Book contents
- Frontmatter
- Contents
- Preface to the second edition
- Preface to the first edition
- 1 Introduction
- 2 Developing a coding scheme
- 3 Recording behavioral sequences
- 4 Assessing observer agreement
- 5 Representing observational data
- 6 Analyzing sequential data: First steps
- 7 Analyzing event sequences
- 8 Issues in sequential analysis
- 9 Analyzing time sequences
- 10 Analyzing cross-classified events
- 11 Epilogue
- Appendix: A Pascal program to compute kappa and weighted kappa
- References
- Index
6 - Analyzing sequential data: First steps
Published online by Cambridge University Press: 13 October 2009
- Frontmatter
- Contents
- Preface to the second edition
- Preface to the first edition
- 1 Introduction
- 2 Developing a coding scheme
- 3 Recording behavioral sequences
- 4 Assessing observer agreement
- 5 Representing observational data
- 6 Analyzing sequential data: First steps
- 7 Analyzing event sequences
- 8 Issues in sequential analysis
- 9 Analyzing time sequences
- 10 Analyzing cross-classified events
- 11 Epilogue
- Appendix: A Pascal program to compute kappa and weighted kappa
- References
- Index
Summary
Describing versus modeling
If all the steps described in previous chapters – developing coding schemes, recording behavioral sequences reliably, representing the observational data – are in order, then the first fruits of the research should be simple description. Introductory textbooks never tire of telling their readers that the basic tasks of psychology are, one, description, and two, explanation. Similarly, almost all introductory textbooks in statistics distinguish between descriptive statistics, on the one hand, and inferential statistics, on the other. This distinction is important and organizes not just introductory statistics texts but this and the next four chapters as well.
Much of the material presented in this and the following four chapters, however, assumes that readers want first to describe their data, and so description is emphasized. Problems of inference and modeling – determining if data fit a particular model, estimating model parameters – are touched on only slightly here. These are important statistical topics and become especially so when one wants to move beyond mere description to a deeper understanding of one's data. That is why so many books and courses, indeed huge specialized literatures, are devoted to such topics. We assume that readers will use scores derived from observing behavioral sequences as input for anything from simple chi-square or analyses of variance, to log-linear modeling, to the modeling approach embodied in programs like LISREL.
Our task, fortunately, is not to describe all the modeling possibilities available. Instead, we have set ourselves the more manageable task of discussing how to derive useful descriptive scores from sequential data.
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- Chapter
- Information
- Observing InteractionAn Introduction to Sequential Analysis, pp. 91 - 99Publisher: Cambridge University PressPrint publication year: 1997