RESUMEN
Categorical cutpoints used to assess the adequacy of various statistics-like small, medium, and large for correlation coefficients of .10, .30, and .50 (Cohen, Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.)-are as useful as they are arbitrary, but not all statistics are suitable candidates for categorical cutpoints. One such is kappa, a statistic that gauges inter-observer agreement corrected for chance (Cohen Educational and Psychological Measurement, 20(1), 37-46, Cohen, Educational and Psychological Measurement 20:37-46, 1960). Depending on circumstances, a specific value of kappa may be judged adequate in one case but not in another. Thus, no one value of kappa can be regarded as universally acceptable and the question for investigators should be, are observers accurate enough, not is kappa big enough. A principled way to assess whether a specific value of kappa is adequate is to estimate observer accuracy-how accurate would simulated observers need to be to have generated a specific value of kappa obtained by actual observers, given specific circumstances. Estimating observer accuracy based on a kappa table the user provides is what KappaAcc, the program described here, does.