Statistical strategies for small sample research

Author(s)

• Hoyle, Rick H.

Bibliographic Information

Statistical strategies for small sample research

[edited by] Rick H. Hoyle

Sage, c1999

• : hardcover
• : pbk

Note

Includes bibliographical references and indexes

Description and Table of Contents

Description

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.

Table of Contents

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

by "Nielsen BookData"

Details

• NCID: BA42122813
• ISBN:
  • 0761908854
  • 0761908862
• LCCN: 98043490
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Thousand Oaks, Calif.
• Pages/Volumes: xxi, 367 p.
• Size: 23 cm
• Classification:
  • LCC : HA29
  • DC21 : 001.4/22
• Subject Headings:
  • LCSH : Social sciences -- Statistical methods