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

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March 1999 | 392 pages | SAGE Publications, Inc

Newer statistical models, such as structural equation modeling and hierarchical linear modeling, require large sample sizes inappropriate for many research questions or unrealistic for many research arenas. How can researchers get the sophistication and flexibility of large sample studies without the requirement of prohibitively large samples? This book describes and illustrates statistical strategies that meet the sophistication/flexibility criteria for analyzing data from small samples of fewer than 150 cases. Contributions from some of the leading researchers in the field cover the use of multiple imputation software and how it can be used profitably with small data sets and missing data; ways to increase statistical power when sample size cannot be increased; and strategies for computing effect sizes and combining effect sizes across studies. Other contributions describe how to hypothesis test using the bootstrap; methods for pooling effect size indicators from single-case studies; frameworks for drawing inferences from cross-tabulated data; how to determine whether a correlation or covariance matrix warrants structure analysis; and what conditions indicate latent variable modeling is a viable approach to correct for unreliability in the mediator. Other topics include the use of dynamic factor analysis to model temporal processes by analyzing multivariate; time-series data from small numbers of individuals; techniques for coping with estimation problems in confirmatory factor analysis in small samples; how the state space model can be used with surprising accuracy with small data samples; and the use of partial least squares as a viable alternative to covariance-based SEM when the N is small and/or the number of variables in a model is large.


John W Graham and Joseph L Schafer
On the Performance of Multiple Imputation for Multivariate Data with Small Sample Size
Anre Venter and Scott E Maxwell
Maximizing Power in Randomized Designs When N is Small
Sharon H Kramer and Robert Rosenthal
Effect Sizes and Significance Levels in Small-Sample Research
Yiu-Fai Yung and Wai Chan
Statistical Analysis Using Bootstrapping
Concepts and Implementation

 
Scott L Hershberger et al
Meta-Analysis of Single-Case Designs
Cyrus R Mehta and Nitin R Patel
Exact Permutational Inference for Categorical and Nonparametric Data
Rachel T Fouladi and James H Steiger
Tests of an Identity Correlation Structure
Rick H Hoyle and David A Kenny
Sample Size, Reliability and Tests of Statistical Mediation
John R Nesselroade and Peter C M Molenaar
Pooling Lagged Covariance Structures Based on Short, Multivariate Time Series for Dynamic Factor Analysis
Herbert W Marsh and Kit-Tai Hau
Confirmatory Factor Analysis
Strategies for Small Sample Sizes

 
Johan H L Oud, Robert A R G Jansen and Dominique M A Haughton
Small Samples in Structural Equation State Space Modeling
Wynne W Chin and Peter R Newsted
Structural Equation Modeling Analysis with Small Samples Using Partial Least Squares

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