.vw ' ..-.A ”wan-u.» n1" - . -p—ppuv..‘uw‘—a«-~.~.w, A 11191111111: CORRELRRER 11111111319111E TRE SUPPUES 111111 sERv1cEs GENERAL FUND 9111191119 FOR SELECRED ACADEMIC DEPARTMENTS AT . 11111111111911 SIATE UNIVERSITY. 19541995 11119 196.5 19116 T119313 for thé Degree of Ph. D. MiGHIGAN STATE DNWERSITY. THOMAS MASON FREEMAN 1967 LIBRARY [Hub]: This is to certify that the thesis entitled A MULTIPLE CORRELATION ANALYSIS OF THE SUPPLIES AND SERVICES GENERAL FUND BUDGETS FOR SELECTED ACADEMIC DEPARTMENTS AT MICHIGAN STATE UNIVERSITY: 196A-65 AND 1965-66 presented by 1 Thomas Mason Freeman has been accepted towards fulfillment of the requirements for Ph.D. degree in Administration and Higher Education "; ( [MA B v {21211 {€72me Major professor Date 8 November 1967 0-169 ABSTRACT A MULTIPLE CORRELATION ANALYSIS OF THE SUPPLIES AND SERVICES GENERAL FUND BUDGETS FOR SELECTED ACADEMIC DEPARTMENTS AT MICHIGAN STATE UNIVERSITY: 196u—l965 AND 1965-1966 by Thomas Mason Freeman The study was intended as an investigation of Supplies and Services expenditures for the academic departments at Michigan State University for the years l96u-l965 and 1965- 1966. There were four primary study objectives. First, are there significant simple and multiple correlations between Supplies and Services expenditures and the depart— ment use factors which utilize those expenditures? Second, what are the most prominent independent variables which are significantly correlated with the expenditures? Third, how well does a "general" independent variable combination which best explains the user-expenditure relationship for all departments and for all sub-categories of Supplies and Services compare with more specific independent variable combinations which eXplain user-expenditure relationships for department sub-groups and specific Supplies and Services sub—categories? Such a comparison is concerned with the degree of correlation, the independent variables involved, and the general predictive ability of the various regression equations. Fourth, what are the implications which can be Thomas Mason Freeman drawn from the analysis in terms of: (l) the expenditure- user relationship; (2) the relative validity of the independent variables which are found to be significantly correlated with expenditures; and finally (3) the predictive ability of the regression equations? Methodology of the Study The sample consisted of 32 academic departments at Michigan State University for the fiscal years 1964-1965 and 1965-1966. The department sample was utilized in all analysis with a further breakdown of the sample into four department groups which might have an influence on Supplies and Services expenditures. Dependent variables consisted of expenditures for total Supplies and Services and selected sub-categories of that total. Independent variables were those items of data which are accepted measures of departmental workload, size, and special characteristics. Data were limited to general fund operations. Simple and multiple correlation analysis was employed to determine the extent of relationship between the indepen- dent and dependent variables for the total department sample and each of its sub—groups. Findings of the Study It was found that the expenditures for Supplies and Services were significantly correlated with the various measures of department workload and size. The extent of Thomas Mason Freeman correlation varied for each department sub—group and each sub-category of Supplies and Services. The total department sample indicated in simple and multiple correlations a very high correlation with laboratory measures and a somewhat lower correlation with faculty measures. The utilization of multiple correlation analysis improved the level of correlations but the pattern of key variables set in simple correlations did not vary greatly in multiple relationships. The multiple correlations did, in most cases, give attention to various department workload factors but laboratory and faculty measures were predominent. Head count faculty measures usually exceeded the correlation level of full time equivalent (FTE) faculty counts suggest- ing that general fund Supplies and Services expenditures are not limited to general fund faculty. The four department groups had very high correlation results with laboratory oriented departments showing the highest and most acceptable results. The non-laboratory, social science departments had high correlations but the independent variables involved were considered as un- acceptable measures of Supplies and Services needs. The "general" regression equation for all departments and total expenditures discriminates against non—laboratory departments and non-laboratory needs due to the extensive influence of laboratory measures in the equations. Thomas Mason Freeman The best regression equations for predicting expendi- tures relative to actual expenditures were the special department group equations rather than the "general" equation. However, those special group equations are doubtful as formulas because they would appear to perpetuate current eXpenditure levels rather than provide a guide to a more equitable funding for different needs and different depart- ments. Volume variations seem insufficient to fully and adequately explain expenditures. The cost factors involved in certain Supplies and Services categories and for certain departments may help clarify the level of expenditures not explained by departmental volume. The overall results point to a consideration of two major features of adequately funding Supplies and Services needs. First, some equation and/or means of adequately discriminating for various department needs and various types of departments is needed if all departments are to be adequately and equitably funded. Second, a reorganization of the sub-categories of Supplies and Services might provide a clearer and more direct cost analysis of this budget category. The more clearly the expenditures are matched with the user of the funds the more effective the cost analysis and evaluation. A MULTIPLE CORRELATION ANALYSIS OF THE SUPPLIES AND SERVICES GENERAL FUND BUDGETS FOR SELECTED ACADEMIC DEPARTMENTS AT MICHIGAN STATE UNIVERSITY: l96u-l965 AND 1965-1966 By Thomas Mason Freeman A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Administration and Higher Education 1967 To My Parents and Wife This study is dedicated to my parents, and to my wife, Florence ii ACKNOWLEDGMENTS The writer wishes to express his sincere appreciation to Dr. Paul L. Dressel for his gracious support and guidance in preparing this dissertation and for the excellent experi- ence derived while working in the Office of Institutional Research. The writer also wishes to express his grateful apprecia- tion to Dr. Edward B. Blackman for his encouragement and guidance throughout the doctoral program. Also my apprecia- tion is extended to Dr. Rollin Simonds for his support and guidance during my doctoral program. A word of appreciation is due Mr. Philip J. May who very graciously allowed me to serve as the first graduate assistant in the University Business Office. The writer also wishes to thank Mr. Paul Rumpsa, Mr. Merrill Pierson, Mr. Gerald Knapp, and Mr. Howard Grider for their help during my association with the University Business Office. Grateful acknowledgment is extended to all members of the Institutional Research staff, especially Lynn H. Peltier and Betty Giuliani, who offered help throughout the study. A special word of appreciation to Dr. Margaret Lorimer who deserves recognition as a very effective catalyst for doctoral students. Finally the writer wishes to express his appreciation to Dr. Herman King for the worthwhile experience derived from having worked with him. 111 TABLE OF CONTENTS DEDICATION ACKNOWLEDGMENTS . . . . . . . LIST OF TABLES . . . . . . . . . . . LIST OF APPENDICES Chapter I. II. III. RATIONALE FOR THE STUDY Problems of Supplies and Services Nature of Supplies and Services Issues of University Budgeting Dimensions of Supplies and Services Need for Supplies and Services Summary REVIEW OF RELATED LITERATURE AND IDEAS Reactions to Enrollment Pressures Budgeting Objective Data and Procedures Cost Analysis Budget Formulas Staffing Procedures Determination of Supplies and Services Needs. Advantages of Cost Analysis and Formulas Disadvantages of Cost Analysis Areas of Agreement Comparison of Dissertation to Cost and Formula Methods OBJECTIVES OF THE RESEARCH . . . . . . . Summary of the Rationale for the study Basic Objectives Parameters of the Data Specific Objectives Summary iv Page ii iii vi 12 38 Chapter Page IV. DESCRIPTION OF SAMPLE, VARIABLES, AND PROCEDURES OF ANALYSIS . . . . . . . . . . . . A6 Dependent Variables Independent Variables Department Sample Assumptions of the Study Limitations of the Study Statistics Employed Design Analysis Procedures V. ANALYSIS OF RESULTS . . . . . . . . . . . . . . . 61 Objectives of the Research Simple Correlations Summary of Total Sample and Four Groups Multiple Correlations Total Supplies and Services (Department Variable No. A) Subcategories of Supplies and Services Utilizing the Total Department Sample Predictions Versus Actual Expenditures for Departments Derived by Each Major Regression Equation VI. SUMMARY, CONCLUSION, AND RECOMMENDATIONS FOR FUTURE RESEARCH . . . . . . . . . . . . . . 133 Summary of Findings Conclusions Recommendations for Further Study APPENDIX I O 9 O O 0 0 O O 0 O O O O O O O O 0 I O O O 1142 BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . 177 Table 10. LIST OF TABLES Simple Correlations of Independent Variables with the Dependent Variable No. A (Total Supplies and Services) for Total Sample and Four Department Groups. . . . Simple Correlation of Independent Variables with the Dependent Variable No. A (Total Supplies and Services) Total Department Sample. Simple Correlation of Independent Variables with the Dependent Variable No. A (Total Supplies and Services) Group 1 Departments Simple Correlation of Independent Variables with the Dependent Variable No. A (Total Supplies and Services) Group 2 Departments Simple Correlation of Independent Variables with the Dependent Variable No. A (Total Supplies and Services) Group 3 Departments Simple Correlation of Independent Variables with the Dependent Variable No. A (Total Supplies and Services) Group A Departments Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. A (Total Supplies and Services) Total Department Sample . Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. A (Total Supplies and Services) Group 1 Departments . Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. A (Total Supplies and Services) Group 2 Departments . . Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. A (Total Supplies and Services) Group 3 Departments . . . vi Page 101 102 103 10A 105 106 107 108 109 110 Table 11. 12. 13. 1A. 15. 16. 17. 18. 19. 20. Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. A (Total Supplies and Services) Group A Departments . . . . . Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. 17 (Supplies and Materials) Total Department Sample. Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. 20 (Total Supplies and Services Less Supplies and Materials) Total Department Sample Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. 10 (Travel) Total Department Sample . Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. 6 (Faculty Related Expenditures) Total Department Sample. Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. 21 (Total Supplies and Services Less Supplies and Materials and Contractual‘Services) Total Department Sample Multiple Correlation Values of Combined Independent Variables Correlated with the Dependent Variable No. 8 (Equipment Related Expenditures) Total Department Sample. . . Multiple Regression Results for Total Department Sample: Dependent Variable No. A (Total Supplies and Services). . . . . . Multiple Regression Results for Group 1 Departments: Dependent Variable No. A (Total Supplies and Services) . . Multiple Regression Results for Group 2 Departments: Dependent Variable No. A (Total Supplies and Services) vii Page 111 112 113 11A 115 116 117 118 119 120 Table Page 21. Multiple Regression Results for Group 3 Departments: Dependent Variable No. A (Total Supplies and Services) . . . . . . 121 22. Multiple Regression Results for Group A Departments: Dependent Variable No. A (Total Supplies and Services) . . . . . . 122 23. Multiple Regression Results for Total Department Sample: Dependent Variable No. 17 (Supplies and Materials) . . . . . . . . . . . 123 2A. Multiple Regression Results for Total Department Sample. Dependent Variable No.20 (Total Supplies and Services Less Supplies and Materials). . . . . . . 12A 25. Multiple Regression Results for Total Department Sample: Dependent Variable No. 10 (Travel). . 125 26. Multiple Regression Results for Total Department Sample: Dependent Variable No. 6 (Faculty Related Expenditures) . . . . . . . . . 126 27. Multiple Regression Results for Total Department Sample: Dependent Variable No. 8 (Equipment Related Categories). . . . . . . . . . 127 28. Multiple Regression Results for Total Department Sample: Dependent Variable No. 21 (Total Supplies and Services Less Supplies and Materials, and Contractual Services) . . . . 128 29. Comparison of Multiple Regression Equation Residuals for Individual Departments Expressed as Percentages Above or Below the Actual Department Expenditure for the Specified Dependent Variable (Group 1 Departments) . . 129 30. Comparison of Multiple Regression Equation Residuals for Individual Departments Expressed as Percentages Above or Below the Actual Department Expenditure for the Specified Dependent Variable (Group 2 Departments) . . 130 31. Comparison of Multiple Regression Equation Residuals for Individual Departments Expressed as Percentages Above or Below the Actual Department Expenditure for the Specified Dependent Variable (Group 3 Departments) . . 131 viii Table Page 32. Comparison of Multiple Regression Equation Residuals for Individual Departments Expressed as Percentages Above or Below the Actual Department Expenditure for the Specified Dependent Variable (Group A Departments). . . 132 33. Simple Correlation of Independent Variables with the Dependent Variable No. 17 (Total Supplies and Services) Total Department Sample. 171 3A. Simple Correlation of Independent Variables with the Dependent Variable No. 20 (Total Supplies and Services Less Supplies and Materials) Total Department Sample . . . . . . . . 172 35. Simple Correlation of Independent Variables with the Dependent Variable No. 6 (Faculty Related Expenditures) Total Department Sample. . . . 173 36. Simple Correlation of Independent Variables with the Dependent Variable No. 10 (Travel) Total Department Sample . . . . 17A 37. Simple Correlation of Independent Variables with the Dependent Variable No. 8 (Equipment Related Expenditures) Total Department Sample. . . . 175 38. Simple Correlation of Independent Variables with the Dependent Variable No. 21 (Total Supplies and Services Less Supplies and Materials and Contractual Services) Total Department Sample . 176 ix LIST OF APPENDICES Appendix Page A. Independent and Dependent Variables Used in Correlation and Regression Analysis of Supplies and Services . . . . . . . . . 1A3 B° Supplies and Services Subcategories (Dependent Variables) . . . . . . . . . . 150 C. "Standardized" Use Variable Combinations (Independent Variables) . . . . . . . . . 156 D. Variables for LS Routine Analysis . . . . . 162 E. Sample of 32 Michigan State University Academic Departments from the Two Fiscal Years l96A-1965 and 1965-1966 (N = 6A) for the Supplies and Services Analysis . . 166 F. Simple Correlation Results for Sub— category Dependent Variables . . . . . . . 170 CHAPTER I RATIONALE FOR THE STUDY Developing, allocating, and projecting Supplies and Services budget needs for those academic departments which have common and diverse needs for Supplies and Services creates a difficult problem for any university. Deter- mining allocations which will sustain departments with needs as different as chemistry, art, and history in a generally fair and equitable manner requires objective data on which decisions can be based. Problems of Supplies and Services Department chairmen perform a critical task of bud- geting funds for salaries, labor, equipment, and supplies and services. During the last decade the task of securing and holding qualified faculty has been the most crucial aspect of their job. Maintaining a quality faculty in a major university places a special burden on resources with the result that adequate attention has not always been given to such support functions as equipment, clerical personnel, and supplies and services. Because these support budgets have often been the last to be considered, they have often been substantially reduced when overall funds were reduced. Although this is defensible as a temporary measure, the Supplies and Services budget is an important element in long-term faculty satisfaction, and serious attention must be given to this category. How effectively or in what manner Supplies and Services is allocated or used by departments at Michigan State University and presumably at other institutions is not really known since data collection and analysis of this budget category have not been organized so as to relate the expenditures of Supplies and Services to various depart- mental factors (students, faculty, etc.) which utilize the budget category. How well budgeting is performed is substantially dependent upon adequate information about the current budget situation. Budgeting, while future oriented, is never divorced from the present situation. Most budgeting for future needs starts with "what is," or the current practice, and then moves into the future in varying ways. Therefore a basic need and problem in any budget operation is an examination of the present situation so as to improve the possibility of objective appraisal of Supplies and Services budgeting. Nature of Supplies and Services This problem arises, in part, because of the following circumstances: First, Supplies and Services is a hetero— geneous budget category that contains a range of categories such as travel, laboratory supplies, and clerical supplies. Second, the departments which use the Supplies and Services' funds are diverse and yet common in character. Zoology and history have a common need for funds in travel, telephone, books and magazines, but a different need for supplies and materials because of the nature of their instructional program. Third, there would appear to be no single criter- ion (number of faculty, students) in departments which could be used as a sole measure of need for all departments. In other words, to compare and project budget needs based only on number of faculty or students could create issues of equity and comparability. Furthermore, the use of a single criterion of need, however valid, creates the problem often raised concerning statewide budget systems. Fourth, not knowing the current situation hampers any future projec- tion of need. It may be that the current user—expenditure relationship is such that it could be develOped as a guide or formula for determining future needs, but this is not known. In most cases enough data now exist so that an analysis of the user-expenditure relationship is feasible. First, there are considerable data available on the various use factors in departments, such as: number of faculty, students, and class size. Second, expenditure data for Supplies and Services are available for a two-year period. Third, the use of regression and correlation analysis in industry and in comparable circumstances where analysis of relationships seems possible has provided valuable insights into user—expenditure relationships. In other words, with the data available, it is possible to analyze the relation— ship of the use factors to expenditures in the academic departments. This analysis would provide valuable infor- mation about the current budget situation and its possible use as a guide for future needs. Issues of University Budggting Concern for the effective budgeting of Supplies and Services in a major university is a general outgrowth of certain identifiable trends and influences in higher edu- cation as well as specific problems of Michigan State University. Since the early 1950's, higher education has been subjected to a major increase inthe demand for its ser- vices. Enrollments have grown faster than resources with the result that institutions of higher education have been forced to give more consideration to the management of the resources available. Seymour Harris, Selma J. Mushkin, DexterflM.Keezer, and others1 have documented the pressures placed upon universities from the interaction of increasing enrollments and limited resources. State legislative and university officials, when faced with the problem of increasing demands and limited resources, were forced to turn toward objective data and objective analysis which could aid in justifying need as well as justifying a particular allocation. M. M. Chambers, Moos and Rourke, Rourke and Brooks, and James L. Miller, Jr.2 have traced, within the universities and within state systems of higher education, reactions to and experiences with the use of "objective data" which have been increas- ingly required for justification of budget requests. The growth of statewide budgeting systems, cost analysis techniques, institutional research, curriculum models for increased faculty productivity, and COOperative cost studies