Measurements of benefit as a means of cost analysis and decision making in education

The purpose of the study was to determine quantifiable measures of educational benefit that have the potential to facilitate educational decision making. Such measures should: 1. Reference to a realistic value system. 2. Contain criterion-measurements. 3. Provide components for the decision making process. 4. Contain options that would enable flexibility of application. 5. Fit into a framework for cost/benefit analysis. Four subprograms were studied in two Texas school districts. The subprograms were: 1. Transportation. 2. Data processing for administration. 3. Advanced senior high mathematics. 4. Vocational data processing. Information was gathered from computerized files, regular files, interviews, school district publications and from a survey of bus drivers, students, teachers and administrators. With the exception of the bus drivers in District B, virtually all of the participants responded to the questionnaire. The survey asked respondents to allocate imaginary resources among possible subprogram goals as they should be allocated; then they were asked to allocate resources again as they were presently being allocated. Analysis of the data gathered for this study revealed the following findings: 1. By adding a simple and easily administered survey to their present data bank, the school districts studied can develop the following information for each of the subprograms studied: a. Costs by subprogram. b. Nature of subprogram benefit. c. Duration of subprogram benefit. d. Characteristics of the students who are benefit recipients. e. Perceptions of participants as to the desirable and actual goals of the subprograms studied. f. Agreement between groups as to desirable and actual goals. 2. The following quantifiable measures of educational benefit met the criteria established. a. Profiles of employees. b. Time spent in class by students. c. Miles that students are transported. d. Time that equipment is operated. e. Participant satisfaction indices as determined by correlation coefficients between perceptions of desirable resource allocation and actual resource allocation. 3. Advanced accounting methods will make a great deal of the data gathered for this study available by computer in the near future. The experience of cpthering usable cost/benefit information in the two school districts studied made possible the following recommendations for school district administrators interested in implementing cost/benefit analysis: 1. Whenever possihb, cost analysis should be goal related. 2. Longitudinal studies of educational benefit such as studies of job success of graduates should be related to the costs of subprograms. 3. District testing programs that show pupil growth as the result of subprograms should be related to per pupil costs of subprograms. 4. Subprogram costs of support subprograms, both instructional and administrative, are probably not as available and useful on a per pupil basis as they are on a more immediate goal assignment basis. Costs per time expended may be more useful for decision making. 5. Districts interested in quantifiable analysis should consider adopting a multi-purpose survey of subprogram participants such as the one developed in this study. 6. Further investigation into using computers to develop quantifiable indices of subprogram quality is suggested by the present and expected future abundance of computerized data.Education, College o