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Related Concept Videos

Ratio Level of Measurement00:54

Ratio Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated. For...
Introduction to z Scores01:06

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
Introduction to z Scores01:05

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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Bioequivalence: Overview01:16

Bioequivalence: Overview

Pharmaceutical equivalents, by definition, are drug products with the same active ingredient in the same quantities, encapsulated in identical dosage forms, and intended for the same administration routes. These pharmaceutical equivalents are deemed bioequivalent if the bioavailability of the active entity in the drug preparations is similar. Moreover, pharmaceutical equivalents demonstrating bioequivalence are also regarded as therapeutically equivalent. This means that when used as directed,...
Bioequivalence Data: Statistical Interpretation01:16

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The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
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Related Experiment Video

Updated: May 7, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Observed-score equating: an overview.

Alina A von Davier1

  • 1Educational Testing Service, Princeton, NJ, USA, avondavier@ets.org.

Psychometrika
|October 5, 2013
PubMed
Summary

This paper provides an overview of observed-score equating (OSE) within a unifying framework, discussing key issues and challenges in test equating and evaluation using real licensure test data.

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Observed-score equating (OSE) is crucial for fair test score interpretation.
  • Existing OSE methods can be unified under a comprehensive statistical framework.
  • Understanding OSE is vital for accurate large-scale assessment.

Purpose of the Study:

  • To present a unifying framework for all observed-score equating approaches.
  • To discuss critical issues in test equating, including common items and sampling designs.
  • To illustrate the equating process with a practical example.

Main Methods:

  • A unifying framework for observed-score equating.
  • Discussion of test characteristics, common items, and sampling designs.

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  • Step-by-step illustration using licensure test data.

    • Main Results:

    • The unifying framework encompasses all OSE methodologies.
    • Key challenges and assumptions in equating are identified.
    • Practical application demonstrates the equating process effectively.

    Conclusions:

    • A unified perspective simplifies the understanding and application of OSE.
    • Addressing issues like common items and sampling designs is essential for valid equating.
    • The presented framework and example aid in robust test equating and evaluation.