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Precision and Sample Size Requirements for Regression-Based Norming Methods for Change Scores.

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|April 28, 2020
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Summary

Interpreting change scores requires norming methods like regression-based change and T Scores for Change. This study compares their precision and determines minimum sample sizes for accurate score interpretation.

Keywords:
T Scores for Change methodchange assessmentnormingregression-based change approachregression-based norming

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Area of Science:

  • Psychometrics
  • Statistical analysis
  • Educational measurement

Background:

  • Interpreting individual change scores necessitates transforming them into comparable metrics, known as norms.
  • Norming involves establishing a reference distribution for change scores.
  • Common methods include regression-based change and T Scores for Change.

Purpose of the Study:

  • To compare the precision of regression-based change and T Scores for Change norming methods.
  • To identify the minimum sample size requirements for each method to achieve satisfactory precision.
  • To elucidate the similarities and differences between these two norming approaches.

Main Methods:

  • A simulation study was employed to systematically evaluate the precision of both norming methods.
  • The study systematically varied sample sizes to determine minimum requirements.
  • Statistical precision was the primary outcome measure.

Main Results:

  • The simulation results provide empirical evidence on the precision of each norming method under varying conditions.
  • Minimum sample size requirements for achieving reliable norming were established.
  • Key differences and similarities in the performance of the two methods were highlighted.

Conclusions:

  • The findings offer guidance on selecting appropriate norming methods for change scores based on sample size and desired precision.
  • Recommendations are provided for researchers and practitioners regarding sample size considerations in norming studies.
  • This research contributes to the robust interpretation of individual differences in change over time.