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.