Confidence intervals for the kappa parameter, with application to the semiconductor industry

• Published in: Communications in statistics. Simulation and computation Vol. 29; no. 2; pp. 647 - 665
• Main Authors: Wilkinson, Rachelle C., Bruce Schaalje, G., Collings, Bruce Jay
• Format: Journal Article
• Language: English
• Published: Colchester Marcel Dekker, Inc 01.01.2000; Taylor & Francis
• Subjects: Applications; bias-corrected bootstrap; bootstrap; Exact sciences and technology; kappa statistic; Mathematics; measures of agreement for categorical data; Nonparametric inference; Parametric inference; Probability and statistics; Reliability, life testing, quality control; Sciences and techniques of general use; square root transformation; Statistics; Bootstrapping; Agreement; Categorical data; Square root; Bias; Confidence interval; Semiconductor industry
• ISSN: 0361-0918; 1532-4141
• DOI: 10.1080/03610910008813633

In the semiconductor industry, test tapes are used to classify microprocessors into one of several functional categories before packaging. Test tapes must be changed frequently, and so it is necessary make decisions about whether the new tape performs similarly enough to the old tape. The kappa statistic a chance-corrected measure of agreement for categorical data, would be useful for this purpose if a reliable confidence interval procedure were available. Unfortunately, confidence intervals for κ have not been investigated for applications in which the true value is expected to be close to 1, more than two categories are involved, and marginal distributions are not uniform. Simulation was used to study properties of 5 confidence interval procedures for κ under these conditions. Confidence intervals were compared based on average confidence interval length and coverage rate. Although no confidence interval method consistently performed better than the rest, the bias-corrected bootstrap method generally performed well. A square root transformation method also perormed well, and is less computationally-intensive than the bootstrap method.