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.

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