were methods and procedures for handling the bud- geting problem. This concern for more objectively-based budgeting has often been centered on staffing and space utilization to the exclusion of other budget needs. The time has come to consider an analysis of budgets, such as Supplies and Services, which perform significant roles in academic support. Budgeting Supplies and Services funds is a necessary task if the needs of the academic programs of Michigan State University are to be reasonably and fairly met. Lacking unlimited resources, this University faces a demand, both internal and external, that the decisions about its budget be arrived at within a general context of objective analysis. Legislators, faculty, students, and university administrative personnel are working within a social and business milieu which requires that policy decisions (which include budgets) about an institution be arrived at through some systematic analysis of the problem. To do an adequate and reasonable job of developing budget re- quests and determining budget allocations for Supplies and Services this University must develop and analyze data on current expenditure-user relationships. This infor- mation can aid in determining future budget needs as well as assist administrative personnel in appraising past allocations. Dimensions of Supplies and Services The budget category Supplies and Services is defined and detailed in various ways at different institutions, but at Michigan State University, Supplies and Services includes the following nine major object class sub—categories: Travel (all categories), Communication Services (telephone, postage and related items), Rentals and Utility Services, Printing and Binding, Physical Plant Services, Off-Campus Contractual Services, Other Contractual Services, Supplies and Materials, and Books and Magazine Subscriptions. In a more general sense, Supplies and Services is a major support budget for departmental teaching and research. This budget category, while small relative to the salary budget, is vital to a department because of its direct influence upon teaching, research, and the general operation of a depart- ment. Adequate, equitable, and properly allocated Supplies and Services not only strengthen instruction and research but sustain it. Without sustained support, the effi- ciency and effectiveness of the academic and professional staff are severely curtailed and this in turn weakens the instructional program. Each of the various sub-categories of Supplies and Services performs a significant task for the academic departments. Travel funds are necessary in order to provide faculty personnel with a means of attending conferences and meetings for the purpose of maintaining active contact with their academic disciplines. Travel funds are also vital to a department for recruitment of new faculty. Books and Magazine Subscriptions represent an active communications process for academic disciplines and must be maintained if department personnel, including students, are to remain aware of current work in their field. Telephone facilities and postage are necessary features of a modern university and as such require an out- lay of funds. These funds promote communications among faculty, students, and administrators and may serve in lieu of travel funds. Physical Plant Contract Services, Off-Campus Contractual Services, and other Contractual Services sustain equipment repair, and provide for services which are essential to the maintainance of laboratories. Supplies and Materials, a major heterogeneous sub- category, covers a wide range of items such as clerical materials (paper, mimeograph materials, and other clierical supplies) as well as materials (art supplies, drafting materials, glassware, and chemicals) directly related to teaching and research in a major university. Students, as well as faculty, are directly affected by this category which has a definite influence on the quality of an academic program. Insufficient clerical materials, art supplies, test tubes, chemicals, slides, and other such items pose a direct handicap to teachers and often put additional burdens on their time. For example, if adequate and proper supplies are not available, then faculty must revert to antiquated methods of teaching. Need for Supplies and Services The President's Commission on Higher Education states the most effective policies regarding salaries and personal and professional security will fail their purpose unless the institution provides for support of the faculty in labora- tory apparatus, supplies, and books . . . the additional cost would be far outweighted by the enhanced effectiveness of the faculty member. In another report by the President's Commission on Higher Education there is a plea for support of instruction with an adequate quantity of services and supplies and other such aids which sustain high quality instruction.Ll At Michigan State University, President John A. Hannah in his 1966 "State of the University" message called attention to the problem of Supplies and Services and emphasized the need for the University to give atten- tion to this matter since it had not done so for several years.5 Provost Howard Neville and assistant provost Herman King in numerous conversations6 have urged the investigation of Supplies and Services because of the persistent problem of adequate and equitable funding of the budget needs of diverse academic departments. Their concern has centered on projecting needs of academic departments as well as desiring to know the current-expenditure relationship in Supplies and Services. Rollin Simonds, although he was referring specifi- cally to faculty compensation, nevertheless stated rather well the issue for Supplies and Services when he said, within a university it will be necessary to dispel any impression among the faculty that funds are not allocated among colleges and departments on a systematic and reasonably equitable basis. A definition of adequate support and equity may be subject to debate, but a formal and generally acceptable definition 10 can be bypassed by determining the relationship between department use factors and expenditures for Supplies and Services by regression and correlation analysis. Summary Effective resource projection and allocation requires an analysis of the current expenditure-user relationship in Supplies and Service. It is important to know the identi- fication of those department variables which actively use or are related to Supplies and Services expenditures because this information is basic to developing a means of funding departmental needs. This will provide admin— istrative personnel with information that can aid in deciding future needs in some objective procedure as well as providing information which can help appraise the current situation. CHAPTER I FOOTNOTES lSeymour E. Harris, Higher Education: Resources and Finance (New York: McGraw-Hill Book Company, Inc., 19627? Selma J. Mushkin, Economics of Higher Education (Washington: U. S. Department of Health, Education and Welfare, 1962); Dexter M. Keezer, editor, Financing Higher Education 1960-70 (New York: McGraw-HillBook Company, Inc., 1959); James L. Miller, Jr., State Budgeting for Higher Education: The Use of Formulas and Cost Analysis (Ann Arbor: The University of Michigan, 196A). 2M. M. Chambers, Voluntary Statewide Coordination in Public Higher Education—(Ann Arbor: The University of Michigan, 19617; M. M. Chambers, Financing Higher Education (Washington: The Center for Applied Research in Education, Inc., 1963); Malcolm Moos and Francis E. Rourke, The Campus and the State (Baltimore: The Johns HOpkins Press, 1959); Francis E. Rourke and Glenn E. Brooks, The Managerial Revolution in Higher Education (Baltimore: The thns Hopkins Press, 1966). 3George F. Zook, Chairman, Higher Education for American Democracy; Financing Higher Education (Washington: The President's Commission on Higher Education, 19A7), p. 59. “George F. Zook, Chairman, Higher Education for American Democracy; Financing Higher Education (Washington: The President's Commission on Higher Education, 19A7), p. 15. 5John A. Hannah, A Year of Appraisal, "State of the University" Address (East Lansing: Michigan State University, February 1A, 1966), no pages given. 6Howard R. Neville and Herman L. King, Conver- sations during preparation of study, Office of the Provost, (East Lansing: Michigan State University, 1966 and 1967). 7Rollin H. Simonds, "To Increase Man-Hour Output in Higher Education," The Educational Record, XXXIX, A (October, 1958), p. 338. 11 CHAPTER II REVIEW OF RELATED LITERATURE AND IDEAS Literature which is directly concerned with supplies and services budgeting is very limited. A perusal of leading journals and publications in higher education reveals that only a few brief passages are devoted to this topic. Discussion of supplies and services budgets is mentioned mainly in the literature on statewide budget systems, the emphasis being on the procedures used to determine future needs for that budget. While there is a lack of literature bearing directly on the subject there is a basic framework of budgeting and more specially of cost analysis and formulas which serves as a focal point for this dissertation. This dissertation fits into the budgeting framework of cost analysis, budget formulas, and objective data analysis because of its orientation and concern. Budgeting practices in higher education exhibit trends which have direct relevance for a study of supplies and services. First, budgeting practice in higher education 1&3 making greater use of objective data as an aid in budget EAnalysis. Numerous writers have drawn attention to this trend of using objective data, cost analysis, and budget 12 l3 formulas for evaluating and allocating budget needs.l Second, the concept of relating expenditure and user variable to one another in some systematic manner is a major example of how objective data are utilized in cost analysis and budget formulas. Neither cost analysis nor budget formulas are accepted without controversy but the concept of attempting to quantify the user—expenditure relationship offers possibilities for improving budgeting in higher education. Up to now no better system than cost analysis and formula procedures has been devised as a reasonable guide to relative need and to an evaluation of that need.2 Reactions to Enrollment Pressures The concern for budgeting in higher eduCation and the use of cost analysis, formulas, and objective data is a direct response to enrollment pressures. During the last decade enrollment increases and limited funds have forced institutions to become more concerned with the 3 management of their resources. The greater the pressures, the greater the burden of budget allocations, and the greater demand for documentation of budget needs. Four possible solutions to the enrollment pressures can be broadly categorized as those which advocate: (l) curric- ulum reorganization, (2) analysis of possible sources for additional funds, (3) improved general university manage- ment, and (A) improved data collection and procedures for 1A analysis of budget needs. They have in common a concern for maintaining quality in higher education by promoting an increase in resources for universities and by effec- tively utilizing those resources once they are obtained. The curriculum reorganization concepts can be effectively characterized by the works of Dressel, and of Ruml and Morrison.” Their concern is centered on filling out unused classroom space, more effective utilization of faculty, and re-evaluation of curriculum patterns which would better utilize faculty and student time. A more careful analysis of funds is recommended by Harris, Keezer, and Mushkin. They have effectively docu- mented the extent of enrollment pressures facing higher education and evaluated the prospect for additional funds through higher tuitions and greater government appro- priations.5 The general management improvement concept is advo- cated by Dobbs, Millett, Henderson and many others.6 They share a common concern for more effective university management through a better understanding of university organization, direction, and objectives. In most cases these authors share their personal experiences and obser- vations as actual administrators in higher education and their writings are devoted largely to issues which they have faced; the writings are descriptive rather than analytical. 15 Improved data—gathering procedures are demonstrated by the growth of institutional research, data collection systems, cost analysis techniques, budget formulas, and program budgeting for the purpose of more effective analysis of budgets. A primary use of objective data and objective procedures has been as an aid in budget evaluation and projection. Budgeting as outlined in the following descrip- tions has a particular need for objective data and objective analysis. Budgeting It is through budgets that institutions attempt to express the educational program of an institution.7 Budgeting is an active process of planning, controlling and evaluating the use of resources for the purpose of achieving certain institutional objectives. As such, a budget should reflect what a university is doing or intends to do. The budgeting process may vary with the type of institution but certain aspects of budget decision making are common to all circumstances. It is always necessary to project expenditures, to determine available resources, to establish priorities, and to match resources to programs. Burkhead sees budgeting as consisting of three interacting functions: "eXpertise, communications, and responsibility."9 Expertise consists of obtaining as much information as possible about costs, of determining the relationship of expenditures to programs, and of deciding on the probable 16 effects of several alternatives. Communication consists of hearing and evaluating the views of various groups which are affected by the budget process. Responsibility consists of making budget decisions and bearing the responsibility for the decision.10 John Dale Russell sees budgeting as being concerned with a study of the relations of input and output. "Input" may be considered as the dollars or resources used for any or all parts of an institutions's program. "Output" is what is accomplished with these resources.11 Russell points out that unit budget analysis requires the following three items: "(1) selection of an expenditure category to be analyzed; (2) selection of appropriate and measurable units of service; and (3) relating the expen- ditures to the measure of service."12 In each of these budget descriptions there is a key element which has direct meaning for all budgeting and certainly for supplies and services. In each case, the attempt to match expenditures to need (program objectives) in some systematic manner is a key element of budgeting. In fact, it is the concept of systematically relating expenditures to programs or needs and evaluating that relationship which is the essence of any objective budget procedure. As Herbert Simons points out, "a budget that is no more than a lot of salaries and other expenses is 13 useless for managing a college." 17 Budget analysis which utilizes data, cost analysis, formulas, and program concepts differs from traditional "line item" budgeting. Budget analysis using cost analysis and formulas promotes a more coordinated analysis of need with a greater emphasis on differentiating and evaluating budget uses. "Line item" budgeting tends to promote across-the-board increases rather than increases based on need, function, or program objectives.lu Objective Data and Procedures The utilization of cost analysis and budget formulas for budgeting places a heavy emphasis on "objective data" and "objective procedures." "Objective data" refers to any quantified information used in financial analysis. It includes (a) programs and activities, (b) costs, (0) and the relationship of programs and costs.15 In higher edu- cation, examples of such units of financial information are: (a) in program measurement, the use of student credit hours to measure the volume of teaching done, or square feet of floor space to measure the amount of building custodial service that is necessary; (b) in cost measure- ment, the identification of the number of dollars spent on salaries for teaching faculty; (0) in the measurement of relationships between cost and program, the computation of the dollar cost per student credit hour of graduate instruction. 18 "Objective procedure" involves the use of objective data and employs a series of steps. An objective procedure can be repeated, and so long as the same objective data are used, the results will be the same. Cost analysis formulas and program budget techniques are examples of objective procedures used in budget analysis.l6 Objective analysis in each of its forms is based on the concept that more effective decisions can be reached whenever an institution can generate and evaluate objective data about its operations. It is obvious that objective data are a significant aid to answering complex issues of higher education and without such data the decisions which are reached are based on a more subjective, rule-of—the- thumb procedure.l7 Misused or misunderstood objective data are dangerous but such data are not inherently bad. As John Dale Russell points out, A vital issue is at stake if unwise formulas, standards, or other measures are devised and employed, because great damage can be done to higher education. At the same time, unnecessary and ill-founded opposition to the use of any measure, no matter what its forms, is a barrier to good adginistration and the pursuit of excellence.1 There are two major forms of objective budget analysis which are utilized as guides to budget development and eval- uation. These two forms, cost analysis and formulas, are rather well documented in the budget literature as to their techniques and uses along with their advantages and disad- vantages. Actual examples of their useenmasomewhat limited 19 but the literature of statewide budget systems serves as a primary source of the uses and applications of cost analysis and formulas. Secondary sources such as articles, pamphlets, and chapters in various books have drawn attention to these methods as they have gained recognition and use.19 Cost Analysis Cost analysis has the longest documented history with the works of Reeves and Russell in the 1930's being among the first.20 The actual procedures and methods of cost analysis are widely cited by other writers.21 Cost analysis may refer to various systematic procedures of objective data and objective procedures to establish relationships between eXpenditures (costs) and users or programs. These rela- tionships are expressed in quantitative terms such as dollar costs, ratios and percentage relationships.22 This unit cost technique involves selecting an expenditure cate- gory to be analyzed, selecting an apprOpriate and measurable unit of service and relating the expenditure to the measure Of service. Indiana's statewide budget system uses the cost analysis approach for developing cost data. These data are then used for evaluation of programs and for the purpose of budget presentation to the state legislature. The actual Computations are very detailed and elaborate, but the end result is a cost per student for each of five student levels for each institution in the state. The actual procedures 20 are outlined in work of Evans and Hicks published by Purdue University and to a great extent follow, with modi- fications, the California and Western Conference Cost and 23 Statistical Study in which Indiana was a participant. Cost analysis provides two major results. First, it provides data which can be used for evaluation. Second, it provides data which can be used as a guide to future budget needs. In other words, cost studies are of value in the internal administration of an institution. The determination of costs may be considered as an impor- tant first step in the evaluation process. Variations in cost over time and among administrative units should signal further analysis of class size, faculty teaching load, curriculum offerings and the general efficiency of the use of the facilities of the educational plant.2Ll To the extent that the cost data seem adequate or acceptable they can be used in a "formula" for estimating future budget needs, as in Indiana. Properly used in conjunction with budget preparation and review, there appears to be agreement that unit costs can be "useful in raising questions about departmental practices, user-expenditure relationships, in calling attention to undernourished departments, and in combating extravagance in others."25 21 Budget Formulas Budget formulas refer to an objective procedure for estimating future budgeting requirements of a university through the use of objective data about future programs, and the relationships between costs and programs, in such a way as to derive an estimate of future expenditures and needs.26 The use of budget formulas is primarily fOund in the statewide budget systems of Florida, Tennessee, Kentucky, California, and Oklahoma. Chambers, Rourke, Glenny, and more recently Miller have written extensively on these systems. Also officials within the statewide systems have published reports about the techniques and procedures utilized by the various states. The actual procedures employed by the state systems vary in degree of detail and emphasis, but they all share certain common elements. These common elements are the following: (1) Each state system has as its primary objective an attempt at objectivity in budget analysis with an emphasis on equity and maintenance of adequate support. (2) Each state develOped its system with the belief that the results should serve as a guide to future budget needs as well as a guide to budget evaluation. (3) Each system utilizes objective data concerning faculty and expenditures in some relationship pattern which they believe best expresses faculty workload and institutional need for funds. 