title: Sample size determination for categorical responses.
abstract: Procedures are reviewed and recommendations made for the choice of the size of a sample to estimate the characteristics (sometimes known as parameters) of a population consisting of discrete items which may belong to one and only one of a number of categories with examples drawn from forensic science. Four sampling procedures are described for binary responses, where the number of possible categories is only two, e.g., licit or illicit pills. One is based on priors informed from historical data. The other three are sequential. The first of these is a sequential probability ratio test with a stopping rule derived by controlling the probabilities of type 1 and type 2 errors. The second is a sequential variation of a procedure based on the predictive distribution of the data yet to be inspected and the distribution of the data that have been inspected, with a stopping rule determined by a prespecified threshold on the probability of a wrong decision. The third is a two-sided sequential criterion which stops sampling when one of two competitive hypotheses has a probability of being accepted which is larger than another prespecified threshold. The fifth procedure extends the ideas developed for binary responses to multinomial responses where the number of possible categories (e.g., types of drug or types of glass) may be more than two. The procedure is sequential and recommends stopping when the joint probability interval or ellipsoid for the estimates of the proportions is less than a given threshold in size. For trinomial data this last procedure is illustrated with a ternary diagram with an ellipse formed around the sample proportions. There is a straightforward generalization of this approach to multinomial populations with more than three categories. A conclusion provides recommendations for sampling procedures in various contexts.
authors.affiliation: School of Mathematics and The Joseph Bell Centre for Forensic Statistics and Legal Reasoning, The King's Buildings, The University of Edinburgh, Mayfield Road, Edinburgh EH9 3JZ, U.K.
authors.forename: Dimitris
authors.initials: D
authors.lastname: Mavridis
authors.forename: Colin G G
authors.initials: CG
authors.lastname: Aitken
mesh_terms: D001499:Bayes Theorem; D005554:Forensic Medicine; D013287:Illicit Drugs; D016013:Likelihood Functions; D018401:Sample Size; D013607:Tablets
chemical_list: D013287:Illicit Drugs; D013607:Tablets