DOCUMENT RESUME 576 - ERICDOCUMENT RESUME ED 107 188 HE 00 576 ... the Austin-San Antonio region of...
Transcript of DOCUMENT RESUME 576 - ERICDOCUMENT RESUME ED 107 188 HE 00 576 ... the Austin-San Antonio region of...
DOCUMENT RESUME
ED 107 188 HE 00 576
TITLE Post-Secondary Education Planning in Texas:Techniques for Policy Analysis. Number 8.
INSTITUTION Texas Univ., Austin. Lyndon B. Johnson School ofPublic Affairs.
PUB DATE 75NOTE 93p.; Report by the Post-Secondary Education Policy
Research Project
EDRS PRICE MF-$0.76 HC-$4.43 PLUS POSTAGEDESCRIPTORS *Educational Assessment; *Educational Trends;
Enrollment Trends; *Higher Education; *PolicyFormation; *Program Development; Research Projects
ABSTRACTThe Lyndon B. Johnson School of Public Affairs has
established interdisciplinary research on policy problems as the coreof its educational program. A major part of this program is thepolicy research project, in the course of which three facultymembers, each from a different profession or discipline, and about 15graduate students with diverse backgrounds, research a policy issueof concern to an agency of government. This document is a report ofone of the policy research projects conducted during 1973-74.Following a brief overview of the postsecondary educationorganizational environment in Texas (chapter 2), consideration isgiven (chapter 3) to county-to-institution student flows and theirimplications for institutional development. Statistical analysis ofthe Austin-San Antonio region of Texas is provided in chapter 4.Chapter 5 approaches institutional and program development from aregional perspective and examines in detail the intrainstitutionalprogram development procedures and interinstitutional coordination inthe Austin-San Antonio region. Areas for future investigaticn arenoted in chapter 4. (Author/KE)
coco,---Ir,_ LYNDON B. JOHNSON SCHOOL OF PUBLIC AFFAIRS
CDr--I POLICY RESEARCH PROJECT REPORT
U S DEPARTMENT OF HEALTH.EDUCATION WELFARENATIONAL INSTITUTE OF
EDUCATIONTHIS DOCUMENT HAS BEEN REPRODUCE D EXACTLY AS RECEIVED FROM
THE PERSON OR ORGANIZATION ORIGINATING IT POINTS OF VIEW OR OPINIONSSTATED DO NOT NECESSARILY REPRESENT OFFICIAL NATIONAL INSTITUTE OF
EDUCATION POSITION OR POLICY
0\: B (Jo
*A`csi
Number 8
Cf) POST-SECONDARY EDUCATIONPLANNING IN TEXAS: CP
TECHNIQUES FORPOLICY ANALYSES
r..1
r)P PUBLW
0 .1 Report ht0 The Post-Secondary Education Policy Research Project
Lyndon 11 Johnson School of Public Affairsti The Unirervity of Texas at Austin
/975
2/3
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Library of Congress Card Number: 74-620161
©1975 The Board of RegentsThe University of Texas
FOREWORD
The Lyndon B. Johnson School of Public Affairs hasestablished interdisciplinary research on policy problems asthe core of its educational program. A major part of thisprogram is the policy research project, in the course ofwhich three faculty members, each from a differentprofession or discipline, and about fifteen graduate studentswith diverse backgrounds research a policy issue of concernto an agency of government. This "client onentation-brings the students face to face with administrators.legislators. and other officials active in the policy process.and demonstrates that research in a policy environmentdemands special talents. It also illuminates the difficultiesof using research findings to bring about change wherepolitical realities must be taken into account.
Post-Secondary Education Planning in Texas.Techniques for Poky Analyses is a report of one of theLBJ School's policy research projects conducted during1973-74. Other publications resulting from this
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post-secondary education projeLt, which was conducted finthe Coordinating Board. Texas College and UniversitySystem, include the Texas Atlas of higher Education andthe MAPPER Users Manual. These reports seek to describeanalytic techniques developed by project participants tomore effectively assess student demand supply patterns andtheir impact upon the polices and practices of stateagencies and education institutions
The intention of the LBJ School is both to develop men,.nd women with the capacity to perform effectively mpublic service and to produce research that will enlightenand inform those already engaged in the policy process. Theproject which resulted m these reports has helped toaccomplish the former. it Is our hope and expectation thatthe reports themselves will contribute to the latter.
William B. CannonDews
PREFACE
This document is the third publication resulting fromthe Post Secondary Education Policy Research Projectconducted by the Lyndon B Johnson School of PublicAffairs. 1 he University of Texas at Austin. during theacademic year 1971-74. The purpose of the pioject is toassist state an d education institution officials in the
detelopment of improted methods and processes for moreeffecutely analyiing data and making decisions in theeducational environment faced with planning issues inTexas
Critically important in the completion of this projecthate been the untlaggine efforts of LBJ School faculty andstudent participants in the policy research project. The staffof the Coordinating Board. Texas College and UniversitySystem prmided intaluable assistance. Supply and demand'is,ues and data were discussed with administiatite and staffpersonnel from the Texas Education Agency . the TexasAdvisory Council for Technic1,1Nocational Education: theTina, Employ ment Commission, the Texas IndustrialCommission: the Office of Information Services. The Officeof the Goternor the Texas Association of ProprietarySchools: the Association of In depeodent Colleges and
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Universities of Texas: and the education committees of theTexas State Legislature. Project participants also visited 4range of post-secondary education institutions in Texas. aswell as individuals in federal regional offices and in selectedother states. The Bureau of Business Research of TheUniversity of Texas at Austin designed the map-generatingcomputer program forming the basis of tie project's firsttwo publications, the Texas Atlas of Higher Education andthe MAPPER Users Manual *
We are grateful to many other individuals who havecontributed to the success of this project but are.
unfortunately . too numerous to list here. The project waspartially, supported by Ford Foundation funds and by aplanning grant awarded under Title .1 of the HigherEducation Facilities Act of 1963. as amended, from theDitision of Academic Facilities of the U.S. Office etEducation, through the Coordinating Board. Texas Collegeand University System
Kenneth W. ToloProject Director
'.1 he lilac and the ( sirs Manual are .11.1) as JILIN': from rise °Hiteof Publit Mums. the Won II John.on School of Public Attain,t he lnner.it% of tes.i at Au.iin. A u.ttn, le \ .1.. 78712
POLICY RESEARCH PROJECT P 1RTICIPANTS
Michael Berner, B.A. (Philosophy). The University of Texas at AustinDave Fege. B.A. (Economics). Kalamazoo CollegeLydia Gardner, B A. (Government and History), Southwestern UniversityBarbara Parness, B.A. (Journalism). Michigan State UniversityCan ie Sewell, B.A. (Sociology), The University of Texas at AustinAbdul ShaLiwy, A.B. (Government), Indiana UniversityPatricia Siemen, B.A. (History), Siena Heights CollegeKarl Spock, B.A. (Economics). The University of Texas at AustinRobin Tillman, B.A. (Government). The University of Texas at AustinMelvin Waxier, B.A. (Government and Psychology), The University of Texas at AustinJanet Weisko t t (Economics), Brooklyn CollegeDavid West, B.A. (Political Science), Austin CollegeJames Williamson, B.A. (Government),, The University of Texas at AustinJan Younglove, B.A. (Sociology), The University of Texas at Austin
James A Fitzsimmons, Associate Professor of Management. B.S.E. (Engineering University of Michigan. M.B.A. (BusinessLInunistration Western Michigan University: Ph.D. 'Management). UCLA
Kingsle Hanes. Associate Professor of Public Affairs. B.A. (History., Geography Political Science), Western Michiganuversity. M.A. (Geography). Rutgers University: Ph.D. (Geography and Enviionmenuil Engineering), The Johns
Hopkins tritiersityKenneth W Tolo, Project Director, Associate Professor of Puhhc Affairs. B.A. (Mathematics), Concordia College. Moorhead.
Minnesota. M..4, (Mathematics), University of Nebraska. Lincoln. Ph.D. (Mathematics). Universto of .Vebraska,Lincoln. M.A. (Public Affairs), Univers:1Y of Minnesota
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SUMMARY
Post-secondary education decisionmakers in Texas mustbe able to effectively assess both current and proposedpolicies in the c9ntext of student and employer demand/supply patterns The policy research project has identifiedand developed several approaches for examining suchinformation. These approaches, together with recom-
mended applications and courses of action, are excerptedhere.
TEXAS ATLAS 01: IIIGIIER EDUCATION.11APPER USERS MA ,VIJA
These research techniques and documents enable educa-tion planners to identify changes and trends in institutionalenrollments and student service areas Decisions on facilityand program development can then he based upon theneeds of the population served (e.g., regional rather thanstatewide). Institutional "marketing' strategies can be
improved by fo,.using on appropriate geographic areas.program LonLentrations, and student groups. Recom-mended courses of action include
The Atlas should periodically be updated and ex-panded in its Loverage (e.g., to include proprietaryschools)The MAPPER system should he modified to producemaps sho\\ mg student flows by educational programand;or by student ty pe (ex.-. minority. under-
graduate:giaduate).The MAPPER system could be applied to substateregions of Texas. using the county rather than theinstuntion as the focal unit. to provide more detailedcharactervations of regional needs and flows
STATEWIDE REGRESSION ANALYSIS
County .speLifiL sanables thought to he associated withinstitutional enrollment \ dilations by county (both totaiparticipation and rate of participation) are identified andanalyied In order to increase the utility of this analysis foreducation planners in Texas. efforts such as the followingare recommended.
Student-specific variables (e.g., sex, ethnic back-ground. educational attainment level(s) of parent(s).undergraduate/graduate status) should be included inthe regression analysis to improve its predictivecapability.
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The Coordinating Board's Uniform Reporting Systemshould he the primary source of state student-specitliinformation, and should be expanded both in thescope of the questions and in the range of institu-tional iespondentsImproved techniques for obtaining information onstudents' educational preferences (institutional andgeographical. as well as programmatic) need to beimplemented, with the results incorporated into theanalysisMethods for updating and otherwise improvingcounty-based information should be sought (e.g
collecting information at the smallest feasible geo-graphic level).
STUDENT ALLOCATION MODEL
This approach seeks to estimate an institution's"drawing power rpgarding potential students throughcounty information on student demand (i e preference)for various types of post-secondary education institutions
Current information on student flows and on presentand planned institutional student capacities must beobtained and incorporated into the model to achievea significant level of predictability.
INSTITUTIONAL SERVICE AREA ANALYSIS
An application of the Atlas and MAPPER Users lianualtechniques to sub-state regions of Texas. the institutionalservice area analysis can further refine both regional
county-tonstitution student flows and the extent to whicha (regional) county's student population receives it post-
secondary education within the region An improvedunderstanding of institutional service areas is criticallyimportant for policy decisions related to such issues asprogram and facility development (e.g whether to provideadditional student residential space or commuter parkinglots). Recommendations are similar to those relating to theAtlas and Manual. with primary emphasis on integrating theinstitutional service area analysis with the project's otheranalytic approaches.
INSTITUTIONAL PROGRAM DEVELOPMENT
An institutional survey and a serie, of in-depth institu-tional analyses in the Austin-San Antonio region reveal
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'sr-Sean:clan. Hucation
substannal interaction both within the public and privatei.ollegiate sector and within the proprietary schpol sector.Between the two sectors, however, there is minimalcommunication. Program development responsibility in theexamined institutions is generally assumed by facultypersonally interested in the new program but often unin-formed about sources of supply/demand information.Vamying emphasis is placed upon student ant: employerdemand All Institutions, however. would akel; benefitfrom improved techniques for better under'Aanding presentdemand (e.g., student flows) and antIL:pating future re-quirements Recommended steps fo- improving this processinclude.
Post-secondary edc-,ition institutions of all typesct-i ie harder to incorporate the planning
conce:ns of other institutions in their respectiveservice areas into their planning and decisionmakihgprocesses.
The institutional responsibility for investigating the
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feasibility and desirability of new programs in thecontext of present and projected student andemployer demand should be assigned to a singleinstitutional office, with appropriate statewide co-ordination exercised by the staff of the CoordinatingBoard
The feasibility of using sub-state jurisdictions (e.g.,councils of government) as clearinghouses forsupply/demand information related to post-secondaryeducation program and facility development shouldbe more closely examined.Guidelines for the establishment and operation ofvocational education advisory committees should beieviewed
Additional student information concerning both pre-matriculation interests and follow-up/placement datais necessary for effective institutional program devel-opment and state-level coordination.
'FABLE OF CONTENTS
FOREWORD m
PREFACE I,
POLICY RESEARCH PROJECT PARTICIPANTS pi/
SUMMARY ix
CHAPTER I INTRODUCTION I
Recent Trends I
I 202 Commissions 2
Project Development 3
CHAPTER II. POST-SECONDARY EDUCATION IN TEXAS:IHE ORGANIZATIONAL ENVIRONMENT 4
State Boaid of Education, Texas Education Agency (TEA) 4Coordinating Board 5
Other Institutions and Associations 7
CHAPTER III STATEWIDE POST-SECONDARY EDUCATION PLANNING:DATA PRESENTATION AND ANALYSIS TECHNIQUES
Texas Atlas of Higher EducationMAPPER Users Manual 10Statewide Regression Analysis 12Student Allocation Model 19
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CHAPTER IV ANALYTIC TECHNIQUES FOR PLANNING:A REGIONAL AND INSTITUTIONAL APPROACH
CHAPTER V PROGRAM DEVELOPMENT: A REGIONAL ANDINSTITUTIONAL PERSPECTIVE
Overview of Program DevelopmentRegional Post-Secondary Education SurveyInstitutional Program Development Analyses
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63636466
CHAPTER VI: AREAS FOR FURTHER INVESTIGATION 71Vocational-Technical Sector 72Academic (Collegiate) Sector 73
Authorli of and Coordination BetweenState Post-Secondary Education Agencies 73
BIBLIOGRAPHY
APPENDIX A' DEFINITION OF POVERTY LEVEL
APPENDIX B STUDENT ALLOCATION MODEL
APPENDIX C RESPONSES TO REGIONAL POST-SECONDARY EDUCATION SURVEY
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CHAPTER I
INTRODUCTION
Education beyond the high school level has become oneof the largest enterprises in the United States. The annualenrollment in the nation's approximately 3,000 public andprivate institutions of higher education exceeds nine millionstudents, while the estimated 7,000 post-secondary pro-prietary schools annually enroll several million additionalstudents.* The total income annually received by post,se,..ondary education institutions now exceeds $30 billion.(National Committee on the Financing of Post-SecondaryEducation 1973. 14, 16, 67, 431)**
The Texas profile is simjlarly impressive. Presentlyserving the academic and vocational post-secondary needsof the state are over 125 colleges, institutes, and universitycenters, plus several hundred proprietary schools. Fall 1973enrollments in Texas colleges and universities totaled452,000, placing the state third among all the states; earneddegrees conferred by these institutions in 1972 exceeded45.000 (HEW 1974: 69,94): Estimated annual completionsin Texas proprietary schools is approaching 20.000(Advisory Council 1974). State and federal funding appro-priated through the state's appropriations act for fiscal year1974 totalled 5618 million for the senior colleges anduniversities and 5106 million for Texas State TechnicalInstitute (TSTI) and the junior colleges (Advisory Council1974
The tremendous growth of education in the 1950s and1960s led most states in the nation to create and expandthe scope of their state boards and agencies responsible forpost-secondary education. The orderly and effective devel-
*"Illeher education" refer, to the collegiate sector, and includescommon* colleges, tour -year liberal arts colleges, major researchtino,ersities, professional schools, and similar institutions "Pest-secondary education," as used in this report, consists of Mgt ereducation plus other learning opportunities offered by certifiededucational institutions that primarily serve persons who havecompleted secondary education or who are beyond the compulsoryschool attendance age "Post-secondary education" includes pro-prietary schools I e , prnately-operated business enterprises thatprovide specific training in occupation-related skills, whereas
"higher education" does not.
.I-or complete bibliographic informatton, see page 75.
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opment of post-secondary education has been a difficultobjective to achieve, however. Not only has the publicsector frequently developed in isolation from the privatesector, but also vocational programs -both public andprivate-have evolved apart from academic programs.Clearly needed are planning perspectives and techniquesthat are able to provide greater insight concerning theenvironment within which such coordination must occur.
RECENT TRENDS
Current social and economic trends make state-levelcoordination even more imperative. If current nationalpopulation trends persist, for example, it is unlikely thatpost-secondary education will remain a growth industry.The number of five-year-olds in the United States declined15 percent between 1960 and 1970. The nation's birth rateis at its lowest point in history, and it has not yet stabilized:the number of births dropped three percent between 1970and 1971-and nine percent between 1971 and 1972. TheBureau of the Census projects a substantial decline in thenumber of college-age youth in the 1980s, and furtherdeclines are likely after 1990 unless the live birth rateincreases in the near future (Glenny 1973 I. A recentprojection prepared for the Carnegie Commission (1973)also indicates a sharp decrease in the rate of college anduniversity enrollment growth in the 1970s. followed by anabsolute decline in enrollment in the following decade.
Similar population trends are likely in Texas. Thepercentage increase in the state's population between 1960and 1970 was less than the 1950.1960 percentage increase;in addition, there were 108,200 fewer births in Texas in1960-1970 than in the preceding decade (Bradshaw andPoston 1971: 105. A recent study by The University ofTexas Population Research Center (Poston et a/. 19731 alsosuggests that the number of college -age youth will begin todecline in all areas of the state in the late 1970s
In recent years there has also been a growing recognitionthat a college degree is not the only route to success in oursociety and that vocational education plays a very signifi-cant role Consequently, the rate of enrollment increase hasbeen much greater in proprietary and industrial schoolsthan in traditional higher education institutions This trend
pow -sea) Wary Education
is likely to continue. particularly if federal student aidbecomes available in greater amounts for proprietary schoolenrollees. In addition. 85 percent of the increase in recentyears in the number of first-time students in cu lei,,ateinstitutions occurred in community colleges; further im-pacting the operation of senior colleges and universities(Glennv 1973)
Program and appropriations trends in Texas bear out thisincreased interest in post-secondary vocational education.For instance. the number of industrial occupation programshas increased from 35 to 357 since 1966. while technicaloccupation programs have jumped from 39 to 133. As stateappropriations for junior colleges and TSTI increased fromS3$ million to 5117 million between fiscal years 1969 and1974. the amount allocated to occupational (rather thanacademic) programs increased from 30 percent to morethan 45 percent. Enrollments in public post-secondaryoccupational programs in Texas have increased from 6,000in 1962.63 to almost 80.000 in 1973.74; further increases
are also likely, since secondary vocational education enroll-
ments have increased from 167,000 to 412,000 over the
same period (Advisory Council 1974)
1202 COMMISSIONS
Recognizing the iinplications of these (mid other)emerging trends. post-secondary education organizationsand institutions (including the Education Commission ofthe States and the State Higher Education ExecutiveOfficers Association) pressed Congress prior to the enact-ment of the 1972 Education Amendments (Public Law
92-318) to assist the states in developing coordinatedpost-secondary education delivery systems. Arguments
cited in support of such action included:
the current number and variety of public and privateinstitutions in each state.the apparent imbalance between manpower needs..student interests. and educational programs;the scarcity of resources for post-secondary educa-
tion.the increased recognition of the desirability of insti-tutional and programmatic complementation, ratherthan duplication and competition:andthe emergence of new or improved management andplanning techniques for post-secondary educationinstitutions and systems. (U.S. House of Representa-tives 1971. 804-811)
Responding to these expressed needs. Congress included
in the 1972 Education Amendments an authorization(Section 12021 for financial and technical assistance tostates desiring to create new agencies or designate existing
agencies as State Post-Secondary Education Commissions
("1202 Commissions") (20 U.S.C. I142a). These 1202Comtniss ons were to he "broadly and equitably representa-t;ye of the general public and of public and privatenonprofit and proprietary in.l.:ations of post-secondaryeducation in the state," including junior colleges. areavocational schools.. technical institutes, and four-year col-leges and universities. The functions of each Commissionwere to include the initiation of comprehensive inventories
and studies of all public and private post-secondaryeducation resources in the state.. as well as the development
of plans related to vocational education.In the latter half of 1972 and early 1973. the United
States Office of Education (USOE) actively sought todevelop regulations for the appointment. operation. andfunding of the 1202 Commissions During this period,Texas was one of 13 states to respond; with GovernorPreston Smith designating the Coordinating Board. TexasCollege and University System as the state's 1202 Commis-sion. Upon assuming office in January 1973, GovernorDolph Briscoe affirmed this designation. On March 7:1973.however, the U.S. Commissioner of Education announcedthat 1202 Commissions were unnecessary in view of theprogram cuts included in President Nixon's proposed fiscalyear 1974 budget and set aside plans for their implementa-
tion.Following a year of inaction.. USOE in March 1974
asked each state whether or not it wanted to establish a1202 Commission. This action was at least partially sparkedby Congressional language in the fiscal year 1974 educationappropriations bill which directed that a substantial portionof a 53 million allocation for state education commissionsshould be made available to the 1202 Commissions forplanning purposes (The Chronicle of Higher Education1974).
In April 1974 Governor Dolph Briscoe of Texas re-sponded affirmatively to USOE. Rather than designating anexisting board as the Texas 1202 Commission. however. hecreated the I 7-member Governor's Advisory Committee onPost-Secondary Education Planning to fulfill the Com-mission's responsibilities. These responsibilities include areview of the state's present post-secondary educationalplanning process. particularly with respect to the qualityand availability of planning data and the suitability ofpresent administrative/governance structures.
Whether or not the establishment of the Governor'sAdvisory Committee is viewed a permanent resolution ofthe 1202 Commission issues is unclear, as is the level ofeffectiveness it will b. able to achieve in its planning andcoordination efforts it is y likely,. however. that theCoordinating Board and it, .staff will assume a major role in
future 1202 Co ion related planning activities The
analyses and matellt; described herein should provideassistance in these efforts.
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Post-Secondary Education in Texas: The Organi:atioal Enrironment
PROJI-C1 1)FVFLOPNII-N1
Pi ellinulais planning lot this protect began in the L.B.IS.11001 of Public Affairs in the spring and summer of 1973,tiIliA% mg the in itwl designation of the Coordinating Board.le \a. College and lluversity System as the state's 1.7.02Commission the Board needed to hioaden its informationbase and policy analyses related to supply and demandissues in both the academic and vocational scorns. and thisprotect was initiated to complement the Board's efforts inmeeting anticipated increased planning responsibilities.
1 he first phase of the protect culminated in a reportdescribing the current organizational environment in Texas%%ithin which post-secondary education decisions are madeand issues raised 11.131 School 1974a). A major finalproduct of the policy research project is the Texas Atlas ofHigher Education (111.1 School 1974b ), a comprehensivewoes of maps depicting enrollments m the state's publicand pnvate institutions of higher education (includingISII ). by county of student origin. for the years 1968 and172 A related project publication, the MAPPER Users
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,%fanual (LW School 1974e). provide. documentanon torthe Atlas and discusses useful modifications in thecomputer generated mapping techniques
This final summary report of this I.R1 School PolicyResearch Project on post-secondary education planning inTexas seeks to integrate the research efforts underlying theabove publi-anons with related deand/supply analysesFollowing a brief ow' view of the post-secondary educationorganizational environment in Texas (Chapter II1, consid-eration is given in Chapter III to eountytomstitutionstudent Bows and their implications for institutionaldevelopment. Statistical analyses of variations in studentenrollments are included, as are analyst . based upon astuder t allocation model. The application of the statewideanalyst: in Chapter ill to the AustinSan Antonio wpm ofTexas is illustrated in Chapter IV. Chapter V approachesinstitutional and program development from a regionalperspective, examining in detail mitainstitution programdevelopment procedures and interinstitution coordinationin the AustinSan Antonio region. Areas for future invest,-gat,on are noted in Chapter VI.
CHAPTER II
POST-SECONDARY EDUCATION IN TEXAS:THE ORGANIZATIONAL ENVIRONMENT
The environment within which post-secondary educationinstitutions and policies in Texas function is composed ofstate education agencies. advisory boards, interagencycommittees. voluntary associations of schools, and legisla-tive committees. Apart from the Governor's Office and theTexas State Legislature, the major components of thisenvironment are the State Board of Education and itsadministrative staff, the Texas Education Agency; and theCoordinating Board, Texas College and University Systemand its staff. These and selected other organizations arebriefly discussed in this chapter to provide a framework forthe analyses in subsequent chapters; further details on theseagencies, boards. and related bodies are included in anearlier project report I LBJ School 1974a).
STATE BOARD OF EDUCATION:TEXAS EDUCATION AGENCY (TEA)
In 1949, as a result of the Gilmer-Atkin Act: a majorreorganization occurred in the management of publiceducation in Texas In place of the elected State Superin-tendent of Public Instruction and the appointed StateBoard of Education, the 51st Texas Legislature establisheda central education agency composeu of an elected StateBoard of Education. which operates as the State Board ofVocational Education when considering vocational-
technical matters, a Board-appointed Commissioner ofEdwation, and a professional. technical, and clerical staffknown as the Texas Education Agency (LEO School 1972).Members of the Board, one elected from each of the state's24 Congressional districts for a six-year term, conveneregularly to review the state's educational needs. to adoptplans to meet those needs, and to evaluate educationprograms under its direction.
