Statistical Strategies for Small Sample Research

This book provides encouragement and strategies for researchers who routinely address research questions using data from small samples. Chapters cover such topics as: using multiple imputation software with small sets; computing and combining effect sizes; bootstrap hypothesis testing; when to use latent variable modeling; time-series data from small numbers of individuals; and sample size, reliability and tests of statistical mediation.

Provides strategies for analysing data from small samples (less than 150 cases) which have the sophistication and flexibility of large samples.

On the Performance of Multiple Imputation for Multivariate Data with Small Sample Size - John W Graham and Joseph L SchaferMaximizing Power in Randomized Designs When N is Small - Anre Venter and Scott E MaxwellEffect Sizes and Significance Levels in Small-Sample Research - Sharon H Kramer and Robert RosenthalStatistical Analysis Using Bootstrapping - Yiu-Fai Yung and Wai Chan Concepts and ImplementationMeta-Analysis of Single-Case Designs - Scott L Hershberger et alExact Permutational Inference for Categorical and Nonparametric Data - Cyrus R Mehta and Nitin R PatelTests of an Identity Correlation Structure - Rachel T Fouladi and James H SteigerSample Size, Reliability and Tests of Statistical Mediation - Rick H Hoyle and David A KennyPooling Lagged Covariance Structures Based on Short, Multivariate Time Series for Dynamic Factor Analysis - John R Nesselroade and Peter C M MolenaarConfirmatory Factor Analysis - Herbert W Marsh and Kit-Tai Hau Strategies for Small Sample SizesSmall Samples in Structural Equation State Space Modeling - Johan H L Oud, Robert A R G Jansen and Dominique M A HaughtonStructural Equation Modeling Analysis with Small Samples Using Partial Least Squares - Wynne W Chin and Peter R Newsted