taker as a sum of the average item difficulty and an average of the logits over the plurality of items; and

iteratively performing the actions of estimating the probability of the correct response, determining the logit, estimating the item difficulty, determining the average item difficulty, and estimating the ability, wherein the most recently estimated ability replaces the total test score on each iteration,

wherein said estimating said ability of each test-taker has greater internal validity for a given test length than estimating an ability of each test-taker by equally weighting all item responses.