22 Staffing Procedures While the focus of attention in this study is on supplies and services, the methods employed and the factors considered in determining faculty for each state give a brief but important insight into the results of the use of formulas. The determination of how many faculty and what support funds a university will need for next year or at some future date is crucial to a university's continued success. The allocation of people and support funds to specific departments determines the direction a university will take. It would be simple to increase the resources for every department by a fixed percentage as is often the case in "line item" budgeting, but the fixed percentage increase ignores need. In order to judge how well the allocation responds to need there must be some basic data on, for example, current and future workload. Establishing need and differentiating for the degree of need is a basic objective of any quantitative procedure. Differentiation of need involves a multitude of considerations but the following are a few which should be considered: (1) present student load; (2) type of student load; (3) type and number of courses taught; (A) the amount of advising done, and (5) the amount of research performed. The following is a brief outline of methods of estimating the number of faculty required for future budgets. 23 Kentucky and Tennessee use institution-wide student- faculty ratios with no differentiation of need for instruc- tional levels or subject matter fields. Both states allow a lower student-faculty ratio for smaller institutions.27 Oklahoma uses a base faculty complement method with addi- tional faculty provided on an institution-wide student- faculty ratio basis. They differentiate for faculty needs by instructional levels, by type of institutions (junior college, four-year college, and major university), but they do not generally differentiate by subject matter area. Small institutions are guaranteed a base number of faculty regardless of enrollment but as enrollment increases, faculty are added at a slower rate (higher ratio) until the institution approaches the student-faculty ratios of the larger institutions.28 Texas calculates need for faculty by using separate student-faculty ratio for sixteen fields and three instruc- tional levels with a ratio ranging from 19:1 for under- graduate institutions in teacher education to Azl at the doctoral level for all fields.29 Texas differentiates for undergraduate, masters, and doctoral instruction and dif- ferentiations are employed in sixteen subject fields. No special consideration is given to smaller institutions. Florida utilizes past faculty productivity of student-credit-hours as its basic workload guide. There is no differentiation of the workload pattern for subject 2A matter fields but there is differentiation for instruc- tional levels. Florida then utilizes this basic student- credit-hour workload factor to develop the number of instructional staff needed. All other faculty functions such as research, administration, and public service are then calculated in relationship to the original number of faculty needed for instruction by using certain standard ratios. For example, in 1961—1962 the University of Florida had the following faculty productivity in student-credit-hours: 375 student credit hours for lower division, 235 student credit hours for upper division, and 120 student credit hours for graduates. One research position was added for each 3.7 teacher portions with comparable ratios being used for extension, counseling, and administration.30 The state college system in California uses the most extensive and detailed system for estimating their need for faculty by using a course-by-course consideration of subject-matter, teaching method involved, and the level of instruction. There is a differentiation of instructional level and subject matter fields with over thirty subject matter fields being considered. It would appear that the detailed California system is based on current or esti- mated workload factors rather than on past indices.31 The above descriptions are extremely brief and hardly do justice to the considerable effort made by the 25 states to develop their data, but the overall concept of trying to develOp methods which can aid in estimating need for faculty is, hopefully, conveyed. Estimating need for faculty is vital to an institution and as such received the greatest effort, but it is not the sole item con- sidered in the developing of budgets by these formula methods. Library, administration, physical plant oper- ations, and other instructional costs and activities are considered within the framework of formulas and cost analysis used by each state. Determination of Supplies and Services Needs Supplies and services budgets are determined by the various state systems in the following manner: California provides no systematic method with the result that each institution requests these funds on a non-formula basis.32 Oklahoma, Tennessee, and Texas calculate their needs for supplies and services as a percentage of instructional salaries. The general basis for determining the percentage to be used is actual expenditures during prior years. In Oklahoma the percentages are different at different insti- tutions, ranging from 20 per cent to 33 per cent depending 33 on institution's needs. Texas uses a base formula to compute departmental Operating expendses. The base figure used is the total estimated semester credit hours, and the amount allowed per credit hour for supplies and services is 26 different for each academic program and for three instruc- tional levels. Dollar amounts per credit hour range from $0.75 for undergraduate liberal arts to $50.00 for doc- toral level engineering.3u In Florida, supplies and services expenditures are calculated separately for each institution on the basis of a cost per student-credit-hour but with no differential programs or instructional level. In the year 1959-1960 the instructional allocation per credit hour for supplies and services was $1.21 for the University of Florida and each of its academic departments.35 It would appear that supplies and services are allocated with fewer computations and less concern for differentiation of need than faculty. The extent of internal allocations by the individual college or univer- sity is not known, but it is presumed that differentials are applied. Nevertheless the attempt to differentiate need for resources by various means in the state systems indicates an awareness by the states that program and instructional differentials among and within institutions require consideration. Miller raises an interesting and disturbing point about the methods utilized to determine supplies and services needs. He points out 27 . . . that there is no evidence that any of these units of measurement used to determine supplies and services needs is a reliable basis for determining the amount needed for this purpose. The units of measurement employed in the formulas for supplies and services do not relate directly to those items of expenditure or to the workload factor which directly affect them.36 This issue is one that creates problems of evaluation, equity, allocation, and projection. It does little good to base budget evaluation and projection on variables which do not adequately or realistically measure need for expenditures. Nor is it worthwhile to establish a formula for projection unless the relationship of current allocations to need is known and evaluated. Advantages of Cost Analysis and Formulas Advantages of cost analysis, formulas, and objective data as aids to budgeting focus on their contribution to facilitating comparisons among requests, equity, adequacy of support, efficiency, and as an aid in focusing atten- tion on policy issues. It would appear that formulas and cost analysis are used for three distinct budgeting pur- poses: (l) to facilitate the analysis of budgeting needs, (2) to help in the presentation of budgeting information, and (3) to focus attention on the major issues and problems of budgeting. Accurate comparisons among activities and adminis- trative units are greatly facilitated by objective budget 28 analysis. Through these procedures a large amount of apparently non-comparable information about a number of administrative units can be organized in a comparable manner and presented in terms of uniform units of measure- ment which make comparisons and evaluation possible. Since colleges and universities do not have the advan- tage of a market system as does industry, a university must substitute analytical studies of its activities and therefore quantitative data resulting from cost analysis and budget formulas aid in accomplishing this analysis.37 Through cost analysis and formulas institutions can know with some degree of precision how much time and effort they give to various institutional objectives. Further- more such data provide some assurance that future needs will be considered. Such data must also serve as an important first step in further analysis of the reasons for these relationships. Equity is cited as one of the most notable successes scored by cost analysis and formulas.38 It has provided a method for comparing administrative units, identifying and correcting inequities and thereby treating all admin- istrative units in a comparable manner in projecting future financial requirements. Highlighting basic policy questions would appear to be a significant asset of objective analysis because the very existence of such data makes it easier for interested personnel to concentrate on the key policy questions. The existence of the data offers some minimum assurance that needs are analyzed in a systematic manner rather than being handled in a totally subjective or non-discrimi- nating way. With the assurance that overall needs of the various administrative units have been considered, atten- tion can turn to specific aspects of the analysis which have been dealt with inadequately by the objective anal- ysis or which need to be questioned. Statewide budget systems with their much larger administrative units find the use of formulas and cost analysis an aid to analysis of budget requests. The size and complexity of higher education seems to require a systematic organization of procedures in order to facil- itate management of the budget process. There are numerous other advantages and objectives of cost analysis and formulas, but the major asset of such approaches may not be in their actual use or employment but in their fos- tering an atmosphere of concern about equity, objectivity, and efficiency in higher education. Disadvantages of Cost Analysis and Formulas While support for objective analysis is generally enthusiastic there are certain definite criticisms of their usefulness in helping administer a university. A major shortcoming of formulas and cost analysis techniques is the tendency toward standardization which can lead to mediocrity. Logan Wilson warns that indiscriminate standardization of 3O workloads, class size, and apprOpriations per student can be very dangerous to higher education because such prac- tices may not allow for institutional differences in role, scope and programs; that is, the results tend to have a leveling influence.39 While formulas and cost analysis procedures can be extremely useful in analyzing university needs, they cannot make the final decisions which are so necessary in budgeting. A decision to emphasize a certain aspect of a university's instructional program must often be made on the basis of information which cannot be reducible to a formula. Formulas, cost data, and all forms of objective data can facilitate analysis, guide and highlight issues but final decisions are still required. Often choices must be made about allocations to comparable departments having the same quantitative needs but due to resource limitations they cannot be supported equally. The greatest single limitation of formulas and cost analysis procedures is that they cannot make policy or determine the effects of non-quantitative issues on the decision process. These procedures can facilitate the analysis which should precede policy-making and then they can be used to facilitate the translation of a policy decision in specific quantitative terms but there is still a significant and important element of subjective evalua- tion which remains. It is worth noting that of the many 31 writers who generally advocate the use of quantitative measures in budget analysis there are none who dismiss or fail to recognize the serious shortcomings of quanti- tative methods. Areas of Agreement It is generally conceded that the use of quantita- tive measures such as cost analysis and budget formulas grew out of the enrollment pressures of the last decade with their primary use being in statewide budget systems. There is evidence that such procedures are being employed within universities but not to the extent reported by state systems.“0 Whether these methods are formally employed for budget evaluation and preparation is not as important as the atmosphere of concern for greater objectivity and equity in managing colleges and universities which these methods have helped create. There would appear to be general agreement that the utilization of quantitative measures in higher education is an aid to analysis of need and helps support evaluation of past performance. It is generally agreed that cost analysis is primarily a measure of past effort, whereas formulas attempt to measure future needs with both measures being utilized in varying degrees by statewide budget systems and various universities. Regardless of the specific method or its proposed use a major objective in using these measures 32 is directed toward establishing the basis for budget need and relating that basis to expenditure as a means of budget evaluation and projection. Comparison of Dissertation to Cost and Formula Methods This dissertation has the major objective of estab- lishing the existing expenditure-user relationship of supplies and services for thirty:two academic departments within Michigan State University utilizing objective data on those departments with correlation and regression as theyprimary tool of analysis. This study has certain features in common with cost analysis and formulas but also departs from those procedures in a few ways. First, this dissertation has in common with cost analysis and formulas the use of quantitative data as a basic ingredient of analysis. Second, there is an attempt ‘to relate eXpenditures to factors which utilize those expenditures. Third, there is a common concern that the expenditure-user relationship will serve as a guide to evaluation as well as a potential guide to budget projec— tion. Finally, there is a common goal of providing infor- mation which will aid in administering the budget more effectively. This study differs from cost analysis and formulas in certain specific ways rather than in general objectives. First, the study is devoted to one aspect of university 33 budgeting which is only briefly mentioned in the liter- ature. Therefore, this study does not build in any major way on any previous documented study about supplies and services. Second, primarily this study is designed to inform and aid in evaluation rather than to project future needs. Third, the study approaches the problem of user-eXpenditure relationships by utilizing regression and correlation as a means of determining the many actual current relationships that may exist between expendi- tures and department variables. In other words, because there are so many department variables which might be related to expenditures, the use of correlation and regression permits a more complete analysis of the rela- tionship to the place. By using this analysis one does not have to assume any relationship exists between user and expenditure. However, if expenditures and use factors are in fact related, the extent and apprOpriateness of the relationship can be evaluated. Whether budget projection is derived from the analysis is subject to the extent of relationship and relative validity of that relationship. CHAPTER II FOOTNOTES lMalcolm Moos and Francis E. Rourke, The Campps and the State (Baltimore: The Johns HOpkins Press, 1959), pp. 82-88; Francis E. Rourke and Glenn E. Brooks, TEe Managerial Revolution in Higher Education (Baltimore: The Johns Hopkins Press, 19667, p. 72; James L. Miller, Jr., State Budgeting for Higher Education: The Use of Formulas and Cost Analysis (An Arbor: The University of Michigan, 196A), p. 150. 2John M. Evans and John W. Hicks, "Studies in Higher Education," An Approach to Higher Educational Cost Analysis, No. Al (Lafayette: Purdue University, 1961), p. 25. 3 Selma J. Mushkin (ed.), Economics of Higher Education, Bulletin 1962 (Washington: U. S. Department of Health, Education, and Welfare, 1962), pp. 3 and 20; Dexter M. Keezer (ed.) Financing Higher Education 1960- 1970 (New York: McGraw Hill Book Company, Inc., 19597 p. 15. “Paul L. Dressel, The Undergraduate Curriculum in Higher Education (Washington: The Center for Applied Research in Education, Inc., 1963), pp. vii and 110. Paul L. Dressel, "Large Credit Blocks--Pro and Con," Liberal Ecucation, LI, No. 3 (October, 1965); Beardsley Ruml and Donald H. Morrison, Memo to a College Trustee (New York: McGraw Hill Book Company, Inc., 1959), pp. v and 9A. 5Seymour E. Harris, Higher Education: Resources and Finance (New York: McGraw Hill Book Company, Inc., 1962), pp v and 713; Keezer, loc. cit.; Mushkin, loc. cit. 6Harold W. Dobbs, The Academic President: Educator or Caretaker? (New York: McGraw Hill Book COmpany, Inc., 196277'pp. v and 29A; John D. Millett, The Academic Community (New York: McGraw Hill Book Company, Inc., 1962), pp. vii and 265; Algo D. Henderson, Policies and Practices in Higher Education (New York: Harper and Brothers, 1962), pp. v and 338. 3A 35 7American Council on Education, College and University Business Administration, I (Washington: American Council on Education, 1952), p. 23. 8G. M. Morris, Modernizing Government Bugget Administration (Chicago: Public Administrative Service, 1962), p. 11. 9Jesse M. Burkhead, Government Budgeting (New York: John Wiley and Sons, Inc., 1956), p. 56. 10Ibid. llJohn Dale Russell, An Overview of Budgeting Analysis (Tallahassee, Florida: Institute on Insti- tutional Research, Florida State University, July 12, 1960), p. 1. 12Ibid., p. 5. 13Herbert A. Simonds, "The Job of a College President," Educational Record, XLVIII, No. 1 (Winter, 1967), p. 70. 1”John F. Briggs, A Refined Program Budget for State Governments, Bulletin No. 6 (Washington: The American University, 1962), p. 8. 15Miller, op. cit., p. A. 16 Ibid., p. 5. 17Francis E. Rourke and Glenn E. Brooks, pp. cit., pp. 1 and 12. 18Western Interstate Commission for Higher Education, Yardsticks and Formulas in University Budgeting (Boulder: Western Interstate Commission for Higher Education, 1959), p. 15. 19Seymour E. Harris, Op. cit., Chapters A2 and A3; Clarence Scheps, Accounting for Colleges and Universities (Baton Rouge: Louisiana State University Press, 19A97, Chapter 13; Robert L. Williams, The Administration of Academic Affairs in Higher Education (Ann Arbor: The University of Michigan Press, 1965), Chapters 8 and 9. 20Floyd W. Reeves, Nelson B. Henry, and John Dale Russell, Size and University Costs, Vol. XI of Tge University of Chicago Survey (Chicago: The University of Chicago Press, 19327} pp. xxi and 229. 36 21John Dale Russell and James I. Doi, "Analysis Of Institutional Expenditures" (a series Of twelve articles), College and University Business, XIX (September, 1955), pp. 19-21; (October, 1955), pp. 27-29; (November, 1955), pp. AA-A7; (December, 1955), pp. 39-A1; XX (January, 1956), pp. Al-A5; (February, 1956), pp. A7-51; (March, 1956), pp. Al-A3; (April, 1956), pp. 35-37; (May, 1956), pp. A7-A8; (June, 1956), pp. A8-5l; XXI (July, 1956), pp. A3—A6; (August, 1956), pp. A2-A7; John M. Evans and Joh W. Hicks, loc. cit.; American Council on Education, Computation Of Unit Costs (Washington: American Council on Education 1955, reprinted from Financial Reports for Colleges and Universities published by the University of Chicago Press, 1935), pp. 177 and 2A9; California and Western Conference Cost and Statistical Study (Berkeley: University of California, n. d{7, pp. xvi and 129. 22 Miller, Op. cit., pp. 5-6. 23Evans and Hicks, loc. cit.; L. E. Hull, "Pitfalls in the Use of Unit-Cost Studies," Journal of Higher Education, XXXII (October, 1961), pp. 371-376. 2“American Council on Education, Computation of Unit Costs, Op. cit., p. 177. 25 26 Dobbs, Op. cit., pp. l7A-l75. Miller, Op. cit., pp. 6-7. 27Ibid., p. 118. 28Oklahoma State Regents for Higher Education, Financing Current Operating_Costs of Higher Education in Oklahoma, Self-Study Of Higher Education in Oklahoma, Report A (Oklahoma City: Oklahoma State Regents for Higher Education, January, 1963), pp. 56-61. 