How to minimize measurement error in foundational learning assessments?

What is measurement error in student assessment?

Measurement error in student assessments refers to the difference between a student's true ability or knowledge and the score they receive on an assessment. Various factors can contribute to measurement errors in student assessments, including the test's design, administration, scoring, student factors , random errors (such as guessing or making careless errors) etc.

An example of measurement error - A student who has a good understanding of mathematics takes a standardized math test. However, on the day of the test, they are feeling unwell and have difficulty concentrating. As a result, the student makes several careless mistakes and scores lower than their actual ability. The difference between the student's true mathematical ability and the score they received on the test is a measurement error.

Measurement errors can affect the reliability and validity of student assessment results, making it difficult to draw accurate conclusions about student learning and achievement. It can also impact the fairness and equity of assessments, as students who are affected by measurement error may receive scores that do not accurately reflect their true abilities.

How to minimize measurement errors in foundational / early grade assessments?

• Develop high quality assessment tools - Ensure that assessment items are age-appropriate, clear, and well-designed. Test items should adequately represent the range of skills and knowledge being assessed, and they should be culturally relevant and unbiased. It is important to ensure that foundational literacy assessments are conducted in the language that the students are most familiar with and use regularly, typically in mother tongue or the primary language of instruction at their school.?
• Standardize the administration procedures - Consistent test administration procedures can help reduce measurement error due to variations in the testing environment. Provide clear instructions to both students and test administrators, maintain a quiet and comfortable testing environment, and ensure that all students have the necessary materials to complete the assessment.
• Train the enumerators/test administrators - Proper training for test administrators can help minimize errors in test administration and scoring. Training should include practice test administration and scoring exercises, discussions about potential challenges, and calibration activities to ensure consistent scoring among raters. High agreement between raters indicates low measurement error.?
• Pilot test and validate assessments - Before implementing an assessment on a large scale, conduct pilot tests to identify and address potential issues. Analyze pilot test data to ensure that the assessment is reliable and valid, and refine the assessment as needed based on the pilot test results
• Include multiple measures - Incorporating a variety of assessment methods, such as observations, performance tasks and others can help reduce the impact of measurement error on any single measure. Multiple measures provide a more comprehensive picture of a student's learning progress and can help mitigate the impact of potential errors in individual assessments.
• Monitor assessment data- Regularly analyze assessment data for potential patterns of measurement error, such as consistent difficulties with specific test items or discrepancies between different assessment methods. Use this information to refine the assessment and improve its accuracy.

How Item Response Theory (IRT) reports measurement error?

IRT can be used to estimate the measurement error for each individual's test score. In IRT, measurement error is reported in terms of the Standard Error of Measurement (SEM), which is derived from the test information function. The SEM represents the uncertainty or variability in an individual's estimated ability or skill level. A smaller SEM indicates a more precise estimate of the individual's true ability, while a larger SEM indicates a less precise estimate.

In IRT, the measurement error is not constant across all test takers or ability levels. The error varies depending on the individual's position on the latent trait (underlying construct , for example numerical ability) continuum, the test items' difficulty, and the discrimination parameters of the items. IRT models allow for the calculation of the SEM at each ability level, providing a more accurate representation of the measurement error for each individual test-taker.

By considering the measurement error reported by IRT models, test developers and researchers can make more informed decisions about test design, item selection, and test length to minimize measurement error and improve the accuracy and reliability of assessments.