Fr. 186.00

Statistical Strategies for Small Sample Research

Inglese · Tascabile

Spedizione di solito entro 1 a 3 settimane (non disponibile a breve termine)

Descrizione

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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.

Sommario










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


Riassunto

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; latent variable modeling; and time-series data from small numbers of individuals.

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