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Related Concept Videos

Expected Frequencies in Goodness-of-Fit Tests01:19

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Comparability of item quality indices from sparse data matrices with random and non-random missing data patterns.

Edward W Wolfe1, Michael T McGill

  • 1Pearson, Mailstop 125, 2510 N. Dodge St., Iowa City, IA 52245-9945, USA. ed.wolfe@pearson.com

Journal of Applied Measurement
|February 24, 2012
PubMed
Summary
This summary is machine-generated.

This study evaluated item quality indicators for computerized adaptive tests with missing data. Weighted fit indices and point-measure correlation showed consistent performance across missing data patterns, especially near the passing score.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Data Analysis

Background:

  • Computerized adaptive testing (CAT) is widely used for certification and licensure exams.
  • Missing data is a common issue in operational testing environments.
  • Item quality indicators are crucial for maintaining test integrity.

Purpose of the Study:

  • To evaluate the performance of five item quality indicators under various missing data conditions.
  • To compare the consistency of these indicators across random and conditional missing data patterns.
  • To assess indicator performance relative to item difficulty.

Main Methods:

  • Simulation study design.
  • Inclusion of five item quality indicators: weighted/unweighted mean square fit, weighted/unweighted standardized mean square fit, and point-measure correlation.
  • Manipulation of missing data: high/low amounts, random/conditional patterns.

Main Results:

  • Weighted fit indices, especially standardized mean square, and point-measure correlation demonstrated consistent performance across missing data types.
  • These indices performed more reliably for items near the passing score compared to those with extreme difficulty.
  • Unweighted indices showed less consistent performance.

Conclusions:

  • Weighted fit indices and point-measure correlation are recommended for item quality assessment in CAT with missing data.
  • Careful consideration of item difficulty is advised when interpreting fit indices.
  • These findings aid in improving the quality and fairness of high-stakes examinations.