TEA, the admmistiato.e unit of the State Board, hasconsisted of four departments Occupational Education andTechnology. Administration, Teacher Education and In-structional Services, aid Special Education and SpecialSchools (A reorganization is currently being unplemented.)Of these four, the first has been most significantly involvedin post-secondary education issues and activities in Texas
Specifically charged wit:. administering vocational-
technical programs and certifying fexas proprietary
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schools, the Department of Occupational Education andTechnology consists of five operating divisions. The Divi-sion of Adult and Continuing Education provides consulta-tive services to local agencies in the development andcoordination of adult education, civil defense, and man-power training programs, approving program applicationsand developing evaluation procedures. The Division ofOccupational Research and Development advises, assists;monitors, and coordinates research projects in occupationaleducation developed and implemented by school districts,post-secondary institutions, and others. The Division ofPublic School Occupational Programs is primarily con-cerned with the development and accreditation of voca-tional-technical programs in public high schools. TheDivision of Post-secondary Occupational Education andTechnology provides post-secondary institutions withtechnical assistance for the development, maintenance, andfinancing of vocational-technical programs. The functionsof the Division of Proprietary Schools and VeteransEducation include advising, certifying, and regulating pro-prietary schools, as well as approving programs and teacherqualifications for the training of veterans.
Responsibilities delegated to TEA in the area of post-secondary education have necessitated its involvement invocational-technical program development, financial
management, and proprietary school certification. Withrespect to program development, TEA reviews proposedvocational programs submitted by community colleges forapproval prior to receipt of state or federal support andevaluates existing vocational-technical programs, services,and activities. Financial management responsibilities ofTEA include the verification of programs and programfunding requests in the budget submissions of the state'scommunity colleges.
An expanding area of post-secondary education respon-sibility within TEA in the past three years has been the'rtification and regulation of proprietary school% and their
programs under the Texas Proprietary School Act of 1971(Texas Education Code 1972 Chapter 32) This actexpressly prohibits any non-exempted proprietary schoolfrom advertising, soliciting for, or conducting any course ifinstruction in Texas without first obtaining a certificate ofapproval from TEA stating that it provides quality training
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instructors and administratro. in adequatetats title, I urthermore. the school must be financiallyi rods Maintain proper student recoRls. implement all
app:oved tuition refund policy. and obtain a bond to coveror expenses resulting from violations of these
legulation, the certificate of approval must he renewedcacti year and may he revoked by TEA if violations occur.Die number of schools holding current certificates ofapproval 's a' 239 as of November 5. 1973 * About 200 ofthese schools have their headquarters in lexas. As anaiclicaiion ot the scope of TEA\ responsibility in this area.bemeen January 1072 and May 1973 approximately 450proprietary schools Were visited at least Once by TEArcpie.entatives. with 300 visited at least twice (TEAI r 31)) By recently undertaking evaluations of variouscourse.. nd curricula. tuition refund policies. and interrup-tion policies for unsatisfactory attendance in affectedproprietary schools, TEA is seeking to place regulation ofproprietary schools on a more comparable basis with otherrecognized public and private post-secondary educationiosioutions in Texas.
COORDINATING BOARD
In 1955 Texas statutorily recognized the need for ac.:ntral coordinating institution in the field of highereducation by creating the Texas Commission on Higher
ducation Unfortunately, the Commission's success wasiinnted by a lack of money and its inability to effectivelyco, ,rdinat e program development and facilities construc-tion State respnses to these difficulties culminated in thepassage ot the Higher Education Coordinating Act of 1965(House Bill I. texas State Legislature). winch establishedthe Coordinating Board. Texas College and University
tem
11%7 Coordinating Board is composed of 18 membersippointed by the governor, with the approval of the Texas(senate. tot overlapping six-year terms: the chairman and'lie vice Amman are designated by the governor ACoirmissioner ot Higher Education, appointed by the
( .,orddating Board. serves at the Board's pleasure and is01; chiet executive ()nicer of the staff The staff numbers'e-s th.m 100 individuals. constituting five administrativewilt. the Division ot Financial Planning. the Division ofViministiation the Division of Program Development, theDivision or Student Services. and the Division of CampusPlanning and Physical Facilities Development (CoordinatingBraid 19-1. Coordinating Board statI interviews 1974).
1,%1111 this ingainzational structure the Coordinating
i hi, thaire sta, secured. wait the permission of TEA, from aPronrierary ,,110111 rile in the Division of Proprietary Schools and
tins tdu,itnrn, Department of Occupational 1 ducation and1,', lin,i1,,es shish list, those s,hools holding a it A certificate ofippr 11
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The Orgaimational Environment
Board endeavors to carry out its statutory mandate toprovide "leadership and coordination tot the Texas Iteducation system" (Texas Education Code 1972 Section61.002). With the exception of public community collegeprograms (subject to the approval it the State Board forVocational Education 1 and community college construction(financed by local property taxes). the Coordinating Boardis authorized to coordinate the activities of the state'spublic colleges and universities. It is also instr.a.ted tocooperate with the independent institutions of higher
education. coordinating programs with them %culla' consti-tutional and statutory limits and considering their degreeand certificate programs prior to authorizing new collegiateprograms in public institutions.
Statewide planning and coordiration Is pursued by theCoordinating Board and its staff in four basic areasinstitutional development, program development, financialmanagement,. and facilities planning. In matters of postsecondary institutional development, the Board roust advise
the legislature as to the state's need for a new publicfour-year institution before such an institution may heauthorized. The creation of a public community college.designed to provide general collegiate education. technicaltraining. and compensatory and continuing education. isnot subject to this statutory requirement. Because theCoordinating Board believes all potential students in thestate should be within reasonable distance of such a college,it has divided the state into 53 geographic regions Theseregions represent groupings of potential community collegestudents and areas within which at least one communitycollege is feasible within the next decade or two (Coordi-nating Board I 9684. These regions do not represent taxingjurisdictions: nor do they restrict the attendance ofstudents at colleges outside the region in which they reside.
Prior to the creation of a new community collegedistrict, the proposed district must have I.I 1 e nonunionassessed valuation. the community need for a college. thepotential student clientele. and the tmancial ability to
support the creation and operation ot the institution Theinitial step in the creation of a Loirmiumt minor college isa local responsibility A steering conmuttee rs .ustornardyappointed to serve as liaison between the local communityand the Coordinating Board Responsible for cnducting alocal survey of the needs and potential 1)1 the area. thesteering committee prepares and presents to the ( 001nating Board a petition certified by the appropriate countyboard(s) of education. The Coordinating Board is requiredby state law to consider the needs and welfare ot the statearid the welfare of the community involved bean': actingon the request Favorable Board actton then results m .1local election.
As a result of Senate Resolution 209 adopted by theOrd Legislature in the spring ot 1'a' t. there prontly
1'
I), is! -cce, urchin. Lducan, in
ems!, a tearpolaty motatolium on the teation or e \pan.situe ot pubh, ,olleges and unite' ,ltle III Tesa. Thelong-n.110 inip,i,t of this iesolution On UW111010111 diVelOp-11101r puhle's In re \ al is Still
111th respect to progiam detelopment. a public senior,ollege mutt:1.'1y must se,ure the approval of the
ooldidating Board prau to the implementation of a newdegree piogram At the ,ommunity college level, Boardaprotal must be obtained for specific academic coursesthat parallel lower division unitersity Lotuses. This is to in-sure toll t'anster ot ,redt's to levy, public senior,olleges and unwerques. 1 septions are made if (1) the.once is able to substantiate a "unique need" for the.nurse. or (2) the ,ollege is required to otter compensatoryedtkation ,ortrfes to fulfill the commitment of an admis-'ions polky entouraging, the enrollment of disadvantagedst orients.
The ( oordinating Board influences the ,approval andfunding ot %0,ational-teclinicai programs through its parti-opation. lgediet with the State Board of EduLation .
the Stare Boaid tor o,ational Education) and the Adti-foiy tqui.ii for Te,hin,al-Vocational Education. in theJoint Committee ( fesas Edu.:ation Code 1972 Section
31 1) The mote nominal Joint Program Retiew Com-mute, in operation prior to the establishment of the Jointnutinee in I Ow). was orgamzed to a,commodate the
otcliappmg Ititisdl.tiulis ot the Coordinating. Board and theStar- Boat(' ot I du,ation with respe,t to tocational-t,.,Iiin,alpror lin, ofteled in the conummity .alleges
In addition. the ( oordinating Board has the power toorder the -deletion of ,onfolidation of any ,ourses. lotseal ... as well as olleee% and unit ersities)Atte; _icing due to 11.e with reasons for that action andafter pon.iding healing it one is requested by the
gol.eriuric hoard int olted- tTexas 1.dikation Code 1972Se,tior, However. the agen,y rarely eser,ises this
itil ttyu T C.1.011% the Board does not hate the,tact 1,e,e,,,,r to ette,titely implement such a control.
saeine 'if priorities. and (2) Coordinatinglit 'aid inteitention In the area of ,ourse approtal raises tileemotional issues ot institutional autonomy and academictreedin
,esoin,e, tor higher edu,ation hate in,reased at arite . 1111.111,1,11 management 11.15 heLoine a more
important responsibility tot the Coordinating Board. Theprimal. dug ot the Board in this area is to develop, withthe assisIalk e tit ieprLsentato.es troll' "'Cu,. senior collegesMid unitrsir.e, and ,olleges. appropriations
[tlae ptotiding .20 equitable distribution of stategenital roeritie toad, Formulae lor senior colleges havebeen approted in ten areas (reneral Administration and
Settles. F.K idly Salaries. Departmental OperatingI \ P;*1 1.1hiat Organized Research. Building Mamie-name, (u,ti,ahal Serlit.es, Instructional Administration.
(1
Faculty Development Leat, s. and I acuity and Stall GroupInsurance. Formulae allow: ons constitute about 145 per-Lent of the state's appropi _awn, for senior colleges anduniversities, with about 1- percent allocated for specificpurposes to individual mstioitions.
In 1972 the Coordinating Board adopted a new formulato more equitably distribute state appropriations to publiccommunity colleges for the 1973-1975 biennium. This newformula computes approp,ia:ions for academic programson the basis of the number of contact hours between thestudent and teacher during the previous academic y earAppropriations are determined by multiplying the totalnumber of contact hours for each type of course by therate reominended by the Legbiative Budget Board for thatcourse. Those colleges which exceed the contact hours ofthe previous years (upon which appropriations had beenbased) receive additional resources from a contingency fundappropriated by the legislature to eliminate such deficits.
The Coordinating Board and its staff also-provide enrollment projections for state senior insti-tutions to he used in determining the distribution offunds collected through the state ad ralorem propertytax:recommend tuition policies for the different types ofpublic colleges and anwersities in texas. and
recommend to the governor and the F egislatite
Budget Board supplemental contingency appropria-tions to provide for increases in enrollment at thepublic institution, of fugher education in Texas.
A fourth area of C.h,;(1,natirg Board responsibility isthat of campus and faciliti:, r fanning While the institu-tional expansion of the 1960s was required to meetincreasing enrollments ID higher education during thatperiod. the 1970s require more careful planning. withprojected enrollment stabilization and the continuing needfor up-to-date and complete facilities on state ,ampusestaken into account. Included among the campus planningfunctions of the Coordinating Board and 'ts staff are thedetermination of space utilization formulae for ,ill educa-tional and general buildings and facilities at higher educa-tion institutions. and the approval or thsapprotal of allmajor construction and rehabilitation 01 educational insti-totions when such nnprovenicai, are financed from fundsother than the ad valorem tn. ic.efpis of public ommunitycolleges ( The Board. hower. exercises no powers ofapproval with regard to roje, Is financed out of thePermanent University Fund I Private institutions are en-
% outraged to participate in the Board's planning activitiesNo state funds are appropriated for campus pl.n and
facilities deseloptnent for public community colleges. nordoes the Coordinating Board have iuristliLtion over con-struction finatked by local property uses However, theCoordmatIng Board does inform the c(inummity colleges ofesistIng federal grants and 10.110, for which they may qualify
and ippl and. if desired; assists them in developinglung -range plans for campus development.
OCHER INSTITUTIONS AND ASSOCIATIONS
A variety of other voluntary associations. advisorybodies. state agencies. and institutions also help shape thepostsecondary education environment in Texas.
Idrisory Council for Technical-VocationalEducation in Texas
As a result of a report submitted in 1967 by the firsttederal Advisory Council on Vocational Education, theVo,:ational Education Amendments of 1968 (Public Law90-570) mandated that state advisory councils on voca-tional and technical education be created. Although theleas State Board of Education had appointed a statevocational education advisory council in 1964, the 61stTexas State Legislature in 1969 created (in Senate Bill 261)the Advisory Council for Technical-Vocational Education(Texas Education Code 1972. Chapter 31). .
The purpose of the Texas Advisory Council for
Technical-Vocational Education is to establish "a climateconducive to the development of technical, vocational. andmanpower training in educational institutions in the Stateof Texas to meet the needs of industrial and economicdevelopment of the state." It is to plan. recommend: ande%aluate "educational programs in the vocational-technical,adult eduLation. and manpower training areas at the statckvel m the public secondary and post-secondary educa-tional institutions and other institutions" (Texas EducationCode 1972 Section 31.31).
One responsibility of the Advisory Council is to partici-pate. together with the Coordinating Board and the StateBoard for Vocational Education. on the Joint Committee.Other responsibilities include providing up-to-date data onstate employment oppertunities and carrying out studiesand forums on vocational education initiated by the
-dvisory Council itself. the State Board for VocationalEdu,ation, the governor. the state legislature, the Legisla-me Budget Board, and other state agencies.
The Advisory Council consists of 21 citizens recom-mended b} the governor. appointed (for overlappingsix-} ear terms) by the State Board of Education, and,ontirined bx the Texas Senate. The members must beselected in accord with 17 specific membership categories.sith approximately one-third educators, one-third corpora-tion exe,.titmes. and one-third representatives of variousspecial groups
It is important to emphasize the advisory nature of theTexas .V.isisory Council for Technical-Vocational Educa-tion It participates neither in the allocation of funds nor inthe administration of programs The Advisory Council
7
The Organizational Environment
believes that such an approach would compromise itsobjective and independent evaluations. its plans. and itsrecommendations. Moreover.. the State Board for Voca-tional Education has the final authority to accept or rejectany recommendation of the Advisory Council (TexasEducation Code 1972: Section 31.39).
Association of Independent Collegesand Universities of Texas
The Association of Independent Colleges and Univer-sities of Texas (ICUT) represents the private colleges anduniversities of Texas before state agencies. the Governor'sOffice, and the State Legislature. Although ICUT has noformal representation on the Coordinating Board, the latterdoes consider the activities of ICUT member institutions inits coordination and planning.
In 1967. for instance, the Coordinating Board sponsoredan ICUT study of Texas independent colleges and univer-sities which included among its recommendations that theexisting physical facilities and programs of independentschools should be recognized in Coordinating Board plan-ning as a means of fulfilling state needs. More recently, theCoordinating Board recommended and the Texas StateLegislature passed a state tuition equalization program topartially offset the economic incentives for Texas citizensto attend public colleges and universities.
Texas Association of Proprietary Schools (TAPS):Proprietary School Advisory Commission
Other than through the certification process involvingnon-exempted proprietary schools in Texas and the Divi-sion of Proprietary Schools and Veterans Education. TexasEducation Agency. interaction between the state post-secondary education agencies and the proprietary schoolsector occurs primarily through the Texas Association ofProprietary Schools (TAPS) and the Proprietary SchoolAdvisory Commission.
TAPS, formed in 1970 by the merger of the UnitedBusiness Schools Association and the Texas Association ofTrade and Technical Schools. represents the interests of itsmember schools to state agencies. the State Legislature. andthe Governor's Office. Proprietary schools in Texas alsomaintain limited contact through TAPS with the NationalAssociation of Independent Colleges and Schools and theNational Association of Trade and Technical Schools.
The Proprietary School Advisory Commission. createdby the 1971 Texas Proprietary School Act. consists of ninemembers appointed by the State Board of Education tooverlapping six-year terms. Four members shall he managersor executive officers of proprietary schools covered by this1971 act, three shall he public school officials, and twoshall be distinguished and informed citizens of Texas. The
Post-secondury Education
Proprietary Si.hool Advisory Commission acts solely in anad% isory ,apatity to the State Board of Education andTEA
Texas Employment Commission (TEC)
The Texas Employment Commission (TEC) and TEAwoi I, together to provide the vocational-technical training,education. and counseling necessary for individuals to beable to obtain. retain. and perform jobs in the state. Forexample. lo,a1 offices of TEC assist Instructors of distribu-tie education and industrial cooperative training byscreening and selecting high school students for participa-tion in programs directed by TEA. TEC also annuallyproides information to TEA for the latter's use in
submitting required federal data relating to vocationaleducation
Texas Industrial Commission (TIC)
The Texas Industrial Cotnmission (TIC) is responsiblefor planning. organizing. and operating a program forattracting and locating new industries in Texas and forpromoting the expansion of existing industries in the state.TIC cooperates with TEA in identifying training needs of anew or expanding industry. in locating resources that maybe used to provide training, in translating the industry'straining needs into programs for training Institutions. inestablishing training programs, and in assuring that thetraining programs are properly administered.
TIC has recently worked closely with both TEA andTEC to develop programs designed to help meet the
8
immediate manpower needs of industries considering plantlocation in Texas. The agencies have also worked togetherto produce state Industrial Start-up Training programs usingthe occupational training facilities and capabilities of Texas'junior colleges, the four campuses of Texas State TechnicalInstitute, and many of the state's independent schooldistricts (TIC 1973).
Texas State Technical Institute ITSTI)
As Texas' only state-level public post-secondary educa-tion institution providing solely technical training, theTexas State Technical Institute (TSTI) occupies a uniqueposition in the state. TSTI operates as a separate system,independent of the state's community college system.Whereas community colleges in Texas offer vocational-technical programs in conjunction with their regular
academic curriculum, all of the TSTI programs are gearedtoward training students for immediate employment, ratherthan for broad education in the traditional academic sense.
Nine regents, appointed by the governor, serve as thegoverning board of TSTI. The main campus is located atWaco, with other branches (all authorized by the TexasState Legislature) at Amarillo, Harlingen, and Sweetwater.
As an independent, state-supported post-secondary
education institution, TSTI is funded primarily throughdirect appropriations by the State Legislature. However,since vocational and technical education programs offeredby TSTI are subject to TEA approval, TEA is able topartially affect the funding, programs, and activities ofTSTI.
CHAPTER III
STATEWIDE POST-SECONDARY EDUCATION PLANNING:DATA PRESENTATION AND ANALYSIS TECHNIQUES
As described in Chapter I, post-secondary educationprograms, appropriations, and enrollments in Texas haveexhibited dramatic changes within the past decade. Particu-larly important have been enrollment trends, since theyform the primary basis for many program development andfinancing changes. Whereas only a few years ago collegesand universities were faced with rapidly expanding studentpopulations, enrollments in many of these institutions havenow stopped growing or may soon do so. In contrast to thiscollegiate trend, however, have been the increasing enroll-ments in the state's post-secondary vocational-technicalclasses.
Enrollment changes such as these, as well as currentsocial and economic trends, have complicated the highereducation -and, more generally, the post-secondary educa-tionplanning process in the state. Given changing educa-tional demands, old planning policies cannot he followedrigidly. Instead, education decisionmakers must evaluateshifts in enrollment (i.e., student demand) patterns andtheir possible causes to assess the changing educationalrequirements of the state and the need to establish newpolicies to meet those requirements. Success is not depen-dent upon the formation of new agencies, committees, ororganizational arrangements. Rather, the effectiveness ofthe new policies will greatly depend upon the quality andutility of the data and the analytic techniques used todescribe and explain the state's higher education enrollmentpatterns.
As part of its study, the Lyndon B. Johnson School ofPublic Affairs Policy Research Project on Post-SecondaryEducation Planning in Texas has developed a variety ofapproaches for examining and analyzing informationrelated to student demand for higher education in Texas.Research results related to the use of one such techniqueare included in two separate publications: the Texas Atlasof Higher Education (LBJ School I 974b), which graph-ically depicts enrollments in Texas' public and privateinstitutions of higher education, by county of studentorigin, for the years 1968 and 1972; and an accompanyingtechnical document, MAPPER Users Manual (LBJ School1974c), which provides documentation for the Atlas andsuggests useful modifications in the computer-generatedmapping techniques. Other analytic techniques that have
9
. 18
been developed and applied by project participants are alinear regression analysis that examines variations in theenrollment of Texas students in Texas higher educationinstitutions in an effort to explain county enrollmentdifferences using county characteristics, and a studentallocation model that seeks to predict likely future studentflows in Texas on the basis of existing higher educationinstitutions and programs.
TEXAS ATLAS OF HIGHER EDUCATION
The Texas Atlas of Higher Education presents, through aset of more than 200 maps, the 1968 and 1972 enrollmentpatterns for the state's public and private senior and juniorcolleges, public technical institutes, and public and privatemedical, dental, nursing, and allied health schools. Themajority of institution's-litluded in the Atlas are those iden-tified in "Institutions of Higher Education in Texas, 1972-73" (Coordinating Board 1973b).
The Atlas does not contain student enrollment informa-tion for the proprietary vocational-technical schools inTexas for two reasons. first, the great number of theseinstitutions precluded their treatment in the available timeand space (e.g., the Texas Education Agency currentlycertifies about 200 such schools); and, second, enrollmentdata are frequently unavailable for these private schools.
The enrollment pattern maps in the Atlas arecomputer-generated, using a basic program developed bythe Bureau of Business Research, The University of Texasat Austin, and modified by LBJ School of Public Affairsproject participants. The MAPPER Users Manual providesadditional details on the production of these maps.
Three kinds of information are provided in theseenrollment pattern maps. Taken together, the 1968 and1972 maps for a given institution show:
changes in the school's enrollment of Texas residentsover this four-year period;the school's service area (i.e., the geographic areas ofthe state from which its students come); andshifts occurring in the service area over this timeperiod.
This information can be of central importance in deter-
pee,rseceinclary
mining, for example. the need for expanding an institutionor ,tearing a 11,'A one in the primary service area of anexisting institution.
Organcation of the .1 tlas
The Atlas is divided into seven sections corresponding tothe categories used by the Coordinating Board; Texas
ollege and University System to classify the state's highereducation institutions. The sections are titled ( I) PublicSenioi Colleges and Universities: (2) Independent SeniorColleges and Universities. (3) Public Community Colleges:(4) Independent Junior Colleges. (5) Public Medical,Dental. Nursing, and Allied Health Schools: (6) independent Nledkal. Dental, Nursing, and Allied Health Schools,and 17) Public Technical Institutes
Each of the seven sections begins with a map showingthe location of the institutions within that group; plus alisting of the city and county within which each is located.The remainder, and major part. of each section is comprisedof the 1968 and 1972 enrollment pattern maps preparedfor each school included therein
Derelopinent of the Atlas
It is important that the reader be aware of tour decisionsinvolved in the preparation of the Atlas. together with theirunderly mg rationales:
1 It was decided that the enrollment statistics in theItlas should indicate only the number of Texas residentsattending (as undergraduates or graduates, in the fall term)the state's various higher education institutions.
This decision was obvious, given the focus of the Atlason Texas. but the need for a clear understanding of thelimitations of the Atlas necessitates its mention
2 It was decided that the Atlas should illustrate 1968and 1972 enrollment patterns for the institutions includediii its contents
Three reasons underlay this decision First, time and,pae ,,onstraints made it impractical to prepare more thannso enrollment pattern maps for each institution. Second.the desire to give the Atlas as much current applicability aspossible made it necessary to base one of these two mapson the latest enrollment data available at the start of thisprotet_ , namely . 1972 data. Finally, the idea that the Atlasshould show enrollment trends and changes in service areassuggested that the earlier period precede the first by four tosix years The 1968 period was selected because of therelative completeness of enrollment information for thatyear.
3 It was decided that where inclusion of an institutionintroduces a special difficulty (e.g. a change from atvv o-y ear to a tour-year college) a footnote should providean explanation of the specific situation.
10
Central to this decision was the desire to increase theusefulness of the Atlas by making it comprehensive andeasily understood.
4. It was decided that the legend (or key) describing apair of enrollment pattern maps should be developedseparately fer each institution.
This decision was necessitated by the extreme variationbetween schools in total enrollments and county atten-dance concentrations. After much experimentation itbecame apparent that a "standardized" legend. or sets oflegends. for all maps would not adequately describe thewidely divergent enrollment patterns of the institutions inthe Atlas. In addition; these variations made it impracticalto develop enrollment map legends through the generalapplication of a standardized statistical method.
Uses of the Atlas
The Texas Atlas of Higher Education is envisioned as adescriptive publication that should be invaluable as areference and planning document for representatives ofhigher education institutions and state agencies Much dataare summarized in easily understood form: e g.. the readercan vividly see shifts that have recently occurred in aninstitution's service area and in the service areas of thevarious groups of colleges and universities included in theAtlas (Caution should be used in attempting to compareservice areas of different institutions, however, because ofthe varying legends for the maps and the significantdifferences in total enrollments among schools.)
The Atlas maps provide an indication of the role eachinstitution is assuming in the Texas higher education"system." The extent to which an institutiodhas a local,regional. or state focus can be observed, as well as shifts inits scope. Higher education institution administrators cancompare their perceptions of their institutions' service areaswith those illustrated in the Atlas. This would permitinstitutional advertising and student recruitment efforts tobe focused on geographic' areas that deserve greater atten-tion, resulting in a more carefully planned marketingstrategy.
The rapidly changing focus of higher education in Texasmakes it inevitable that the usefulness of the specific datain this edition of the Texas Atlas of Higher Education is
transitory However, time should not diminish the impor-tance of this type of information in planning for highereducation in the state, nor the importance of the techn-iques used in the production of the Atlas
MAPPER USERS MANUAL
A technical publication accompanying the Texas Ate..of Higher Education is the MAPPER Users Manual (LBJSchool 1974c). which describes the computer system
19
MAPPER and provides documentation for the systemmodifications used in the production of the Atlas Thecapability of the MAPPER system to display computer-based geographic data foi policy planning is demonstratedin the Atlas, and the Manual has been produced to sharethis technique with other education planners in Texas.
