The numerical data for programs have been compiled from several sources. These are discussed in the data dictionary, which is available elsewhere. Calculated data have the formulae provided, and some explanation of the rationale for each value. Similarly, the nature of counts is explained, as needed.
The main sources are various tables and aggregated reports in Banner, together with the Leardi database, which a set of student-related data compiled from Banner. The Leardi data were compiled for other purposes, and so may not meet our exact needs, but it is providing substitute data to some reasonable level of utility. Various data items have been extracted from the Banner tables, then searched to compare various derived values. For example, there is a count of unique students in a program, so that the extracted list of all students who have taken courses in the program is searched to delete duplicates of student names.
Most of these searches and manipulation of data are fairly straightforward. However, some of the cost and revenue calculations may seem to be incorrect. This is usually because the calculations have been simplified, so that they are actually able to be calculated in a reasonable time. Therefore, the total student tuition that a program brings in assumes in-state rates for all students, and does not include out-of-state tuition. By the same token, tuition waivers are also ignored. This will bias the total tuition for all programs, but it should be reasonably consistent across the campus. A program that has a high proportion of out-of-state students will have that information appear in the proportion of in-state to out-of-state students calculated under another statistic. The difficulty of tracking how much each student actually paid would have added significantly to the complexity of the overall process, for no real improvement in the overall result. What we will have is some idea of the typical value of the tuition to UAA, on a program-by-program basis.
Because of the difficulty of determining the proportion of a course that may be dedicated to different programs in which it is used, each course is counted in every program in which it is used. This will significantly bias the income each course generates, but it has not been possible to determine the programs in which each student uses the course, and for some courses, it may actually be used in more than one program. Again, this bias is unavoidable, but it is expected to be similar across the campus.
Student numbers are counted at the official freeze dates, so may miss some students. It is expected that the number of missed students will be both small and fairly evenly distributed across all programs.
Similarly, calculations of cost do not include all possible factors. This is because there are diminishing returns with each additional search for greater detail, owing to the growing complexity of each search and the progressively smaller impact of each additional factor. While the costs are biased as a consequence, the bias should be similar for all programs.
Because of these biases in some of the data, there is no intention to use these numerical data as ‘absolute’ values. The best that can be done with them is to look at relative costs and revenue for different programs. The data will allow programs to look at their costs and revenue in more detail, from the point of view of the value they provide to the university as a whole.
Programs with tripartite faculty are also at a disadvantage, because faculty costs per credit are based on bipartite loads, but this can be explained in the discussion. A program with tripartite faculty could indicate the proportion of tripartite to bipartite faculty and perhaps indicate a ‘correction factor’ for faculty costs.
When reviewing programs, the AcTF will not be looking at the absolute values of the various numerical calculations, as we are very aware of the shortcomings and limitations of these data. That said, the data produced are far better than no data, and give everyone the chance to see costs and revenue at a program level. Since the bias in certain values should be reasonably consistent across the campus, relative values are able to be used. It is realized that even these relative values are approximate, so the AcTF will not be placing great weight on them, but will be using them as guides.
In the future, it is hoped that these types of measures will be improved and made more readily available all the time, to assist on-going program management. Understanding costs and revenue, and the implications for these from various potential changes in how programs are operated and delivered, will help programs work with the university as a whole to deal with the impending budget cuts, hopefully without any major reductions in employees or what we do.