Kappa

The Kappa statistic, also known as Cohen's Kappa, measures the degree of agreement between two or more raters on nominal or ordinal data. They are often used in quality control, medical diagnosis, and other fields where subjective assessments are made. Kappa helps assess if raters are consistent with themselves (within-rater agreement) or with each other (inter-rater agreement).

To put the way Kappa is calculated into perspective, consider a situation where you take a multiple-choice test you didn’t study for. Even though you were unprepared and may be picking answers at random, it’s likely that you will get some answers on the test correct by chance. The kappa statistic takes this element of chance into account. During inspections, appraisers won’t necessarily agree 100%—but they will agree to some extent by chance.

The formula for calculating kappa is:

Where Po is the actual probability of agreement among observers, and Pe is the expected probability of chance agreement among observers.

The higher the Kappa’s calculated value, the stronger the agreement. The Kappa value varies between 0 and 1, where:

• 0= Agreement is equivalent to chance
• 0.10-0.20= Agreement is slightly above chance
• 0.21-0.40= Fair agreement
• 0.41-0.60= Moderate agreement
• 0.61-0.80= Substantial agreement
• 0.81-0.99= Near-perfect agreement
• 1= Perfect agreement

Negative values may occur when the agreement is less than expected by chance, however, this rarely happens.

Kappa statistics and studies are helpful for businesses in quality assurance, especially when evaluating the agreement between multiple raters or appraisers. They can help prevent defects and improve customer satisfaction by ensuring consistent evaluations of product quality.

Kappa statistics and studies are beneficial across various industries and can be applied to different types of assessments beyond visual inspection. Kappa is used in used in multiple fields, including:

• Manufacturing: Kappa statistics help ensure that inspectors are consistently identifying defects, leading to improved quality control.
• Healthcare: They can be used to assess the reliability of diagnostic tests or procedures, ensuring that different doctors or nurses arrive at the same conclusions.
• Psychology and Social Sciences: Kappa statistics can help determine the level of agreement between researchers or clinicians when classifying or rating individuals or behaviors.
• Machine Learning: They are useful for comparing the performance of an AI model to human experts, ensuring that the model is accurate and reliable.
• Research: Kappa can be used to assess the reliability of data collection methods, such as questionnaires or interviews, ensuring that the data collected is consistent and accurate.