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Screening screeners: calculating classification indices using correlations and cut-points.

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Summary
This summary is machine-generated.

Accurate reading problem screeners require a high correlation (over .9) between the screener and outcome measure. Current screeners often fall short, necessitating improved reliability for effective universal screening.

Keywords:
ClassificationIdentificationScreening

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Area of Science:

  • Educational Psychology
  • Psychometrics
  • Developmental Psychology

Background:

  • Universal screening for reading problems is increasing.
  • Accurate identification of at-risk children is crucial.
  • Existing screening tools' classification accuracy varies across samples.

Purpose of the Study:

  • To develop a method for calculating comprehensive classification information for any screener cut-point.
  • To provide an example using empirical data to validate estimation procedures.
  • To inform the development and selection of effective reading screeners.

Main Methods:

  • Utilized a bivariate normal distribution to estimate classification metrics.
  • Calculated metrics based on screener-outcome correlation, outcome base rate, and screener cut-point.
  • Developed an online tool for calculating classification information.

Main Results:

  • A correlation greater than .9 between screener and outcome is needed for good classification accuracy.
  • Current screeners generally do not meet this high correlation threshold.
  • Base rate significantly impacts positive predictive power.

Conclusions:

  • Current reading screeners may not be sufficiently accurate for universal screening.
  • Improving screener reliability or using multiple measures is essential.
  • Gated screening strategies can be beneficial in low base rate populations.