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Interval likelihood ratios: Applications for gated screening in schools.

David A Klingbeil1, Ethan R Van Norman2, Peter M Nelson3

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Using interval likelihood ratios for academic screening may reduce the number of students needing further evaluation. This method offers a more nuanced approach to identifying at-risk students in math education.

Keywords:
GatedInterval likelihood ratiosMathMiddle schoolScreeningUniversal

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Academic Screening

Background:

  • Traditional universal screening in education dichotomizes scores, losing risk magnitude information.
  • Medical research uses ordinal categories and interval likelihood ratios for screening evaluation.
  • Interval likelihood ratios have not been applied to academic screening research.

Purpose of the Study:

  • To evaluate differences in screening accuracy and efficiency using dichotomous versus interval likelihood ratios.
  • To apply interval likelihood ratios within a gated screening model for middle school math.
  • To compare the interpretation of screening performance using different methods.

Main Methods:

  • Reanalyzed data from a middle school math screening study.
  • Employed a gated screening model with a prior year achievement test as the first gate.
  • Used permutations of curriculum-based measures and standardized tests to predict future math proficiency.
  • Compared dichotomized score interpretation with ordinal category interpretation using interval likelihood ratios.

Main Results:

  • Interpreting screening performance as ordinal yielded wider likelihood ratio ranges than dichotomization.
  • The number of tests needed to classify students ranged from two to three.
  • Diagnostic accuracy was similar between interval likelihood ratios and dichotomized results.
  • Interval likelihood ratios may reduce the number of students requiring additional screening.

Conclusions:

  • Interval likelihood ratios offer a potentially more efficient method for academic screening.
  • This approach may improve the classification of at-risk students without sacrificing diagnostic accuracy.
  • Further research and replication are needed to confirm these findings in educational settings.