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Related Experiment Videos

Using a Model of Analysts' Judgments to Augment an Item Calibration Process.

Carl Hauser1, Yeow Meng Thum1, Wei He1

  • 1Northwest Evaluation Association, Portland, OR, USA.

Educational and Psychological Measurement
|May 26, 2018
PubMed
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This study developed a model to automate item review processes, reducing the need for extensive human reviews (HR). The new decision rules significantly decrease the workload associated with calibrating field test items.

Area of Science:

  • Educational Measurement
  • Psychometrics
  • Data Analysis

Background:

  • Item review is crucial for assessing field test (FT) item fit to item response theory (IRT) models.
  • Current manual review processes are time-consuming and prone to human error, especially with large datasets.
  • Behavioral decision-making research suggests parametric models can outperform human judgment.

Purpose of the Study:

  • To develop a model that mimics analyst decision-making in FT item reviews.
  • To create a rule-based system for classifying item status, distinguishing between clear-cut decisions and those needing human review (HR).
  • To reduce the burden of manual item calibration and improve efficiency.

Main Methods:

  • Utilized behavioral decision-making principles to model analyst integration of statistical and graphical FT item data.
Keywords:
human decision makingitem calibrationitem fit statisticsitem reviewlogistic regression

Related Experiment Videos

  • Developed a set of decision rules based on this model to predict item status.
  • Evaluated the model's performance in classifying items requiring HR.

    • Main Results:

    • The developed model successfully mimics analyst integration of FT item statistics and performance plots.
    • The proposed decision rules achieve a desired level of classification accuracy.
    • Implementation of these rules led to an estimated 65% reduction in calibrations requiring human reviews (HR).

    Conclusions:

    • A data-driven, model-based approach can effectively automate aspects of the FT item review process.
    • This methodology significantly enhances efficiency by reducing the need for extensive human reviews (HR).
    • The findings support the use of predictive models in psychometric analysis for improved workflow and accuracy.