Interrater Reliability: Why It Matters

Interrater reliability is a cornerstone of high-quality assessment. When multiple educators or evaluators score the same student using a rubric or tool, interrater reliability reflects how consistently they apply the criteria. High interrater reliability means that different evaluators are interpreting the rubric in a similar way and producing dependable results (McHugh, 2012).

In Career and Technical Education and Work Based Learning environments, consistency is especially important because students’ employability skills, professionalism, and workplace readiness are often assessed by multiple staff members. Reliable scoring ensures fairness for students and credibility for programs.

Interrater reliability refers to the degree of agreement or consistency between two or more evaluators assessing the same performance using the same criteria (Tinsley & Weiss, 2000).

When evaluators disagree significantly, scores become subjective, and the validity of the assessment is weakened. When evaluators demonstrate strong agreement, the assessment results are more accurate, defensible, and equitable.

1. Ensures Fairness and Equity for Students

Consistent scoring prevents bias and guarantees that all students are evaluated using the same standards (Moskal & Leydens, 2000).

A rubric only works if everyone uses it the same way. Interrater reliability confirms that rubric criteria are clear and measurable.

Reliable data allows educators to identify authentic skill strengths and areas for growth across students, pathways, and programs.

When scoring is inconsistent, districts or organizations can face challenges related to grading fairness, special education compliance, or program accountability.

Calibrate Rubric Expectations

Bring evaluators together to review each criterion, define performance levels, and discuss examples. Calibration is one of the most effective strategies for improving rater agreement (Jonsson & Svingby, 2007).

Providing sample “benchmark” performances at each rubric level helps evaluators develop shared mental models (Brookhart, 2013).

Have all evaluators score the same student work independently. Then compare results, discuss discrepancies, and clarify expectations.

Interrater reliability is not a one-time task. Regular refreshers help maintain consistency over time, particularly when new staff join the team.

Rubrics that use objective, observable descriptors tend to result in higher reliability than those using vague or subjective language (Arter & McTighe, 2001).

PES was built to help schools collect consistent, meaningful evaluation data across classrooms and work sites.

Our platform:

• reduces subjective scoring through standardized descriptors
• provides clean, uniform evaluation tools for all raters
• supports calibration conversations with stored, comparable data
• ensures scores remain consistent across evaluators and programs

When teachers, job coaches, and employers evaluate students with shared expectations, student soft skill development becomes more accurate and actionable.

 Arter, J., & McTighe, J. (2001). Scoring rubrics in the classroom: Using performance criteria for assessing and improving student performance. Corwin Press.

Brookhart, S. M. (2013). How to create and use rubrics for formative assessment and grading. ASCD.

Hallgren, K. A. (2012). Computing interrater reliability for observational data: An overview and tutorial. Tutorials in Quantitative Methods for Psychology, 8(1), 23–34. https://doi.org/10.20982/tqmp.08.1.p023

Jonsson, A., & Svingby, G. (2007). The use of scoring rubrics: Reliability, validity, and educational consequences. Educational Research Review, 2(2), 130–144.

McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282.

Moskal, B. M., & Leydens, J. A. (2000). Scoring rubric development: Validity and reliability. Practical Assessment, Research, and Evaluation, 7(10).

Tinsley, H. E., & Weiss, D. J. (2000). Interrater reliability and agreement. In H. E. A. Tinsley & S. D. Brown (Eds.), Handbook of applied multivariate statistics and mathematical modeling (pp. 95–124). Academic Press.