Statistical Strategies for Small Sample Research

Hoyle Rick H.

ISBN 10: 0761908862 / ISBN 13: 9780761908869

Published by Sage Publications, 1999

About this Item

Print on Demand pp. 392 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Seller Inventory # 8335166

Bibliographic Details

• Title: Statistical Strategies for Small Sample Research
• Publisher: Sage Publications
• Publication Date: 1999
• Language: English
• ISBN 10: 0761908862
• ISBN 13: 9780761908869
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

About the Author

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.