Statistical Strategies for Small Sample Research
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Statistical Strategies for Small Sample Research

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Rick H. Hoyle
744 g
229x152x25 mm
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 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
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.