Book contents
- Frontmatter
- Contents
- Figures
- Preface
- 1 Introduction to Observational Methods
- 2 Coding Schemes and Observational Measurement
- 3 Recording Observational Data
- 4 Representing Observational Data
- 5 Observer Agreement and Cohen’s Kappa
- 6 Kappas for Point-by-Point Agreement
- 7 The Intraclass Correlation Coefficient (ICC) for Summary Measures
- 8 Summary Statistics for Individual Codes
- 9 Cell and Summary Statistics for Contingency Tables
- 10 Preparing for Sequential and Other Analyses
- 11 Time-Window and Log-Linear Sequential Analysis
- 12 Recurrence Analysis and Permutation Tests
- Epilogue
- Appendix A Expected Values for Kappa Comparing Two Observers
- Appendix B Expected Values for Kappa Comparing with a Gold Standard
- References
- Index
9 - Cell and Summary Statistics for Contingency Tables
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Figures
- Preface
- 1 Introduction to Observational Methods
- 2 Coding Schemes and Observational Measurement
- 3 Recording Observational Data
- 4 Representing Observational Data
- 5 Observer Agreement and Cohen’s Kappa
- 6 Kappas for Point-by-Point Agreement
- 7 The Intraclass Correlation Coefficient (ICC) for Summary Measures
- 8 Summary Statistics for Individual Codes
- 9 Cell and Summary Statistics for Contingency Tables
- 10 Preparing for Sequential and Other Analyses
- 11 Time-Window and Log-Linear Sequential Analysis
- 12 Recurrence Analysis and Permutation Tests
- Epilogue
- Appendix A Expected Values for Kappa Comparing Two Observers
- Appendix B Expected Values for Kappa Comparing with a Gold Standard
- References
- Index
Summary
The summary statistics described in the previous chapter could be called one-dimensional because they were computed for individual codes. In contrast, the statistics described in this chapter could be called two-dimensional because they are derived from two-dimensional contingency tables whose rows and columns are defined with two or more codes. Still, the overall purpose is the same: Summary statistics are computed for individual sessions, and those scores are then described and analyzed using whatever design and statistical procedures are appropriate.
Statistics derived from two-dimensional contingency tables are of three kinds. First are statistics for individual cells; these are primarily descriptive. Second are summary indices of independence and association for tables of varying dimensions (e.g., Pearson chi-square and Cohen’s kappa); these are generally well known or, in the case of kappa, already described in Chapters 5 and 6. Third, and most important for sequential analyses, are summary statistics specifically for 2×2 tables; these contingency indices often turn out to be the best way to address sequential questions as detailed in subsequent chapters.
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- Publisher: Cambridge University PressPrint publication year: 2011