29Texas Commission on Higher Education, Basis of Commission Recommendation on Institutional Requests for Legislative Appropriations 1963-65 Biennium (Austin: Texas Commissions on Higher Education, n. d.), pp. 1 and 11. 3OBudget Director's Office, University System of the State of Florida: Educational and General Budgets (Tallahassee: Budget Director's Office, Biennium 1961- 1963), p. 8. 37 31California Department Of Education, Memorandum to Deans Of Instruction, From Jon S. Peters, Subject: Genesis-- The Faculty Staffing Formula. California State Department of Education, June, 1961, p. 30. (Mimeo) 32Miller, op. cit., p. 121. 33Oklahoma State Regents for Higher Education, op. cit., p. 57. 3”Texas Commission on Higher Education 1963-65 Biennium, op. cit., p. 3. 35University System of the State of Florida, pp. cit., p. 35- 36Miller, op. cit., p. 122. 37Harry Williams, Planning for Effective Resource Allocation in Universities (Washington: American Council on Education, 19667, p. 12. 38Miller, op. cit., p. 152. 39Logan Wilson, "Analyzing and Evaluating Costs in Higher Education," The Educational Record, XXXXII, NO. 2 (April, 1961), p. 102. ' ORourke and Brooks, op. cit., p. 80. CHAPTER III OBJECTIVES OF THE RESEARCH Summary Of the Rationale for the Study Effective resource projection and allocation requires an analysis of the current expenditure-user relationship in Supplies and Services. It is important to know the identification of those department variables which actively use or are related to Supplies and Services expen- ditures because this information is basic to develOping a means Of funding departmental needs. These data will provide administrative personnel with information that can aid in deciding future needs in some Objective pro- cedure as well as providing information which can help appraise the current budget situation. Basic Objectives The basic Objectives of this dissertation are to answer four questions concerning Supplies and Services eXpenditures in academic departments at Michigan State University. First, are there significant correlations (simple and multiple) between expenditures (dependent variables) for Supplies and Services and use factors (independent variables) in departments? Second, what are the major independent variables (use factors) which are 38 significantly correlated with the expenditures? Third, how well does a "general" independent variable combination which explains the user-expenditure relationship for all department types and sub-categories of Supplies and Services compare with more specific independent variable combin- ations which explain user-expenditure relationships for specific department sub-groups and specific Supplies and Services sub-categories? Such a comparison is concerned with degree of correlation, independent variables involved, and general predictive ability. Fourth, what impressions or implications can be drawn from the analysis in terms of: (l) the expenditure-user relationship; (2) the effect Of department sub-groups and Supplies and Services sub- categories on total expenditure-user relationships; (3) the relative validity of the independent variables which are found to be significantly correlated with eXpen- ditures; and finally, (A) the predictive ability of the regression equations. Parameters of the Data Although in the analysis and procedures section a description of the data will be given, it is necessary to briefly identify the parameters Of the data so that the Objectives are more clearly understood. First, there is the sample of thirty-two academic departments with two years' data. The two years' data are being treated as a single set Of data and therefore the thirty-two departments A0 are actually equivalent to sixty-four. This department sample can be treated in total or it can be subdivided into four equal groups which have similar needs. Second, there is total Supplies and Services expenditures for each department which is the dependent variable in the analysis. This dependent variable for each department can be treated in total or it can be subcategorized into several cate- gories. Third, there are the seventy-two use factor department variables, such as students and faculty, which are the independent variables in this analysis. These are employed in various combinations for analysis of all dependent variables. The independent, predictive variables ( use factors) will be used in all the analysis and as such are not a major concern in terms Of being subgrouped or subcate- gorized. However, as pointed out, the dependent variable, expenditures, can be subcategorized into several cate- gories and the total department sample can be subgrouped into four groups. Therefore, the data consist of the following: (1) independent variables known as use factors; (2) the dependent variable Supplies and Services with certain major subcategories; and (3) a total department sample, with both the dependent and independent variables, which can be subgrouped into four similar groups. Al Specific Objectives The first Objective of the analysis is to determine the extent of simple correlations between the dependent variable, expenditures, and the independent variables, use factors, for the total department sample and for total Supplies and Services. This Objective should provide a general picture of the user-expenditure rela- tionships. Further and more detailed analysis will come through multiple correlation. The second Objective of the study is to investigate, through multiple correlation and regression analysis, the use factor-eXpenditure relationship for the following: (1) the most "general" independent variable combination which explains expenditures for total Supplies and Services for the complete department sample; (2) specific independent variable combinations derived from the results of the major subcategories of Supplies and Services utilizing the complete department sample; and (3) specific variable combinations derived from total Supplies and Services with the department sample being subdivided into four groups. The results of this analysis will afford broad com- parisons Of the "general" independent variable combination which explains the user-expenditure relationship for total Supplies and Services and the total department sample with the more specific independent variable A2 combinations which explains the user-expenditures rela- tionship for the subcategories of Supplies and Services and the subgroups of the department sample. It will help determine whether a single, "general" independent variable equation is sufficient to explain the use factor- expenditure relationship for different department types and different subcategories or whether more specific equations are required. The third Objective of this study, which is an outgrowth and somewhat of a duplication of the multiple correlation, is to determine the "general regression equation for total Supplies and Services utilizing the total department sample. Then, as in the multiple corre- lation analysis, the objective is to determine the more specific equations for the four subgroups Of the department sample and the subcategories of Supplies and Services. The analysis will be concerned with comparing the results of the "general" equation which explains the user-expenditure relationship for all departments and all subcategories with the more specific equations which explain the user-expen- diture relationship for subgroups and subcategories. This latter Objective is concerned with the predic- tive qualities Of the "general" equation and the specific equations in two ways. First, there is a statistical analysis Of predictive qualities which is concerned with the overall predictive ability of each equation. Second, there is an analysis which is concerned with results as reflected in actual versus predicted values for each department. The first analysis is concerned with general predic- tive qualities of a multiple correlation and regression equation which include the following: (1) the difference in independent (use factors) variables which show up in the analysis; (2) the degree Of correlation; (3) the total amount Of variance (coefficient of determination) eXplained by the combined independent variables; (A) the variance explained by each separate independent variable in the total combined equation; (5) the standard error of estimate. The second analysis is concerned with the specific departments in the sample rather than overall statistical measures which are concerned with the general state Of predictive analysis but which do not say anything about individual results. Therefore, results of the regression equation will show predicted and actual values with residuals for each department. In addition, the predictive ability of the equation will be analyzed for each depart- ment and department group as a percentage difference between actual and predicted values. A fourth and final Objective is concerned with evaluating the results from the above four Objectives. The evaluation will be concerned with the following items: AA 1. A general discussion Of the simple correlation results with particular attention focused on the variables which show high correlation. 2. Discussion Of the multiple correlation analysis with emphasis on the independent variables involved; the variance in expenditures explained by the combined inde- pendent variables; the variance of individual, independent variables which make up the multiple correlation; the differences in results for the "general" versus specific variable combinations, and finally, possible reasons for the results. 3. Discussion Of the regression results as to the independent variables involved, their variance, the results of the predictive qualities of the "general" versus "specific" equations, and possible explanation for the results of the "general" and "specific" equations. A. Discussion Of problem areas which showed up in the analysis. Possible problem areas might be low corre- lations; high correlations but the validity of independent variables seems questionable; indications of inequities, and possible reasons for the differences between the "general" variable combinations and the more "specific" variable combinations. 5. Discussion Of the possible uses of the results in terms Of evaluating the present situation in Supplies and Services and the possibility of utilizing the regression results for future budgeting. A5 Summary The Objectives of this research are concerned with the following: (1) the extent of correlation between independent (use factors) variables and dependent (expenditures) variables; (2) the identification of the independent variables which are significantly correlated with eXpenditures; and finally, (3) a multiple correlation and regression comparison of the "general" results for the total sample, total eXpenditures with specific results for sample subgroups and expenditure subcategories. CHAPTER IV DESCRIPTION OF SAMPLE, VARIABLES, AND PROCEDURES OF ANALYSIS In cost analysis and formulas there is usually a single measure which is commonly used to estimate budget needs, with a heavy emphasis on projection. As noted in the review of literature, any number of factors is employed to estimate the need for supplies and services funds in state systems but few show any relationship to supplies and services. Therefore one Of the major reasons for using regression and correlation for analysis purposes is its ability to aid in revealing the many relationships which may exist between budget uses and expenditures. Regression and correlation, unlike cost analysis or formulas, is not limited to or dependent upon a single variable as an estimate of need or as a guide to evaluation. By using regression and correlation it is possible to determine numerous simple and multiple rela— tionships, the strength Of those relationships, the differences in relationships, and the effect of maintaining those relationships in the future. In analyzing the relationship between varying quan- tities as in regression and correlation analysis, dependent and independent variables are usually established. When a A6 A7 change in the value of one variable corresponds to a change in the value of the other variable, a functional relation- ship is said to exist. In this study, the expenditures for supplies and services of thirty-two academic departments for a two-year period will serve as the dependent variables, and those departmental use factors which characterize departmental workload will serve as the independent variables.1 Explanations of the statistical techniques employed in analyzing the functional relationships between the dependent and independent variables will follow a discussion of the sources from which the basic data were derived. Dependent Variables The dependent variables are the Supplies and Serivces eXpenditures for thirty-two academic departments for the fiscal years 1965-1965 and 1965-1966. The dependent variables2 include total Supplies and Services expenditures with five major subcategories of that total being employed for analysis. For each department in the sample the dependent variables are the following: (1) total Supplies and Services expenditures (Variable No.A); (2) total Supplies and Materials (Variable No. 17); (3) total Supplies and Services expenditures less Supplies and Materials expen- ditures (Variable NO. 20), this derived category includes all Supplies and Services expenditures except Supplies and Materials; (A) total travel expenditures (Variable NO. 10); A8 (5) total faculty-related expenditures (Variable No. 6), including travel expenditures, printing expenditures, Off- campus contractual eXpenditures (072), book and magazine subscriptions, and telephone, telegraph, and postage expenditure. These eXpenditure data for each department are Obtained from the annual "Object Class Report" of the University Business Office. This report serves as a primary source of data for the Michigan State University Annual Financial Report. Independent Variables The independent variables consist Of 73 kinds Of data which are collected on a quarterly basis by the Office Of Institutional. Research from various University sources. Thirty-six are collected in the fall quarter of each year, 22 items collected for the full year which match comparable fall data, and another 15 items of faculty data which can be counted in either category. These independent variables can be broadly classified as general department data, faculty and staff data, and student data and can be said to provide a reasonable guide to a department's workload and expenditure needs. The general department data consist Of such items as the number Of courses taught, class hours of instruction, the number of sections taught, and the equipment inventory of a department by type Of equipment. Faculty and staff A9 data consist Of faculty head count, full time equivalent faculty, faculty time distribution, A and B faculty counts, and faculty head counts by rank. Student data consist of student credit hours, majors, and student credit hours by type of section. The above listings are not complete or detailed but provide examples of the type Of data utilized.3 Department Sampje Thirty-two academic departments assumed to be repre— sentative of all University departments were chosen as a sample which included departments with a variety of pro— grams and functions, along with variations in size. These departments were chosen on the basis of department type and on the basis Of size variations in the independent variables. The departments vary in overall department size from the small (entomology) to the large (chemistry and history) with all intermediate sizes. A major concern in choosing a sample of departments was to include enough to provide a wide range of size coupled with variation in functions that might have an influence on supplies and services expenditures. While in certain analyses the thirty-two departments chosen are treated as a single group, they are also broken down into four groups of eight departments each for other analysis. These four groups are based on a National Science SO Foundation scheme for departmental groupings. The four groupingsLl are: 1. Group 1 are laboratory science departments with evidence of direct student consumption Of supplies and are classified as basic science disciplines by NSF. 2. Group 2 are laboratory science departments with evidence of direct consumption of supplies and are clas- sified as applied science disciplines by NSF. 3. Group 3 are non-laboratory departments having no laboratory sections, no evidence of direct student consump- tion of supplies and are classified as belonging to the social sciences and the humanities by NSF. A. Group A are laboratory-oriented departments having laboratory sections with evidence of direct student consumption of supplies and are classified as social sciences and humanities by NSF. Assumptions of the Study The assumptions of this study are the following: 1. While departments may have individual and special preferences for Supplies and Services resources the broad and more general subcategories chosen in this analysis, as well as the total expenditures for Supplies and Services, should respond to various common workload factors in every department. Volume variations should be a primary and first approximation Of need for each department. 51 2. Supplies and Services, and especially Supplies and Materials, should reflect the laboratory needs of department groups 1, 2, and A. 3. The large number and variety of independent variables should account for and eXplain variations in department needs for Supplies and Services. A. That there is a linear relationship between independent and dependent variables. 5. Normal distribution of variables-—assumption not necessary because skewness and kurtoses values (Bastat 5) of all variables indicate a generally normal distribution of values with few observable exceptions. 6. That the thirty-two department sample is reasonably representative of all academic departments. A check on this was provided by comparing the mean values and standard deviations of selected sample variables to the mean value and standard deviation of the University total. The results confirm the assumption that the thirty-two departments are representative of the total University. Limitation of the Study The limitations of the study are the following: 1. The department sample is limited to academic departments and to university general fund money and does not include non-general funds resources from research grants or the Agricultural Experiment Station. 52 2. Dependent variable expenditure data are limited to the two-year period l96A-1965 and 1965—1966 when expen- diture data were first available in its present form. 3. The departmental groupings (A major groups) may have some overlap; however, these department groups represent a fair approximation of departments having similar characteristics and needs. A. Quantitative relationships (volume) between dependent and independent variables may not be sufficient to totally or adequately explain department needs but the independent variables in the study should be a first approximation of need. 5. The use of regression and correlation analysis may not uncover all the factors useful in estimating the need for Supplies and Services but the analysis is a beginning of such an evaluation. Statistics Employed In analyzing the functional relationship between the dependent and independent variables, a multiple correlation and regression analysis was employed. Correlation permits us to establish the extent two things are related, to what extent variations in the one go with variations in the other. Regression's main use is to predict the most likely measurement in one variable from the known measurement in another, for example, predicting the amount Of expenditures in Supplies and Services for a department where the size of 53 its faculty is known. The higher the correlation between independent and dependent variables the greater is the accuracy of prediction from regression and the smaller the errors of prediction. Multiple correlation and regression extends the analysis beyond the establishment of relationships between two items at a time and the prediciton of some variable y from another variable x. The coefficient of multiple correlation indicates the strength of relationship between one variable and two or more variables taken together. To facilitate analysis three Michigan State Univer- sity STAT series computer programs were utilized. STAT Series No. 5 (Bastat) was utilized to develop basic statistics on the data, including simple correlation, means, standard deviation, skewness, kurtoses and test statistics on the correlations. STAT Series No. 9 (LSADD) program, which is explained later in more detail, was utilized to reduce the large number of independent variables to a number usable in the STAT Series No. 7 program (LS). STAT Series 7 (LS) was utilized as the primary analysis program for multiple regression and as a further check on the multiple correlation results of STAT Series 9.5 The basic statistical measures of the study will be outlined here in order to facilitate reporting the results in these statistical terms. Analysis results will be reported primarily in overall statistical measures but 5A measures that pertain specifically to individual variables and department will also be reported. Overall statistical results include the following:6 1. r is the simple correlation coefficient. This measures the extent to which variations in one item are associated with variations in the other.7 2. R is the multiple correlation coefficient. This indicates the extent of relationship between a dependent variable and two or more independent variables. +1.00 is a perfect positive relationship, —l.00 is a perfect negative relationship. The following verbal description of coefficients by Guilford gives a general guide for interpreting R.8 Less than .20 Slight, almost negligible rela- tionship .20 - .A0 Low correlation; definite but small relationship .AO - .70 Moderate correlation; substan- tial relationship .70 - .90 High correlation; marked relationship .90 — 1.00 Very high correlation; very dependable relationship 3. R2 is the coefficient of multiple determination or variance (the square of the multiple correlation coefficient). This measure indicates the prOportion of variance in the dependent variable that is accounted for by the independent variables combined with the regression weights used. R2 of .8878 indicates that 88.78 per cent of the variance in variable y is eXplained by the varia- tion in z and x. K2 (1-R2) is sometimes used to indicate 55 the percentage of variance still to be accounted for. This is known as the coefficient of multiple non-deter- mination.9 A. R2 (R Bar 2) is the coefficient Of determination adjusted by degrees of freedom. It takes into account the number of independent variables relative to the number of observations. Though not commonly reported, it is mentioned in case the size of R Bar 2 differs considerably 10 from R2. 