title: Optimization of sample size in controlled experiments: the CLAST rule.
abstract: Sequential rules are explored in the context of null hypothesis significance testing. Several studies have demonstrated that the fixed-sample stopping rule, in which the sample size used by researchers is determined in advance, is less practical and less efficient than sequential stopping rules. It is proposed that a sequential stopping rule called CLAST (composite limited adaptive sequential test) is a superior variant of COAST (composite open adaptive sequential test), a sequential rule proposed by Frick (1998). Simulation studies are conducted to test the efficiency of the proposed rule in terms of sample size and power. Two statistical tests are used: the one-tailed t test of mean differences with two matched samples, and the chi-square independence test for twofold contingency tables. The results show that the CLAST rule is more efficient than the COAST rule and reflects more realistically the practice of experimental psychology researchers.
authors.affiliation: Facultad de Psicología, Departamento de Psicología Social y Metodología, Universidad Autónoma de Madrid, Cantoblanco s/n, 28049 Madrid, Spain. juan.botella@uam.es
authors.forename: Juan
authors.initials: J
authors.lastname: Botella
authors.forename: Carmen
authors.initials: C
authors.lastname: Ximénez
authors.forename: Javier
authors.initials: J
authors.lastname: Revuelta
authors.forename: Manuel
authors.initials: M
authors.lastname: Suero
mesh_terms: D035841:Behavioral Research / Q000379:methods*; D003198:Computer Simulation; D003661:Decision Support Techniques; D006801:Humans; D018401:Sample Size