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; application of latent variable modeling; time-series data from small numbers of individuals; and sample size, reliability and tests of statistical mediation.
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; application of 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 <i>N</i> 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 lÓ9