It is not uncommon for public agencies to collectconsiderable data concerning their areas of responsibility.Although the data may be stored in readily accessiblecomputer tiles, their usefulness for planning and decision-making may be minimal unless presented in a manner thatwill assist planners in understanding the problems at hand.For example, identification of the enrollment patterns(both spatial and longitudinal) of post-secondary educationinstitutions are important in the state's education planningprocess. These patterns are frequently difficult to identifythrough examination of the raw data on students' countiesof origin; they are more easily observed, however, when thedata are presented in the form of geographic maps shadedaccording to enrollment concentrations by county.
Maps displaYang student flow information or other dataof int -t to education planners can be produced quicklythrough MAPPER. Developed by the Bureau of BusinessResearch of The University of Texas at Austin, theMAPPER system consists of two computer programs,MAPDAT3 and MAPPER. The former is a preprocessorprogram that reads county data from cards and constructs adata tile for display. The MAPPER program uses theMAPDAT3 file to generate a choropleth map of countyoutlines on the CALCOMP plotter for the entire state ofTexas for any smaller region of contiguous counties).County data are displayed on the map through the use ofdifferent shading patterns, each of which is associated witha distinct range of data.
A modified MAPPER system (Student Flow Version),the basis for the Atlas maps, has been developed by the LBJSchool to plot student flows by institution. Modificationsinclude the design of MAPDAT4 and MAPDAT5. Theformer is a program designed specifically to read from cardsstudent enrollment data by county of origin (i.e., residence)for each Texas institution of higher education. It creates atape tile that in turn is used as input by MAPDAT5, astrident flow version of MAPDAT3. The student flowversion of MAPPER. called MAPPER2, has been desigrAto then produce a pair of Texas state maps showing thenumber of students from each Texas county attending eachTexas institution of higher education for two selectedacademic years. As evidenced by the Atlas, the titles andlegend are located on the map proper to produce a finishedexhibit ready for direct inclusion in a publication.
The Atlas and the Manual:Applications and Conclusions
The Texas Atlas of Higher Education has value in and of
11
20
Data Presentation and Analysis Techniques
Itself, e.g., as a reference document and as a guide to stateand institutional planning (including student recruitment)decisions. So, also, does the modified MAPPER systemdescribed in the MAPPER Users Manual, for it permitsperiodic updates of the Atlas on the basis of more recentenrollment data.
Potentially more important, however, are other applica-tions of the modified MAPPER system (Student FlowVersion) that are feasible and require little additional work.The Atlas and this modified version of the MAPPER systemare based upon (total) student enrollment data by countyof origin by institution. Similar descriptive analyses are alsopossible for any county based student enrollment datacollected by the institutions. For instance, if institutionalenrollment data were collected by educational program,maps could be produced to illustrate student flows byprogram within an institution.
Data availability is the primary stumbling block. Mapscould be developed (and map-based analyses performed)which would show an institution's drawing power acrossTexas in any one of a number of student categories (e.g.,seniors, graduate students, male students, Mexican-American students, transfer students, scholarship students).What is necessary, however, is student enrollment data; byinstitution and county of (student) origin, for the desiredstudent categories. Recent developments with regard to theCoordinating Board's Uniform Reporting System (URS) areencouraging, in that additional student data should now beavailable annually on an institutional basis.
The Manual techniques can also be applied (and ex-tended) at the sub-state level to any set of contiguous Texascounties (e.g., to any or all of the 24 state planningregions). For instance, a computer program has beendeveloped that, for each institution and any county in aselected region of Texa , gives annual data on:
the number of students in the institution from thedesignated county;the percentage of the regional portion of students inthis institution coming flo-n the designated county;andthe percentage of all Texas students attending theinstitution coming from the designated county.
The application of such (regional) in,titunonal servi,e areaanalyses is described in Chapter :V-
A diff-rent type of map-b ,.trd lltalysts is possible, foreither the entire state or a regit Jf the state, with regardto any student category to which the above modifiedMAPPER system (as extended) applies. Rather than use theinstitution as the primary unit of representation, it is
possible to use the county. That is, for each county inTexas the flow of students from that county to the variousinstitutions of higher education within the state can bepictorially represented.
P,)st-Secondary Education
Stiaighttorward modifications of the Users Manual
would penult the production of student flow maps (similarto those in the Atlas) that would provide the afore-mentioned types of information. The policy research
project participants felt, however. that further map produc-tion could best be done by the staffs of the CoordinatingBoaid or other state agencies on the basis of their perceivedpost-secondary education planning needs in Texas and theay adability of usable data. Thus the Atlas and Manual notonly provide an efficient procedure for displaying collected,but too often neglected, student enrollment data, but alsoshould prove to be an incentive for improving planning anddata collection policies among education decisionmakers inTexas
STATEWIDE REGRESSION ANALYSIS
The regression analysis component of the project sought
to explain the variations in the enrollment of Texasresidents in higher education institutions across the state.county by county,, and to provide a basis for predictingchanges in these county enrollments by monitoring selectedindependent variables. Student demand analyses that in-clude the entire post-secondary education sector are neededas well. regrettably, however, the appropriate student dataare currently unavailable from the non-collegiate sector
De phrase "regression analysis:" when used in a
statistical sense, refers to the methods by which estimatesof one variable (called the dependent variable) are madefrom information about the values of one or more othervariables (called the independent variables). and to themeasurement of errors associated with such estimation. The
phrase "correlation analysis" refers to methods formeasuring the strength. or degree; of the association (or,correlation) among these variables, in this study. suchmethods are also subsumed under "regression analysis. "*
Two dependent variables were considered in the regres-sion analysis in this study. The first (D1) was the number oftexas iesidents attending an institution of higher educationin Texas in the fall of 1970, by county of studentresidence. This information was provided by the staff of theCoordinating Board, Texas College and University Systemon the basis of its Educational Data Center reporting form.Included were student enrollments in any course at leastone term in length in an undergraduate, graduate, orprofessional program Data on the Texas State TechnicalInstitute and private colleges and universities were onlypartially available.
The second dependent variable (D2) examined in the
"I or a further discussion of regression Jn.ily sis. see Hubert M.Blalock. Jr.. Sotruf Stanstres (New York MLGrau 11111 Book
Company. 1972) or Morris Hamburg. Statistical -Malt xis forDeuston Waking INea York Harcourt, 13race and World, Inc ,1970)
project was the "college-going rate." by county of studentresidence This refers to a county's extent of participationin Texas higher education relative to its total college-agepopulation. It is calculated, for each county in the state, bydividing the number of county residents attending Texashigher education institutions (DI ) by the county's popula-tion in 1970 between the ages of 18 and 24_ This age groupwas selected primarily because it was used by the Coor-dinating Board staff in 1%8 in calculating "college-goingrates" of Texas counties, even though most states onlyinclude 18 to 2I-year-olds in the college-age category(Coordinating Board, 1968b). This expanded grouping isalso more likely to include potential graduate and profes-sional students, as well as older-than-average students in theundergraduate programs.
Characteristics of the (Texas) counties of student originwere used as the independent variables. due to the lack ofhistorical information available on individual students orgroups of students enrolled in Texas colleges and uni-versities. These county-based data were obtained primarilyfrom Bureau of the Census (1973: Chapters B and C)information and Texas Public School Finance .4 Majorityof Exceptions (Texas Research League I 972 ) In formationon individual institutions was obtained from the Coor-dinating Board staff and., in the case of some privatecolleges and universities. from the institutions themselves.
It was hoped that variations in each of the two
dependent variables could be largely explained by anappropriate linear combination of independent variables.The 19 independent variables included in this analysis wereselected on the basis of anticipated relationships with thedependent variables and the availability of data in a usableform. The selected variables. each of which had a value foreach Texas county. were.
number of junior colleges within a 50-mile com-muting radius of the county,*number of senior colleges within a 50-mile com-muting radius of the county,:,distance from the county to the nearest public seniorcollege:distance from the county to the nearest public juniorcollege,distance from the county to the nearest private junioror senior college:percentage of the county population that is betweenthe ages of 18 and 24:
*Since a county occupies area and measurement trom all parts ofthe area is Impossible, the population center (or, centroid) of thecounty. as determined by the U S. Bureau of the Census, was usedas the point from which to measure distance Since fesas countiesare generally both small in sin and regularly shaped, this wasregarded as a reasonable assumption,
12
21
percentage of the county population that is betweenthe ages of 25 and 34;percentage of the county population that is blaLK,percentage of the county population that has aSpanish surname,percentage of the county population that is urban,percentage of the county population with an annualincome below the 1969 Social Security Administra-tion poverty level (see Appendix A):percentage of the county civilian labor force that isunemployed;median income of county residents;mean income of county residents;per capita income of county residents;median education school years completed) ofmales in the county;median education of females in the county;amount of total current operating costs per averagedad} attender in the public schools (grades K-12) inthe county; andtotal county population.
Among the county-based independent variable candi-dates considered and then rejected were: county populationchange between 1960 and 1970; county fertility rate;number of county residents enrolled in grades 9.12;percentage of high school graduates among county residents25 and over; median earnings of county residents inselected occupations:and number of county residents livingin military base housing.
For consistency, 1970 dependent variable data wereused with the independent variable data from the 1970Census. To have used the fall 1973 data from theCoordinating Board's Uniform Reporting System (URS)would also have been impossible; since the URS responseswere incomplete when the regression analysis was initiated
The use of these data and variables suggest the need forcare in analyzing the results. For instance, adult andcontinuing education enrollees were not distinguishableCrum students in other categories; part-time students weregrouped with full-time. enrollments in technical programsand academic programs in the two-year institutions couldnot he separated: and the stated county of residence (i.e.,origin) of a student could differ from the county ofresidence prior to attendance at the institution. Availabilityof data and the desire for consistency necessitated their usehowever Suggestions for improving the analysis are dis-cussed below.
The computer program used for the regression analysiswas selected from the Statistical Package for the SocialSciences (SPSS) (Nie, Bent, and Hull 1970).
In analyzing the relationships among the county-basedindependent and dependent variables listed above, correla-tions between each independent variable and each depen-
13
Data Presentation and Analysis Techniques
dent variable were first examined. Using the characteriza-tion that any two variables having a correlation coefficientbetween -.20 and +.20 are "not significantly correlated,"Table III-1 summarizes these results. Table 111-2 contains acomplete listing of correlation coefficients between eachindependent variable and each dependent variable.
The independent vanable most positively correlated withthe first dependent variable (D1) was "total countypopulation," having a correlation coefficient of 0.994. Alsoexhibiting significant positive correlation with variable DIwere "mean income of county residents" (0.4C3) and"amount of total current operating costs per average dailyattender in the public schools (grades K-I2) in the county"(0.453). The only independent vanable having a significantnegative correlation with DI was "percentage of the countypopulation with an annual income below the 1969 SocialSecurity Administration poverty level" (-0.217).
The second dependent variable (D2), on the other hand,exhibited significant negative correlations with seven inde-pendent variables and significant positive correlations withonly three (as compared with one and nine, respectively, inthe case of D1). The greatest negative correlation wasbetween D2 and "percentage of the county population thatis between the ages of 18 and 24" (-0.460), with the nextgreatest being "percentage of the county civilian labor forcethat is unemployed" (-0.393). Such correlations were notsurpnsing. For instance, one might expect that a larger totalcounty population between the ages of 18 and 24 would heassociated with a lesser "county participation rate" and,Indeed, this was the case.
It is interesting to note (Tables Ill -1 and 111-2) whichindependent variables were significantly correlated withboth of the two dependent variables, Only "per capitaincome of county residents" and "median education ofmales in the county" were (significantly) positively cor-related with both DI and D2, while only "percentage of thecounty population with an annual income below the 1969Social Security Administration poverty level" was (signifi-cantly) negatively correlated with both DI and D2. Thesecorrelation results would indicate that these three indepen-dent variables are important ones to include in any analysisof the variation in enrollments of Texas higher educationinstitutions.
The independent variables significantly correlated withneither of the dependent variables were "number of juniorcolleges within a 50-mile commuting radius of the county,""distance from the county to the nearest public seniorcollege," "distance from the county to the nearest publicjunior college," and "distance from the county to thenearest private junior or senior college." It appears,therefore, that factors other than distance to highereducation institutions influence student attendance pat-terns, by county. However, the difference between the
A w-.S'econtlan. EducationTABLE III-I
CORRELATIONS BETWEEN INDEPENDENT AND DEPENDENT VARIABLESIN STATEWIDE REGRESSION ANALYSIS
Dependent Variables
n even ent ariables
Number of studentsfrom the countyattending any'ollege or um-ersity in Texas(D1)
PositivelyCorrelated
*Nuinber of Texassenior collegeswithin 50 miles
"''' of county 25-34
*c'r of county urban
*County medianincome
*County meanincome
*County per capitaincome
*County medianeducation' male
*County schooloperating costs
*Total countypopulation
NegativelyCorrelated
*% of countywith incomebelow povertylevel
NotSignificantlyCorrelated
*Number of Texasjunior collegeswithin 50 miles
*Distance to nearestTexas public seniorcollege
*Distance to nearestTexas public juniorcollege
*Distance to nearestTexas private college
*% of county 18-24
*%of county black
*% of county withSpanish surname
*County medianeducation: female
*% unemployed incounty
County partici-pation rate (D2)
*County percapita incon,e
*County medianeducation. male
*County medianeducation.female
23
*Number of Texassenior collegeswithin 50 miles
*% of county18.24
*7( of countyblack
*7 of countyurban
*% of countywith incomebelow povertylevel
*% of countywith Spanishsurname
*% unemployedinkcounty
*Number of Texasjunior collegeswithin 50 miles
*Distance to nearestTexas public seniorcollege
*Distance to nearestTexas public juniorcollege
*Distance to nearestTexas private college
*% of county 25.34
*County median income
*County mean income
*County school oper-ating costs
*Total countypopulation
Data Presentation and Analysis. Techniques
TABLE 111-2
CORRELATION COEFFICIENTSBETWEEN INDEPENDENT AND DEPENDENT VARIABLES
IN STATEWIDE REGRESSION ANALYSIS
Independent Variables
Number of Texas junior collegeswithin 50 miles (VAR001)
- Number of Texas senior collegeswithin 50 miles (VAR002)
-Distance to nearest Texas publicsenior college (VAR003)
-Distance to nearest Texas publicjunior college (VAR004)
-Distance to nearest Texas privatecollege (VAR005)
Percent of county 18-24 (VAR006)
Percent of county 25-34 (VAR007)
-Percent of county black (VAR008)
-Percent of county urban (VAR009)
-County median income (VAROI 1)
--County mean income (VAR012)
-County per capita income (VAR013)
-Percent of county with income belowpoverty level (VAR014)
Percent of county with Spanishsurname (VAR015)
County median education: male (VARI6)
County median education: female(VAR017)
--Percent unemployed in county (VAR018)
-County school operating costs (VAR019)
- Total county population (VAR020)
2415
Dependent Variables
DI D2
0.198 -0.169
0 362 -0.240
-0.167 0.177
-0.057 0.096
-0.073 0.120
0.177 -0A60
0.386 -0.181
0.090 -0.308
0.356 -0.224
0.375 0.125
0.403 0.123
0.336 0.321
-0.217 -0.241
-0.014 -0.214
0.260 0.236
0.178 0.259
0.035 -0.393
0.453 -0.046
0.994 -0.081
roNt-.Sec()Pidart I:duenom
onelations of the densit% 111 nearby moult colleges withD.' and of the density ot neatby senior colleges with D2indicates that swill(); collect, emollinent in 1 ex.'s may beless sensitise to densit% 11 e . neatness) considelations than
iumot college enrollmentrnator purpose 01 the regiession an:6,N was to
examine the teasibility of using these 19 county-basedindependent satiable, to predict %amnions in higher educa-tion pat ticipanon. k county .ind by institution For eachof the two dependent satiable, (DI and D2), a multiplelinear telnession equation relating it to the independentsatiable, was therefore (lensed and anal fed using the SPSSregression program tables Ilk; and 1114 contain listings Ofcoettments devised for these two equations. as well as
t11 5,1110115 statMILA measures
in the case of the regression equation invoking DI, the%Attie ot the multiple cot relation coefficient is greater than()'igs Hence these independent variables (under the
}meatus assumption) account tot,oi explain, more than 99pe,cent ot the %amino!' in DI the total number of studentsattending college from a gken county. This result is largelydue io the 'e r\ high correlation (01)')31 between DI and"total counts population- (see Fables 111.1 and 111-2 )
hi the second regression equation, the value of themultiple conelation coetncient is significantly less (0.69)1 Ins means that the linear combination of these indepen-dent %ambles accounts for only about one-half of the%aflame in 1)2. In whet words, about one-half of the%a: tall. e must be explained 1)% other factors
thus it would appear that the 19 county characteristicsconsidered a. independent variables In this regression
analy 515 are. 1)% themselves insufficient to accuratelypredict how mans s111dents. from a e \as county will attenda texas Milege of urns ersw, 11 e to predict D2), othersanables must also he included in the analysis The LBJ
Policy Research Project participants believe thatthese oiliet factors are largek student specific informationon tic h.t,kground and current status of indisidualsa.'u,fils attending fcs.is higher education institutions
Ilse I !Worm Rcpot wig Sy stem (URS) presently in use11% the Coordinating Boat d staff ls now able to supply sonic
ot this studentspecific data to complement the countydata 1 or instance a students se \. ethnic background, andundergraduate graduate status. as well as county of resi-dence (according to the location of the high school
attended) can be easily obtained front the URS. Otherintonnation can also he extracted nom the URS through,ro.sreterenc mg. although this is inure difficult. (Thisdun, ult% relates to the atorementioned lack of separation
t kik.. of students programs. etc ) The UniformReporting System is able. tot instance, to provide thenumber it half's for which a student is emolled,whthe he she is a pa; t-time or I till-time student, and the
16
courses taken by the siudent. I e.. whether the student isenrolled in somonal programs or at adenuc programs.
Conclusions and Recommendations
The LB1 School Policy Research Project participantsbelieve that multiple regression (and correlation) analyststechniques can be useful in explaining and predictingvariations in county enrollment and paiticipation patternsul po,t-secondary education across the state. provided usersare aware of underlying assumptions and piedictive limitations
When the regression analysis in this project was begun,the decisu,n was made to use county characteristics as theindependent sanables. These data were available and wereregarded as appropriate, and project participants had noadvance knowledge about the st,-mgth of the correlationsand the amount of dependent variable variance explainedby a linear combination of these independent variables Theresults of this regression analysis were helpful in illustratingwhich independent variables were highly correlated witheach of DI and D2, and the explanatory capability of thesecounty-based independent variables Moreoyr. the empha-sis on county characteristics in this analysis was entirelyappropriate Nevertheless, it is clear that additional vari-ables need to he included in the analysis of countyparticipation rates. In particular, background and student-specific information on individuals attending Texas postsecondary education institutions is needed Some of thisinformation in the higher education sector is now beingprovided by the Coordinating Board's Uniform ReportingSystem: other information is still not being generated.
Some suggestions that flow from this regression analysisare
1. The Uniform Reporting System (URS) should heregarded as the primary Instrument for gathering thestudent information needed in further regression analysesof this type. This would necessitate changes in the types ofinformation gathered through the URS, as well as anexpansion in the types of institutions completing andsubmitting these forms.
2 The Uniform Reporting System (URS) should heexpanded to include additional student background infor-mation that preliminary analyses indicate would he usefulSuch information might include
level of education of each prenttype of occupation of each parent,Income of parents, andlap code number of parents' residence.
Knowledge of parental income lesels while difficult todetermine directly, might be obtainable indirectly fromother types of background information
21;
Data Presentation a I Analysis Techniques
TABLE 111-3
MULTIPLE REGRESSION
Dependent Variable: DI (Number of Students)
INDEPENDENTVARIABLE MULTIPLE R R SQUARE RSQ CHANGE SIMPLE R B BETA
VAR1320 .99446 .98894 .98894 .99446 .03324 .98494VAR016 .99476 .98954 .00060 .26023 112.33796 .02995VA R006 .99490 .98983 .00029 .17723 23.36387 .01823VA 8004 .99499 .99000 .00017 -.05714 -2.46548 -.01531VAR015 .99504 .99011 .00011 -.01428 4.56944 .01955VAR019 .99508 .99018 .00007 .45256 -.02011 -.00888VAR013 .99511 .99023 .00006 .33646 .35960 .02982VAR002 .99513 .99028 .00005 .36166 -20.82976 -.01041VAR005 .99516 .99033 .00006 -.07299 -.93719 -.00911VAR009 .99517 .99037 .00004 .35610 1.91140 .01099VAR018 .99519 .99040 .00003 .03497 -25.78941 -.00728VAR007 .99520 .99043 .00002 .38571 -13.60054 -.00484VAR014 .99521 .99044 .00001 -.21715 3.90706 .00772VAR017 .99521 .99045 .00001 .17801 -32.68362 -.00840VAR008 .99521 .99045 .00000 .08974 1.8154! .00357VAR012 .99521 .99045 .00000 .40279 -.02540 ..00684VAR003 .99522 .99045 .00000 -.16659 -.26687 -.00155(CONSTANT) -1465.28175
FINAL ANALYSIS OF VARIANCE
Due to DF Sum of Squares Mean Square FRegression 17 6967008693.08200 409824040.76953 1428.23060Residual 234 67145197.24732 286945.28738
Standard Deviation of Residuals 535.67274
Variable names are given in TABLE 111 -2.
17
l'ot-Seomdary Educatum
TABLE 111-4
MULTIPLE REGRESSION
Dependent Variable' D2 (Participation Role)
INDEPENDENTVARIAELE-' MULTIPLE R R SQUARE RSQ CHANGE SIMPLER B BETA
VAR006 46023 .21181 .21181 -.46023 -.57694 -.29848
VAROI 6 .57285 .32816 11635 .23567 1.71572 .30328
VAR008 .60965 37168 .04352 - 30810 -.17205 -.22460
VAR007 .64507 .41612 .04444 -.18062 -1.05066 -.24815
VAROIS .65958 .43505 .01893 -.39260 -I 02914 - 19256
VAR002 66572 44318 .00813 -.23966 -.33491 -.11096
VAROI I .66870 .44716 .00398 .12546 .00194 .35367
VAR014 67401 .45429 .00713 -.24145 .27252 35699
VAR013 .67608 .45709 .00280 .32108 .00756 A1597
VAROI_ .68389 .46771 .01062 .12273 - 00197 -.35201
VAR005 68512 .46939 00168 11989 .01638 .10563
VAROO4 68749 .47265 .00326 .09641 -.01767 - 07275
VAR001 .68759 .47278 00013 -.16886 08937 .02063
VAR015 .68768 A7291 .00012 21397 -.01377 -.03906
VAROI7 68781 .47308 .00017 .25869 -.23174 -.03949
VAR020 68785 .47314 .00006 -.08066 00000 00942
(CONSTANT) 12.31644
FINAL ANALYSIS OF VARIANCE
Due to DF Sum of Squares Mean Square F
Regression 16 7571.37176 473.21073 13.18978
Residual 235 84311 1394 35 87708
Standard Deviation of Residuals 5.98975
*Variable names are given in TABLE 111-2.
18
27
3 The feasibility of generatint, or collecting student-specific information should also he seriously considered bythe Coordinating Board staff. For instance, it might behelpful to obtain Scholastic Aptitude Test scores of highschool students (possibly obtained through the TexasLducation Agency or through the institutions themselves),as well as a statement of post-secondary education inten-tions by each high school student who is approachinggraduation. This latter information iniglit be obtained in ahigh school interest surrey that would record the student'ssocial security number (for cross-referencing survey resultswith the URS data); the student's intentions with regard toattendance at a four-year college. two-year college, publictechnical institute, or proprietary school, as well as specificschool preferences. and the student's desired area ofconcentration. Such items as these would seem to ber.le%ant in predicting post-secondary education participa-tion in both academic and vocational sectors.
4. If data on Texas counties continue to be used in suchpredictions and we believe they should he they should inmany cases be collected at the smallest possible geographic
level less.. urban area tracts or ZIP code areas. Moreover, itis important that county data, if they are to play a majorrole in future analyses of this sort, be capable of beingfrequently updated. Such surrogate indicators as newhousing starts and new automobile registrations may alsohave to be included.
5. Alternative approaches to expanding institutionaliinoRettient in the Uniform Reporting System should beassessed for their feasibility. Effort.) might be made toinclude not only public and private senior and juniorcolleges unnersities but also Texas Education Agencycertificated proprietary schools in the set of institutionstiling completed CRS forms with the Coordinating Boardstaff. This would be a first step toward the development ofa state capability to analyze potential post-secondary
education not just higher education student participation.
ST1 DENT ALLOCATION MODEL
While the regression model is useful in assessing thestatistical significance of major determinants of countyenrollment, and participation rates. its predictive capabilityfor allocation purposes is somewhat limited by the nonful-fillment of certain underlying conditions. A visual inspecnon suggests a non-linear relationship between the depen-dent variable and the independent variables. In addition, acounty's flow of students is not tied to a single college ortumersity Perhaps most important of all, to design amultiple regression analysts that is operationally effectivefor Audent allocation purposes would require increasedregional homogemety and product specificity: this would
19
Data Presentation and Analysis Techniques
render the analysis almost useless for other regions andother points in time.*
As part of the demand study a methodology forassessing student demand at various institutions was in-vestigated. This technique is based on a spatial allocationprocess which assigns students by county of origin toinstitutions of higher education. The process is structuredto account for variations in the character of destinationinstitutions by subdividing institutions into categoriesMajor Research Institutions, M.A. and B.A.-Granting Insti-tutions. and Community and Junior Colleges These sub-divisions are further refined by separating the private andpublic educational systems.
