Statistical Strategies for Small Sample Research

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SAGE, Mar 30, 1999 - Reference - 367 pages
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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; application of latent variable modeling; time-series data from small numbers of individuals; and sample size, reliability and tests of statistical mediation.

 

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Contents

On the Performance of Multiple Imputation
1
Maximizing Power in Randomized Designs
31
Maximizing Power in Randomized Designs
37
An Introduction
60
Effect Sizes and Significance Levels
63
Effect Sizes Across Studies
70
References
76
MetaAnalysis of SingleCase Designs
107
Discussion and Conclusions
245
Strategies
251
Small Samples in Structural Equation State
285
Structural Equation Modeling Analysis with
307
The Standard Partial Least Squares Algorithm
315
Formal Specification of the Partial Least
321
The Issue
328
Summary
335

Exact Permutational Inference for Categorical
133
Tests of an Identity Correlation Structure
167
Sample Size Reliability and Tests
195
Pooling Lagged Covariance Structures Based
223
Dynamic Factor Analysis of Pooled Lagged
234
Author Index
343
Subject Index
349
About the Contributors
361
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About the author (1999)

Rick Hoyle received Ph.D in Psychology from the University of North Carolina at Chapel Hill and is currently a Research Professor at Duke University for the department of Psychology. Rick's areas of research interest include the foundations of self-esteem, the role of personality in problem behavior, and strategic applications of structural equation modeling, and related techniques for the purpose of modeling complex processes that unfold over time.

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