Confidence interval‐based sample size determination formulas and some mathematical properties for hierarchical data. (7th September 2019)

• Record Type: Journal Article
• Authors: Usami, Satoshi
• Is Part Of: British journal of mathematical & statistical psychology. Volume 73(2020)Supplement 1
• Page Start: 1
• Page End: 31
• Publication Date: 2019-09-07
• Subjects: confidence interval -- experimental design -- hierarchical data -- sample size; Psychometrics -- Periodicals; Psychology -- Mathematical models -- Periodicals; Psychology -- Statistical methods -- Periodicals; 150.727
• DOI: 10.1111/bmsp.12181
• Languages: English
• ISSNs: 0007-1102

Abstract

Abstract : The use of hierarchical data (also called multilevel data or clustered data) is common in behavioural and psychological research when data of lower‐level units (e.g., students, clients, repeated measures) are nested within clusters or higher‐level units (e.g., classes, hospitals, individuals). Over the past 25 years we have seen great advances in methods for computing the sample sizes needed to obtain the desired statistical properties for such data in experimental evaluations. The present research provides closed‐form and iterative formulas for sample size determination that can be used to ensure the desired width of confidence intervals for hierarchical data. Formulas are provided for a four‐level hierarchical linear model that assumes slope variances and inclusion of covariates under both balanced and unbalanced designs. In addition, we address several mathematical properties relating to sample size determination for hierarchical data via the standard errors of experimental effect estimates. These include the relative impact of several indices (e.g., random intercept or slope variance at each level) on standard errors, asymptotic standard errors, minimum required values at the highest level, and generalized expressions of standard errors for designs with any‐level randomization under any number of levels. In particular, information on the minimum required values will help researchers to minimize the risk of conducting experiments that are statistically unlikely to show the presence of an experimental effect.