This allocation process recognizes the homogenizingfeatures within these broad groupings by assessing the past"drawing power" of the individual institution relative tothe level within which the institution is located. Althoughthis is basically historical in nature, it is hoped that thepresent impact of the multitude of forces that define the"attractiveness" of a given institution can be representedthrough the use of recently observed behavior (Le.. 1970data).
The potential supply of students to institutions of highereducation is defined in two stages. The first is tl't numberof college-age students available in each county now or atsome future date. This information has been supplied to theCoordinating Board by the Population Research Center(Poston et al. 1973) through the use of the cohortsurvival- migration technique. Once the primary population"at risk" (i.e.. college-age population) is determined. thesecond stage occurs. This stage is the determination ofcollege-going rates by type or group of institution attended.When this is multiplied by the college-age population. apotential supply of students to institutions is determined_These elements are then entered into an allocation model inorder to determine the relative demand of student spaces atgiven institutions.
The model used in this study is a modification of oneformulated originally by David L. Huff to predict "con-sumer spatia: behavior" (Huff and Blue 1966). Animportant attribute of this work is that "consumer spatialbehavior" is shown to be explainable by utility theory(Luce 1959). The Huff model has shown an adaptability toa wide variety of multiple origin/multiple destinationproblems. For instance,. Huff ( 1 973) has used it to describe
a national system of planning regions based on urban
In addition. the error terms generated by the regression equationare not independent, and the regression residuals do nut display anormal distribution. Set N.R Draper and II Smith. AppliedRegrescion Analysis (New York John Wiley and Suns. In .1966/.p 86
28
Post-Secondary Education
spheres of influence. David E. Ault and Thomas E.Johnson. Jr. (1973) have used the model to plan hospitalservice areas. George H Haines. Jr.. Leonard S. Simon, andMarcus Alexis (1972a. 1972b) have analyzed central citytrade areas using this technique
Appendix B contains a technical description of theallocation model used in this project.
For each Texas county the flow of students from thatcounty was determined for Texas universities and collegesaccording to the aforementioned six institutional groupings(e.g.. Public Universities and Colleges granting Bachelor andMaster Degrees (N =17)). These flows determined therelative role that distance played in allocating students froma given county to alternate institutional destinations. TableIII-5 provides an example of this procedure applied to onecollege and university group (consisting of 17 institutions)for one particular county. This procedure was repeated foreach of the six college and university groups. for each ofthe 254 Texas counties
In Table III-5. lambda represents the allocation factorand U is a statistic of goodness of fit. (See Appendix B for afurther discussion of lambda.) If U is less than one itindicates that the fit is better than a trend projection of thesame data. In all cases in which this model was applied tostudent flows. U was equal to or less than one.
The primary purpose of this phase of the researchproject was to calculate the probability distribution ofstudent attendance for a given group of schools. forstudents from a specific county. When summed by countyfor a particular institution. the student drawing power ofthat school on that county is determined. With the help ofthe Educational Data Center of the Coordinating Board.this information was incorporated into a 1980 institui :onalenrollment projection procedure based on 1970 county-to-institution student flow information.
The elements of the projection methodology are fairlystraightforward. once the probability distribution whichmatches student attendance from each county to eachinstitution is determined. The projection elements are
the projected college-age population (18.24) in eachTexas county in 1980 (Poston et al 1973 ):the 1980 college-going rate of each county projectedfrom existing state trends allocated to the countiesbased upon their 1970 rates of higher educationattendance (Educational Data Center staff interviews1974).the share of student enrollment that each insti-tutional group (two-year colleges. BA/MA institu-tions. research institutions) will draw in 1980 (Edu-
cational Data Center staff interviews 1974): and
20
the probability distribution of students in a givencounty attending a given college or university, basedupon the variant of the Huff model developed by theLBJ School to analyze student flow data
An application of this protection procedure to oneuniversity (East Texas State University) is given in TableIII-6. Table 111-7 summarizes the 1980 enrollment projec-tions for the six institutional groupings used in this analysis.
Uses of the Allocation Model
It is important to fully understand the limitations of thisallocation model analysis and the resulting projectionsFirst. the projections are based upon the reported fall headcounts of Texas residents at Texas higher educationinstitutions in 1970. Hence there is a built-in underesti-mation of approximately 14 percent resulting from missingdata, inaccurately reported data, Texas residents attendingeducation institutions outside the state. and non-Texasresidents attending Texas institutions.
The interpretation of these results is also quite difficultdue to the dynamic nature of the Texas "system" of highereducation during the years immediately following the fallof 1970 head count. This projection procedure onlyestimates student demand for (and thus only allocatesstudents to) Texas higher education institutions thatexisted in 1970. Student demand for newly establishedTexas institutions (and programs) and the impact of thisdemand on potential enrollments of existing institutions arenot taken into account: neither are changes in the highereducation "systems" in neighboring states considered. Itshould therefore be clear that, if the methodology demon-strated here is to be successfully applied for enrollmentprojection purposes, education planners must use the mostrecent student flow information and the institutionsexisting in the projection base year. and assume that nonew institutions will be created during the projectionperiod.
The 1980 enrollment projection figures for North TexasState University. The University of Texas at Arlington. andTexas Women's University are likely to exceed actualenrollment due to the post -1970 development of TheUniversity of Texas at Dallas campus. Similarly, the
enrollment projection for Southwest Texas State Universitydoes not take into account the impact of the new SanAntonio campus of The University of Texas. On the otherhand, the estimate for The University of Houston is likelylow, since enrollment at its new Clear Lake City campus isnot considered.
This model is also culturally blind: it may, for instance,slightly underestimate the drawing power of Pan American
29
Data Presentation and Analysis Techniques
TABLE III-5
LAMBDA ERROR U
2.5278640 .1545143 .45927793.4721360 .1695191 .45373861.9442719 .1499157 .47051841.5835921 .1502425 .48168012.1671843 .1510141 .46528061.8065045 .1497160 .47438791.7213595 .1497898 .47702411.8591270 .1497467 .47285211.7739820 .1497260 .47537281.8266045 .1497210 .4737928
Fibonacci Search Ended In 11 Steps
Lambda = 1.81
LOCATIONIDENTIF.
U = .474
LOCATIONSIZE
DISTANCE FROMCOUNTY
PROBABILITYDISTRIBUTION
ACTUAL DISTR.OF STUDENTS
EXPECTED DISTROF STUDENTS
1- I 3740 2 04 .11 13 20.51-,- 1_ 10210 693 03 3 6.13
3- 3 3641 3.43 04 3 7.76
4- 4 4939 6.61 .02 0 3 '35- 5 4095 5.11 02 0 4 26
6- 6 0769 5.69 .05 I 8 38
7- 7 9633 4.36 07 69 13.33
8- 8 9200 6 15 .04 11 6 859. 9 2368 1 99 07 38 13 5'
10- 10 2905 3 47 .03 3 6 08
11- 11 7517 5 83 03 0 6.15
12- 12 265 5 17 00 0 27
13- 13 68 6 72 00 0 04
14- 14 4803 6.11 02 C 3 61
15- 15 13574 414 II I 20.65
16- 16 9470 2 75 16 2 _ 30 22
17. 17 7554 2 23 19 42 35 021 00* 186* 186 000*
Post-Secondary Education
TABLE 111.6
COORDINATING BOARD, TEXAS COLLEGE AND UNIVERSITY SYSTEMEDUCATIONAL DATA CENTER
ENROLLMENT PROJECTIONS 1980EAST TEXAS STATE UNIVERSITY
COUNTYNAME
1980 COUNTYCOLLEGE GOING
RATE
SHARE BYTYPE INST.
FACTORPROBABILITYDISTRIBUT!ON
1980 COLLEGEAGE POPULATION ENROLLMENT
CLAY 0.454 .240 .100 000,739 00,008LIMESTONE 0.302 .240 .100 001.435 00,010SABINE 0.280 .240 .110 000,705 00,005COOKE 0.764 .240 .120 002,789 00,061NAVARRO 0A80 .240 .120 002,693 00,037SAN AUGUSTINE 0.299 .240 120 000,786 00,006GRAYSON 0.442 .240 150 016,339 00,259FREESTONE 0.377 .240 .160 000,771 00,011NACOGDOCHES 0.180 .240 .170 007,441 00,054HENDERSON 0.421 .240 190 003,316 00,063PANOLA 0.465 240 .190 001,195 00,025SHELBY 0.403 .240 .190 001,852 00,034ROCKWALL 0.337 240 .200 000,833 00,013ANDERSON 0.490 .240 .220 002A79 00,064KAUFMAN 0.270 .240 .220 003,622 00,051RUSK 0.428 240 .240 002,976 00,073BOWIE 0.412 .240 .280 008,030 00,222CHEROKEE 0.345 .240 .290 003,023 00,072HARRISON 0.343 .240 .290 013326 00,327
CASS 0.370 .240 300 002 A 70 00,065MARION 0.341 .240 .330 000,860 00,023FANNIN 0.439 .240 340 001,955 00,070LAMAR 0.420 .240 .350 003345 00.132SMITH 0.488 .240 .350 011,162 00,457
RED RIVER 0.356 .240 .360 001,125 00,034VAN ZAN DT 0.404 .240 .390 002,277 00,086GREGG 0.483 240 430 008,889 00.443
RAINS 0.322 .240 .460 000,408 00,014MORRIS 0.406 .240 .490 001,290 00,061
DELTA 0.491 .240 .500 000,275 00,016CAMP 0.427 .240 .580 000378 00.046FRANKLIN 0.330 .240 610 000.506 00.024UPSHUR 0.324 .240 730 002,363 00,134TITUS 0 364 .240 .780 001559 00.106
HUNT 0.288 .240 .810 007.755 00,434WOOD 0.360 .240 830 OW ,809 00.129
HOPKINS 0.310 240 .930 002.262 00.156
Total Projected 1980 Enrollment for East Texas State University 007133
22
3i
Data Presentation and Analysis Techniques
TABLE 1II-7
1980 ENROLLMENT PROJECTIONS*
Public Sr.Research & Ph.D. 143,7804 yr. & 1st level
grad.119,534
Subtotal 263,314Public Comm. 218,553
Subtotal 481,867
Private Sr.
Research & Ph.D. 31,6534 yrs. & 1st level
grad.36,147
Subtotal 67,800Private Jr. 1,526
Subtotal 69,326
TOTAL 551,193
*Based upon the Huff allocation model variant developed by theLBJ School in this project.
23
, 3 2
Post-Secondary Education
University as a center of Mexican-American education inthe South Texas area. Student pricing policies and otherinstitutional factors are also ignored, for good reason, inthis analysis.
In summary,. the methodology is regarded as valid by theproject participants. Up-to-date information on studentflows and institutional existence and capacity must be used,
24
however. When combined with the other project analyses ofstudent flow data at the regional level, this Huff modelvariant could be of considerable value in effectivelyincorporating demand/supply factors and data into post,secondary education planning in Texas. Such regionalapplications are discussed in Chapter IV.
IAPTER IV
ANALYTIC TECHNIQUES FOR PLANNING:A REGIONAL AND INSTITUTIONAL APPROACH
This report has examined a variety of analytic tech-niques for incorporating supply/demand information intopost-secondary education planning in Texas. Included have
been the Texas Atlas of Higher Education, the student flowversion of the MAPPER programs (as documented in theMAPPER Users Manual), the statewide regression analysisof county participation rates in higher education, and theLBJ School student flow variant of the Huff allocation(probability) model. These provide improved informationdisplay and analysis techniques to facilitate state educationplanning At the other end of the spectrum, institutionalplanning procedures, with an emphasis on program develop-ment. are examined in Chapter V through survey responsesof post-secondary education institutions in one sub-stateregion and in-depth studies of a few selected schools. Theseanalyses focus on present practices and clearly indicate theneed for the development of regional and institutionalplanning techniques that complement the statewide efforts.
An important initial task in relating state planning toinstitutional planning is the application of statewide tech-niques in different regions of the state, thereby prowlingthe student flow information needed by education institu-tions and planners to effectively coordinate their activities
One such tool, the Institutional Service Area (ISA)analysis, has been developed in this project to demonstratethe post-secondary education dynamics of any Texas region
, of any set of contiguous Texas counties). This analytictechnique is a straightforward application to a region of the
Atlas and MAPPER Users Manual information display toolsdiscussed in Chapter III Both institution-based and county-based matrices of county-to-institution student flows canbe provided for all counties and higher education institu-tions in the legion, for a five-year period
To illustrate the Institutional Service Area analysis,project participants decided to focus on a single sub-stateregion. The Austin - San Antonio area was selected as the
region of study. Included were the 21 counties constitutingthe Capitol Area Stare Planning Region (CASPR) and the
Alamo Area State Planning Region (AASPR).* County
*Barnes County has, at difterent times, been a member ofAASPR and the Coastal Bend State Planning Region and is notincluded in this e \ ample
34
characteristics of the region are summarized in Table IV-1The selection of the Austin San Antonio region in
preference to other regions of the state was based on severalfactors. These included
regional population growth;shifting economic patterns;the presence cf metropolitan centers; andrepresentative numbers of ethnic minorities.
In addition to these general factors, the region wasevaluated in terms of institutional mix, physical accessi-bility. information availability, and the presence of bothurban and rural characteristics While recognizing thediversity of sub-state regions, the project participants feltthat the Austin San Antonio area was as heterogeneouswith respect to population: economic activity, urban/ruralmix, and institutions as most other areas in Texas.
With respect to the institution-based analysis, the 14CASPR-AASPR collegiate institutions in existence duringthe period 1968.1972 are considered. Tables IV-2 throughIV-15 provide the basis for the discussion.
In each of these 14 tables, the institution's name is inthe upper left corner. Counties are listed on the far leftside. Below the counties appear the titles "Region,""State," and "Total." For each of the five years. the firstnumber associated with "Region" represents the totalnumber of students attending the institution who origi-nated from the selected region. i.e , from the counties listedon the table. The first "State" figure denotes, for each year.the total number of students attending the institution whocome from Texas counties outside the selected regionThese numbers have also been converted into percentagesto give reg.on and state breakdowns indicating the scope ofthe institution. i.e., whether it is predominantly state Orregional in terms of its student service area
The three columns under each of the years 1968 through
1972 provide more detailed student flow information foreach of the selected counties. In each of these tables, these
columns indicate, respectivelythe number of students in that institution whooriginate from the designated county:
- the p reentage of the regional portion of students in
Post Secondary Education
TABLE IV-1
CASPR AND AASPR:
COUNTY CHARACTERISTICS
Counties
LandArea
(sq. mi.)(I)
TotalPopulation
(I)
PercentUrban
(I)
PercentUnder 18
(I)
PercentUnemployed
(1)
PercentMinority
(I)
No. inpublic
high school(2)
No. incollege
(2)
Atacosa 1.206 18,696 45.0 38.3 3.5 1.1 1,296 127
Bandera 763 4,747 0.0 28.4 3.5 0.4 407 26
Bastrop (c) 890 17,297 57.3 32.6 2.4 26.5 1,217 65
Bexar 1,246 830,460 94.9 37.8 4.2 7.9 57,229 22,Q63
Blanco (c) 719 3,567 0.0 28.1 1.4 .>.,..' 7 241 6
Burnet (c) 996 11,420 25.1 27.5 1.5 2.0 671 36
Caldwell (c) 544 21,178 52.9 37.5 2.9 22.0 1,785 205
Comal 567 24,165 73.9 33.3 2.6 2.1 1,728 299
Fayette (c) 934 17,650 17.5 25.9 1.7 12.0 1,133 128
Frio 1,116 11,159 49.7 42.7 4.8 0.8 728 47
Gillespie 1,055 10,583 50.5 30.4 5.3 0.8 719 11
Guadalupe 714 33,554 59.6 34.6 3.7 9.6 2,020 1,053
Hays (c) 650 27,142 68.2 29.4 2.1 4.4 1,486 6,068
Karnes* 758 13,462 52.6 37.9 3.4 3.4 1,009 136
Kendall 670 6,964 0.0 30.6 2.2 0.6 526 24
Kerr 1,101 19,454 65.1 26.6 3.3 4.2 1,031 242
Lee (c) 637 8,048 34.6 29.8 1.6 22.2 646 36
Llano (c) 941 6,979 37.4 22.3 1.2 0.5 322 9
Medina 1,352 20,249 43.4 38.6 3.7 1.1 1,539 129
Travis (c) 1,012 295,516 89.5 32.0 1.8 11.9 16,931 32,197
Williamson (c) 1,164 37,305 50 5 33.1 1.9 12.6 2,367 921
Wilson 802 13,041 28.9 37.2 3.0 1.5 957 29
(c) Denotes CASPR county*Karnes County has, at different times, been included in the AASPR and in the Coastal Bend State Planning Region.
Sources.
(I) Bureau of the Cen-Js, 1973.(2) CASPR and AASPR
26
36
this institution coming from the designated county,and
the percentage of all Texas-resident students attend-ing the institution who originate from the designatedcount%
As an example, consider Table 1V-13. Examining theclimes opposite "Region" for the years 1968 through1972, it is clear that St Phillip's College is becoming evenmore regional in its scope. Whereas 95 8 percent of thecollege's (Texas) students came from the selected region in1968, this figure had risen to 994 percent by 1972.Ft cusing on the counties, it is turther shown that, in 1972,96 8 percent of these regional students came from BexarCounty. Thus St Phillip's College Is not only regional in itsorientation but, in fact., very local.
Other institutions exhibit opposite trends. SouthwestTexas State University, for example, is experiencing anexpanding service area (see Table IV-2). The "State"proportion of its Texas-resident enrollment has beenincreasing at the expense of its regional enrollment
This institution-based analysis, as the Atlas, provides adescription lif each institution's service area. By examiningthe extent of the goegraphic area from which eachinstitution draws its students, post-secondary educationplanners can develop improved policies concerning facilityconstruction (and modification) and program development,Fur example, i college or university serving an increasingpercentage of students from counties outside the immediate%minty would need to consider the capacity of its existingdormitories. On the other hand. an institution such as St.Phillip's College that is serving an increasing member ofregional and local students may need to reassess its facilitiesin light of its shrinking service area. Such a reassessmentmight result in the construction of parking lots forcommute' students rather than dormitories. for example
Program development is similarly affected by a school'sserice ,tea Piograms should he designed to meet theeducation needs and the employ ment opportunities anddemands of the residents of the service ,Jea. Identificationof an institution's sercice area is a critical first step informulating institutional naming progiams
Student flow trends noted through institution-basedmay sis pros ide useful data in projecting future enrollmentstot colleges or umea sines The analysis also may aid theCoordinating Board staff in re-examining the scope ofinstitutions Further development and application of thisanalytical tool could provide the Coordinating Board staffwith another means of assessing the impact of newprograms on the distobution ut students among institu-tions
Cables 1V-16 through IV -36 illustrate the county-basedamity sis for the selected region In each of these 21 tables,the county name is pen in the upper left corner, with thecollegiate institutions in the region listed on the far left
27
Analytic Techniques for Planning
side. At the conclusion of this list of institutions appear thetitles "Region," "State," and "Total." The number ofstudents from the county attending colleges or universitieswithin the region is located in the "Region" category, thenumber of students from the county attending Texas highereducation institutions outside the region is located in the"State" category; and the total number of students fromthe county attending Texas colleges and universities is
located in the "Total" category.Detailed enrollment data are provided in three columns
for each institution, for each year from 1968 through 1972.The number of students from the county enrolled in eachinstitution is given in the first column, the percent ofcounty-resident students educated in the region attendingeach institution in the second column, and the percent ofcounty-resident students educated in the state attendingeach institution in the third column.
The proportion of students attending institutions in theregion relative to students attending schools outside theregion remained stable °vet this five-year period for most orthe CASPR and AASPR counties. A few counties, however,exhibited discernible shifts. For example, the percent ofWilson County (Table IV-36) students attending collegesand universities in the region increased steadily from 52 0percent in 1968 to 66.3 percent in 1972. On the otherhand, Travis County (Table IV-34 ) experienced a shift of itscollege-age population to schools outside the region overthe same period. In 1968 only 14 0 percent of -DavisCounty students were enrolled in out-of-region institutions,by 1972 this figure had jumped to 25.7 percent.
County-based analysis can assist an institution in betterprojecting its future student body we and composition(with respect to residence). Examining trends in countiesthat contribute heavily to its student population can helpthe institution detect shifts in student preference and canindicate whether the shift is an institutional or regionalphenomenon
County-based analysis also may serve to Identify inequi-tably apportioned educational resources. For instance. theobservance of counties with excessively high proportions oftheir respective college-age population attending out-ofregion schools may indicate inadequate regional facilities ora lack of programs designed to meet regional needs Suchidentification of potentially "deficient.' counties and/orinstitutions might enable the Coordinating Board staff tosuggest improved alternatives,
The Institutional Service Area (ISA) analysis can heapplied easily to any region of the state consisting ofcontiguous counties This potential application of ISAanalysis offers institutional planners an improved analyticaltechnique for self-improvement, while increasing the stateplanners' capability to facilitate comprehensive post-
secondary education institution coordination and developmeat.
TA
BL
E I
V-2
SOUTHWEST
TEKAS
1968
1969
1970
1971
1972
111
121
131
(11
121
131
111
121
131
111
121
131
111
121
131
ATASCOSA
55
1.2
.7
54
1.1
.676
1.4
.8
88
1.4
.7
75
1.3
.6
8ANDERA
25
0.3
38
.6
.3
23
.4
.2
12
.2
.1
16
.3
.1
BASTROP
55
1.2
.7
54
1.1
.6
76
1.4
.8
80
1.4
.7
75
1.3
.6
BEKAR
1515
31.9
18.5
1755
34.2
19.1
1916
35.7
19.9
2274
38.7
28.6
2379
39.7
28.5
BLANCO
43
.9
.5
45
.9
.5
43
.0
.4
43
.7
.443
.7
.4
BURNET
49
1.8
.6
Si
10
.6
54
1.11
.6
49
.8
.4
40
.7
.3
CALDWELL
173
3.6
2.1
202
3.9
2.2
181
3.4
1.9
186
3.2
1.7
165
3.1
1.6
CDMAL
271
5.7
3.3
276
5.4
3.0
276
5.1
2.9
324
5.5
2.9
304
5.1
2.6
FAYETTE
61
1.3
.7
62
1.2
.7
' 57
1.1
.6
52
.9
.5
46
.8.4
FRIO
IS
.4
.2
21
.4
.2
36
.7
.4
.6
.3
33
.6
3DILLESP/E
63
1.3
.868
1.3
.7
69
1.3
.7
::
1.8
89
1.5
.8GUADALUPE
229
4.0
2.11
262
5.1
20
263
4.9
2.7
291
5.11
2.6
290
4.11
2.5
HAYS
671
14.1
8.2
642
12.5
7.8
628
11.7
6.5
648
11.0
5.9
622
18.4
5.4
KENDALL
31
.7
.4
46
.9
.5
44
.8
.5
36
.6
.3
43
.7
11
KERR
52
1.1
.6
440
1.8
.5
68
1.1
.649
.8
sii
41
.7
.4
LEE
11
2.1
IS
.2
.1
13
.2
122
.4
.2
28
.3
2LLAND
31
.7
.4
24
.5
.324
.2
23
.4
.2
25
.4
2MEDINA
I10
8.1
8SO
11.8
95
1.:
14
08.8
8.8
119
2.8
1.1
TRAVIS
1182
24.9
14.4
1049
24.4
13.6
1218
22.7
12.6
1374
23.4
12.4
1363
22.7
11.8
WILLIAMSON
156
3.3
1.9
159
3.1
1.7
149
2.0
1.5
156
2.7
1.4
128
2.1
1.1
WILSON
55
1.2
.7
67
1.3
.7
541
1.1
.6
59
1.0
.5
62
1.8
.5
REGION
4744
57.11
5126
55.9
5368
55.7
5876
53.2
5996
510
STATE
3464
41.2
4839
44.1
4270
44.3
5179
46.8
5585
48.2
TOTAL
sass
11165
9630
11855
11583
AN ASTERISK INDICATES THAT ENROLLMENT DATA WAS NDT AVAILABLE
11/41NROLLNENT FROM COUNTY IN INSTITUTION
121AFERCENT OF REGIONAL STUDENTS ATTENDING INSTITUTION ORIGINATING IN OESIGNATED COUNTY
(314PERCENT OP STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
1 ABLE IV-3
UT **US t IN 19613 1970 1911 1972113 123 (3) 111 12r 131 t I 1 123 131 113 (2) 133 (1) 123 113
ATASCOSA 41 .4 .1 3S .3 .1 3S .3 .1 3S .3 .1 33 .3 .1
BANDER A 10 .1 .0 11 .1 .0 12 .1 .8 18 .1 .0 1 .1 .8
BABTA0p 86 .8 .3 86 *2 .3 67 .6 .2 66 .6 .2 15 .7 .2
BEKAA 2341 22.3 80 2861 25.1 9.2 3201 28.3 .6 3338 30.3 9.8 3368 31.3 9.8
BLANCO 16 .2 .1 17 .2 .1 18 .2 .1 12 .1 .0 9 .1 .0
Sweat 42 .4 .1 43 .4 .1 41 .4 K1 S2 .5 .2 Se .S .2
CALDWELL 73 .7 .3 70 0 .2 S6 .8 .2 44 .4 .1 44 .8 .1
COPAL TS .7 .3 TA .7 .3 76 .7 .2 70 .6 .2 TT .7 .2
LAYETTE 76 .7 .3 18 .7 .8 78 .7 .2 88 .7 .2 II .8 .2
TRIO 17 .2 .1 13 .1 .0 20 .2 .1 22 .2 .1 25 .2 .1
GILLESPIE 60 .6 2 64 .6 .2 60 .6 .2 SS .5 .2 60 .6 .2
GuAOALUPE 76 .7 .3 OS 94 .0 .5 9S .7 .3 114 .9 .3
HAYS 88 .8 .3 96 .9 .3 49 419 .3 1411 .9 .3 00 .9 .3
CAD KENDALL 23 .2 .1 26 .2 .1 27 .2 .1 25 2 .1 26 .2 .1GOD IJ
.r.3 WEAR ST .5 .2 67 6 71 4 .2 71 0 .2 70 0 2LEE 17 .2 .1 (7 .2 .1 22 .2 .1 14 .1 0 13 .1
LLANO 23 .2 .1 24 .2 .1 IS .1 .0 16 .1 .0 II .1 .0
MEDINA 37 .3 .1 43 .8 .1 39 .3 .1 48 .11 .1 34 .3 .1
TRAVIS 1210 6801 WO 7186 64.6 MO 1107 62.0 21.0 6610 60.1 19,4 6330 54.9 14.4
W/LLIAMOON 196 1.8 .7 244 1.4 .7 256 a.: a 219 2.0 *4 224 1.9 .6
WILSON 15 .1 .1 22 .2 .1 24 .2 .1 27 .2 .1 15 .1
REGION 10599 37.8 11124 35.7 11472 33.9 11001 32.3 10745 ma
STATE 17074 61.0 20476 64.2 22359 66.1 22040 67.7 23720 420
TOTAL 20473 21204 33851 24041 34465
AN ASTERISK INDICATES THAT ENROLLMENT DATA 442 NOT AVAIL441.2
tilmeNnoLow IRON COUNTY IN INSTITUTION
1214PEACENT OF REGIONAL STUDENT, ATTENDING INSTITUTION ORIAINATINO IN 0E0IONATE0 COUNTY
(33KPEACEN. OP STUDENTS ATTENDING INSTITUTION ORIOINAT/NO IN DESIONATED COUNTY
HUSTON TILLO'SO
146
1949
FA
BLE
IV-4
1470
1971
1,72
117
127
13)
111
111
131
111
illtit
(II
(27
111
111
121
131
AUSCOSA
00
0.
