Estimating the standardized mean difference with minimum risk: Maximizing accuracy and minimizing cost with sequential estimation.The standardized mean difference is a widely used effect size measure.  In this article, we develop a general theory for estimating the population standardized mean difference by minimizing both the mean square error of the estimator and the total sampling cost.  Fixed sample size methods, when sample size is planned before the start of a study, cannot simultaneously minimize both the mean square error of the estimator and the total sampling cost.  To overcome this limitation of the current state of affairs, this article develops a purely sequential sampling procedure, which provides an estimate of the sample size required to achieve a sufficiently accurate estimate with minimum expected sampling cost.  Performance of the purely sequential procedure is examined via a simulation study to show that our analytic developments are highly accurate.  Additionally, we provide freely available functions in R to implement the algorithm of the purely sequential procedure.  (PsycINFO Database Record
R
Estimating the standardized mean difference with minimum risk: Maximizing accuracy and minimizing cost with sequential estimation.The standardized mean difference is a widely used effect size measure.  In this article, we develop a general theory for estimating the population standardized mean difference by minimizing both the mean square error of the estimator and the total sampling cost.  Fixed sample size methods, when sample size is planned before the start of a study, cannot simultaneously minimize both the mean square error of the estimator and the total sampling cost.  To overcome this limitation of the current state of affairs, this article develops a purely sequential sampling procedure, which provides an estimate of the sample size required to achieve a sufficiently accurate estimate with minimum expected sampling cost.  Performance of the purely sequential procedure is examined via a simulation study to show that our analytic developments are highly accurate.  Additionally, we provide freely available functions in R to implement the algorithm of the purely sequential procedure.  (PsycINFO Database Record
R