5. S is the standard error of estimate—-a standard deviation measure for correlation and regression which provides an overall measure of how far the predicted values of a regression equation would deviate from the actual values.11 6. F value for significance testing. This tests the hypothesis, at a prescribed significance level (.05) that the relationship between dependent and independent variables is different from zero and not the result of a chance happening.l2 Statistical results pertaining to individual variables: 1. Regression coefficient13 is an.obtained value for each independent variable in a linear least squares equation which tells how many units y increases for every increase of one unit in x. Regression coefficients indi- cate the rate of change in one variable per unit of change in the other. 56 2. Beta weightslu provide a standard comparison of individual variables by expressing each variable in terms of its own standard deviation. Beta weights provide a means of establishing the relative contribution each independent variable makes to the total variation explained by all independent variables. R2 (coefficient of deter- mination) is the sum of the product Of beta times its correSponding simple r. 3. Residuals are the difference between the pre- dicted and actual expenditure values for each department.15 Design To study the relationship between the dependent variables and the independent variables multiple correlation and regression analysis was utilized. The pattern of analysis included correlation and regression results between the independent variables and total expenditures for the complete sample of thirty-two departments. A further independent-dependent variable analysis of four a priori selected subgroups of the entire sample was made. Additional analysis included utilizing the entire sample with a breakdown of the total dependent variable into 5 subcategories. Pearson-product-moment correlations for simple correlations were calculated with the least squares method being utilized to calculate multiple correlation and regression. Significance tests at .05 level of confidence were employed in all the analysis. 57 Analysis Procedures The large number of independent, predictive variables in the study necessitated utilizing the Bastat 9 (LSADD) and Bastat 7 (LS) computer routines. The LSADD and LS routines are basically the same in developing overall multiple correlation and regression statistical results with significance testing, but they differ in two ways: First, the LSADD routine develops only overall correlation and regression results and does not develop results for individual variables such as regression coefficients, beta weights, partial correlation and importantly, residuals for each department. The LS routine goes beyond the LSADD routine in performing the above-mentioned calculations. Second, the LSADD routine performs a vital task of selecting (denoting) from a larger set of independent variables those variables in combination which have the highest multiple correlation with the dependent variable. Sixteen independent variable combinations were chosen for the LSADD routine. Some were chosen on an a priori basis of probable relationship to the dependent variables and two sets of variables, one for all fall data, the other for the full year, included all independent variables in the study. These sixteen sets of variables provided complete coverage of all independent variables in the study that might be significantly correlated with the dependent variables. These sixteen combinations were 58 processed with the LSADD computer routine for each dependent variable and sample combination. The LSADD routine approxi- mates the Wherry-Doolittle Methodl6 which begins with the selection of the most valid predictor, other predictors are added, one at a time, until the multiple correlation, corrected for shrinkage, exhibits no further appreciable increment. The LSADD routine has several features that merit its use in multiple correlation and regression: (1) whenever the number of independent variables is too large for use on the LS routine; (2) the program handles the normally difficult problem of variable selection for multiple correlation and regression in a manner approximating the Wherry-Doolittle method; (3) the program overcomes any personal bias in selecting from a large number of inde- pendent variables which could not be utilized on the LS routine. The significant variable combinations derived from the LSADD routine were then utilized in the Bastat 7 (LS) routine for computation of the following statistics: (1) multiple correlation coefficients of R2, R Bar 2, R Bar and the standard error of estimate along the F test statistic variables; (2) regression coefficients for each independent variables with beta weights, standard errors of coefficients and betas, t and f statistics, and partial correlation coefficients; (3) predicted and actual expen- diture values for each department along with residuals. CHAPTER IV FOOTNOTES lSee Appendices E and A for a complete list of the department sample, the dependent and the independent variables. 2See Appendix A for a complete listing of dependent variables. 3See Appendix A for a complete listing of inde- pendent variables. A See Appendix E for a complete list of departments by group. 5Michigan State University Agricultural Experiment Station, Stat Series Descriptions No. 5 (Bastat), NO. 7 (LS), and No. 9 (LSADD), (East Lansing, Michigan: Michigan State University, 1967). 6J. P. Guilford, Fundamental Statistics in Peychology and Education (3d ed.; New York: McGraw Hill Book Company, Inc., 1966), Chapters VIII, X, XV, and XVI; Philip H. Dubois, An Introduction to Psychological Statistics (New York: Harper and Row, 19657, Chapters VI, VII, and VIII; Helen M. Walker and Joseph Lev, Statistical Inference (New York: Henry Holt and Company, 1953), Chapters X and XIII; Mordecai Ezekiel, Methods of Correlation Analysis (New York: John Wiley and Sons, Inc., 19Al7, pp. 50—5A, 128—130, 136—139, 196-152, Chapter IX, pp. 208—218, and 312-325; John E. Freund and Frank J. Williams, Modern Business Statistics (Englewood Cliffs, J. J.: Prentice- HalI, Inc., 1958), Chapters XIII and XIV. 7Guilford, Op. cit., p. 135. 8Ibid., p. 1A5. 9Ezekiel, op. cit., p. 159 and Guilford, op. cit., p. 397. 10 Guilford, op. cit., pp. 398-399. llEzekiel, op. cit., p. 159 and Guilford, Op. cit., p. 360. 59 60 12Walker and Lev, o . cit., pp. 251-255; Freund and Williams, op. cit., pp. 226—227, and Guilford, Op. cit., p. 207. 13Guilford, op. cit., p. 366. luIbid., p. 39A. 15Walker and Lev, Op. cit., p. 236. 16Harry E. Anderson, Jr., and Benjamin Fruchter, "Some Multiple Correlation and Predictor Selection MethodsJ'Psychometrika, XXV, No. 1 (March, 1960), p. 63. CHAPTER V ANALYSIS OF RESULTS Objectives of the Research The basic objective of this dissertation is to answer four questions concerning Supplies and Services expenditures in academic departments at Michigan State University. First, are there significant correlations (simple and multiple) between expenditures (dependent variables) for Supplies and Services and use factors (independent variables) in departments? Second, what are the most prominent independent variables (use factors) which are significantly correlated with the expenditures? Third, how well does a "general" independent variable combination which best explains the user-expenditure relationship for all department types and subcategories of Supplies and Services compare with more specific independent variable combinations which explain user-expenditure relationships for specific department subgroups and specific Supplies and Services subcategories? Such a comparison is concerned with degree of correlation, independent variables involved, and general predictive ability. Fourth, what impressions or implications can be drawn from the analysis in terms of: (l) the expenditure— user relationship; (2) the effect Of department subgroups 61 62 and Supplies and Services subcategories on total expenditure- user relationships; (3) the relative validity of the independent variables which are found to be significantly correlated with expenditures; and finally, (A) the predic— tive ability of the regression equations? Simple Correlations Findings As shown in Tables 1 through 6 an analysis of the data indicates a wide range of positive and negative corre- lations for the total department sample and total eXpen- ditures. Each of the four subgroups of the total sample reveals significantly high correlations between the dependent variable and the various independent variables with Group 3 departments having the lowest and fewest significant corre- lations. The results for fall and full year data do not indicate any significant differences in results so that- fall data results will be reported. Total Sample The total sample results in Tables 1 and 2 indicate high positive correlations for a large number of the inde- pendent variables. Using J. P. Guilford's scheme (as outlined in Chapter IV) for a verbal description of coeffi- cients, there were 5 variables showing significant correlations greater than .70 which is considered high correlation with a marked relationship. Nine independent 63 variables had correlations from .A0 to .70 which is moderate correlation with a substantial relationship. Thirteen variables showed low correlation with a small but definite relationship. Correlations for these latter 13 variables ranged from .20 to .A0. There were 22 variables having correlations below .20 and therefore were considered having negligible relationship. Negative correlations were not large enough to be considered significant. Variables above .26 correlation were significant at the .05 level of confidence. The five independent variables above .70 correlation included Variable 32, total number of laboratory sections, with the highest correlation of .83. Variable 68, total part-time FTE faculty, was second with a correlation of .81. Following at correlations of .75, .71, and .70 are Variable 51, total laboratory student credit hours, Variable 65, total part-time faculty, and Variable 66, total head- count faculty. These variables are common to all depart- ments with the exception of Variables 32 and 51 which are peculiar to Groups 1, 2, and A, but not Group 3. Group 3 is non-laboratory departments. Variables 68, 65, 66 are faculty variables which cut across all departments. It is reasonable to assume that Variables 32 and 51 would show a high correlation due to the laboratory-related function of all departments with the exception of Group 3. Variable 68, with its strong graduate assistant bias, is not a really 6A good measure of faculty need independent of laboratory influence. It more likely reflects laboratory Operations than it reflects ranked faculty. The strength of several faculty variables is a welcome indication that expenditures for Supplies and Services are related to a major user of such variables. Of particular significance is the strength of the head-count faculty (Variables 65 and 66) relationship to expenditures. Their high correlation suggests that the use of Supplies and Services :fihmis is not clearly segregated into general fund versus non-general faculty. Expenditures for certain department functions do not appear to be based solely on the number of general fund (FTE) faculty. A good example is telephone expenses. These expenditures apparently serve all faculty regardless of the individual faculty member's source of salary funds. It might be worthwile to question why departments expend general fund resources on non-general fund faculty if this latter group is supposedly self-sustaining from funds which supply their salaries. The most reasonable answer would seem to be the inability or desire of a department to segregate funds for the various derived faculty categories. Independent variables which might have been expected to show higher correlations were Variable Ag, total student credit hours, and Variable 72, total A faculty count. Variable A9 had a correlation of .36 which was significant, 65 and Variable 72 had a correlation of .32 which was not statistically significant. The low correlation of Variable 72 seems to be in the nature of the variable and what it represents. Variable 72 is the position count of A and B faculty as of July 1 each fiscal year. Since it is a position count and not a count of people filling positions, as is the fall FTE faculty count (Variable 69), the corre— lations with expenditures might be low. Comparing the faculty counts for Variable 72 and Variable 69, its fall counterpart, there is sufficient difference in the faculty size of these two variables to support this interpretation. Furthermore the much higher and significant correlation of .61 for Variable 69 supports the belief that actual faculty are more directly related to the level Of expenditures for Supplies and Services than a position count. An evaluation of Variable 72 as a guide to department needs may be required if the pattern for Supplies and Services is true for other department requirements and is true for all departments. The low correlation of Variable A9, total student credit hours, suggests that the uses of Supplies and Services are more specific than general in nature. While all students might influence the size of expenditures for Supplies and Services, the influence is less than the direct influence of faculty needs, laboratory needs, or 66 other comparable measures which reflect the expenditures for Supplies and Services in a more specific manner. Subgroups The four subgroups of the department sample show Group 1 (Table 3) to be the closest to the results for the total sample in terms Of high correlations and comparable variables. Group 1, which includes laboratory, basic science departments, shows 27 independent variables having a positive and significant correlation greater than .70. Of those variables, 12 are above .90 which is considered very high correlation with a very dependable relationship. Group 1departments show more independent variables with an overall higher correlation than the other three groups. Faculty, students, and general department data are ade- quately represented. The highest correlation Of the independent variables is Variable 51, number of student credit hours in laboratory sections. The next eleven independent variables suggest that the expenditures in Group 1 are highly correlated with those variables, faculty and students, which have a legitimate need for Supplies and Services. Once again, the highest variables reflect the basic character of these departments. Both variables, 51 and 32, measure laboratory needs and both are among the higher correlations. There are other independent variables which have correlations almost as high as Variable 51 and Variable 32, indicating that the expenditures in Group 1 67 are also covered by more general variables. Variable A9, total student credit hours, and Variables 60 and 62, concerned with department service load, support the corre- lation results of Variables 51 and 32. The high and numerous correlations of Group 1 suggest, in general, a group of departments that have received and expended funds sufficient to cover all their basic needs. There are faculty-related needs, student-related needs, and the more specific needs of laboratory--all are represented with significant and high positive correlations. It would seem that volume considerations for Group 1 may more ads- quately reflect need than the total sample. The relative homogeneity of a special group may help explain these results but it must also be considered that laboratory demands may have created a special leverage for total Supplies and Services funds that other departments have not had. Therefore, most of their other needs are also ade- quately funded under an umbrella of laboratory leverage. Group 2 Departments Group 2 (Table A) which is applied laboratory science departments shows a marked drop in overall correlation results. The six highest significant independent variables range from .50 to .72 correlation which is considered as moderate correlation with substantial relationships. The two highest correlation variables, 36 and 68, represent 68 total graduate weighted average section size and part-time FTE faculty. Variable 36 is a variable which exhibits a fairly high correlation, but it is doubtful that it can be considered significant in a face validity sense. A measure such as graduate weighted average section size might not be considered a reliable measure of need due to the complexity of what it purports to measure. Variable 68 is a more representative measure of need, as is Variable 66, both of which are faculty measures; however, more general measures of need as in Group 1 are not evident, nor are specific laboratory measures such as Variables 32 and 51 present. There is justification in eXpecting that Group 2, having laboratory needs, would show the above two variables in high positive correlation comparable to Group 1. However, the very low correlation Of Variables 32 and 51 is signif- icant, not statistically, but practically. Variables 32 and 51 show correlations of .12 and .00 which would indicate that the expenditures for Group 2 Supplies and Services are not correlated with two major independent variables concerned with laboratory Operations. Several explanations seem feasible in light of these results. First, both the specific and general needs of these departments are not adequately reflected in laboratory and general department variables because volume variations may not be sufficient to explain the basic needs of these departments. Second, the laboratory and general needs of 69 these departments may be supplemented by non-general fund sources to a greater extent than the other groups and therefore are sufficiently funded. Finally, the reaction to low correlations for laboratory variables tends to place the burden on insufficient funds; however, the cause of low correlations could be excessive funds relative to need. This latter conclusion seems more justified when residuals are examined in Table 30. Group 3 Departments Group 3, non-laboratory departments, (Table 5) shows simple correlations comparable to Group 2 but generally they are lower and less valid than any of the four groups. Of the top four independent variables, which range from .A5 to .71, which is considered moderate correlation with substantial relationship, there is no general variable that indicates that the overall expenditures of Supplies and Services for this group are represented. Variable A8, doctoral SCH, has the highest significant correlation of .71. There is no particular characteristic of these departments which would indicate that doctoral students would place a special burden on Supplies and Services. Variable 29 at .A5 correlation is also a doctoral measure. Variable 35, undergraduate weighted average section size, and Variable 5A, non-general fund faculty, appear to represent a more general basis of need but neither is sufficient alone to 7O serve as adequate measures of department need. Group 3 has no special need for supplies and services as was true for Groups 1 and 2, but the requirements for supporting faculty for travel, books and magazine subscriptions, telephone facilities, and general department operations would justify much higher correlations with faculty measures. None of these faculty variables show a statis- tically significant correlation except Variable 75 at .A9 correlation. Basic expenditures for students, while not directly identifiable as is true with laboratory depart- ments, should also show correlations at a higher level than is shown. Variable 26, number of courses, and Variable A9, total student credit hours, show correlations of -.1A and .26 respectively. Generally Group 3 appears deficient in significant correlations comparable to the results of Groups 1 and 2. GrogppA Departments Group A, (Table 6) social science and humanities departments with laboratory section, has high correlations comparable to Group 1 with 18 independent variables having significant correlations between .72 and .91. Results in this range are considered high correlations with marked