0.0
0.
.0
0.0
00.0
0.0
SANDER*
SO
SO
SO
SO
SO
SO
S0
4.0
eASTRO
72.7
1.
S20
1.
3.6
1.6
3.4
1.0
OEXAR
47
17.
6.7
20
12.1
0.6
16.
3.0
10
7.6
3.9
BLANCO
SO
SO
SO
SO
..
0SO
Oa
BURNET
1.0
.t
t0
.2
SO
SO
SO
.0
CALORELL
ISO
SO
21.
.4
S2.)
1.
2se
.44
CCM'.
0.
..
0.
..
.0.
FAYETTE
2a*
.1
21
.4
2.9
.4
S2.1
1.1
/RIO
SO
SO
SO
SO
..
.00
GILLESPIE'
SO
SO
SO
.0
SO
SO
060
GUADALUE
SO
1SO
SO
1sE
.2
31.3
.7
HAYS
1.0
.1SO
SO
SO
SO
1.0
.2
to./
KENDALL
SO
SO
SO
eSO
SO
0SO
CD
KERR
.SO
SO
SO
SO
SO
0.
0.0
Lee
e2.3
.9
21.
00
2.7
1.2
2.0
.4
Cog;
Cr
LLANO
SO
.SO
SO
SO
SO
00.0
0.0
NEO/NA
SO
SO
SO
SO
..
.0.
704010
196
74.2
27.
1S7
79.4
4.
102
02.
36.3
190
03.2
43.3
KILLIAHSON
S1.9
.7
S2.11
tee
31.9
.6
1.4
.2
WILSON
.SO
SO
SO
SO
SO
dP.O
00
REGION
264
37.5
14
37.
222
44.3
230
S2.1
STATE
44
62.5
326
62.2
279
55.7
214
47.9
TOTAL
744
S24
S01
AS7
AN ASTERISK
INO/CATE4
THAT ENROLLMENT004
WAS
NOT AVAILABLE
11111ENROLLHEHT FROM COUNTY IN INSTITUTION
1214ERCENT OF REGIONAL STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
131a0ERCENT OF STUDENTS ATTEND/44 INSTITUTION ORIGINATING IN DESIGNATED COUNTY
T A
BU
' IV
-5
INCARNATE .010
MS
196
197e
1971
1972
(1)
(2)
(3)
(1)
121
(3)
II/
121
(31
111
121
(31
Cl)
121
13)
ATASCOSA
0.11
.8
801.
50
.8
3.3
.24
.4
8A40EMA
1.1
.1.
sip.
so
so
...
1.0
so
so
ASTROP
2.2
.2
i.1
.1
1.1
.1
co
go
aa.
so
laxAR
81 .1
75.
gas
3ee1
700
4.8
0.2
um 5.28
102
11.7
SLANCO
8.8
8.0
1.1
.1
1.1
.1
8.0
.0
..8
URNET
. .
8.
.8.
.0
88.
0.
..
CALDWELL
. 8.
898.
..
..
..
CONAL
S.5
s.4
.3
.0
a0
.S
0.
FAYETTE
2.2
.2
1.1
.1
00.0
0.
0.8.
..
IRIS
2.2
.2
so
coo
1.1
.1
2.2
.1
.3
GILLESPIE
3.3
.3
1el
.1
1.1
.1
1.1
.1
1.1
.1
GuAOALuPt
11
1.2
1.
11
10
..7
.5
.NAYS
0 .
1.1
.1
1.1
.1
3.3
.22
.2.2
KENDALL
2.2
.2
iD
07
7S
..
11
19
KERB
7.
5.
03
.3
.2
.7
..
eS
LEE
. .8
..
..
.8.
8.
.NANO
1.1
.1
..
..
..
MEDINA
.7
.11
..3
S.S
.S
..
S0
TRAVIS
.7
01
el
el
1.1
1.3
.NILLIANSON
. .
8.
..
..
1.1
.1
WILSON
7.15
7.7
13
1.2
1.1
1.7
REGION
10.1
83
81.
10
113
.1003
2.2
STATE
22
19,3
22
Ilea
ICI
11.
201
15.1
235
ITTOTAL
113
1207
123
1335
131
AN ASTERISK
INDICATES THAT ENROLLMENT DATA44
NOT AYAILALE
111REWROLLMENT PROS COUNTY IN INSTITUTION
l218PERCENT OP REGIONAL STUDENTS ATTENOIN INSTITUTION ORIGINATING IN DESIGNATED COUNTY
I319PERCENT OP STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
TA
BL
E P
V-)
OUR 1.40Y OF
1.40(
1468
1969
1970
1971
1972
(11
(21
(31
111
121
(31
111
12/
13/
111
121
131
111
(21
(31
ATASCOSA
14
1.6
1.3
16
1.3
1.1
20
1.3
1.2
15
.8
.8
19
1.0
se
8440E46
00.0
0.0
3.2
.2
3.2
.2
2.1
.1
0SO
8.0
BASTROP
00.0
P.O
ee.e
0.0
000
0.0
40.0
0.0
00.0
0.0
SEXAR
1128
93.5
77.8
1158
92.5
79.2
1441
92.9
83.1
1698
95.4
87.0
1868
95.3
32.8
eL.Nco
00.0
0.0
00.0
...
0e.e
0.0
00.
00.
00
0.5
Oa
BURNET
000
e.e
I0.5
0.11
1.1
.1
00.0
0.0
00.0
P.O
c.LowELL
I.1
.1
1.1
.1
s.3
.3
4.2
.2
2.1
.1
COMM.
2a
.1
3.2
.2
3.2
.2
4.2
.2
3.2
.1
FAYETTE
ee.e
e;e
A.2
.1
E.1
.1
1.1
.1
1.1
.2
F610
a.0
.1
4.3
.3
a.3
.2
a.3
.3
I1
.4
SILLIC
0ea
ea
1Al
.1
a.1
.1
a.1
.1
2.1
.1
oulkoALuee
s.0
.3
0a
.4
T.5
.4
9.3
.3
6.3
.3
NAYS
9.7
..4,
5.9
.3
111
.41
.6
7.9
.a
I.1
.5
KENDALL
is
.5
.7
6.3
.8
4.3
.2
3.2
.2
5.3
.2
KERR
0SO
so
1.1
.1
11
.1
21
.14
.2
.2
LEE
0e.e
e.e
aea
.1
1.1
.1
II
11.11
SO
II
PO
00
LLANO
eso
e:e
e5.5
000
0000
PO
0OA
00
0PO
DO
MEOINA
24
8.0
1.7
SO'
11.4
2.1
34
2.2
2.e
IS
',II
.4
15
.4
.8
TRAVIS
1.1
.1
IS
.5
.7
6.4
.3
5.3
.3
1.5
.9
NILLIANSON
1.1
el
1.1
.1
0cis
so
I.1
.1
0.3
.3
4200N
4.3
.3
3.2
.2
701
6.3
.3
10
a.4
REGION
120:
113.2
MI
011.6
1051
54.4
1750
41.2
1661
64149
STATE
248
16.0
211
14.4
164
le..
172
5.11
296
13.1
TOTAL
14SO
1465
1130
1452
2257
AN ASTERISK INDICATES THAT ENROLLMENT DATA MAS NOT AYAILASLE
MarNADLLNENT FROM COUNTY IN INSTITUTION
(1349ENCENT OF REGIONAL STVOINTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
131FERCENT OF STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
I A131.1. IV-7
ST. 10wARDS 1660* 1966 14117 1971 1972(II (2) (3) (1) (2) (3) (1) (21 (3) (1) (2) 131 (1) (2) (3)
ATASCOSA 0 , 0 0.0 .0 0 110 0.0
BANOEMA , ,4 .2 1 .2 .1 1 .2 .1
BASTROP 4 1.4 0 s .4 .6 3 .3 .3
BEXAR 37 13.3 7.1 37 2.4 40 37 6,3 4.1
BLANCO 11 SOS SOP P.O SO 1 .2 .1
SuRmET 2 .7 .4 2 .4 .2 2 .3 .2c21.222u. 1 43 .2 2 .4 .2 2 .3 .2
COAL S SOS SOS SO . I . SOS
FAYETTE 1 .4 .2 2 .4 .2 2 .3 .2PRIO 0 0. SO SO .0 0 SO OaGILLESPIE S 1,4 a 2 1.1 .7 7 1.2 .
02 Gu404LuwE SOS SO SOS . SOS O.r:.)
td4
NAYS 3 1.1 .9 4 .7 .3 s .9 .9
.44 KENOALL . .0 SO . I SO Sol
KERR 3 1.1 .2 4 .7 .5 4 .7 .0
LEE . 0 GO GO OOP OILLANO SO .0 SO .0 0 0.0 SOMEDINA SO SO CO SO 1 .2 .1
TRAVIS 213 76,3 111. 435 64,7 33.11 SOS 6,4 56,1
wILLIAKSOK IS 3.2 1.9 IS 3.4 11.1 13 2.2 1.4
WILSON SO SO 1 .2 .1 2 .3 .2
REGION 279 33,7 337 63, DSO 63,
STATE 241 4443 313 37 31T 33
TOTAL SAS 132 SOS
AN ASTERISK INDICATES THAT ENROLLMENT DATA WAS NOT AVAILABLE
11/4ENROLLmENT PROM COUNTY IN INSTITUTION
(2)SPERCENT OP REGIONAL STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
1314PERCENT OP STUOEMTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
1:
TA
BL
E. I
V8
ST. MARY S
1968
1069
1978
1971
1972
II)
121
13)
II)
12)
13)
II)
12)
13)
II)
12)
13)
I1)
12)
13)
ATASCOSA
S.1
.1
9.2
.2
9.3
.3
I.
.2
.2
13
.S
.4
OANDERA
5.2
.2
I.8
.8
880
0.8
18
.0
2.1
.1
SASTROP
08.1
1.8
88.8
0.8
01.0
8.8
80.0
0.0
08.0
0.0
MAR
3396
98.4
910
3590
98.3
13.1
3042
97.8
90.6
2909
97.9
91.4
2682
960
8292
SLANCO
88.8
8.8
80.8
8.8
I.8
.8
I.1
.0
00.8
0.0
SURNET
4.1
.1
2.1
.1
1.1
.8
4.1
.1
3.1
.1
CALDWELL
88.8
6.6
80.8
8.8
81.8
8.8
880
0.8
1.8
.8
COMAL
2.1
.1
a.1
.1
9.3
.3
11
.4
.3
12
.4
.4
PAYETTE
S.1
.1
3.1
.1
3.1
.1
2.1
.1
1.2
.0
/RIO
S.1
.1
301
.1
6.2
.2
a.1
.1
8.3
.2
GILLESPIE
2.1
.1
s.1
.1
1.2
.2
1.2
.2
2.1
1GUADALUPE
6.2
.2
7.2
.2
is
.3
.3
6.2
.2
9.3
.3
HAYS
81.8
8.0
3.1
.1
a.1
.1
3.1
.1
S.2
.2
KENDALL
6.2
.2
9.2
.2
8.3
.2
10
.3
.3
3.1
.1
KERR
a.1
.1
1.2
.8
3.1
.1
2.1
.1
1.2
.2
LEE
88.8
8:8
88.8
8.1
00.1
8.8
88.1
8.8
1.8
.0
LLANO
88.8
8.0
08.8
80
88.8
8.0
81.8
8.1
88.0
11.21
NEOINA
1.1
.11
2.1
.1
2.1
.1
iii
so
so
1.2
.0
TRAVIS
S.1
.1
7.2
.2
I.
.2
.2
I.
.2
.2
32
1.2
1.8
WILLIAMSON
88.8
8.0
1.1
.8
88.8
8.8
10
.0
1.8
.8
WILSON
..2
.2
4.2
.2
7.2
.2
4.2
.2
1.0
.0
RES/ON
34SS
93.1
3661
94.8
3112
92.6
1971
93.4
2778
OS.1
STATE
2SO
8.9
212
S.2
247
70
611
8.8
406
14.9
TOTAL
3713
3863
3359
3181
3164
AN ASTERISK
INDICATES TWAT ENROLLMENT DATA WAS NOT AVAILABLE
II)KENROLLNENT PROM COUNTY IN INSTITUTION
(1)6EBEENT OP REGIONAL STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
ts).PERCENT SP STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
TA
BL
E I
V-9
SOUTHWESTERN
UN
1968
1969
1970
1971
1972
111
(2]
131
(1]
121
(3]
(11
(2]
(3]
(1]
(2]
131
113
(2]
(3]
ATASCOSA
1.5
.1
00.0
0.0
00,0
0.0
00.0
0.0
00.0
00
BANDER.
00.0
0.0
00.0
0.0
00.0
0.0
00.0
0.0
00.0
0.0
BASTROP
31.6
.4
31.6
.4
21.0
.2
2.9
.2
00.0
0.0
BExAR
41
21.8
5.3
45
23.4
5.6
41
19.5
5.1
43
19.0
5.4
52
22.6
6.8
BLANCO
21.1
.3
31.6
.4
21.0
.2
31.3
.4
00.0
0.0
BURNET
42.1
.5
21.0
.3
1.5
.1
41.8
.5
31.3
.4
CALOWELL
00.0
0.0
00.0
0.0
15
.1
14
.1
1.4
.1
CO4AL
21.1
.3
1.5
.1
a1.9
.5
41.8
.5
31.3
.4
FAYETTE
00.0
0.0
1.5
.1
1.5
.1
00.0
0.0
1.4
.1
FRIO
1.5
.1
31.6
.4
31.4
.4
2.9
.2
1.4
.1
GILLESPIE
1.5
.1
00.0
ea0
0.0
0.0
1.4
.1
00.0
0.0
GUAOALUPE
00.0
0.0
00.0
0.1
0ea
0.0
41.8
.5
31.3
.4
HAYS
1.5
.1
00.0
SO
1.5
.1
00.0
0.0
00.0
0.0
KENOALL
21.1
.3
1.5
.1
21.0
.2
2.
.-9
.2
2.9
.3
KERR
21.1
.3
00.0
0.0
21.0
.2
2.9
.2
52.2
.7
LEE
00.0
0.0
00.0
0.0
00.0
0.0
00.0
0.0
0CO
0.0
LLANO
00.0
0.0
1.5
.1
1.5
.1
00.0
0.0
000
CO
MEDINA
84.3
1.0
17
0.9
2.1
SI
15.2
4.11
21
9.3
2.6
19
0,3
2.5
TRAVIS
33
17.6
4.3
36
18.0
4.5
39
10.6
4.9
49
21.7
6.1
50
21.7
6.5
MILLIAmSON
87
46.3
11.3
77
40.1
9.6
77
36.7
9.6
67
38.5
10.8
89
38.7
11.6
WILSON
00.0
0.0
21.0
.3
1.5
.1
1.4
.1
1.4
.1
REGION
188
24.3
192
24.0
210
26.3
226
28.1
230
30.0
STATE
565
75.7
607
76.0
590
73.7
577
91.9
536
70.0
TOTAL
773
799
000
003
766
AN ASTERISK
INDICATES
THAT ENROLLMENT DATA
NAS
NOT AVAILABLE
(116ENROLLmENT FROM COUNTY IN INSTITUTION
1236FESCENT OF REGIONAL STUDENTS ATTENDING INSTITUTION ORIGINATING INDESIGNATED COUNTY
1334FERCENT OF STUDENTS ATTENDING INSTITUTION ORIGINATING INDESIGNATED COUNTY
TABLE IV-10
TEXAS LoymemAN
1968
1966
1470
1971
1972
111
121
131
111
121
131
111
121
131
111
121
131
111
123
131
ATASCOSA
2.6
31
.3
.2
2.5
.3
1.2
.1
2.4
.3
SANOERA
1.3
.2
88.0
8.11
2.5
.3
2.4
.3
1.2
.1
OASTRO
41.3
.7
2.
.3
3.8
.4
3.7
.4
4.9
.5
IEXAR
85
27.1
14.1
189
32.5
16.4
128
32.8
17.9
157
34.5
19.8
196
42.2
24.8
BLANCO
2.6
.3
2.6
.3
41.8
.6
3.7
.4
4.9
.5
BURNET
1.3
.2
1.3
.2
88.8
0.8
80.0
0.0
08.0
0.0
CALDWELL
31.0
.5
2.6
.3
1.3
.1
24
35
1.1
.6
COMAL
16
5.1
2.7
28
6.0
3.0
16
4.1
2.2
24
5.3
3.8
20
4.3
2.5
FAYETTE
11
3.5
1.8
13
3.9
2.8
14
3.6
2.8
92.8
1.1
4.9
.5
FRIO
00.0
SO
00.0
0.8
08.0
0.8
00.8
0.0
1a
.1
GILLES/E
72.2
1.2
61.8
.9
13
3.3
1.8
92.8
1.1
91.9
1.1
GUADALUPE
119
37.9
19.7
117
34.9
17,6
139
35.6
190
175
38.5
22.1
155
33.4
190
HAYS
88.8
8.0
1.3
.2
1.3
.1
51.
1.6
2.4
.3
KENDALL
41.3
.7
2.6
.3
2.5
.3
4.9
.5
3.6
.4
..)
a`
KERR
51.6
.8
51.5
.2
61.5
.8
92.0
1.1
71.5
.9
LEE
31011
.5
88.1
0.8
880
0.0
1.2
.1
1.2
.1
1101.
LLANO
01.1
P.O
1.3
.2
00.8
0.0
00.0
0.0
00,0
0.0
az
MEDINA
2.6
.3
41.2
.6
3.8
.4
1.2
.1
00.0
0.0
TRAVIS
38
9.6
501
38
9.8
4.5
38
7.7
4.2
21
4.4
2.7
21
40
2.7
WILLIAMS"
92.9
1.0
0204
1.2
13
3.3
1.8
11
2.4
1.4
14
3.0
1.8
WILSON
18
3.2
1.7
11
3.3
1.7
13
3.3
10
18
4.0
2.3
15
3.2
1.4
REGION
314
52.1
335
58.5
390
54.6
455
57.4
464
58.7
STATE
289
47.9
329
49.5
324
asol
337
42.6
326
41.3
TOTAL
683
664
714
792
798
AN ASTERISK INDICATES THAT ENROLLMENT DATA WAS NOT AVAILABLE
111aPiRoLLRENT FROM COUNTY IN INSTITUTION
1210PERCENT OF REGIONAL STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
C316FERCENT OF STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
TABLE IVI I
TRINITY %Angelis
1968
1949
197
1971
1972
113
123
133
111
(21
(3)
113
(2)
(3)
(13
(2)
(33
CO
(23
(33
AUSCO8A
2.2
.1
1.1
.0
2.1
.1
1.1
.4
2.1
.1
SANDERS
3.3
.2
3.2
.1
4.3
.2
2.1
.11
.1.0
()ASTRO.
8.
8.
I.1
.41
.1
.8
1.1
.1
.1.
EXAR
973
92.8
S1.4
1154
99.6
36.7
1385
94.1
586
146
94.8
38.9
1981
94.3
38.9
BLANCO
88.88
8.
2.1
.1
2.1
.1
2.1
.1BURNET
88.8.
8.8
Go
2.1
.1
2.1
.1
2.1
.1CALDWELL
4.4
.2
.1
1.1
.2
.1
.1
4.9
.2
CORAL
3.3
.2
3.2
.1
3.4
.2
9.9
.4
13
.8
.5
FAYETTE
.e
1.1
.1
.1
.2
.1
.1
88.
868
MD
3.3
.2
4.3
.2
3.2
.13
.2
.1
1.1
.4
GILLESPIE
88.8.
..
2.1
.1
2.1
.1
1.1
.44.
c.J
GuADALupi
WAYS
S.5 O
.3 .
4.5
.3 6
I.4
I.
.4
0.
4 1
.3
.1
.2 .
5 3
.3
.2
.2
.1
t....,
---1
KENDALL
1.1
.1
405
.3
7.5
.3
5.3
.2
3.2
.1
KERR
5.5
.3
7.4
.3
7.3
.3
4.4
.2
9.4
.4
LEE
1.1
.1
0SO
CIO0
1.1
0COI
OOP
LLANO
0OOP
..
1.1
..
OOP
0.
OOP
MEDINA
4.4
.2
4.3
.2
5.4
.2
2.1
.1
2.1
.1
TRAVIS
43
4.1
2.3
27
2.2
1.3
25
1.8
1.1
21
1.4
.8
23
1.3
.9
WILLIAMSON
4.4
.2
3.2
.1
3.2
.1
4.4
.2
4.4
.2
WILSON
1.1
.1
1.1
3.2
.1
.3
.3
4.4
.2
REGION
1848
39.4
1223
All
1387
41.4
154
42.1
1347
42.3
STATE
845
88.6
814
411.
