Single Subject Research: Strategies for Evaluating Change presents an overview of strategies used to evaluate change in single subject research, a particular approach referring to time-series paradigms in which each subject is used repeatedly. These strategies include research design, graphical analysis, and statistical tests, and it will be apparent that such designs extend beyond those commonly employed in applied behavioral research.
The book opens with a brief historical perspective of time-series and other small group research. Separate chapters follow covering the analysis of graphic data as being of singular importance in single subject research; the units-of-analysis issue and a nonparametric randomization approach to data analysis; and the growing awareness among researchers that understanding the individual characteristics of interacting subjects may be inadequate for understanding the process that has taken place between them. The final chapter reviews the advantages and disadvantages of the analytic methods presented in the previous chapters.
This volume will be of interest to those individuals actively engaged in educational and psychological experimentation for which single subject research strategies are or may prove to be valuable. Students desiring to supplement their knowledge of a unique and rapidly emerging area of statistical analysis as applied to single subject research designs will also find this volume of importance.

List of ContributorsForewordPreface1 Foundation of Time-Series Research I. Time-Series Research: A Brief Historical Perspective II. Design Considerations III. Time-Series Research: Experimental Validity IV. Time-Series Research Design V. Considerations in the Choice of a Time-Series Design VI. Measuring Change in Time-Series Research References2 The Analysis and Presentation of Graphic Data I. Introduction II. Visual Analysis of Graphic Data III. Forms of Graphic Representation IV. The Construction and Presentation of Graphs V. Summary and Conclusions References3 N = Nonparametric Randomization Tests I. Introduction II. A Randomization Framework for Small-Sample Behavior Change Experiments III. Examples of Randomization Tests in Small-Sample Behavior Change Experiments IV. A Few Final Words References4 Analysis of Interrupted Time-Series Experiments I. Introduction II. Issues in the Statistical Analysis of Behavioral Data: The Insufficiency of "Eyeballing" Graphs III. Time-Series Analysis IV. Conclusion Appendix I. Anomalous Values References5 Sequential Analysis of Observational Data Using Markov Chains I. Introduction II. Matrices III. Types of Markov Chains IV. Occupation Times V. Nth Order Models VI. Reduction in Uncertainty VII. Multivariate Informational Analysis VIII. The Sackett Alternative to Markov Chain Analysis IX. Special Applications X. Conclusion Appendix 1. Matrix Analysis Applied to Markov Chains Appendix 2. Derivation of N-(I-Q)-l Appendix 3. Random Walk as Null Hypothesis References6 Choosing a Statistical Method for Analysis of an Intensive Experiment I. Some Introductory Comments II. Commentary on Suggested Analytic Methods III. Exploratory and Confirmatory Data Analysis IV. Selecting an Analytic Approach in Practice V. Concluding Comments ReferencesIndex