rela- tionships. Variables 69 and 72 are comparable faculty counts with Variable 69 being the full—time equivalent count-(FTE) and 72 being the total A and B faculty count. The conclusions 71 and implications about Variable 72 (A and B faculty) which were reached in discussion of the total sample are not fully supported in Group A where the correlations for Variable 72 and Variable 69 (FTE faculty) are the same. It must be noted that Group A is joined by Group 1 in this pattern whereas Groups 2 and 3 deviate most prominently when Variable 72 is considered. The effect of Groups 2 and 3 is sufficient to cause the overall decline in correlation for the total sample and therefore to single out Groups 2 and 3 for investigation seems justified. Of the next nine variables ranging from .8A to .90 correlation, five variables are faculty measures, two variables are student- related measures, and two variables are general department measures. These results indicate rather strongly that these departments are well represented by faculty, student, and general department variables. Variables 51 and 32 which were very high for the total sample are less for Group A but are nevertheless significant at .60 and .6A correlation. Group A displays the ideal pattern of having numerous faculty, student, and general department measures signif- icantly correlated with total Supplies and Services eXpen- ditures; however, the more prominent variables of this group are relatively suppressed when the total sample variables are perused. Perhaps it is unrealistic to expect the same independent variables to have identical 72 high correlations across the four groups but there is a hope that the total results will reflect the group patterns more uniformly Imither than reflecting one or two of the groups predominantly. Summary of Total Sample and Four Groups A summary of the total sample and the four department subgroups indicates significantly high correlations. Groups 1 and A have the highest correlations and the best cross section of independent variables. Groups 2 and 3 have moderately high correlations but the independent variables have less validity. Primarily, the independent variables that have the highest correlations for these latter two groups are specific in character and more_restrictive than expected. That is, the results show specific sub-units of a more general variable. For example, Variables A8 and A7 for Group 3 are sub-units of Variable A9. Both Variables A8 and A7 show relatively high correlations but Variable A9 does not show a comparable level. Furthermore, the needs of Group 3 are not justifiably reflected by these specific values when faculty and other independent variables show up so poorly. If a variable is specific in nature, as Variables 51 or 32 for Group 1, then a predominant need of that group must be laboratory needs. Group 3 has needs commensurate with its character, and its basic orientation is more general than masters and doctoral level work. 73 Three results seem to stand out from the simple corre- lation results. First, faculty-related measures are most prominent across departments as significant independent variables. This result is encouraging because faculty measures cut across all department types and faculty members are expected to be prominent users of Supplies and Services. While various faculty measuressflunvup as significant, the presence of head-count faculty measures suggests the line Of demarcation between sources of funds is not always followed, nor perhaps can it be followed. Second, the lower correlations and less prominent variables of Groups 2 and 3 suggest that these departments have not reacted in the same manner as Groups 1 and A according to volume consideration. There is no guarantee that the funds available for expenditures in departments will be sufficient because volume variations may not do justice to actual need when cost factors are also con- sidered; however, if volume is a first approximation to need, then the departments in Groups 2 and 3 have not responded in the same general manner as l and A. It should not be concluded that the lack of correlation is due alone to insufficient funds. Third, laboratory measures stand out as prominent independent variables but it was expected since laboratory Operations utilize a sizable proportion Of Supplies and Services in the form of Supplies and Materials (Variable 17). 7A What was not expected was the overall influence of these factors in the total sample. Group 3 does not possess these variables, and Group22shows lower correlations than 1 or A; however, laboratory measures dominate the total sample with Group 1 appearing to have a major and sub- stantial influence on the total results. Multiple Correlations Multiple correlation analysis was desired in order to ascertain the level of correlation and variance accounted for by several significant independent variables in com- bination when these variables were correlated with various dependent variables. A second reason was to determine the relative contributions several independent variables might make to a single multiple equation which would best explain expenditure levels. Further it was hoped that multiple correlation analysis might bring into a single equation enough different variables to adequately explain and represent different department types. While 16 variable combinations were run for each cell of analysis, there was considerable repetition. Therefore the results reported focus on the multiple correlation and regression results with the lowest standard error of estimate, the highest correlation, and the highest variance accounted for by a combination of significant, independent variables. In all cases the best results were fall and full year variable com- binations with fall term results being the better. 75 Total Supplies and Services (Depertment Variable No. A) Total Sample An independent variable combination which included seven significant variables (Table 7) produced a multiple correlation of .96, a high variance at .93, and a relatively low standard error of estimate of $8,919. Variable 32, total number of laboratory sections, had the highest correlation at .83 with a variance of .70. Variable 68, part-time FTE faculty,tmt with a very heavy laboratory bias, added 11 per cent to the variance while Variable 26, total number of courses taught, added another six per cent to the overall variance. These first three variables accounted for 87 per cent of the total variance in Supplies and Services expenditures. The remaining four variables, although statistically significant, added only six per cent to the total explained variance. The results of the multiple correlation analysis show the laboratory influence being most Obvious. The remaining independent variables, with the exception of Variable 51, have a less specialized orientation. These latter variables may be able to account for the other needs Of departments even though that influence is less than 20 per cent of the total explained variance. 76 Group 1 Departments Group 1 (Table 8) generally duplicates the total results in level of correlation at .99 and a total variance at .99. In Group 1, Variable 51, the number of laboratory student credit hours, replaces Variable 32 as the predom— inant laboratory influence accounting for 95 per cent of the explained variance in expenditures for this group. The other two variables in this multiple combination add only four per cent to the total explained variance. The results of this analysis are expected but not to this extent. It is understandable that laboratory measures would be important in this particular department group but it is disturbing that expenditures in these departments are so dominated by laboratory needs. The simple correlations suggest that more general measures of need are also highly correlated so that this particular laboratory emphasis does not distract from other department requirements. In fact, the leverage for these departments provided by laboratory requirements may be a key element in producing funds for other department needs. Group 2 Departments Group 2 departments (Table 9) Show an equation which has Variable 68, part-time FTE faculty, and Variable 32, number of laboratory sections, producing a standard error of estimate of $3,289, a total variance of .66 with a multiple correlation Of .81. Part-time FTE faculty accounts 77 for 51 per cent of the total variance which leaves 10 per cent of the variance explained by the number of laboratory sections. The results are disappointing relative to Group 1 but as pointed out in simple correlations this group may not be expected to duplicate Group 1 even though it has laboratory needs. Group 3 Departments Group 3 (Table 10) shows the combined results of its most prominent but relatively invalid simple correlations. The analysis shows Variable A8, doctoral student credit hours; Variable A7, masters and graduate-professional student credit hours; Variable 61, majors in department courses; Variable 37, masters and graduate-professional class hours; and finally, Variable A6, upper division student credit hours, accounting for a .96 variance with a standard error Of estimate of $1,065 and multiple corre— lation of .98. This group, as pointed out in a discussion of simple correlation, reflects statistically significant high correlations but the independent variables are subject to question as valid indicators or measures of Group 3's needs. While trying to avoid forcing every department group or dependent variable subcategory into a fixed set of desirable independent variables, the needs and character of these departments do not suggest why doctoral and masters student credit hours should account for 65 per cent of the variance in level of expenditures. There is a 78 temptation to accept these multiple correlation results because their combined effect is so much greater than any of the simple correlations. The high correlation results are very impressive; however, knowing that Group 3 does not have special and unique features reinforces a belief that a rejection of the above results is justified. If the major purpose of this study was to Obtain high corre- lations in order to project current practice into the future, then the Group 3 results are satisfactory. But the results suggest, and they are not discredited by other evidence, that these departments do not appear to have their expen- ditures correlated with notable independent variables comparable to the other departments. Therefore the results serve as a signal for further inveStigation. Group A Departments Group A departments have high correlation with a low standard error of estimate produced by three significant independent variables. As was true in the simple corre- lations, faculty-related variables play a prominent role in Group A. Table 11 shows that Variable 69, total FTE faculty, accounts for 83 per cent of the variance with Variable 27, lower division class hours, and Variable 25, total number Of graduate courses, explaining another 15 per cent of the total variance. In combination the three variables produced a correlation of .98 with a variance of .96 and a standard error of estimate of $1,566. 79 Each of the above three variables reflects the program efforts of these departments. TOtal FTE faculty should and apparently does account for a very high proportion of the Supplies and Services expenditures in this department group. The two other variables also reflect two distinct qualities of these departments. The amount of lower division class hour work produced is significant because class hours reflect to some extent the laboratory orien- tation of these departments. The third variable, total number Of graduate courses, also gives some recognition to a sizable function of these departments. Although direct laboratory variables such as Variables 32 or 51 do not appear in this multiple correlation, they do show up in simple correlations. Furthermore Variable 27, lower division class hours, may be an adequate substitute for these variables. Summary of Four Groupe An overall impression of the total results and of each separate group indicates high correlation and high variance with faculty and laboratory variables being most prominent. The results for the total sample reflect specific needs of certain departments by giving weight to laboratory needs. Faculty and staff needs and the general overall department needs have far less emphasis given to them by the "general" equation. The very high weight given to laboratory needs 80 would appear to be a primary reflection of Group 1 and a much lesser result of Groups 2 and A. Variables 68, part- time FTE faculty, and 26, total number of courses, come fairly close to reflecting the other non-laboratory needs Of all departments. Also it was hOped that these total equation variables would also reflect the overall needs of Group 3 but this appears doubtful. Group 2 is disappointing because it does not reflect, in level Of correlation8 8} Independent :36 8 g Variable g g: '3 g Variable g ,5 '3 :5 H 0 CU :5 0H 0 Cd 2 (DUI—I z (DUI—l 51 Laboratory SCH 97* 26 Total No. of Courses ,72* 30 Total Class Hours 93* 55 Total Number Majors .71* 32 No. Laboratory Sec. 92* 6A Instr.-Prof.Head Count .69* 7A Total B Faculty 92* 67 FTEF Instr.—Prof. .69* 52 Total Cls.Ungrad.SCH 91* 29 Doctoral Class Hours .68* 6O No.Undergrad. in 31 No. Non-Laboratory Sec..66* Department Courses .91* 58 No. Undergrad.Majors .6A* 62 No. Non-Majors in 57 No. of Doctoral Majors .63* Department Courses .91* A6 Upper Division SCH .62* 63 Total in Dept.Courses .91* 75 Non—Gen.Fund Faculty .62* 68 Part-Time FTEF .91* A8 Doctoral SCH .61* 27 Lower Div. Cls. Hrs. .91* 73 Total A Faculty .58* A5 Lower Division SCH .90* 25 No. of Grad. Courses .53* A9 Total Stu. Credit Hrs. .90* 7O % Instruction .51* 56 No. of Masters Majors .87* 76 Prof., ASsoc. Prof. .A2* 69 Total FTEF .86* 53 Total Classes Grad.SCH .33 72 A + B Faculty .85* A7 Masters & Grad.-Pro.SCH.3l 28 Upper Div. Cls. Hrs. .8A* 36 Graduate Weighted 50 Lect. and Recit.SCH .83* Average Section Size .30 61 Total Majors in 37 Grad.-Pro.,Masters Department Courses .83* Class Hrs. .29 2A No. of Doctoral Courses .77* 33 N. Graduate Sections .23 5A Grad. Indep. Var. SCH .75* 3A Laboratory Weighted 65 Part-Time Faculty Average Section Size .23 Head-Count .75* 23 Masters and Grad.-Prof .07 66 Total Head Count .75* 35 Undergrad.Weighted 59 Undergrad. Majors in Average Section Size .07 Department Courses .7A* 71 % Research -.13 77 Asst. Prof., Instr. .73* 22 No. of Undergraduate Courses .72* *Correlation value significant at .05 level. 10A TABLE A.--Simple Correlation of Independent Variables with the Dependent Variable No. A (Total Supplies and Services) Group II Departments. 3 Independent 234,8 8 Independent 'gcbg Q Variable QSAH L) Variable QSAH E SSS-.43 E 8:44.) 5 r1060 3 r10CU z moa z moa 36 Graduate Weighted 72 A + B Faculty 17 Average Section Size 72* 27 Lower Div. Class Hrs. 15 68 Part-Time FTEF 71* 71 % Research 15 A7 Masters & Grad.Pro.SCH .57* 73 Total A Faculty .15 66 Total Head Count .53* 31 No. Non-Laboratory Sec..l2 53 Total Cls.Grad.SCH 51* 32 Number of Lab. Sec. .12 57 No. of Doctoral Majors .50* 60 Total Undergraduates 65 Part-Time Faculty in Department Courses .09 Head—Count .A8* 29 Doctoral Class Hours .03 25 No. of Graduate Courses A5* 51 Laboratory SCH .OO 75 Non—Gen.Fund Faculty .A5* 69 Total FTEF .OO 26 Total No. of Courses A0 A5 Lower Division SCH -.OA 23 Masters and Grad.-Pro. .38 A9 Total Stu. Credit Hrs.—.OA 2A No. of Doctoral Courses .37 61 Total Majors in A8 Doctoral SCH .36 Department Courses -.06 5A Grad.Indep.Var. SCH .33 A6 Upper Division SCH -.12 33 Number of Grad.Sec. .33 35 Undergrad.Weighted 3A Laboratory Weighted Average Section Size -.l5 Average Section Size .33 55 Total Number of Majors-.21 22 No. of Undergraduate 52 Total Classes Courses .25 Undergraduate SCH —.22 63 Total in Dept.Courses .2A 6A Instr.-Prof.Head Count-.22 56 No. of Masters Majors .23 67 FTEF Instr.—Prof. -.22 37 Grad.-Pro. & Masters 50 Lect. & Recit. SCH -.23 Class Hours .23 58 No. of Undergraduate 62 No. Non-Majors in Majors —.2A Department Courses .22 59 Undergraduate Majors in Dept. Courses -.2A 76 Prof.,Assoc.Prof. .21 7A Total B Faculty .19 30 Total Class Hours -.28 77 Asst. Prof., Instr. .19 28 Upper Division Class 70 % Instruction .18 Hours —.38 *Correlation value significant at .05 level. 105 TABLE 5.--Simple Correlation of Independent Variables with the Dependent Variable No. A (Total Supplies and Services) Group III Departments. lation a on c L. 318 2 Independent '3. g .2 ,8 Independent SE: 5, Variable E 34;; 5 Variable g; g z Ulora 2 A8 Doctoral SCH 71* 31 No. Non—Laboratory ax» .ll 35 Undergraduate Weighted 36 Graduate Weighted Average Section Size 67* Average Section Size .11 75 Non-Gen.Fund Faculty A9* 56 No. of Masters Majors .11 29 Doctoral Class Hours .A5* A5 Lower Division SCH .10 25 No. of Graduate Courses .AA* 63 Total in Dept. Courses .10 65 Part-Time Faculty 72 A + B Faculty .10 Head Count .AA* 73 Total A Faculty .10 71 % Research .A3 69 Total FTEF .09 2A No. of Doctoral Courses .A2 7A Total B Faculty .06 A6 Upper Division SCH .38 26 Total No. of Courses .03 66 Total Head Count .33 32 No. of Laboratory Sec. .00 57 No. of Doctoral Majors .30 51 Laboratory SCH .OO 53 Total Classes Grad.SCH .28 6A Instr.-Prof:HamiCount -.01 A9 Total Student Cr.Hrs. .26 67 FTEF Instr.—Prof. -.Ol 62 No. Non—Majors in 28 Upper Division Department Courses .2A Class Hours - 08 3A Laboratory Weighted 37 Grad.-Pro. and Average Section Size .23 Masters Class Hours - O8 68 Part—time FTEF .23 22 No. of Undergrad. Courses —.11 33 No. Graduate Sections .19 27 Lower Div. Class Hrs. -.1A A7 Masters & Grad.-Pro.SCH .19 77 Asst. Prof., Instr. — 1A 50 Leo. & Rec. SCH .18 55 Total No. of Majors — 18 5A Grad. Indep. Var.SCH .18 59 Undergraduate Majors 23 Masters and Grad.-Pro. .17 in Dept. Courses - 18 52 Total Classes 61 Total Majors in Undergraduate SCH .17 Department Courses — 19 30 Total Class Hours .13 58 Number of 60 Total Undergraduates in Undergraduate Majors - 21 Department Courses .13 7O % Instruction —.35 76 Prof., Assoc. Prof. .12 *Correlation value significant at .05 level. 106 TABLE 6.--Simp1e Correlation of Independent Variables with the Dependent Variable No. A (Total Supplies and Services) Group IV Departments. 8 Independent £38 6 Independent 3:8 8 a Variable S E 33 Variable 2* E: 3 5 r10€0 r10 m z U30r4 010.4 69 Total FTEF .91* 77 Asst. Prof., Instr. 63* 72 A + B Faculty .91* 65 Part-Time Faculty 26 Total No. of Courses .90* Head-Count 62* 61 Total Majors in 51 Laboratory SCH .60* Department Courses .89* 68 Part-Time FTEF .59* 56 No. of Masters Majors .89* 62 No. Non-Majors in 73 Total A Faculty .88* Department Courses .57* 76 Prof., Assoc. Prof. .87* 31 No. Non-Laboratory Sec..52* 27 Lower Division C1.Hr. .86* 53 Total Classes Grad.SCH .52* 66 Total Head Count .85* 7A Total B Faculty .52* 6A Instr.—Prof.Head Count .8A* 28 Upper Division C1.Hr. .51* 67 FTEF Instr.-Prof. .8A* 57 No. of Doctoral Majors .51* 59 Undergraduate Majors 2A No. of Doctoral in Department Courses .83* Courses .50 30 Total Class Hours .80* 50 Lec. & Rec. SCH .A8 60 Total Undergraduates 29 Doctoral Class Hours .A6 in Department Courses .76* A8 Doctoral SCH .A2 22 No. of Undergraduate A6 Upper Division SCH .35 Courses .75* 35 Undergraduate Weighted 63 Total in Dept. Courses .75* Average Section Size .32 A5 Lower Division SCH .72* 37 Grad.-Pro. and Masters 5A Grad. Indep.Var. 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Amy COHuwHoCCoo oHQHuHCz mCOHumsum mHQEmm uCoEuhmnmo kuoa .msdm . mpHCmmm COprCcm . «Cam .msdm .msum .maom .CCUM .Csum .msam sm> Hm H a HH 2...... a H r I” .H z Csoac > > > > > > > .HmpCoEquCoo : dsoaov mHomHCm> pCmoCoCoQ UoHMHomdm oCu COH mCCpHoCmme mpCoEHHQOQ Hmspo< me 30Hmm Co m>oo< mmwmquoCmm mm oommmCme mpCoEpCmCmQ HmsoH>HCCH Com.mHmConmm COHpmsom COHmmmaom oHCHpHCS mo ComHCmoEooII.mm mqm<9 CHAPTER VI SUMMARY, CONCLUSIONS,AND RECOMMENDATIONS FOR FUTURE RESEARCH Providing equitable and adequate funds for Supplies and Services functions requires that information concerning those expenditures be analyzed as effectively as possible. An evaluation of current expenditures compared with factors that might reasonably be expected to use those expenditures should provide administrators a means of evaluating and classifying problem areas. To evaluate the situation in Supplies and Services requires a willingness to experiment in the procedures used for evaluation. Typically Supplies and Services has not been extensively scrutinized by this University although this budget category has posed some of the most difficult problems of funding and hence eval- uation. Evaluation of this budget category has generally failed to receive much attention as evidenced by lack of literature on the subject. State systems of higher educa- tion have done the most with regard to Supplies and Services but typically their attention has been on funding for the future under a standardized ratio or formula. Obviously the issue not faced by the state systems is the question of 133 13A evaluating their practice to a greater extent than is reported in the literature. Also at issue is the tendency to select one factor or measure of need and then utilize this as a guide to budget planning and evaluation even where there is not adequate justification for this factor being used. Correlation and regression analysis, while used extensively in industry as a tool of budget evaluation, has not been effectively utilized in higher education budget analysis or at least it has not been reported in the literature. Correlation and regression analysis provides a tool of analysis which is suitable for instances where volume relationships are important and where no single measure of volume or need has been established as a totally appropriate evaluation criterion. Correlation and regression analysis in this study has been applied to a sample of academic departments with a range of similar, and yet different, needs for Supplies and Services. Dependent variables and independent variables have been chosen for analysis with the dependent variables being various Supplies and Services expenditure categories. Independent variables are data collected by various University agencies which have commonly been used to measure workload in academic departments. Four depart— ment subgroups of the total sample and the total department sample have been analyzed using total Supplies and Services 135 expenditures as the dependent variable. Further analysis has involved utilization of the total department sample with various subcategories of total Supplies and Services serving as dependent variables. The results have focused on the independent variables which were significantly correlated with the various dependent variables. These results have included the extent of correlation, the dif- ferent variables which were correlated, the strength of such variables in a multiple correlation, and finally the ability of various regression equations to predict actual expenditures for the various departments in the sample. Summary of Findings 1. There are statistically significant high corre- lations between the various dependent variables and the independent variables suggesting that expenditures do have some distinct volume relationship to departmental use factors. 