863
38.4
939
37.9
949
37.7
TOTAL
1893
2837
223
2479
2516
AN ASTERISK INDICATES TWAT ENROLLMENT DATA WAS NOT
AVAILABLE
1116ENROLL6r4T FROM COUNTY IN INSTITUTION
[2188[RCENT OP REGIONAL STUDENTS WENOING INSTITUTION
ORIGINATING IN °MONATE° COUNTY
1234PIRCENT OF STUDENTS ATTENDING INSTITUTION ORIGINATING
IN 028/8NATED COUNT',
!ABLE PVI2
SAN ANTONIO CON
1968
1969
1978
1971
1972
111
12)
13/
(II
121
(3)
111
121
13)
111
12/
131
111
12/
131
ATASCOSA
59
.5
.5
81
.6
665
.4
474
.5
.5
90
6.6
BANDERA
419
.0
12
.1
.1
7.0
.0
21
.1
.1
15
.1
.1
swim'
I.0
02
.0
.0
08.0
0.0
00,0
0,8
00.0
8.8
BEXAR
11471
9668
95.9
12830
96.8
95.8
14865
97.3
96.8
14903
97.3
96.7
13583
96.6
94.0
BLANCO
2.0
.8
3.0
.0
90.0
8,0
2.0
.0
4g
.8
BURNET
00.8
0.0
00.0
0.0
1.0
.0
I.0
.0
2.0
.0
CALONELL
1.8
.0
00.0
0.0
4.0
02
.0
.0
2.0
.0
CORAL
SO
.4
.4
SS
.9
.4
48
.3
.3
SS
.4
.9
62
.4
64
PAYETTE
0Oa
860
80.0
8,8
88,8
0,8
00.0
0.0
00.0
0,0
FR/0
23
.2
.2
20
.2
.1
9.1
.1
11
.1
.1
15
.1
.1
GILLESPIE
4.8
.0
2.0
se
7.0
.0
4.0
.0
80.0
0.0
GUADALUPE
BS
.7
.7
94
00
86
.6
.6
78
0.5
109
.8
.8
MAYS
9.1
.1
IS
.1
.1
10
.1
.1
S0
.0
08.8
Oa
KENDALL
28
.2
62
29
.2
.2
22
.2
.2
26
.2
.2
39
.3
.3
xKERR
13
.1
.1
7.1
.1
5.8
.0
is
.1
.1
9.1
.1
LEE
08.8
060
1.0
.0
00,8
0.8
000
0.0
80.8
0.0
LLANO
88.8
868
88.8
0.8
4.0
.0
10
.5
28
.0
MEDINA
63
.5
070
.9
.9
83
.6
.6
98
.6
.6
82
.6
.6
TRAVIS
7.1
.1
5.8
.0
2.5
.0
1.0
.0
70
.0
WILLIAMSON
00.0
0.0
00.0
0.0
00.0
la
00.0
0.0
1.0
.0
WILSON
IS
.2
.2
32
.2
.0
30
.2
.2
48
.3
.3
43
.3
.3
REGION
11845
99.8
13268
IPSO
14498
99,5
15324
99,5
14865
97.4
STATE
119
1.0
135
1.0
76
.5
81
.3
381
2.6
TOTAL
11964
11595
14524
15485
14446
AN ASTERISK INDICATES
THAT ENROLLMENT
DATA
PAS NOT AVAILABLE
11128NROLLmENT PROM COUNTY IN INSTITUTION
t2I6PIRCENT OP REGIONAL STUDENTS ATTENDING INSTITUTION ORIGINATINGIN4OESIGNATIO COUNTY
1310PERCENT OP STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
TA
BL
E I
V-1
3
ST. PHILLIPS
1968
1969
1970
1971
1972
(11
121
(31
(11
121
(31
(11
(21
(37
(11
al
(37
(11
(21
(37
ATASCOSA
3.3
.3
1.1
.1
7.3
.3
4.1
.1
6.2
.2
SANDER*
1.1
.1
00.0
8.0
00.0
8.0
1.0
.0
1.0
.0
easTeo
00.0
0.8
00.8
0.0
80.0
0.0
80.0
0.0
00.0
0.0
8EKAR
1088
95.7
91.7
1688
97.1
414.41
2098
96.2
94.9
2957
98.1
97.8
3059
97.4
96.8
OLANCO
0SO
0.0
0SO
04
I.0
.0
10
.0
00.0
0.0
some,
1*
i.e
0.0
1.1
.1
0so
so
e0.0
0.0
00.0
0.0
CALOwELL
00.8
00
2.1
.1
1.0
.0
00.0
SO
00.0
0.0
COMAL
9.0
.8
7,4
.9
6.3
.3
a.1
.1
9.3
.3
FAYETTE
00.0
0.8
80.0
8.8
00.0
0.0
8i.e
8.0
1.e
.0
FRIO
00.8
0.0
eea
e.e
00.0
0.0
00.1
0.0
2.1
.1
eiLoreeza
0i.e
i.e
1.1
.1
0SO
0.0
90.0
0.8
0so
0.0
GuADALuE
16
1.4
1.9
14
.0
.1
3S
1.6
1.6
32
1.1
1.1
43
1.4
1,4
HAYS
s.4
.9
2.1
.1
3.1
.1
1.0
.0
1.0
.0
'...)
4W4i-
KENDALL
0so
0.0
6so
so
ee.e
0.0
4.1
.1
.1
.1
CX)
KERR
0e.e
sop
si.e
e.e
0e.e
so
eee
e.e
Ie
.0
LEE
00.8
0.0
1.1
.1
00.0
0.0
80.0
e.e
Iss
8.0
LLANO
00.0
0.0
a00
00
00.0
e.e
o0.0
0.0
08.8
8.0
MEDINA
S.4
.4
S.3
.3
13
.6
.6
2.1
.1
18
.3
.3
TRAVIS
76
.6
98
.*
.9
80.0
0.0
2.1
.1
WILLIAMSON
80.0
0.0
00.0
foe
00.0
so
e0.1
e.e
oso
0WILSOM
3.3
.3
79
.4
8.9
.9
9.3
.3
2.1
.1
REGION
1129
95.8
1738
97.7
2180
98,6
3015
4141.7
3141
419,4
STATE
49
4.2
41
2.3
38
1.4
18
.3
as
4,9
TOTAL
1178
1779
2210
302S
3161
AN ASTERISK
INDICATES THAT ENROLLMENT DATA
WAS NOT AVAIL/18LE
;118ENR0LLHE NT FROM COUNTY IN INSTITUTION
(215 PERCENT OF REGIONAL STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUN'Y
(3111ERCENT OF STUDENTS ATTENDING INSTITUTION DRIGINAT'NO IN DESIGNATED COUNTY
I ABU IV-14
CONCORDIA 1966 1969 1970 1971 1972111 121 131 111 121 131 111 121 131 CI) 121 131 111 121 131
ATASCOSA 0 0.0 0.0 5 5.9 3.2 1 .8 .5 0 0.0 0.0 1 .8 .5
SANOERA 0 0.0 0.0 0 0.0 0.11 0 0.0 0.0 0 0.0 140 0 0.0 0.0
61AoTRop 2 2.6 1.2 1 1.2 .6 0 0.0 0.0 1 .8 .5 1 .8 .5
BERAR 7 9.0 4.1 1 1.2 .6 7 5.7 3.3 6 6.7 4.1 12 9.3 5.9
BLANCO 0 0.0 0.0 0 0.0 0.0 0 0.0 0.0 0 0.0 0.0 0 0.0 0.0
SuRNET 0 0.0 0.0 0 0.0 0.0 0 0.0 0.0 0 0.6 0.0 0 0.6 0.0
CALOPELL 0 0.0 DO a 0.0 0.1 a 0.0 0.0 0 0.0 0.0 11 0.0 00
COMM. 0 61.0 0.0 1 1.2 .6 I .0 .5 0 61.61 11.0 0 0.0 0.0
PAyETTE 1 1.3 .6 0 0.0 0.0 1 .8 .5 1 .8 .s 1 .0 .5
FRIO a 001 0.0 a as 0.0 0 61.0 0.0 0 0.0 Oa 0 0.0 Oa
GILLESPIE 0 Oa 0.0 0 0.0 0.0 0 0.0 0. 0 0.0 0.0 I 0.0 0.0
GUADALUPE 11 0.0 0.0 2 2.4 1.3 1 .1 .5 1 . .5 2 1.6 1.0
MAYS 11 0. 0.0 0 0.0 0.0 3 2.4 1.4 2 1.7 1.0 2 1.6 1.0
KENDALL 0 0.0 0.0 a 0.0 0.0 0 61.0 0.0 0 0.0 0.0 0 11.0 Oa
KERR a 0.0 0.0 0 0.0 0. 0 0.0 0.0 0 0.0 0.0 1 .6 .5
LEE 4 5.1 2.3 1 1.2 .6 1 . .5 a 0.0 0.0 2 1.6 1.0
LLANO II oo Co o o,o 10 0 cop 0.0 0 0.0 0.0 0 0.0 0.0
NEOINA 0 0.0 0.0 0 0.0 0.0 0 0.0 0.0 0 0.0 CO I 0.0 0.0
TRAVIS 61 76.2 35.5 64 75.3 40.5 101 02.1 47.6 106 66.3 53.8 105 61.4 51.2
NILLIAmSON 3 3.6 1.7 10 11. 6.3 7 5.7 3.3 1 .8 .5 2 1.6 1.0
NILSON a 0.0 0.0 0 0.0 0.0 0.0 0.0 0 0.0 0.0 0 0.0 0.0
REGION 76 45.3 65 53.6 123 56.3 120 60.9 129 62.9
STATE 94 54.7 73 44.2 46 41.7 77 39.1 74 37.1
TOTAL 172 156 211 147 205
AN ASTERISK INDICATES THAT ENROLLMENT DATA PAS NOT AVAILABLE
61)4ENROLLNENT /ROM COUNTY IN INSTITUTION
12116PERCENT OF REGIONAL STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNT'
6314PERCENT OF STUDENTS ATTENDING INSTITUTION ORIGINATING IN DESIGNATED COUNTY
TA
BU
IV
SCMRCINER
1966
1969
197
1971
1972
11/
121
133
III
(2)
131
11/
I21
133
111
(21
(33
(II
121
13)
*TWOS*
I. .0
54,3
2.4
I.9
.9
1.
.4
ANDERA
23.2
1.1
1.9
1.
.9
21.
.6
ATR0*
Cl
SO
SO
SO
1O
02
1.
.4
EKAR
15
23,
.3
116.2
.1
17
14,.
23
26.7
SO
LANCO
66,6
6.6
1.
.5
1.
.9
66,
6,6
SuRNET
23.2
1.1
SO
SO
21,7
1.
6,6
U.S
CALOMEL
SO
.e
SO
SO
1.9
.5
ei.e
COMAL
23,2,
1.1
11
.5
SO
U.S
SO
0.
LAYETTE
11.6
.44
SO
SO
SO
SO
SO
P120
SO
SO
SO
2101
.6
ILLEPIE
711.13
64.6
8.4
44
9.2
5.1
II
,6
6,
GuADALOE
11.6
.6
SO
SO
SO
SO
SO
6,6
Cog
NAYS
23.2
1.1
1.
.9
..
1.9
4=
4a
KENDALL
I1,6
*6
92.6
1.4
SO
SO
32,7
1,2
KERR
22
34.
12;2
6 119
33
75
65.2
36,
55
52.3
U.S
LEI
I1,6
.6
U.S
U.S
SO
SO
SO
SO
LLANO
SO
SO
ISO
SO
Oa
Oa
S.
SO
MEDINA
I1.6
.44
38,6
1.4
21,7
1.6
1.
.4
TRAVIS
97,
2.6
43.4
1.
43.9
2.1
43.6
1.6
WILLIAN999
. SO
10
tS
1.2
.2
CO
0.0
w:LIION
11.6
.6
43.4
1.9
32,6
1,6
43.6
1,6
REGION
63
36;6
117
66.
119
99,6
III
43
STATE
117
S2
44
74,4
147
97,
TOTAL
12
193
25
AN ASTERISK
INDICATES THAT ENROLLNENT DATA 94 NOT ATAILALI
1136ENROLLNINT IRON COUNTY IN INSTITUTION
12IKPERCENT 04 REGIONAL TUDENT ATTENDIN INSTITUTION ORIGINATIN IN DESIGNATED COUNTY
01166ERCINT 04 STUDENTS ATTENDING INSTITUTION ORIGI4ATIN IN 02$12NAT2D COUNTY
ASTACOSA
1968
1969
TA
BL
E I
V -
I6
1971
1971
1972
11I
123
133
113
I2/
/31
II/
123
133
/13
123
133
113
123
133
SOUTBKEST TEXAS
SS
29.4
14.2
56
26.11
12.9
76
34.1
17.9
SO
36.4
19.3
75
30.5
18.8
UT6AUSTIN
41
21.9
16.6
15
160
8.4
35
15.7
8.2
35
15.9
8,5
33
13.4
7.9
MUSTON0TILLOISO
08.8
0.0
08.8
8.8
86
0.8
col
00,0
P.O
oso
403
INCARNATE .040
0so
Po
o10
80
S2.2
1.2
31.4
.7
41.6
1.0
OUR LADY OF LAX
19
18.2
4.9
16
7.7
3.11
28
9.8
4.7
15
6,0
3.6
19
7.7
4.6
ST EDWARDS
04
90
11.0
mil
Oa
Oa
01.11
90
0OOP
8.8
000
00
ST MARY'S
52.7
1.3
94.3
2.1
96.8
2.1
62.7
1.4
13
5.3
3.1
SOUTHWESTERN UN
1,5
.3
88.8
0.8
800
00
oso
o.o
00.0
0.0
Texas LUTHERAN
21.1
.5
1.5
.2
2.9
.5
1.5
.2
2.9
.5
i
A t4TRINITY UNIVERS
21.1
.5
1.5
.2
2.9
.3
1.5
.2
2.8
.5
SAN ANTONIO COW
SIP
31.6
13.2
81
38.9
19.3
65
29.1
15.3
74
33.6
17.9
90
36.6
21,6
C.1.:
ST. PHILLIPS
31.6
.8
1.5
.2
73.1
1.6
41.8
1.0
62.6
1.4
im
CONCORDIA
IOA
So
S2,4
1.2
1.4
.2
I0.0
O.@
1.4
.2
SCHREINCR
08.8
8.0
52.4
1.2
1.4
412
1.5
,2
1.4
.2
REGION
187
88.2
288
611.6
223
52.5
220
53.1
246
59.8
STATE
281
51.8
211
58.6
282
47.5
194
46.9
171
41.0
TOTAL
388
819
425
414
417
AN ASTERISK IMO/CATES
THAT ENROLLMENT
WAS NOT
AVAILABLE AND
AN ESTIMATE
I! USCG
111ENROLL$ENT RON COUNTY IN INSTITUTION
/214PERCENT OF COUNTY'S STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
/31PERCENT OF COUNTY'S STUDENTS EDUCATED WITHIN THE
STATE ATTEND/MG DESIGNATED INSTITUTION
TA
BL
E I
V-1
7
SANOERA
1968
1969
1978
1971
1971
(11
(21
(31
(13
(21
(31
111
(21
(31
(13
(21
(31
(13
(21
(31
SOUTmwFST TExAS
25
46.3
21.9
IS
41,4
22.7
23
43,4
16,1
12
22.9
18.5
16
34.8
14,8
utAusriv
20
18.5
8.8
11
17.7
8.3
12
22.6
8.4
10
18.7
8.7
715.2
6.5
musToN.TILLOTS0
08.0
0.0
8SO
5.6
O.
8,8
0.0
8O.@
Oa
06.8
8.8
INCARNATE .040
11.9
.9
I196
1.1
6SO
9.8
I11.1
la
aSO
6.0
OuR LAD', OF LAN
88.9
8.0
24,8
2.3
35,7
2,1
23.7
1.7
OSO
0.9
ST. EONAR05
11.9
.9
11.6
.8
11,9
.7
11.9
.9
12,2
.9
ST. mARY.S
611.1
5.3
11.6
.8
88.8
0.8
11.9
.9
24,3
1,9
SOUTHWESTERN 04
08.8
8.0
66.6
8.8
6SO
9.9
66.6
6.6
811.11
8.0
0TEXAS LUTHERAN
I1.9
.9
8SO
5.6
23,8
1.4
23.7
1.7
12.2
.9
TRINITY uNIYERS
tS.6
2.6
34.8
2.3
470
2.11
23.7
1.7
12.2
.C
AV.) 4=,
SAN ANTONIO COW
47.4
3.3
12
19.4
9.1
713.2
9.9
21
39.3
18.3
IS
32,6
13.9
ST. PHILLIPS
11.9
.9
88.8
8.8
O8.8
SO
11.9
.9
12.2
.9
DONCORD24
88.8
8.9
SSO
8.8
0SO
So
s8.8
8.8
O8.8
8.8
SCHREINER
23.7
1.6
11.6
.9
11.9
.7
20
1.0
1.3
24.3
1.9
REGION
54
47.4
62
47.8
SS
37.1
93
46.7
46
42.6
STATE
68
52.6
78
83.8
911
62.9
61
S3.3
62
S7.4
TOTAL
224
132
143
114
IDS
AN ASTERISM INDICATES
THAT ENROLLMENT
WAS NOT
AVAILABLE AND
AN ESTIMATE
IS USED
I13sENROLLHENT IRON COUNTY IN INSTITUTION
I236PERCENT 31 COUNTY./ STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
c336f4cENT 01 COUNTY.S STuOINTS EDUCATED WITHIN THE
STATE ATTENDING DESIGNATED INSTITUTION
BASTROP
1966
1969
TA
BL
E I
V-1
8
14711
1671
1972
tl)
(2)
(3)
(1)
15)
131
tl)
121
(3)
111
12)
(31
111
(2)
(3)
SOUTHWEST TffAS
55
33.S
16.3
S4
33.4
16.7
76
47.1
23.6
IS
47.
24.2
75
44.4
21.9
UTAUTIN
66
52.4
25.5
66
53.4
26.7
67
41.5
210.11
66
34.4
2.
75
44.4
21.9
NuSTONwlLLOTS0
74.3
2.1
53.1
1.6
7.
4.11
2.0
4.6
2.4
84.7
2.3
INCARNATE WORD
21.2
.6
1.6
.3
1.6
.3
SO
SO
.S.9
OUR LADY Of 4AK
S.11
11.11
.t,
11.11
0SO
SO
SO
SO
SSO
0.0
ST EDWAROS
4.
2.6
1.3
S2.
1.4
42.5
1.2
S3.0
1.5
310
.9
ST NARY'S
Sso
so
so
so
00
00
00.
0.0
SOUTHWESTERN UN
31.8
.4
31.9
.4
21.2
.6
21.2
.6
0.
1.0
TEXAS LUTHERAN
42.4
1.2
21.3
.6
31.4
.4
31.11
.4
42.4
1.2
TRINITY UNIV.
0.0
0.0
1Gil
.3
1.6
.3
1.6
.3
1.6
.3
4. 4.
SAN ANTONIO CON
1.6
.3
ST. PHILLIPS
S.
1.
21.3
SO
.6
so
SO
so
so
so
O so
so
so
. so
. so
C.:11
CONCOROIA
21.2
.6
1.6
.3
.SO
186
.3
1.6
.3
C1,..
SCHREINER
.0.
.SO
1.6
.3
2.9
.5
21.2
.6
REGION
164
46.7
ISO
44.11
161
S.2
167
S.7
169
49.4
STATE
173
31.3
16)
3.11
16
46'S
163
49.3
173
500
TOTAL
337
322
321
33
342
AN ASTERISK INDICATES THAT ENROLLMENT WAS NOTAVAILASLE AND
AN ESTIMATE
IS USED
mwENRGLLHEHT 'ism, COUNTY IN INSTITUTION
(234PERCiNT OF COUNTY' STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
(314FERCENT OP COUNTY'S STUDENTS EDUCATED WITHIN THE
STATE ATTENDING DESIGNATED INSTITUTION
I AB
E E
IV-1
9
Sf FAR
1968
1969
1970
1971
1972
111
121
131
111
12)
*51
III
12)
13)
II)
12)
13)
)11
121
131
5ouTHHEST TEs48
1515
6.6
5.7
1755
6,7
5.8
1916
6.8
5.9
2274
7.4
6.4
2379
8.0
6.9
0.4usTIN
2361
10.3
8.9
2861
18.9
9.5
3241
11.4
10.0
3338
10.8
9.5
3360
11.3
9.7
*uSTON.TILLOTS0
47
.2
.2
24
.1
.1
14.
.1
.1
IS
se
.0
18
.1
.1
INCARNATE .040
865
3.8
3.2
945
1.6
3.1
448
3,5
3,1
1880
3.5
3.1
1024
3.4
3.0
OuR LACY OF LAK
1128
4.9
4.2
1158
4.4
3.9
1441
5.1
4.5
1698
5.5
4.8
1868
6.3
5.4
ST EDwARDS
17
.2
.1
37.
.1
.1
37
.1
.1
37
.1
.1
37
.1
.1
ST, MARY.S
3398
14.8
12.8
3598
130
U.S
3142
18.7
4,4
2414
4.4
8.2
2682
9.9
7.7
SOUTH423TEAN UN
41
.2
.2
45
a.1
41
.1
.1
43
.1
.1
52
.2
.1
TEXAS LUTHERAN
85
.4
.3
109
.4
.4
128
.5
.4
157
.5
.4
146
.7
.6
TRINITY UNIvER$
973
4.2
3,7
1154
4,4
3,8
1315
4,6
4,8
1468
4,7
4.1
1481
5.0
4,3
SAN ANTONIO CON
11471
49.8
43.1
12830
48,9
42.7
14065
49.6
43.5
14405
48.2
42.2
13583
45.6
34.1
.1.,
ST, PHILLIPS
1080
4.7
4.1
1680
6,4
5.6
2898
7.4
6,5
2957
9.6
8.4
3054
10.3
8.8
CONCORDIA
7.8
.1
1se
.8
7.8
.8
S.8
.8
12
.0
.0
scs0EiNtot
Is
.1
.1
19
.1
.1
17
.1
.1
2801
.1
.1
23
.1
.1
REGION
23823
86.4
26224
87,3
28347
87,7
38894
87.6
29782
85,8
STATE
3684
13.6
3812
12,7
3962
12,3
4376
12.4
4415
14.2
TOTAL
26632
111126
32389
35275
34697
AN ASTERISK INDICATES THAT ENROLLMENT WAS NOT
AVAILASLI ANO
AN ESTIMATE IS USED
IIIKENROLLHENT PROF, COUNTY IN INSTITUTION
12111PERCENT OP COuNTY,S STUDENTS EDUCATED WITHIN THE
0E010N ATTENDING DESIGNATED INSTITUTILR
131APERCENT OP COUNTY,S STUDENTS EDUCATED KITHIN THE
STATE ATTENDING DESIGNATED INSTITUTION
TA
BL
E 1
V-2
0
BLANCO
1968
1969
1970
1971
1972
(11
121
131
(11
121
(31
111
121
(31
111
121
131
111
121
(31
SDUTHwE3T TEXAS
43
46.2
42.4
43
62.5
42.9
43
56,9
40.2
43
63.2
42,6
43
68,3
41.0
UTwAU3TIN
16
24.6
1S.6
17
23.6
16.2
16
24.7
16.8
12
17,6
11.9
914.3
6,6
Hu$10NwTILLITS0
00.0
P.O
00.0
00
00.0
0.0
0009
0,0
00,0
0.0
INCARNATE WORD
00.0
0,0
11.4
1.
11,4
.9
00.0
0.0
0.0
0.0
OUR LADY OF LAX
00.0
0.0
0Oa
0.0
00.0
0.0
00.0
0.0
00.0
0.0
ST, EDwAR03
00.0
0.0
Ow
P.O
SO
00.0
0.0
00.0
0.0
11,6
1.0
3T, MARY.$
00.0
0.0
00,0
SO
11.4
.9
110
1.0
00.0
0.0
SOUTHWESTERN UN
23,1
2.0
34.2
2,9
22,7
1.9
34.0
SO
00,0
0,0
TEXAS LUTHERAN
23.1
2,0
22.6
1,9
45.5
3,7
34.4
3.0
46.3
3.6
TRINITY UNIvE43
00.0
0.0
00.0
0.0
22.7
1.9
22.9
2.0
23.2
1.9
SAN ANTONIO CON
23.1
2.0
34.2
2.9
011.0
001
2tog
2.11
44.3
3.8
ST. PNILLIB
0SO
0,0
00,0
0.0
11.4
.9
11.11
101
00,0
0,0
CONCoreoIA
00.0
01
011.0
.0
0CO
0,0
00.0
0.0
00.0
0.0
SCHREINER
0.0
9.0
11.4
1.1
11.4
.9
110
I'S
0SO
0.0
REGION
65
64.4
72
68.6
73
ASot
66
67,3
63
40.0
STATE
36
35,6
33
31,4
34
31.8
33
32.7
42
40.0
TOTAL
101
10S
107
101
105
AN ASTERISK INDICATES THAT ENROLLMENT WAS NOT AVAILABLE AND AN ESTIMATE IS USED
mAgwaOLLHENT IRON COUNTY IN INSTITUTION
121 PERCENT OP COUNTY'S STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
1S1PPERCENT OP COUNTY'S STUDENTS EDUCATED WITHIN THE
STATE ATTENDING DESIGNATED INSTITUTION
R
SuRNET1668 1969
TABLE 1V-21
1970 1971 1972
t1) 12) 13) II) 12) (3) t1) 121 131 t1) RI 13) (11 t2) 13)
SOOTOWST TEXAS 49 66,7 21,2 S1 10,9 22,1 14 46,2 22,6 49 42,6 20,3 40 36,4 16.6
UT.PAUSTIN 42 40,0 18,2 42 41.2 10,1 47 42,5 16,7 12 61,2 21.6 SO 52,7 24.1
NuSTDN.TILLITso i 1.0 .4 1 TO oR 1. , .6 6 0,0 0.0 6 OOP 4.0
INCARNATE HCIRD 8 0,0 0,0 0 0,0 CO 9 0,11 9,0 0 0.0 0,0 0 Oil 0.0
OUR LADY OF LAM 0 0,0 0,0 0 0,0 Ea 1 .9 4 0 4,0 00 0 0,0 soST EDNARDS 2 1.9 .9 2 2,0 .9 2 1.0 .8 2 1,7 0 2 1,0 .8
ST, mARY,3 4 3,8 1.7 2 2,0 .9 1 .9 .4 4 3,5 1,7 3 2,7 1,2
SOUTHWESTERN UN 4 3,8 1.7 t 2,0 .9 1 .9 .4 4 3,5 1.7 3 2,7 1,2
TEXAS LUTHERAN 1 1.0 .4 1 1,0 .4 0 Flo so 0 0,0 so so 0,6
TRINITY UNIVERS 6 00 0,0 0 0,0 Oa 2 1,0 .8 2 1.7 0 2 1.0 .0
CA 4,
ad - SAN ANTONIO CON 0 SO 0.0 S O.@ 0.0 1 .9 .4 1 .9 .4 2 1,0 .0
ST, PHILLIPS 0 6,6 0,0 1 1,0 .4 0 0.0 00 0 SO 0,0 0 001 0.0
CONCORDIA 0 6,6 0.0 Is 00 so I 5,5 so S so so 0,0 0,0
SCHREINER 2 1,9 .9 0 00 00 2 1,0 6 1 11 4 6 S.S 0.0
REGION 101 45,5 102 44,8 lta 46,8 11S 47,7 110 65,6
STATE 126 14 Its 55,1 127 13,1 126 52,3 131 54.6
TOTAL 231 227 239 241 241
AN ASTERISK INDICATES THAT ENROLLMENT WAS NOT AVAILASLE AND AN ESTIMATE IS USED
tlIaENROLLHENT FRON COUNTY IN INSTITUTION
I2)RRERCENT OF COUNTY'S STUDENTS EDUCATED NITHIN THE REGION ATTENDING DESIGNATED INSTITUTION
mAPEAcENT OF COUNTY,S STUDENTS EDUCATED WITHIN THE STATE ATTENDING DESIGNATED INSTITUTION
CALDWELL1966
111 121 (31
TABLE IV-22
1969 1970
111 121 131 111 121 (31 111
1671
121 131
1972
111 121 131
SOUTHWEST TEXAS 173 68.6 47.3 202 71.5 49.6 141 78.8 48.3 186 74.7 52.2 les 74.0 49.5
UT.AUSTIN 73 20.9 20.0 70 240 17.3 26 21.9 13.8 44 17.7 12.4 64 17.6 11.6
NuSTON.TILLDTS0 0 0.0 0.6 2 .7 .3 46 1.4 .9 3 2.0 1.4 2 .8 0INCARNATE WORD 0 0.0 O.. O 9.0 SOP 0 0.0 60 O 0.0 So 6 0.0 maOUR LADY OF LAW 1 .4 .3 1 .4 .2 5 2.0 1.3 A 1.6 1.1 2 .8 .S
ST. EDWARDS 1 .3 .3 2. .9 .4 1 .4 .3 2 .8 .6 2 .8 .s
ST. MARY'S 0 0.0 0.0 6 0.0 9.6 0 0.0 0.0 0 0.6 0.0 1 .4 .3
SOUTHWESTERN UN 0 0.8 O.@ O Oa 0.0 1 .4 .3 1 .4 .3 1 .4 .3
TEXAS LUTHERAN 3 1.2 .e 2 .7 .S 1 .4 .3 2 .8 .4 S 2.0 1.3
TRINITY uNIVERS 6 0.11 O.@ 2 .7 .3 1 .4 .3 2 .0 .6 6 2.4 1.64=.
