Description
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 Schafer Maximizing Power in Randomized Designs When N is Small – Anre Venter and Scott E Maxwell Effect Sizes and Significance Levels in Small-Sample Research – Sharon H Kramer and Robert Rosenthal Statistical Analysis Using Bootstrapping – Yiu-Fai Yung and Wai Chan Concepts and Implementation Meta-Analysis of Single-Case Designs – Scott L Hershberger et al Exact Permutational Inference for Categorical and Nonparametric Data – Cyrus R Mehta and Nitin R Patel Tests of an Identity Correlation Structure – Rachel T Fouladi and James H Steiger Sample Size, Reliability and Tests of Statistical Mediation – Rick H Hoyle and David A Kenny Pooling Lagged Covariance Structures Based on Short, Multivariate Time Series for Dynamic Factor Analysis – John R Nesselroade and Peter C M Molenaar Confirmatory Factor Analysis – Herbert W Marsh and Kit-Tai Hau Strategies for Small Sample Sizes Small Samples in Structural Equation State Space Modeling – Johan H L Oud, Robert A R G Jansen and Dominique M A Haughton Structural Equation Modeling Analysis with Small Samples Using Partial Least Squares – Wynne W Chin and Peter R Newsted