2. The utilization of multiple correlation analysis generally improved the level of correlations but the pattern of key variables set in simple correlations did not vary greatly in multiple relationships. 3. The multiple correlations did, in most cases, give attention to various independent variables but laboratory measures and faculty measures were predominant. 136 A. Head count faculty measures usually exceeded in level of correlation the FTE faculty counts suggesting that general fund Supplies and Services expenditures are not limited to general fund faculty. 5. Department groups had very high correlations when total expenditures were analyzed,vdifliGroups 1 and A being the highest and most valid. Group 3 had high correlations but generally invalid independent variables. Therefore this group deserves attention beyond this study. 6. Subcategories of total Supplies and Services showed generally high correlations with expected results. Again FTE faculty did not show up as well as the faculty head count measures but in some cases the differences were not signif- icant. 7. Travel, Variable 10, had the worst results of the subcategories with no major faculty variable being corre- lated with these eXpenditures. 8. The "general" equation for all departments and all expenditures discriminates against non-laboratory departments in allocations due to the extensive influence of laboratory measures. 9. The best equations for predicting expenditures relative to actual expenditures were the special department group equations rather than the total or "general" equation and/or the equations for the subcategories. 137 10. The best subcategory equation was Variable 6 while Groups 1 and A did the best jobs for the groups. 11. Volume alone seems insufficient to explain expen- ditures. Those expenditures directly related to labora- tories (Supplies and Materials) may be justified in devia- tion from volume because of differential cost factors not measured in this study; however, volume variations for other department needs seem fairly defensible. In that case, results of subcategory equations 6, 20 and 21 need attention because they show departments with expenditures extensively above or below volume measures. 12. The results suggest a laboratory pattern with Groups 1 and 2 being most closely represented by this pattern but the results for the subcategories suggest that other functions have also been considered. Conclusions The pattern of departmental expenditures generally has followed a fairly rational basis of volume and need with certain notable exceptions. It is gratifying to know that the expenditures in academic departments do bear some identifiable relationship to factors which are believed to use those expenditures. The laboratory influence is overwhelming to such an extent that funds may be derived for departments based on laboratory needs, but they are extensive enough to allow 138 more than adequate funding for all other categories. This statement is difficult to absolutely document, but the analysis seems to point to a laboratory syndrome which indicates that as you move away from the basic science, laboratory departments, and the applied science laboratory departments, the results are either: (1) low correlations, or as in the case of Group 3, high invalid correlations, with insufficient funding or (2) highly Correlated but greatly under-funded departments such as Group A. This conclusion seems substantiated in one major way. The results for the subcategory Supplies and Materials (primarily a laboratory subcategory) duplicate the total equation. Therefore to a very great extent the total equation is really an equation for labofatory needs much more than it is an equation for all needs and all depart- ments. The laboratory influence of Group 1 or Variable 17 should not diminish the exceptional overall correlations found in the various subcategories of Supplies and Services. Whatever the cause, the fact that correlations were signif- icantly high between dependent and independent variables should serve as a focal point for improvement in a situation that is not perfect but is not at the same time hopeless or totally unrealistic. 139 Recommendations for Further Study Several recommendations seem feasible as a result of this study. Further investigation of cost problems for certain departments and certain categories seems required. Volume alone is not sufficient to adequately explain how or why certain departments expend funds at the level that they do. Certainly the cost of certain materials poses a special problem not answered but raised by this study. A second study should be concerned with volume as a measure of need if we are to know to what extent a department's needs are adequately measured by volume considerations. In what cases is volume a good measure and in what cases is it unable to adequately discriminate the extent or degree of need? This applies to all budgets and not alone to Supplies and Services. A study of the feasibility and desirability of changing the current Supplies and Services categories so that the categories will better reflect the direct use of these funds. The more closely expenditures are identified with actual users the better the cost data and the better the evaluation process. For example, at least two major functions are identifiable in Supplies and Services. First, those expenditures which are directly related to teaching or the instructional process. Laboratories are the major factors in this area but there are also eXpenditures directly related to the teaching function which are not lAO laboratory related. At the present time several subcate- gories of Supplies and Services are related to teaching and/or the operation of laboratories. There is no single comprehensive measure of laboratory costs that is readily available for the purpose of evaluating and managing these costs. For example, the subcategory Supplies and Materials contains both laboratory as well as non-laboratory expen- ditures. Furthermore the non-laboratory expenditures also contain general office or clerical supplies and those materials are related to teaching functions which are non- laboratory. Second, there are eXpenditures directly related to faculty. These are also contained in several categories so that there is no measure of how much it takes to add one faculty member to a department. Travel is directly related to both ranked and unranked faculty and so is Telephone, Telegraph and Postage. Part of the clerical outlay contained in Supplies and Materials and certainly the expenditures for Books and Magazine Subscriptions are primarily faculty related but neither these categories nor the preceding categories are easily or distinctly developed in a simple measure of faculty cost. The point being made is that a better system of identifying direct costs in Supplies and Services is a worthwhile consideration. Quite obviously the way in which a department chairman chooses to spend his funds 1A1 would still be his prerogative but his basis for requesting funds would be greatly facilitated. Also the evaluation of that need could be far better performed than is currently the case. So long as certain departments can exert leverage for funds based on laboratory needs, which are not easily identifiable, the issue of adequate and equitable funding for all departments will be unresolved. Other possible studies would include a personal eval— uation of the outlay of funds for Supplies and Services over the past two or three years by department chairmen. Such a study has been initiated and hopefully the results will help clarify some of the conclusions of this disser- tation. Finally a study could focus on trying to determine the influence that non-general fund and general fund resources have on one another. The issue may not be worth the expense of investigation but until the exact influence is known this University seems justified in imposing an overhead charge on non-general fund contracts. APPENDICES 1A2 APPENDIX A INDEPENDENT AND DEPENDENT VARIABLES USED IN CORRELATION AND REGRESSION ANALYSIS OF SUPPLIES AND SERVICES 1A3 1AA The following variables are those items which are collected regularly by various Michigan State University Offices (including the Office of Institutional Research) for the purpose of providing data about the Operations of the University academic departments. The data fall into four broad groups and are concerned with general fund operations only. The groups are (1) dependent variables including total Supplies and Services expenditures and the subcategories of that total; (2) independent variables including (a) faculty data; (b) student population data; and (c) general department data. The source of the data is the Office of Institutional Research quarterly and yearly reports, as well as the Annual Financial Report and "Object Class Report" of the University Business Office. 1A5 Fiscal Year Expenditure Data.from Michigan State University Annual Financial Report and "Object Class Report.Tr Variable No. Total Budget Expenditure. Salaries. Labor. Equipment. Supplies and Services Expenditures (Dependent Variables). A. (l) B. (2) C. (3) D. (5) E. 1. (10) 2. (ll) 3. (7) A. (12) 5. (13) 6. (1A) 7. (15) 8. (17) 9. (19) Travel (020); all travel categories treated as one. Postage, Telephone and Telegraph (OAO). Utilities, Rentals and Leases (050); three combined categories. Printing and Related Expenses, and Bookbinding (060); two combined categories. Physical Plant Department Services (070). Contractual Services (071). Other Contractual Services (072). General Supplies and Materials (082). Books and Magazine Subscriptions (180). F. Categories for analysis. 1. (A) 2. (20) 3. (17) A. (10) 5. (6) 6. (8) Total Supplies and Services Total Supplies and Services less Supplies and Materials. Supplies and Materials (082). Travel. Faculty—Related Subcategory:—-Trave1 (020's), Postage, Telephone, and Telegraph (0A0), Printing and Related Expenses (060), Other Contractual Services (072), and Books and Magazines Subscriptions (180). Equipment-Related Categories including Physical Plant Department Services (070), Contractual Services (071); and Utilities, Rentals, and Leases (050). II. 1A6 General Department Data from Office of Institutional Research quarterly reports. Variable No. Number of Courses Taught from Volume of Instruction. 1. R.) U‘I-ll'w AAA NV“) vvv AAAAA CXDNN N N OQKOWJ: woom vvvvv Undergraduate courses for fall term and full year. Master and Graduate-Professional Courses for fall term Doctoral Courses for fall term. Graduate Courses for fall term and full year. Total Number Courses taught for fall term and full year. Class Hours from Volume of Instruction. 1. \I own:- U) AAA AAA/\A (I) CDWN WCDNCDN 1‘: LAJOKO NNmI—‘N vvv vvvvv A V Lower Division Class Hours for fall term and full year. Upper Division Class Hours for fall term and full year. Master and Graduate—Professional Class Hours for fall term. Doctoral Class Hours for fall term. Grand Total Class Hours for fall term. Undergraduate Total Class Hours for full year. Graduate Total Class Hours for full year. Number Sections Taught from Section Size Analysis. 1. 2. (31) Number Undergraduate Non-Laboratory Sections for fall term and full year. Number of Undergraduate Sections for fall term and full year. Number Graduate Sections for fall term and full year. Total Number Sections for full year. III. 1A7 D. Weighted Average Section Size from Section Size Analysis for fall term only. 1. (3A) Laboratory. 2. (35) Total Undergraduate. 3. (36) Total Graduate. E. Equipment Inventory (Full Year). 1. (9) Clerical Equipment, Scientific Equip- ment and Kitchen Equipment combined. Faculty and Staff Data from July 1 position counts, and fall quarter Teaching Load and Time Distribution Analysis. A. Faculty Head Count from fall quarter Teaching Load and Time Distribution Analysis. 1. (6A) Full Time Faculty (Instructor-Professor). 2. (65) Part—time Faculty. 3. (66) Total Faculty and Other. B. Full Time Equivalent Faculty (FTEF) from Teaching Load and Time Distribution Analysis. l. (67) Full Time (Instructor-Professor). 2. (68) Part-time FTE Faculty. 3. (69) Total FTE Faculty. C. Faculty Time Distribution from Teaching Load and Time Distribution Analysis. l. (70) Per cent offaculty time devoted to Instruction. 2. (71) Per cent of faculty time devoted to Research. D. A & B Faculty in Full Time Equivalent Faculty (FTE) Position Counts. l. (73) A Faculty (Instructor-Professor). 2. (7A) B Faculty (Grad. Asst., Asst. Instructors, etc.). 3. (72) Total A & B Faculty. E. (75) Total Non-General Fund Faculty (Derived by subtracting Total Full Time Equivalent Faculty from Total Faculty Head Count). 1A8 Professor and Associate Professors (FTE). F. (76) G. (77) Assistant Professors and Instructors (FTE). IV. Student Data from Office of Institutional Research quarterly reports and the OIR "Brown Book." Variable No. Student Credit Hours (SCH) from Volume of Instruction Analysis. 1. \10\ (A5) AA SUD OKD O\oo VV VV A v AAA/NA J: .B'z-DJI'J: 5 CW J: \OWNODH \l vvvvv A V Lower Division Student Credit Hours for fall term and full year. Upper Division Student Credit Hours for fall term and full year. Undergraduate Student Credit Hours for the full year. Master and Graduate-Professional for fall term and full year. Doctoral Student Credit Hours for fall term and full year. Graduate Student Credit Hours for full year. Grand Total Student Credit Hours for fall term and full year. Number of Student Credit Hours by Type of Section from Volume of Instruction Analysis. 1. (50) Lecture and Recitation Student Credit Hours for fall term (89) and full year. 2. (51) Laboratory Student Credit for fall term (90) and full year. 3. (52) Total Number Undergraduate Section Student Credit Hours for fall term. A. (53) Total Number Graduate Section Student Credit Hours for fall term (91) and full year. 5. (5A) Graduate Independent Variable Student Credit Hours for fall term (92) and full year. 1A9 Number of Majors from OIR "Brown Book." .1:me Undergraduate Majors. Master Level Majors. Doctoral Level Majors. Grand Total. AAAA U'IUWU‘IUT U'I‘Q mm vvvv Number Majors and Non-Majors Taking Courses Offered by Academic Departments from Course Enrollments byyMajorsLVCurriculum, Class Level and Sex. l. Undergraduate (59) a. Majors. (60) b. Total (Majors and Non-Majors). Combined Undergraduate and Graduate. (61) a. Majors. (62) b. Non-Majors (Services). (63) c. Total. APPENDIX B SUPPLIES AND SERVICES SUBCATEGORIES (DEPENDENT VARIABLES) 150 l51 Total List of Supplies and Services Subcategories from Which Dependent Variables Were Selected Taken from the Michigan State University Manual of Business Procedures 020—-Travel in State: Includes transportation of persons, lodging, and subsistence while in an authorized travel status, and other expenses incidental to travel which are to be paid by the University, as follows: a. The cost of rail, air or bus tickets and tokens when travel is performed by a commercial carrier; mileage allowance granted when traveling by private conveyance or a rented car. b. Subsistence including reimbursement for food and lodging. c. Incidental travel expenses including telephone, telegraph, taxi fares, registration fees at conventions, etc. 02l--Travel within the Home Community Area: Mileage allowance only when authorized by the Dean. O22--Travel Out-of—State: Includes transportation of persons, lodging, sub— sistence while in an authorized travel status, and other eXpenses incidental to travel which are to be paid by the University. 023--Travel Out-of—State Includes transportation only. 152 02A--Travel Interview for Positions: Includes first-class transportation and normal expenses. O25--Travel--Non-University§Employee: (Excluded from study) Includes first-class transportation and normal expenses. O26-HTravel Overseas: Includes travel expenses as authorized by the University. O27--Team Travel: (Excluded from study) O30--Transportation of Things: Does not include transportation on equipment classified 090. OAO--Postage, Telephone & Telegyaph, as follows: a. All postage, telephone and telegraph services. b. Switchboard service charges and telephone installation cost. 0. Leased-wire for Extension Radio. d. Purchase of stamped enve10pes. 051——Utilities: Electricity, gas, water and steam. 052--Rentals: a. Monetary payment for the right of possession and use of land, structures, and equipment owned by another, the possession of which is to be relinquished at a future date. C.‘ 153 Charges for purchase rental agreements. (Under such agreements, until the title to the equipment is acquired, payments should be classified as rentals.) Post Office Box Rentals; also storage. 053--Leases: This code is to be used only where a 10% of the entire cost of equipment is charged on a yearly basis. 061—-Printing & Related Expenses: Cost of all contractual services for the printing of a. b. d. Books, pamphlets, documents, and other publications. University catalogs, bulletins, reports, student publications, technical bulletins. Engravings, cuts, half-tones, zinc etcings, zinc linecuts, and art work for printed matter. Programs, athletic and other. O62--Bookbinding & Misc. Small Printing Not for Publication: Printing of tickets. O70--Physical Plant Department Services Only 07l--Contractual Services: a. Repairs and maintenance to equipment, including maintenance contracts. Photographing, developing, engraving and blue-printing. Multigraphing and mimeographing work if performed by a vendor. 1. \TI .1: Entertainers or entertainment by contract. Entrance fees, membership dues, press news service. Ambulance and taxi services; hospitalization and any work performed by a business establishment. Commissions, fees, etc., for special and miscellaneous services rendered by others. Guarantees. State News delivery Registration of animals. Wiping cloths rental service. Advertising and publication notices. Any contractual service not otherwise classified. 072--Other Contractual Services: a. Alterations, repairs and maintenance to buildings which are not capital improvements. Professional services and physical examinations. Honoraria. Insurance and surety bonds. Laundry and dry cleaning. Payment of insurance premiums carried on retired University employees. 155 O82-—General Supplies and Materials: All commodities which are ordinarily consumed or expended within a comparatively short length of time; or converted into the process, construction, and manufacture of equipment; or form a minor part of it. a. Glassware for laboratories. b. Office supplies. c. Chemicals, surgical and medical supplies. d. Fuels, such as coal, wood, petroleum, or oil used in cooking, heating and generating power. e. Provisions, food and beverages for human con- sumption. f. Forage and stable supplies; food used for live- stock and small animals; also bedding, horse- shoes, collar pads, etc. g. Parts for the repair of equipment and machinery. h. Press clippings. i. Carpeting, drapes and venetian blinds. j. Furnaces, hot-water heaters, sinks, etc., which are permanently attached to the building. k. Hope and garden hose. 1. Small tools. l80--Books and Magazine Subscriptions: All books and magazine subscriptions purchased by the University except those for resale. APPENDIX C "STANDARDIZED" USE VARIABLE COMBINATIONS (INDEPENDENT VARIABLES) 156 157 Each combination or variation in grouping of depart- ments or in the use of different dependent variables (budgets) was analyzed with the same set of sixteen independent variable (use factors) combinations. By using a "standardized" set of independent variables the analysis with the LSADD routine selected the highest significant multiple correlation combination for each dependent variable. The following sixteen variable combinations include fourteen a priori selected sets which appear related to the various dependent variables. Two combin- ations, one for fall and the other for the total year, include all important independent variables in the study. This procedure insures impartial analysis of the dependent- independent relationship. 