Oc SAN ANTONIO COM 1 .4 .3 O 0.0 SOD 4 1.6 1.1 2 .8 .6 2 .8 .5
IT. PHILLIPS 0 6.6 ... 2 .7 0 1 .4 .3 O SO 0.0 O O.§ OacoNcomo/A . ... 00 iii so so iii SO 0.0 O 0.0 14 0 0.0 0.1
scoIREINeR IS ... . so ... 1 .4 .3 1 .4 .3 6 9.9 0.0
REGION 252 69.3 262 69.6 2!! 66.2 249 66.9 2SO 66.8
STATE 112 311,7 122 31.2 112 31.8 1117 31.1 124 33.2
TOTAL 364 404 376 336 374
AN ASTERISK INDICATES THAT ENROLLMENT WAS NOT AVAILABLE ANO AN ESTIMATE IS USED
IIIENROLLEHT FROM COUNTY IN INSTITUTION
/21PERCENT OF COUNTY'S STUDENTS EDUCATED A!TNIN THE REGION ATTENDING DESIGNATED INSTITUTION
I31KRERCENT OF COUNTY'S STUDENTS EDUCATED WITHIN THE STATE ATTENDING DESIGNATED INSTITUTION
TA
BL
E I
V-2
3
COHAL
1968
1969
970
1971
1912
Ill
123
131
(I/
(33
Cl)
(II
Cl)
133
113
121
137
(I/
12/
133
SOUTMNEST TEKA8
271
62.0
8.3
276
60,4
66,2
276
61,3
45,5
324
63,2
47,0
304
590
42,7
UTIDAUSTIN
75
17,2
12.4
79
17,3
13.2
76
16,9
12.5
70
13,6
10,1
77
15.2
10.0
HUSTOWILLIT30
00.0
0,0
S0.0
0.11
9+
9.0
0,0
11
0,0
0.0
00,0
0.0
INCARNATE 6040
S1.1
94
.9
.7
61.3
1.0
81.6
1.2
51,0
.7
OUR LADY OF LAK
2.5
,4
3.7
.5
3.7
.5
4.6
3.6
.4
ST. EDAAADS
0*
0,0
CO
OK
0.0
0.0
00,0
0,0
00,0
0.0
00,0
0.0
IT, MART'S
2.5
.4
a.9
79
2,0
1,5
11
2.1
1.6
12
2,4
1,7
SOUTHWESTERN UN
2.5
.4
1$2
24
.9
.7
4.8
,6
3e.
.9
TEXAS LUTHERAN
16
3.1
2.9
20
4,4
3,4
16
3,6
2,6
24
4,1
3.5
20
3,9
20
TRI ITY UNIVERS
1.1
.5
3.7
.5
51.1
.8
918
1.3
13
2.6
18
SAN ANTONIO CON
50
11.4
8,9
58
12.7
9,7
48
10,7
7,9
55
10,7
0,0
62
12,2
8.7
ST, PHILLIPS
92.1
1.6
71,5
1.2
61.3
1,0
4.5
.6
91.8
1.3
CORCOROIA
00.0
0.0
1.2
.2
1.2
.2
00,0
0,0
00,0
0.0
SCHREINER
2s
.4
1.2
.2
000
0,0
00.0
0.4
00,0
0.0
REGION
437
77.9
457
76.5
450
74,3
313
74,S
508
71.3
STATE
124
22,1
140
23,5
156
25,7
177
25.1
204
28.7
TOTAL
561
597
606
690
712
AN ASTERISK INDICATES THAT ENROLLMENT WAS NOT AVAILAOLE AND AN 1117INATE IS USED
11)4ENROLLHENT PROP COUNTY IN INSTITUTION
121oPERCENT Of COUNTY'S STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
13)4PERCENT OF COUNTY'S STUDENTS EDUCATED WITHIN THE
STATE ATTENDING DESIGNATED INSTITUTION
TABLE IV-24
FAYETTE19114 1949 1975 1971 1972
111 121 13/ III 121 111 117 121 131 117 121 131 117 121 133
SOUTHWEST TEXAS 41 38.3 14, IIE 37.7 12.7 37 35.4 11.8 32 34.4 11.4 46 32.2 11.6
UT.4USTIN 76 47.7 17.5 74 47.4 111 78 48.7 16.1 84 33.4 17.5 81 54.4 2.5
MUSTON.TILLOTS0 1 .4 .2 2 1.2 .4 2. 1.2 .4 2 1.3 .4 3 3.5 1.3
INCARNATE 9040 2 1.3 .3 1 .4 82 . CIO . CO
OUR LADY OF LAW S 4. 5.5 2 1.2 .4 2 1.2 .4 1 .7 .2 1 .7 .3
ST. EDWARDS 1 .S .3 2 .6 .3 1 .4 .2 2 1.3 .4 2 1.4 .5
ST. MARY'S 3 3.1 1.2 3 1.8 .6 3 1.4 .6 2 1.3 .4 1 .7 .3
SOUTHWESTERN UN .. 1 .4 .2 1 .6 .2 1 .7 .3
TEXAS LUTHERAN 11 11.9 2.5 13 70 2.7 14 8.7 20 9 .. 2 4 2.8 14
TRINITY UNIVERS S 5.5 .0 1 .4 .2 1 .6 .2 2 1.3 .4 0 0.0 4.4
SAN ANTONIO COM 4. . . . . . . .ST. PHILLIPS . CO . . 1 .7 .3
CONCORDIA 1 .4 .2 SO CA 1 .6 .2 1 .7 .2 1 .7 .3
SCHREINER 1 .4 .2 . . S . 34 410 . . 34 2.5
S
REGION 159 34.7 144 33.7 14 33.1 131 33. 143 36.2
STATE 273 43.3 323 66.3 323 4110 314 67. 232 63.8
TOTAL 434 4 4S3 437 343
AN ASTERISM INDICATES THAT ENROLLMENT WAS NOT AVAILABLE AND AN ESTIMATE IS USED
tilecNnoLLNENT PROM COUNTY IN INSTITUTION
cunnincENT OP COUNTY.S STUDENTS EDUCATED WITHIN THE REGION ATTENDING DESIGNATED INSTITUTION
I310PERCENT OP COUNTY.S STUDENTS EDUCATED WITHIN THE STATE ATTENDING DESIGNATED INSTITUTION
4
1
FRID
1968
(11
12)
t3)
TA
BL
E I
V-2
5
1969
1970
tl)12
)W
It)t2
)t3
)II)
1971
RI
13)
tI)1972
(2)
t3)
SOUTHWEST TEXAS
18
25.4
10.4
21
30.9
11.2
36
43.9
16.4
33
40.2
14.7
33
33.0
14.6
UT*AUSTIN
17
23,9
9.8
13
19.1
6.9
20
24,4
9.1
22
26.8
9.8
25
25.0
11.1
MUSTON.TILLOTS0
00.0
0.0
00.0
0.0
00.0
0.0
o0,0
0.8
00.0
0.0
INCARNATE *ORO
22,
6132
00.0
0,0
11.
20
22.4
04
4.0
Ise
OUR LADY OF 04
22.8
1.2
45,9
2,1
44.9
1.8
67.3
2.7
08.0
3.5
ST, EDWARDS
00.0
0,0
I.
0.0
0,0
000
0,0
00.0
0.0
00,0
0,0
ST. MARTS
57.0
2.9
34.4
1,6
67.3
2.7
22.4
,9
88.0
395
SOUTHWESTERN UN
11.4
sew
34,4
1.6
33.7
1.4
22.4
.9
11.9
.4
TEXAS LUTHERAN
08.0
0.0
I00
00
00,0
0,0
00.0
0.0
11.0
.4
TRINITY UNIVERS
34.2
1.7
43,9
2.1
33.7
1,4
33.7
1.3
11.0
01
Cra
`2SAN ANTONIO COW
23
32.4
13.3
20
29.4
10.6
911.0
4.1
11
13.4
4.9
15
15.0
6.6
C..
ST, PHILLIPS
060
1.1
0SOO
Oa
00.0
0,0
00.0
0.0
22,0
.9
CONCORDIA
00.1
0.0
o00
IS
0OOP
0,0
I0.0
SO
00.0
0.0
SCHREINER
0OOP
1.0
I0.0
foe
00.0
SO
1*
1,2
.4
22.0
.9
REGION
71
41.0
60
36.2
82
37.3
82
36.4
100
44.2
STATE
102
59.0
120
63.8
138
62.7 N
1C3
63.6
126
55,6
TOTAL
173
188
220
223
226
AN ASTERISK INDICATES
THAT ENROLLMENT
WAS NOT
AVAILABLE AND
AN ESTIMATE
IB USED
tilaENROLLmENT FROM COUNTY IN INSTITUTION
(2/1WERCENT OF COUNTY.S STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
t3)4PERCENT OF COUNTY,) STUDENTS EDUCATED WITHIN THE
STATE ATTENDING DESIGNATED INSTITUTION
1
TA
BL
E I
V-2
6
GIL
LE3P
IEtime
1969
147
1971
1472
(13
(23
131
113
121
133
113
123
133
113
123
133
(13
121
131
SOUTHWEST TEXAS
63
41.6
21.0
643
22
64
34,
21,4
OS
40.
26.2
040,2
27.6
uTwAusTIN
64
34.0
2.4
64
44.4
21.3
44
34.3
21.1
ss
31.6
14.,
66
33.1
10.6
HOUSTON TILLT30
9SO
Sol
SO
Sol
Ow.
.SO
.2
U.S
00
INCARNATE wORO
32.0
Col
1.4
.3
1.4
.3
1.4
.3
1.4
.3
OUR LA07 OF LAN
S0.2
1.1
1.0
.3
21,2
.4
21.1
.4
21.1
.4
ST, EDwARDS
53.2
1,6
Se
3,2
1.7
42,3
1.2
43.4
1.0
73,
2.2
37, mAR7,3
21.3
.7
53.2
1.7
1.6
.3
1.6
.3
21.1
.4
SOUTHWESTERN UN
1.7
.3
2fa0
.,
1.4
.3
22,
,TEXAS LUTHERAN
74,6
2,4
63,11
13
7,54
5,2
2.4
So
2.0
TRINITY UNIVERS
001
20
..2
11.2
.0
21.1
.6
1.0
.3
Cit
,,''.,
SAN ANTONIO CON
42.6
1.4
21.3
.7
74
2,2
42.3
1.2
so
2.
1..
sr. eraLLies
aso
so
1.4
.3
so
Sol
2.
Sol
2.
2.0
CONCORDIA
.SO
SO
SO
SO
SO
SO
Sol
SO
2.0
SOME/NCR
74.6
2.4
53.2
1.7
03,5
1.4
ow
4.6
2.5
Is
5.5
3.1
REGION
151
52.5
15
530
173
53.6
174
53,5
141
56,2
STATE
137
47.5
14
47.
15
46,4
151
46.5
142
44.
TOTAL
HS
242
323
32S
323
AN ASTERISK INDICATES THAT ENROLLMENT WAS NOT AVAILABLE AND AN ESTIMATE is
USED
IllegNsOLLNENT FROM COUNTY IN INSTITUTION
ISWERCENT OP couNT7, STUDENTS EDUCATED WITHIN THE
RESIGN ATTENDING DEIGNATED INSTITUTION
1310V CENT OF COUNTT STUDENTS EDUCATED WITHIN THE
STATE ATTENDING 011I044r10 INSTITUTION
TA
BL
E I
V-2
7
GUADALUPE
1960
1969
1970
1971
1972
(11
121
131
111
123
131
111
121
33)
11)
(21
131
111
121
131
SOUTHWEST TEXAS
229
41.4
32.2
262
43.4
34.1
263
400
31.2
291
42.6
32.3
290
4.2
36
UT4AUSTIN
76
13.7
1.7
05
14.1
11.1
94
14.4
11.2
95
13,4
la.s
94
13.0
9.9
HUSTON TILL0720
0. .
0 .
1.2
.1
1.1
.1
3sa
SINCARNATE WORD
11
2.1
1.5
11
1.5
1.0
91.4
1.1
6.9
.7
64
OUR LADY OF LAW
5.9
.7
619
07
1.1
.6
.9
.7
6.9
.9
ST. EDWARDS
* 0 .
.Coo
.6.
.a
0.
00.0
Soo
ST. MARTS
61.1
.0
71.2
.9
IS
1.5
1.2
6.9
.7
91.2
1SOUTHWESTERN UN
Doe
2.0
.DO
Ooll
4.4
.4
3.4
.3
474
cTEXAS LUTHERAN
119
21.5
16.!
117
19.4
15.2
139
21.3
14.S
175
as
19.0
1S5
21.4
14.4
tvTRINITY UNIVERS
S.9
.7
61.5
.0
II
1.2
1.0
4.9
.4
5.7
.5
V.
WSAN ANTONIO CON
SS
12.4
11.9
94
15.6
12.2
613.2
142
711.2
.4
159
15.2
11.5
ST. PHILLIPS
16
2.9
2,2
14
2.3
1.
35
50
4.2
32
4,6
3.5
43
5.9
4.5
CONCORDIA
Ogg.
2.3
.3
1.2
.1
1.1
.1
2.3
.2
SCPRE/NER
1.2
.1
DO
01
.01
5*
5,5
.2
.oil
REGION
553
77.7
694
7.4
6S3
77,6
699
77.5
725
76.6
STATE
159
22.3
164
21.9
159
22,4
293
22.1
222
23,4
TOTAL
712
76
42
9112
947
AN ASTERISK INDICATES THAT ENROLLMENT WAS NOT AVAILABLE AND AN ESTIMATE IS USED
1119ENROLLmENT PRO* COUNTY IN INSTITUTION
(2) PERCENT OP COUNTY.$ STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
(51.6gRCENT OP COUNTY.S STUDENTS EDUCATED WITHIN THE
STATE ATTENDING DESIGNATED INSTITUTION
TA
BLE
IV-2
8
NAYS
196
1969
1970
1971
1972
(1)
(2)
(3)
(I)
121
133
111
(21
(3)
II)
(2)
(31
111
121
131
SOUTHWEST TEXAS
671
05.0
76.1
all
83.5
73.7
628
82.3
73.0
646
3,1
71,9
622
03.3
73.2
UT.AUSTIN
Se
11.1
ISO
96
110
11.
99
13.1
11.6
IBS
11.1
11.1
Ise
130
110
musroN.TaLorso
1.1
.1
000
0*8
801
88018
1.1
.1
INCARNATE *ORO
00.0
0.0
I.1
.1
1.1
.1
3.4
.3
2.3
.2
OUR LADY OF 01(
91.1
1.0
3.7
.6
le
1.3
1.2
7.9
.1
1.1
.1
ST. EONAROS
3*
o4
046
415
03
00
4.5
.4
50
.6
ST, MARY.S
I0.0
.0
3.6
.3
4oll
.5
30
.3
3.7
.6
SOUTHWESTERN UN
1el
411
8U.S
U.S
1.1
.1
so
cop
006
006
TEXAS LUTHERAN
60.0
0.0
I.1
.1
I.1
.1
5.6
.6
2.3
.2
TRINITY UNIVER3
8.8
cis.
U.S1
1.1
.1
3.4
.4
SAN ANYONIO CO..
91.1
1.0
14
1.0
1.6
II
1.3
1.2
5.6
.0
U.S
U.S
ST. PNILLIPS
50
02
.3
.1
3.6
.6
1.1
.1
1.1
.1
ComCON0I.
0.0
.0
.0
OOP
3.6
.6
2.3
.2
2.3
.2
5cootivEN
2.3
.2
1.1
.1
0606
000
1*
.1
.1
1.1
.1
REGION
789
89.3
768
sea
763
89.7
700
86.6
74S
87.6
STATE
93
16.9
103
11.11
OS
11O
121
13.4
103
12.4
TOTAL
882
871
BSI
911
ISO
AN ASTERISK INDICATES THAT ENROLLMENT NA8 NOT ASA:LADLE AND AN 'SINAI' II USED
(IIKENROLLNENT FROM COUNTY IN INSTITUTION
(2)4PENCENT OF COUNTY.S STUDENTS EDUCATED WITHIN THE
DIM ON ATTENDING DESIGNATED INSTITUTION
0)10ERCENT OP COUNTY.$ STUDENTS EDUCATED WITHIN THE
STATE ATTENDING 0[616N6TED INSTITUTION
KE
ND
AL
L
SOUTHWEST TEXAS
UT.AUSTIN
HUSTON4TILLOTS0
INCARNATE WORD
OUR LADY OK LAM
ST. EDWARDS
ST, AWRY'S
SOUTHWESTERN UN
TEXAS LUTHERAN
TRINITY UNIVERS
cm
SAN ANTONIO COM
ST. PHILLIPS
CONCORDIA
SCHREINER
REGION
STATE
TOTAL
AN ASTERISK INDICATES THAT ENROLLMENT WAS NOT AVAILABLE AND AN ESTIMATE IS USED
filENROLLHENT IRO'. COUNTY IN INSTITUTION
121FERCENT OF COUNTY.) STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
151144cENT OF COUNTY,$ STUDENTS EDUCATED WITHIN THE
STATE ATTENDING DESIGNATED INSTITUTION
1968
1969
TA
BL
E I
V-2
9
1971
1971
1972
(II
(21
(11
111
(21
131
111
121
(31
111
121
131
111
121
131
31
28.7
17.5
46
140
21.9
44
35.6
23.4
36
29.6
17.5
43
30.3
19.2
23
21.3
13.0
26
19.4
12,4
27
22.1
14.4
25
28.6
12.2
26
18.3
11.6
00.8
0.8
08.8
0.11
OK
8,0
8.0
OOs@
8.8
88.8
0.8
21,9
1.1
64.4
2,9
75,7
3,7
54,1
2,4
11
7,7
4,9
tO
9.3
5.6
64.5
2.9
43.3
2.1
32,5
1.5
51.5
2.2
8"
8.11
8,0
0.
8.8
8.8
00.0
8.8
08.8
8.0
08,0
0.0
65,6
3,4
96,7
4.3
66,5
4,3
111
8.2
4.9
32.1
1,3
21.9
1.1
1.7
.5
21.6
1.1
21,6
1,8
21.4
.9
4 1
3.7 .9
2.1
.6
2 6
1,5
40
1.,
2.9
2 7
1.6
5,7
1.1
3,7
4 5
3.3
4.1
1.9
2,4
3 3
2.1
2.1
1.3
1.3
28
25.9
15.8
29
21.6
13.4
22
17.9
11,7
26
21.4
12.7
39
27,5
17.4
68.0
8.0
88.8
001
88.8
8.0
43.3
1.9
42,8
1.8
11
SO
8.8
0fel
9.8
811.8
8.8
88.11
8.5
88.0
8.0
169
.6
3Ea
1,4
00.8
ISO
2.
1.2
.7
32,1
1,3
188
610
13"
61.8
123
65.4
121
59.1
142
63,4
69
39.11
76
36,2
65
34.6
84
40,9
82
36.6
177
2I8
188
20S
224
TA
BL
E I
V-3
0
KERR
111611
196
MO
11171
11172
111
121
131
111
121
(311
111
121
113
(13
(23
(3]
(13
121
131
SOUTHWEST TEXAS
S2
3.
13
49
23.4
126
620.6
13.1
49
21.4
1.6
41
14,3
.1
UTwAUTIN
S7
31.
15.1
67
32.
16.4
71
3.1
1S.S
71
3.
15.4
734.S
17.4
HUSTON.TILLOTS0
SO
SO
SO
SO
SO
SO
SO
SO
CIO
INCARNATE TORO
74.2
1.
.3
1.1
.T
S3.5
1.7
62.7
1.3
OUR LADY OF LAK
GO
SO
i0
.2
1.4
.2
2.
.4
4i.e
.ST. E0wAROS
11.
.4
1.7
.3
1.3
.7
41.7
.41
6ST, NARY'S
21.2
01
.111
.2
31.3
.7
2.
.4
1.8
.2
SOUTHWESTERN UN
21.2
.4
SO
SO
2.8
.4
a.
.8
S2.2
1.1
TEXAS LUTHERAN
S1.
1.3
S2.4
1.2
620
1.3
3.
1.
73.1
1.6
un
TRINITY umpas
Ss
1.3
73.3
1.7
73.
10
62.6
1.3
8.
2,
an
SAN ANTONIO CON
13
7.7
3.8
7343
1.7
S2.1
1.1
IS
4.4
2.2
4.
29
C::
CJ.
ST. PHILLIPS
.OOP
SO
CA
See
Cog
OOP
1.0
.2
CONCORDIA
go
0Oa
0GO
4,
1.4
.2
scrm/Nu
22
13.1
SO
6 32.
16.
7S
31.
16.4
66 2111.
14.4
SO
2S.
12.
REGION
16
44.6
EGO
B1.
236
S1.S
22
44.6
224
44.
STATE
2SS..
14
4.6
222
480
233
3.11
22S
5.1
TOTAL
377
407
43
463
44
AN ASTERISK INDICATED THAT Ems0Low4 sal NOT AVAILABLE AND AN ESTIMATE IS USED
IIIPENROLLNENT FROM COUNTY IN 'NIT/UT-0N
1230PERCENT OF COUNTY. STUDENTS EDUCAM WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
1316PERCENT OF COUNTY.S STUDENTS EDUCATED WITHIN THE
STATE ATTENDING DESIGNATED INSTITUTION
LEE
TA
BL
E I
V-3
1
1966
1969
1970
1971
1972
(1)
12)
131
111
121
111
Ell
121
115
11)
121
13)
111
12)
13)
SOUTHWEST TEXAS
922.
4.9
129.4
3.4
13
31,7
6.6
22
50.0
11.5
20
51,3
11.2
uT*uSTIN
17
41,3
9,3
17
50
9,1
22
53,7
11,2
14
31.0
7.3
13
33.3
7.3
HuSTONTILLOTBD
614.6
3.3
250
1.1
49.0
2,0
613,6
3.1
25.1
1.1
INCARNATE *0110
00.
0.0
00,0
0.
SO
0.
00.0
0.
00.0
0.0
OUR LADY OF LAW
00
0.0
25.9
1.1
12.4
.3
6.8
0.
0.0
0.0
ST. EDKANDs
0SO
So
00.00
0.
.0
00,0
00
00,0
0.8
..4.
ST, NARY'S
..0
0so
.e.
0,0
.1
2.6
.6
SOUTHWESTERN UN
00.0
0.0
00.11
0.0
0so
0.0
00.0
0.0
a:.0
0.0
TEXAS LUTHERAN
37,1
1.6
00
.0
00.0
0.
12.1
.5
12.6
.*
CrA
C.
TRINITY uNIVERS
12.4
.5
00
00
0,0
0,0
12.3
.5
00.0
0,0
vl
---1
SAN ANTONIO CON
0,0
00
12.9
.3
00.
0.0
0.
.0
00.
0.0
ST. PHILLIPS
00,
0.0
12,9
.5
011,0
5.5
40,0
0,0
00.0
0.0
CONCORDIA
49.8
2.2
12.9
.3
12.4
.3
0,0
00
25.1
1.1
SCHREINER
12,4
.5
O.
.0.
0.0
0*
0,0
0,0
00.0
0.0
REGION
41
22,3
34
10.1
41
2.0
44
22.9
39
21.9
STATE
141
77.5
132
81.7
136
79.2
146
77.1
139
76.1
TOTAL
ISO
106
197
192
170
AN ASTERISK INDICATES
THAT ENROLLMENT
WAS NOT
AVAILABLE AND AN ESTIMATE
13 USED
(IIKENROLLHENT FRO% COUNTY IN INSTITUTION
(21*RelicENT OF COUNTY'S STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
(31*FIRCENT OF COUNTY'S STUDENTS EDUCATED */THIN T*1
STATE ATTENDING DESIGNATED INSTITUTION
LLANO191$ 1966
TABLE IV32 .
