How results from the analysis are presented is as important as the results themselves. As discussed in chapter 5, data presented incorrectly can tell a misleading story. But unlike learning data, performance data can get rather complicated, and the evaluator must take care to present it in the clearest way possible and in a way that answers the questions asked of the evaluation. Group data in proper categories for easy comparisons. Bolding, shading, and using color are techniques the evaluator can use to distinguish one area of data from another. For example, shading could be used to highlight comparisons that are statistically different. It is the evaluator's job to determine the best way to display results. Clarity is the goal.

Many of the tables in the sales example are used for analysis and would not necessarily be tables provided in the evaluation report for this program. Group the data in whatever way necessary, so that differences and trends are seen. But be careful how the data are presented to the client. Unless the client has an understanding of what a standard deviation means, for example, or has other programs with which to compare different standard deviations, presenting the range (low and high scores) may be more helpful. The graphs used in the sales example (Figures 8-1 and 8-3) may be useful for the client because it provides them with comparisons at a glance. Grouping the data in categories (Table 8-5) to answer specific questions is good for analysis...