158 Supplies and Services Indppendent Variable Combinations These sixteen variable combinations represent variables which showed a definite relationship to dependent variables in preliminary data analysis as well as two combinations which cover all important independent variables in the study. These combinations were processed on LSADD routines and then the significant results were utilized in the LS routine. 1. Fall Quarter Independent Variables—-Variables No. 9, 22, 2A...28, 3o...36, A5...53, 57, 61, 62, 6A...69, 71...7A. 9 (office and scientific equipment inventory), 22 (No. of undergraduate courses), 2A (No. of doctoral courses), 25 (No. of graduate courses), 26 (total No. of courses), 27 (lower division class hours), 28 (upper division class hours), 30 (grand total class hours), 31 (No. non-lab sections), 32 (No. lab sections), 33 (No. grad. sections), 3A (lab whtd. ave. sec. size), 35 (total undergraduate whtd. ave. sec. size), 36 (total grad. whtd. ave. sec. size), A5 (lower div. SCH), A6 (upper div. SCH), A7 (Master and graduate—professional SCH), A8 (doctoral SCH), A9 (total SCH), 50 (lecture and recitation SCH), 51 (laboratory SCH), 52 (total classes undergraduate SCH), 53 (total classes grad. SCH), 57 (No. of doctoral SCH), 61 (total undergraduate and grad. majors taking dept. 159 courses) 62 (No. undergraduates and grad. non-majors taking dept. courses), 6A (full-time instructor- professor head count), 65 (part-time faculty head c0unt), 66 (total faculty and other head count), 67 (full-time instructor-professor FTE), 68 (part-time faculty FTE), 69 (total FTE), 71 (per cent time devoted to research), 72 (total A + B faculty count), 73 (total A faculty, instructor-professor), 7A (total B faculty; grad. asst., asst. instructors). Total Year Independent Variables--Variables No. 9, 38...AA, 57, 61, 62, 6A...69, 71...7A, 78...92. 9 (office and scientific equipment inventory), 38 (lower div. SCH), 39 (upper div. SCH), A0 (under- graduate total SCH), Al (masters and graduate- professional SCH), A2 (doctoral SCH), A3 (grad. total SCH), AA (total SCH), 57 (doctoral majors), 61 (total undergraduate and grad. majors taking dept. courses), 62 (No. undergraduate and grad. non-majors taking dept. courses), 6A (full-time instructor- professor head count), 65 (part-time faculty head count), 66 (total faculty and other head count), 67 (full-time instructor-professor FTE), 68 (part-time faculty FTE), 69 (total FTE), 71 (per cent time devoted to research), 72 (total A + B faculty count), 73 (total A faculty, instructor-professor), 7A (total B faculty; grad. asst., asst. instructors), 78 (total No. undergrad 160 courses), 79 (No. grad. courses), 80 (total no. courses), 81 (lower div. class hours), 82 (upper div. class hours), 83 (total undergraduate courses), 8A (grad. class hours), 85 (No. non-lab sections, U. G.), 86 (No. lab sections, U. G.), 87 (No. grad. sections), 88 (total No. sections, classes), 89 (No. non-lab SCH), 90 (laboratory SCH), 9l (grad. classes SCH), 92 (grad. ind.-var. SCH). Independent variables--Variables No. A9, 51, 69. A9 (total fall SCH), 51 (lab SCH), 69 (total FTE faculty).. Independent variables--Variables No. 2A, A5, A9, 51, 68, 69. 2A (No. doctoral courses), A5 (fall lower div. SCH), A9 (total fall SCH), 51 (laboratory SCH), 68 (part- time FTE), 69 (total FTE faculty). Independent variables--Variables No. 51, 57, 61, 69. 51 (lab SCH), 57 (No. doctoral majors), 61 (No. majors taking department courses), 69 (total FTE faculty. Independent variables-—Variables No. A7, 51, 68. A7 (masters and graduate-professional SCH), 51 (lab. SCH), 68 (part-time FTE). Independent variables--Variables No. 36, 51, 68. 36 (total grad. whtd. ave. sec. Size), 51 (lab SCH), 68 (part—time FTE). 10. 11. 12. 13. 1A. 15. 16. 161 Independent variables--Variables No. 36, 51, 66. 36 (total grad. whtd. ave. sec. size), 51 (lab SCH), 66 (total faculty and other head count). Independent variableS--Variable No. 32, A7, 68. 32 (No. lab sections), A7 (masters and graduate- professional SCH), 68 (part-time FTE). Independent variables--Variables No. 32, 35, 51, 57. 32 (No. lab. sections), 35 (total U. G. whtd. ave. sec. size), 51 (lab SCH), 57 (No. doctoral majors). Independent variables--Variab1es No. 28, 32, 65, 68. 28 (upper div. class hours), 32 (No. lab sections), 65 (part-time faculty, head count), 68 (part-time FTE). Independent variables-~Variables No. 30, 35, 69. 30 (total class hours), 35 (total undergraduate whtd. ave. sec. size), 69 (total FTE faculty). Independent variables—-Variables No. 31...33, 35, 51. 31 (Non—lab sections), 32 (No. lab section), 33 (No. grad. sections), 35 (total undergraduate whtd. ave. sec. size), 51 (lab SCH). Independent variables-—Variables No. 32, 35, 69. 32(No. lab sections), 35 (total undergraduate whtd. ave. sec. size), 69 (total faculty FTE). Independent variables--Variables No. 22, 32, 51, 57, 68. 22 (total U. G. courses), 32 (No. lab sections), 51 (lab SCH), 57 (No. doctoral majors), 68 (part—time FTE). Independent Variables--Variable No. 35, A9, 73. 35 (total undergraduate whtd. ave. sec. size), A9 (total fall SCH), 73 (total A faculty). APPENDIX D VARIABLES FOR LS ROUTINE ANALYSIS 162 163 The following variables were those variables which were selected as significantly related to the dependent variables based upon analysis results of the LSADD routine. Of the sixteen variable combinations used in the LSADD routine there were obvious duplications. Results as shown here are the best results of the analysis with a minimum of duplication. These results were re-run on the LS routine for three reasons: (1) as a check on LSADD results; (2) as a means of checking the effect of the best variable combination for the entire sample when applied to individual subgroups and subcategories; (3) as a means of developing regression results for the study, i.e., the LSADD routine does not go beyond multiple correlation results. 16A Variables from LSADD Routines for Use on LS Routine Dependent Variable No. A; Total Sample Independent Variables: 86, 68, 80, 32, 68, 26, 68, 51, 69, 57, 8A, 51, 33, 57, 6A A9 Dependent Variable No. A; Group 1 departments Independent Variables: 90, 39, Al, 51, 36, 26 79 Dependent Variable No. A; Group 2 departments Independent Variables: 68, 32 Dependent Variable No. A: Group 3 departments Independent Variables: A2, A1, 61, A8, 35, A7 8A, 39 Dependent Variable No. A; Group A departments Independent Variables: 69, 81, 79 69, 27, 25 Dependent Variables 17; Total Sample Independent Variables: 86, 68, 80, 32, 68, 26, 32, 68, 22, Dependent Variable 20; Total Sample Independent Variables: 65, 86 66, 32, 35, Dependent Variable 10; Total Sample Independent Variables: 91, 82, 80, 53, 28, 26, 57 57, 51,33,56,9,36,62 57, 51 6A. 39 6A, us 165 Dependent Variable 6; Total Sample Independent Variables: 65, 8A, 9, A3 66, 36 Dependent Variable 8; Total Sample Independent Variables: 86, 9 32, 9, 35, 3A, 7A 68, A9, 51 Dependent Variable 21; Total Sample Independent Variables: 65, 86 22’ 3. APPENDIX E SAMPLE OF 32 MICHIGAN STATE UNIVERSITY ACADEMIC DEPARTMENTS FROM THE TWO FISCAL YEARS l96A-1965 AND 1965-1966 (N = 6A) FOR THE SUPPLIES AND SERVICES ANALYSIS 166 167 I. Organized by Type of Department A. Laboratory Science departments; Basic Disciplines from Life and Physical Sciences; 8 Departments (Group 1) CDNO‘Wfi-C'UUNH Physics and Astronomy--Natura1 Science Botany and Plant Pathology--Natura1 Science Chemistry—-Natural Science Entomology--Natural Science Zoology--Natural Science Physiology and Pharmacology--Veterinary Medicine Microbiology--Veterinary Medicine Pathology--Veterinary Medicine Laboratory Science Departments; Applied Disciplines from Life and Physical Sciences; 8 Departments (Group 2) l. CD'\]0‘\U'IJZ‘ DON Mechanical Engineering--Engineering Civil Engineering--Engineering Metallurgy, Mechanics and Material Science (M,M&M)--Engineering Soil Science-—Agricu1ture Food Science--Agriculture Horticulture--Agriculture Nursing--Natura1 Science Foods and Nutrition--Home Economics Non-laboratory Science; Basic and Applied Disciplines from the Social Sciences and Humanities; 8 Departments (Group 3) (ID-<10 UltUJNH English--Arts and Letters History--Arts and Letters Philosophy--Arts and Letters Economics--Business Accounting and Financial Administration (AFA)-- Business Political Science--Social Science Social Work-~Social Science Sociology--Socia1 Science 168 Laboratory Type Departments; Basic and Applied Disciplines from the Social Sciences and Humanities; 8 Departments (Group A) 1. room (I) Noun Urban Planning and Landscape Architecture-- Social Science Art—-Arts and Letters Music--Arts and Letters Textiles, Clothing and Related Arts-- Home Economics Geography--Social Science Psychology--Socia1 Science Business Law, Insurance and Office Administration--Business Journalism--Communication Arts II. Organized by Size of Department (This was not a part of the analysis scheme but it is intended to show the general range in size reflected in these departments). A. }_J Small Departments; Average Undergraduate SCH of 3565 for 196A-l965; 10 Departments l. 2. OKOCDN ONU'I 4:00 Entomology—-Natura1 Science (Group 1) Physiology and Pharmacology--(Veterinary Medicine (Group 1) Pathology--Veterinary Medicine (Group 1) Mechanical Engineering (Group 2) Civil Engineering--Engineering (Group 2) Metallurgy, Mechanics, and Material Science (M,M&M)--Engineering (Group 2) Soil Science--Agriculture (Group 2) Food Science--Agriculture (Group 2) Horticulture--Agriculture (Group 2) Nursing--Natural Science (Group 2) Intermediate Departments; Average Undergraduate SCH of 10,980 for l96A-1965; l2 Departments l. 2. (EN OUT :00 Physics and Astronomy--Natura1 Science (Group 1) Botany and Plant Pathology--Natural Science (Group 1) Zoology-~Natura1 Science (Group 1) Microbiology--Veterinary Medicine (Group 1) Foods and Nutrition—-Home Economics (Group 2) Philosophy--Arts and Letters (Group 3) Social Work--Socia1 Science (Group 3) Urban Planning and Landscape Architecture—- Social Science (Group A) 9. 10. ll. 12. 169 Music--Arts and Letters (Group A) Textiles, Clothing and Related Arts-- Home Economics (Group A) Geography--Social Science (Group A) Business Law, Insurance, and Office Administration—-Business (Group A) Large Departments Average Undergraduate SCH of 3A,A38 for l96A—1965; lO Departments \DCDNQ \n-II‘UIJNH Chemistry--Natural Science (Group 1) English--Arts and Letters (Group 3) History-—Arts and Letters (Group 3) Economics-—Business (Group 3) Accounting and Financial Administration-- Business (Group 3) Political Science--Social Science (Group 3) Art--Arts and Letters (Group A) Psychology—-Social Science (Group A) Sociology--Social Science (Group 3) APPENDIX F SIMPLE CORRELATION RESULTS FOR SUBCATEGORY DEPENDENT VARIABLES 170 171 TABLE 33.--Simple Correlation of Independent Variables with the Dependent Variable No. 17 (Total Supplies and Materials) Total Department Sample. Q . No Independent 8638:? No Independent Ziggif Variable lation Variable lation 32 No. Laboratory Sec. .83* 76 Prof , Assoc Prof. .18 68 Part—Time FTEF .78* 5A Grad. Inder Var. SCH .17 51 Laboratory SCH .76* 29 ‘octoral Class Hours .16 9 Office Equipment .70* 3A Laboratory Weighted . 65 Part-Time Faculty Average Section Size .16 Head Count .66* 30 Leo & Rec. SCH .lb 27 Lower Division Class Hours .65* 56 Ho. of Masters Majors .16 66 Total Read Count .65* 77 Asst. Prof., Instr. .1A 7A Total B Faculty .62* 61 Total Majors in 30 Total Class Hours .59* Department Courses 11 69 Total FTEF .58* 31 Ho. Non-Laboratory Sec. 10 A5 Lower Division SCH .A9* 33 Ho. Graduate Section .09 28 Upper Division Class Hours .A5* 53 Total Classes Grad. SCH .09 2A No. of Doctoral Courses .A2* 73 Total A Faculty O8 57 No. of Doctoral Majors .A2* 26 Total Number of Courses 07 A8 Doctoral SCH .35* 75 Hon-Gen. Fund Faculty .07 62 No. of Non-Majors in Ac Upper Division SCH .05 Department Courses 35* A7 Masters 8 Grad.-Pro. SCH .05 A9 Total SCH 33* 5" Tota1 No. of Majors .01 52 Total Classes 59’ Undergraduate Majors Undergraduate SCH 33* in Department Courses .01 60 Total Undergraduates 70 A Instruction .01 in Department Courses .31* 22 Number of 63 Total in Dept. Courses 31* Under raduate courses .CO 72 A + B Faculty .31* 71 A Research -.02 25 No. of Grad. Courses .22 23 Tasters < Grad —P:o - C3 36 Graduate Weighted Average 35 Undergra’uate Weighted Seciton Size 20 Average ‘ectlon Size -.03 6A Instructor-Professor 58 No. of Undergrad Majors -.03 Head Count 19 37 Grad -Pro. and ”asters 67 FTEF Instructor— Class Hours -.0« Professor .19 *Correlation value significant at .05 level. 172 TABLE 3A.--Simple Correlation of Independent Variables with the Dependent Variable No. 20 (Total Supplies and Services Less Supplies and Materials). Total Department Sample Independent Simple Independent Simple Variable corre‘ ”0' Variable corre- lation lation 65 Part-Time Faculty 33 No. Graduate Section .27 Head Count .78* 28 Upper Division Class Hours .27 66 Total Head Count .78* 6A Instructor-Professor 68 Part-Time FTEF .75* Head Count .2A 32 No. Laboratory Sec. .70* 67 TEF Instr.-Prof. .2A 69 Total FTEF .60* 56 No. of Masters Majors .22 51 Laboratory SCH .57* 50 Lee. & Hec. SCH .21 57 No. of Doctoral Majors .55* 61 Total Majors in 7A Total B Faculty .53* Department Courses .18 2A No. of Doctoral Courses .52* A7 Masters & Grad. Pro. SCH .18 27 Lower Division Class Hours .51* 26 Total Number of Courses .18 A8 Doctoral SCH .50* 3A Laboratory Weighted 30 Total Class Hours .A6* Average Section Size .16 A5 Lower Division SCH .A3* 73 Total A Faculty .1A 36 Graduate Weighted 77 Asst. Prof., Instr. .13 Average Section Size .39* 35 Undergraduate Weighted 76 Professor, Assoc. Prof. .38* Average Section Size .12 25 No. of Graduate Courses .36* 31 No. Non-Laboratory Sec. .11 62 No. of Non-Majors in 23 Masters and Grad.-Pro. .09 Department Courses .35* A6 ~Upper Division SCH .08 52 Total Classes . 22 Number of Undergraduate SCH .33* Undergraduate Courses .06 63 Total in Dept. Courses .33* 59 Undergraduate Majors A9 Total SCH ‘ .33* in Department Courses .06 60 Total Undergraduates 75 Non-Gen. Fund Faculty .06 in Department Courses .32* 37 Grad.-Pro. and Masters 72 A + B Faculty .31 Class Hours .01 29 Doctoral Class Hours .29 7O % Instruction -.dO 5A Grad. Indep. Var. SCH .28 71 % Research —.01 53 Total Classes Grad. SCH .27 55 Total No. of Majors —.17 58 No. of Undergrad. Majors -.lA *Correlation value significant at .05 level. 173 TABLE 35.--Simple Correlation of Independent Variables with the Dependent Variables with the Dependent Variable No. 6 (Faculty-Belated Expenditures). Total Department Sample ' Independent Simple Independent Olmple No. Variable Corre- No. Variable Corre- lation lation 65 Part-Time Faculty 5O Lec. & Rec. SCH .35* Head Count .8A* 56 No. of Masters Majors .3A* 66 Total Head Count .83* A7 Masters & Grad.-Pro. SCH .30* 68 Part-Time FTEF .76* 35 Undergraduate Weighted 57 No. of Doctoral Majors .71* Average Section Size .26* A8 Doctoral SCH .67* 72 A + B Faculty .26* 2A No. of Doctoral Courses .63* 6A Instructor-Professor 69 Total FTEF .61* Head Count .25 32~ No. of Laboratory Sec. .58* 67 FTEF Instr.-Prof. .25 A5 Lower_Division SCH .52* 26 Total Number of Courses .23 25 No. of Graduate Courses .51* 61 Total Majors in 27 Lower Division Class Hours .A9* Department Courses .21 76 Professor, Assoc. Prof. .A9* 31 No. Non-Laboratory Sec. .21 62 No. of Non-Majors in 23 Masters & Grad.-Pro. .20 Department Courses .A8* 28 Upper Division Class Hours .20 30 Total Class Hours .A6* A6 Upper Division SCH .19 7A Total B Faculty .A6* 37 Grad.—Pro. and Masters 51 Laboratory SCH .A5* Class Hours .11 36 Graduate Weighted 73 Total A Faculty .11 Average Section Size .AA* 77 Asst. Prof., Instr. .09 A9 Total SCH .AA* 22 No. of Undergrad. Courses .07 52 Total Classes ' 59 Undergraduate Majors Undergraduate SCH .AA* in.Department Courses .07 63 Total in Dept. Courses .AA* 75 Non-Gen. Fund Faculty .02 60 Total Undergraduates 3A Laboratory Weighted in Department Courses .A3* Average Section Size -.00 29 Doctoral Class Hours .A2* 55 Total No. of Majors -.03 5A Grad. Indep. Var. SCH .A2* 71 % Research -.03 53 Total Classes Grad. SCH .AG* 70 % Instruction -.08 33 No. Graduate Sections .38* 58 No. of Undergrad. Majors -.l3 *Correlation value significant at .05 level. 17A TABLE 36.—-Simple Correlation of Independent Variables with Dependent Variable No. 10 (Travel) Total Department Sample. No. Independent giggif No. Independent gigggf , Variable lation Variable latitn 53 Total Classes Grad. SCH .A8* A8 Doctoral SCH .22 A7 Masters & Grad.-Pro. SCH .A2* 61 Total Majors in 33 No. Graduate Sections .AO* Department Courses .22 A6 Upper Division SCH .37* 66 Total Head Count .20 56 No. of Masters Majors .33* 35 Undergraduate Weighted A9 Total SCH .33* Average Section Size .18 69 Total FTEF .32* 72 A + B Faculty .18 50 Lee. & Rec. SCH .32* 2A No. of Doctoral Courses .16 52 Total Classes 59 Undergraduate Majors in Undergraduate SCH .32* Department Courses .16 No. of Doctoral Majors .30* 26 Total Number of Courses .15 Total Number of Majors .30* 30 Total Class Hours .15 Prof., Assoc. Prof. .29* 77 Asst. Prof., Instr. .15 No. of Graduate Courses .29* 31 No. Non-Laboratory Sec. .1A Masters & Grad.—Pro. .28* 65 Part-Time Faculty Instructor-Professor Head Count .1A Head Count .27* 73 Total A Faculty .la FTEF Instr.—Prof. .27* 29 Doctoral Class Hours .1 68 Part-Time FTEF .27* 5A Grad. Indep. Var. SCH .11 58 No. Undergrad. Majors .26 27 Lower Division Class Hours .08 63 Total Dept. Courses .26 - 22 ‘No. Undergrad. Courses .06 Upper Division Class Hours .25 51 Laboratory SCH .OA No. of Non—Majors in 71 % Research .OA Department Courses .25 37 Grad.-Pro. and Masters Lower Division SCH .2A Class Hours .03 Total Undergraduates 32 No. Graduate Sections .02 in Department Courses .2A 70 % Instruction .01 Total B Faculty .23 75 Non-Gen. Fund Faculty n02 Graduate Weighted 3A Laboratory Weighted Average Section Size .22 Average Section Size :35 *Correlation value significant at .05 level. 175 V TABLE 37.-—Simple Correlation of Independent V v avith tin? Dermaldent Variable No. 8 (Equipment Related Eyrendltures ariables ) T tal Tepartment Sample. C) n A O . (N 0 simple eimp-1e Independent .,.‘ ." ‘ Corre- do. w . Corre- Varlable u? I‘IO a 9 . lation lation No. Laboratory Sec. .69* 25 No. Gra .16 Office Equipment .tt* :2 ”o C 2 9 .. g 68 Part-Time FTEF .bA* Unde 5 T ‘ C. p.) m m S: U‘ IT) (I) (‘4 O C ‘3 (I) (D u) s .,W;-' (1L8 SUCH .16 “ Part-Time Faculty 73 otal A Faculty .16 Head Count .61* 77 loot. F'of , Ins r. .IM .13 H - ,3 (u 0) O ('t C 63 66 Total Head Count .61* 29 Doctoral C 51 Laboratory SCH . 63* 33 No. Graduate Sections .12 7A Total B Faculty .5?* 61 jotal Hajors in 69 Total FTEF .53* Department Courses .12 27 Lower Division Class Hours .A3* 5A Grad. .ndep. Var. SCH .11 30 Total Class Hours .39* 26 loral Number of Courses .10 3A Laboratory Weighted 7v Lon—Sen. Fund Faculty .19 Average Section Size .3“* 70 i Instruction .98 2A No. of Doctoral Courses .33* 53 Total Classes Grad. SCH .66 57 No. of Doctoral Majors .33* 56 Ho. ol Hasters Jajozs .86 72 A + B Faculty .32* pl and “"raduate aioro A8 Ikmotoral EMli .3d* .y. retartrv>x l3; rses do 28 Upper Division Class Hours .36- Cour‘e‘ ‘ r .1 K. >L ‘k F O J I I "1‘. >4? ‘ >— u , i L H I H . LC >44 I f k ( L. b—d “5 Lower Division SCH ."F' 'aduate We shted 36 Graduate Weighted Average Section Llfle .03 Average Section Size .96 in Lee. & flee. SCH .Jé 76 Prof., Assoc. Prof. .23 Fl I Research .WL 60 Total Undergraduates A? Easter; s Grad.—Fro. SCH .-u in Department Courses .3; ll Xe. Hon-Latoratorv .e;. -.F 6A Instructor—Professor do Upper pivisl n SCH -. Head Count .20 37 Grad.-Pro. a Iasters 67 FTEF lnstr.—Frof. .UJ Llass Hours —.fl" 63 Total Dept. Courses .19 ‘5 Wasters a Grad.-“wo. -.;> A9 Total son .3: .; -‘tfl nu LC? of sagoze —.ll 62 No. of Mon-Majors in Co Lo. of o.l “‘rad Ia era -.15 LL Department Courses .1 *Correlation value significant at .l; level. 176 TABLE 38.--Simple Correlation of Independent Variables with the Dependent Variable No. 21 (Total Supplies and Services Less Supplies and Materials and Contractual Services) Total Department Sample. Independent Simple Independent Simple “0' Variable corre' NO' Variable corre‘ lation lation 65 Part-Time Faculty 56 No. of Masters Majors 29 Head Count .77* 50 Leo. & Rec. SCH 26 66 Total Head Count .76* A7 Masters & Grad.—Pro. SCH .25 68 Part—Time FTEF .70* 72 A + B Faculty 2A 32 No. of Laboratory Sec. .60* 6A Instructor-Professor 57 No. of Doctoral Majors .60* Head Count .2 69 Total FTEF .56* 67 FTEF Instr.-Prof. .23 A8 Doctoral SCH .52* 28 Upper Division Class Hours .21 2A No. of Doctoral Courses .51* 26 Total Number of Courses .19 27 Lower Division Class Hours .A8* 61 Total Majors 51 Laboratory SCH .A7* in Department Courses .18 73 Total B Faculty .AA* 31 No. Non—Laboratory Sec. .17 A5 Lower Division SCH .A3* 23 Masters & Grad.-Pro. .16 30 Total Class Hours .A2* A6 Upper Division SCH .10 76 Prof., Assoc. Prof. .A2* 73 Total A Faculty .09 25 No. of Graduate Courses .Al* 77 Asst. Prof., Instr. .09 36 Graduate Weighted 35 Undergraduate Weighted , Average Section Size .Ac* Average Section Size (8 62 No. of Non=Majors in 22 No. Undergrad. Courses 67 Department Courses 37* 59 Undergraduate Majors 52 Total Classes in Department Courses L Undergraduate SCH .35*. 75 Non-Gen. 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