1971 1971 1972
111 121 131 111 121 131 111 121 131 111 121 131 111 12) 133
SOUTHWEST TEXAS 31 56.6 19.1 24 66.2 13.1 26 53.3 17.8 23 S75 152 25 67.6 19.1
UT,AUSTIN 23 411.1 14.2 21 580 16.7 15 33.3 11.1 16 41.1 11.6 10 27.1 7.1
HuRTON.TILLOTSO I Oa 6.6 6 SO U.S S. 1.1 See I SO SO il SO ea
INCARNATE woRD 1 1,6 .1 I SO Sol S ea 6.6 6 6.6 6.6 6 6.6 6.6
OUR LADY OF LAM G 6.6 6.6 6 U.S U.S 6 U.S 6.6 U 6.6 6.11 6 6,6 1.1
St, 201+4406 O. 6.6 6.6 6. 6.6 0.0 6 6.0 6,6 6 6.6 6.6 6 6.6 0.0
$1, RART0 I 6.6 6.6 6 6.6 6.6 6 6.6 6.6 6 6.6 6.6 6 6,6 1.1
SOU7K426724m UN 6 6.6 6.6 1 1.6 .6 1 2.2 .1 6 6.6 6.6 6 6.6 6.6
7ExA6 LUTHERAN I 6.6 6.6 I 1.9 A I 6.6 6.6 6 60 6.6 6 1.1 6.6
TRINITY UNrvERS 0 0.0 1.1 0 1.1 1.1 1 2.2 .7 I 00 OO 0 6,0 1.1
vloo SAN ANTONIO CON I SO 1.1 6.6 6.6 $O 3.1 1 2.5 .7 2 5.6 1.5
ST. PHILLIPS U U.S 1.6 5 U.S 6.6 U U.S6.6 ,U 6.6 60 U 6.6 6.6
CONCORDIA 6 6.6 6,6 6 6.6 1.1 6 6.6 6.6 6 1.1 U.S 6 6,6 6.6
ScHREtNER e 0.0 1.1 0 1.1 0.0 I 00 00 0* 1.1 1.1 0 00 00
REGION SS 36.6 Sa 33.3 41 33.3 AU 264 37 28.2
STATE 187 66.6 164 66.7 66 66.7 111 13.3 96 11.1
TOTAL 112 151 135 151 131
AN ASTERISM INDICATES THAT ENROLLMENT 4A6 NOT APAILA8LE AND AN ESTIMATE 1$ USED
11ImENROLLmEMT FROM COUNTY IN INSTITUTION
121mPERCENT OF COUNTY'S STUDENTS EDUCATED WITHIN THE REGION ATTENDING DEGISMATED INSTITUTION
(31mPERCENT OF COUNTY.S STUDENTS EDUCATED WITHIN THE STATE ATTENDING DESIGNATED INSTITUTION
RE
OIN
.1968
(11
(21
(31
1969
(11
(21
TA
BL
E I
V-3
3
1971
(31
(11
(21
(31
1971
(11
(21
(31
(11
1972
(21
131
SOUTHWEST TEXAS
08.0
e.e
111
SO
Peg
9S
30.4
17,S
111
0.0
0.0
119
4W.9
21.0
UTwAUST/N
37
240
11.8
43
23,6
11.11
39
U.S
7.2
4111
22.2
80
34
11.7
6.6
HUSTONwT/LoTso
I0.8
P.O
gPOI
POI
01,
11.11
8.0
111
0.0
0.0
.1
0.0
ae0
INCARNATE NORD
il
Reg
1.7
42.2
1.11
S186
.9
S2.8
1.1
Sto
1.0
OUR LADY or LAX
24
15.9
7.11
36
160
7.7
34
111.9
6.3
le
111.11
3.8
17
S.8
3.3
111. EDWARDS
S.
8.11
111.0
ge
11.11
8.11
111
11.11
11.11
111
11.11
11.11
1.3
.2
SOUTHNESTERN UN
187
.3
I1.1
02
.6
814
111
11.11
11.11
1.3
.2
ST. MARY.11
8S.3
2.3
17
9.3
4.4
32
18.2
S.9
21
11.4
40
19
60
3.7
TEXAS LUTHERAN
21.3
.4
42.2
1.11
31.6
.4
1.6
.2
88.8
8.8
TRINITY UNIVERS
a2.6
1.2
42,2
1.8
S1.6
189
21.1
eR
2.7
041
47h
Or...
SAN ANTONIO CON
43
41e7
18.3
76
340
17.9
83
26.S
15.3
98
49.11
19.2
82
28,2
15.9
ST. PHILLIPS
S3.3
I.S
S2.7
1.3
13
6.2
2.6
21.1
egi
la
3.9
1.9
CDNCDRD/A
011.11
11.0
88.8
11.8
111
11.0
0.8
08.8
0.8
0so
13.0
4c4RE/NER
1.7
.3
31.6
88
2.6
.4
2.8
,3
1.3
.2
REGION
1S1
43.9
182
46.7
313
S7.6
188
38.4
291
56.3
STATE
193
56.1
2118
53.3
238
42.4
289
61.6
226
43.7
TOTAL
344
398
S43
469
5I7
AN ASTERISK INDICATES
THAT ENROLLMENT
WAS NOT
AVAILABLE AND
AN 1674667(
IS USED
11)wENROLLENT FROM COUNTY IN INSTITUTION
1214FACENT OF COUNTY,S STUDENTS COUCATIO WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
(314ERCENT OF COUNTY,S STUDENTS EDUCATED WITHIN THE
STATE ATTENDING DESIGNATED INSTITUTION
TR
AV
IS196$
111
121
131
1969
III
121
TA
BL
E I
V-3
4
131
111
197$
121
131
III
1971
123
131
III
1972
121
151
SOUTPMEST TEXAS
1142
13.2
11.3
1249
13.9
11.$
1214
13.6
11.4
1374
15.5
12.4
1363
15.7
11.7
UTeAUSTIN
7214
44.4
64:1
7186
04.4
67.11
7157
74.6
66.4
6614
74.0
59.8
6336
73.1
54.3
HUSTON.T1LLOSTI
196
2.2
1.9
157
1.7
1.4
165*1
1.6
182
2.1
1.6
194
2.3
1.7
INCARNATE WORD
6.1
.1
1.4
.5
1.3
.S
6.1
.1
6.1
.1
OUR LADY OF LAK
1.4
.4
II
.1
.1
6:
.1
5.1
.4
9.1
.1
3T. EDwAROS
100
2.4
1.7
200*
2.2
1.41
213
2.4
2.4
455
5.1
4.1
SOO
5.9
4,01
ST. MARY'S
S.1
57
.1
.1
6.1
.1
6.1
.1
32
.4
.3
SOUTHWESTERN UN
33
.4
.3
36
.4
.3
39
.4
.4
49
.4
.4
SO
.4
.4
TEXAS LUTHERAN
34
.3
.3
31
.3
.3
30
.3
.3
21
.2
.2
21
.2
la
an
TRINITY UNIVERS
43
.1
.4
27
.3
.3
25
.3
.2
21
.2
.2
23
.3
.2
oSAN ANTONIO CON
7.1
.1
S.1
.5
2A
.5
10
7.1
.1
ST. PHILLIPS
Y.1
.1
9.1
.1
a.1
.1
I11011
OOP
2A
0C
TC
.14
CONCORDIA
nt
.7
.6
64
.7
.6
141
1.1
.9
1116
1.2
1.0
145
1.2
0$CNRIINER
s.1
.5
4.5
.4
4.5
.5
4.4
.5
4.4
.4
REGION
11466
$6.11
SIM
$4.7
$92111
$3.1
ISIS
79.9
$65$
74.3
STATE
1461
14.4
16211
15.3
1766
16.5
221$
24.1
2994
25.7
TOTAL
15427
1464$
18695
11119$
11640
AN ASTIR/SK INDICATES THAT ENROLLMENT WAS NOT AVAILABLE AND AN ESTIMATE IS USED
(1)*1NmoLLNGNT PROW COUNTY IN INSTITUTION
cusPoRCENT OP COUNTY.$ STUDENTS EDUCATED WITHIN THE
REGION AMINO:NS DESIGNATED INSTITUTION
I314PERCENT OP COUNTY.$ STUDENTS EDUCATED WITHIN THE
STATE ATTINDIN$ DISISNATED INSTITUTION
N ILL:ANON
8OUTHNEAT TEXAS
UTI.AUSTIN
HUSTON*TILLOTSO
INCARNATE 6040
OUR LADY OF LAN
ST. EDMAROS
AT. MARY'S
SOUTHWESTERN UN
TEXAS LUTHERAN
TRINITY UNIVERS
--,
SAN ANTONIO COW
S T. PHILLIPS
coNconoi.
scNnEINEN
TA
BL
E I
V-3
5
1944
1969
11/
12/
13/
11/
12/
13)
11/
156
SLOP
1.6
159
32.9
10
149
196
41.4
23.3
294
42.2
24.
206
51.1
.6
9la
.6
4O
0.0
.0
0.
.1
.2
.1
1.2
.1
0
12
2.5
114
14
2.9
1.6
10
O0.0
0.0
1.2
.1
0
ST
18.0
1,11
77
13.9
9.1
77
91.9
1.1
1.7
.9
13
4se
.S
3.6
.4
3
0.
0.0
90.0
so
0
O0.0
0.0
0.9.
0
3.6
.4
IS
2.1
i.e
7
0.
0.0
1.2
.1
1
REGION
473
56.3
443
96.
920
STATE
367
43.7
367
43.2
342
TOTAL
04
89
902
AN ASTERISM INDICATES THAT ENROLLMENT WAS NOT AVAILABLE AND AN ESTIMATE IS USED
t1)ENnoLLmv4T PROM COUNTY IN INSTITUTION
tElm.EacENT OF COUNTY.S STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
13)RPERCENT OP COUNTY,S STUDENTS EDUCATED WITHIN TWO
STATE ATTENDING DESIGNATED INSTITUTION
1975
1971
1972
(2/
131
11/
Cl)
Is)
11/
12/
13/
2.7
16.5
156
11.0
16.8
128
27,2
14.7
09.2
280
219
43.3
23.5
206
44.3
23.9
..4
3.6
.3
1.2
.1
0.
.0
00.0
0.0
1.2
.1
0.0
0.
1.2
.1
61,3
.7
1.9
1.1
IS
3,6
1.9
13
2.8
1.3
0.0
0.0
1.2
.11
.2
.1
IRO
80
87
17,3
9.4
89
18.9
10.2
2.5
1.4
11
2.2
1.2
IA
3.0
10
.6
.3
61.2
.6
61.3
.7
.0
0.6
00.0
.0
1.2
.1
0.0
0.0
0so
0.0
00.0
0.0
1.5
.8
1.2
.1
2.4
.2
.2
.1
1.2
.10
0,0
0.9
57,6
504
54.2
471
54.0
42.4
426
45.8
401
46.0
93
871
TA
BL
E I
V36
WILSON
1968
1969
1970
1971
1972
11)
(2)
t3)
tit
12)
13)
111
t2)
13)
(1)
12)
131
111
12)
13)
S0UTH0EST TEXAS
39
43.1
22.4
67
41.1
27.8
S9
31.1
24.6
59
31.0
23.4
62
36.7
24.3
UTeAUSTIN
15
11.7
6.1
22
13,5
9.1
24
14.3
10.0
27
14.6
10.7
15
8.9
5,9
04USTON.TILLOTS0
08.8
S.@
00.0
0.0
O.
0.0
0.0
06.0
0.0
00.0
0.0
INCARNATE HORD
64.7
2.4
74.3
2.9
13
7.7
5.8
73.8
2.8
04.7
3,1
OUR LADY OP LAw
43.1
1.6
31.0
1.2
74.2
2.9
63.2
2.4
11
5.9
3.9
ST. EDwARDS
il
6.6
0.0
I.
.6
.4
S0.8
0.0
1.5
AA
21.2
.4
3T. NARY'S
06.3
3.3
63.7
2,5
74.2
2.9
63.2
2.4
14
.4
SOUTNWESTERW UN
00.0
001
It
1.2
.0
1.6
AS
1a
AA
1AA
01
TEXAS LUTHERAN
II
7.1
4.1
11
6.7
4.6
13
7.7
9.4
18
9.7
7.1
15
8.9
5.9
cr,
1.J
TRINITY UN/VERS
1.0
AR
1.6
.4
31.8
1.2
14.3
3.2
63.6
2.4
SAN ANTONIO CON
25
19.5
10.2
32
19.6
13.3
31
17.9
12.9
40
21.6
15.0
43
25.4
160
.,2
17. PHILLIPS
32.3
1.2
74.3
2.9
14.8
3.3
94.9
3.6
21.2
.8
/44s
CONCORDIA
81.8
8.8
S0.8
8.8
80.0
0.8
88.8
8.8
I8.8
8.8
SCHREINER
1.0
.0
42.9
1.7
31.0
1.2
4*
1.9
1.4
42.4
1,6
REGION
120
52.0
163
67.6
168
78.1
185
73.5
169
66.3
STATE
118
48;8
70
32.4
72
30.8
67
26.5
06
33.7
TOTAL
246
241
148
252
255
AN A3TERISw INDICATES THAT ENROLLMENT HAS NOT AVAILABLE AND AN ESTIMATE
IS USED
(1)4ENROLLmENT PROM COUNTY IN INSTITUTION
(2).PERCENT OF COUNTY'S STUDENTS EDUCATED WITHIN THE
REGION ATTENDING DESIGNATED INSTITUTION
(3)4PERCENT OF COUNTY'S STUDENTS EDUCATED WITHIN THE
STATE ATTENOING DESIGNATED INSTITUTION
( 1,11)T ER V
PROGRAM DEVELOPMENT:A REGIONAL AND INSTITUTIONAL PERSPECTIVE
Useful statewide and regional data presentation andanalysis teLlmiques are necessary Lomponents of an efteL-me state post-seLondary plairaing effort The techniquesdescribed in Chapters III and IV. for instaike. can lead toan unproved undeistanding of student demand for educa-tion in le \as by illustrating and e \plaming state andregional higher education emollment patterns Knowledgeof such patterns us in turn cortical for program and faLratiesplanning at the state level,
It is equally important. however. that state plannerscomprehend institutional planning processes within post-secondary education, and the was in which supply"demand taLtois are considered in these processes. This
ampler seeks to clarify these processes by foL using on onespecific issue integrally related to physkal flames andLampus planning. namely , program development
OVERVIEW OF PROGRAM DEVELOPMENT
Although the \Life provides teLlinkal assistance andmist approve or deny in -tuitional requests to create newstate-funded programs. primal) responsibility for initiatingthe program deveiongient proLess is left to the institutionThe stimulus for a new program mss Lome I ram any one ofseveral sources Otter) faculty or student interests serve asthe Latalyst..Alternatively. . requests from the 0)111111'1110y of
iron speLifiL businesses needing trained employ ees stay setthe proLess in motion
Program development and design procedures vary amongpost-secondary education institutions. Sonic school to gTeas Stare Technical Institute) have established a divisionspe,ilk charged with investigating new program pos-sibilities. designing Lin rkula fin new programs and submit-ting new program requests to the appropriate state agerayOther institutions delegate these responsibilities on di' ad//it( basis to those administrators 0, faculty members mostinterested in a new program
If the new program is vocational- technical in nature, theinstitution is required by federal and stare law to L,tablkh.in occupational advisory committee to assist in the
planning and development of the specific program underconsideration. the committee, appointed by the press lentor dean of the institution. is composed of individuals
involved in the speLifiL trade or occupation for which theprogram is designed. The Lomnuttee assists the institutionin determining the need for the program by providing anassessment of the area's training and manpower require-ments The Lonurattee also reviews the proposed programcurriculum and instruLtionol content. assesses the program'sequipment and taLility needs, assists in student recruitmentand placement. promotes public and legislative support forthe timing program, aids in obtaining training equipmentdonations. and assists in program evaluations in order toidentity program dchLienues and areas for gnprovement.
Institutions offering vocational - technical programs arefurther required to appoint a general advisory committeecomposed of high -level representatives of the LommunityMajor businesses. The function of the general advisor)Lommittee is to provide the institution with an assessmentof the area's general economic condition. the emsting andemerging manpower needs, and the overall appropriatenessof the college's occupational programs
I e \as State Technical Institute (-IS-TI) employs a thirdtype of advisor Lonumttee. namely, the school- industryLooperative committee. Unlike the community college.
created to serve a specified Lommunity. TSI I was estab-lished to provide training programs to meet statewidemanpower needs In order to ensure the identitkation ofthese statewide training needs. TE 1 has mandated theestablishment of school-mdustry cooperative committeescomposed of industrial representatives front across thestate Lich such committees orgamted to advise TSI I in
the development of a speLifiL occupational prograill ors wellas to promote cooperation between TSI I and the assoua-ted undo !Hal community Its tialit.tions art: similar to thoseof the oLLupational advisory Loninuttee and include place-ment assistaike, publk relations, obtaining training equip-ment donations. identification of naming needs and
evaluationIn contrast to 5ocatiott.11 programs. academic programs
are not required to employ an athisol cumnuttee in theprograni development pikes,.
Once a new prograill has been de aped it is reviewed hy
the appropriate administrative of fiLials within the college ofuniversity if the institution desires any state finano,,f
();
72
P ,stSt collilary Lineation
suppot I tot the new Imytain and has ieceived the approvalof the institution s goy cluing tmid. it must then submitthe pioglam to the applom late state agency for review and
applos.iiAcademic piogiains ale submitted for review to the
Cool diluting Board, Approy al of the Coordinating Board is
statutooly lequiled bet me a new degree program can heinitiated at the senior college level.
The Coordinating Board also exercises control over theacademic courses offered by the states public two-yearcolleges. It:gulling that all general academic courses offeredin these colleges be universityparallel courses.
Slate review of vocational education policies and pro-ga am, is complicated by the oveilapping responsibilities ofthe State Boaid of Education (and TEA) and the Coordi-nating Boaid. texas College and Univel soy System. TEA isresponsible for dnecong the state's activities in vocational-technical education at the secondary and post-secondarylevel as a result of the Texas TechnicalVocational Act of1°60 The Coordinating Board's concern stems from itslegislame mandate to provide " . leadership and coordina-
tion for the Texas inghei education system (Texas
Education ( ode 1072 Section 61.0021. The Joint Commit-tee. consisting of three representatives each from the Coor-dinating Board. the State Board of Education. and theAd\ holy Council for Technical-Vocational Education.meets regularly to coordinate the Joint policy concerns ofthe hoards
Requests for new yocational-technical programs areinitially submitted 1 TLA and then leYiewed by the JointProgram Review Committee Based on the Committee'steonlimendations the Associate Commissioner for Occupa-tiodal Education and Technology. TEA, either approvesthe piogram, denies it. or returns it to the institution forimprovement or modification If the program is approved,the institution 111,1X begin its implementation It. howeser,
an insttution% (vain iequest is (killed, the institution[mist sillier abandon the proposed program or appeal thedecision in a rehearing A program winch has been returned
to tlit institution for a modification may he resubmittedtor state restless once the identified problems have been
correctedSuccessful program planning at the institutional level
thas require. information that is both available and
applicable this may he data demonstrating student interest
in the program, advice concerning curriculum development,
and or lob availability projection,. This chapter analyzesthe extent to which supply 'demand factors are employed in
pioram development by selected Texas institutions of
post-secondary educationGiyen the magnitude of the post - secondary education
''N,,tetn in Texas, as well as the time and staff limitationsof the protect In examination of the program development
piocesses in each institution within the state was mmos-siblc. and prnect participants decided to focus on a single
sub- state region.The Austin-San Antonio area was again selected as the
region of study, included were the comities constituting theCapitol Atea State Planning Region (CASPR) arid the
Alamo Area State Planning Region (AASPR) Althoughlying outside this region, the James Connally Campus ofTexas State Technical Institute (TSTI) in Waco was alsoincluded. This inclusion was based upon TSTI's expandingrole in the state's post-secondary educational system andthe relative proximity of Waco to the selected region.
Within the selected region two studies were undertakento examine institutional policies related to the considera-tton of supply/demand factors in program development Toprovide an overview of the interaction among educationalinstitutions, a regional survey was developed and dissemi-nated In addition, detailed analyses of program devel-opment within eight diverse institutions in the regionprovided a perspective on mad-institutional policies andprocedures. These studies are described in the next twosections
REGIONAL POST-SECONDARYEDUCAT ION SURVEY
Effective state post-secondary education planning isdependent upon communication and the flow of infoima-lion between all concerned sectors. Thus a survey wasdesigned by project participants to disclose both the degreeof inter-institutional interaction and the extent of inter-action hetsveen post-secondary education institutions andvarious organizations in the post-secondary arena. e g.. theCoordinating Board mid its staff This approach was takenin an attempt to relate the institutional program develop-ment process with state/regional planning concerns LBJSchool project participants felt that the information
generated by such a survey could provide broader
overview of the case study institutions, as well as provide
helpful information concerning institutions not selected formore detailed analysts
The regional post-secondary education survey was
mailed to top administrators in all 42 post-secondaryeducation institutions, proprietary schools as well as
public and private colleges and universities. in CASPRand AASPR Two questions were posed. The first questionrequested the respondent to indicate which of the followingtypes of interaction occur hetsveen the respondent's ilistl-tuition and each of the other post-secondary educationinstitutions In the region.
academic program planning.socationaltechnical program planning,education committees ( e.g . joint institution. regional.
state),education conferences. andinformal interaction among adininistiatois g ,
telephone/mail correspondence)
the second sin yey question asked the respondent toidentify which of the following types of interaction occurbetween the respondent's institution and each of 14
organizations and government agencies
info! manorial .
advisoi y:regulatory:member:budgetary. andlobby.
The quality of the responses to these two questionsvaned significantly among the 42 surveyed institutions. Forexample. the San Antonio College responses represent thecollective judgments of approximately 20 administrators.deans, and department chairpersons. This broad involve-ment appeared to be the exception rather than the rule.however.
The response rate for public and private colleges anduniversities was excellent. with 13 or 16 institutionsreturning completed suneys Proprietary scho,r1 responseswere less complete,. with q of 26 institutions in the regionresponding Although the nine responding proprietaryschools may not he representative of the region's pro-prietary schools, the response rate is reasonable lot a mailsurvey. (The average response rate in such suryeys is onlyabout 15 percent.)
Question I Responses
These results. summarized in Appendix C. clearlyindicate a lack of program planning interaction between (a)the public and poyatc colleges and universities in the regionand lb) the proprietary institutions w.thin the legion. Thiswas noted by the respondents in each educational sectoi.
01 instance. only 4 of the 13 responding colleges anduniYersibes indicated any interaction with any propuetar:school in the region. and in only two of the tour cases didthe interaction consist of any thing other than informalcontact. ('onversely none of the responding proprietaryschools ihdicated interaction of any of the first three typeswith any college or university in the region
One might expect non-communication between theproprietary schools and the public and private four -yearninstitutions. given their different program orientationsMore surprising was the limited interaction between pro-prietary schools and the t WO-) ear colleges Although SanAntonio College does appear to maintain close contact withproprietary schools in San Antonio. only one respondingproprietary school in the region noted interaction with atwo-year college.
In contrast, the responding public and private colleges
65
7 4
Program Development
and universities in CASPR and AASPR indicated extensiveinformal contact with other institutions of this type. as wellas substantial interaction with respect to academic programplanning. education conference participation, and educa-tion committee involvement. Understandably, little Jointactivity related to vocational-technical program planningoccurred within this institutional sector.
Responses to this first question also imply substantialinteraction between proprietary schools. For instance, eightof the nine responding schools noted they had informalprogram planning interaction with other proprietaryschools in the selected region. Seven of the nine indicatedinteraction of this sou t in the context of educationconferences Surprisingly, however, none of the respondingproprietary schools indicated interaction with other pro-prietary schools in the region with regard to vocational-technical program planning.
Question 2 Responses
The responses to the second question (see Appendix C)are not ,is easily categorized and interpreted, due to themultiplicity of post-secondary education organizations andgovernment agencies in CASPR and AASPR. Nevertheless,the primary type of interaction occurring between respond-ing educational institutions and these organizations andagencies is clearly information exchange The survey re-
sponses further indicated that: with the exceptiou of theproprietary schooliTexas Education Agency certificationpiocedures, other types of contact between these govern-ment units and proprietary schools in the region are mini-mal
Conclusions
I he interaction categories used in the two surreyquestions were neither entirely del-111111%e not unambiguous
Neither was there a 100 percent institutional responseNevertheless. some generalizations based upon the
responses appear to he appropriate
I Substantial institutional Interaction occuts withinthe public and private collegiate sec tor. as well as within theproprietary school sec tot . in CASPR and AASPR Howe% CIinteraction among time pioprietaiy schools with legard tovocational-technical progiam planning does appeal to bevirtually non-existent.
2. Little interaction oc,,irs between the more
traditionally academic colleges and unneismes and theproprietary schools in the wpm Less expected. howesci.is the lack of interactive yOcational-technical programplanning between proprietary schools and the public andprivate two-year colleges With substantial e.aphasis nowbeing placed on occupationally um tented education. this
Pint-S'econdary Lineation
non-interaction could possibly I esult in excessive program
duplication.The survey does not describe the impact of inter-
institutional program planning interaction on intra-
institutional planning processes. For instance, surveyresponses indicated that 11 of the 13 responding collegesand universities interacted with similar institutions in theregion on academic program planning matters Whether thisis a public relations activity or actually influences internalplanning is unclear, however. (The eight institutionalanalyses in the next section focus on this issue.)
4. The proprietary school sector in CASPR-AASPR isrelatively autonomous. The only organizations/agencieswith which more than two of the responding proprietaryschools indicated any type of interaction were the TexasEducation Agency (TEA). the Texas Employment Com-mission (TEC). the Proprietary School Advisory Commis-sion (PSAC). and the Texas Association of ProprietarySchools (TAPS).
5. The value of the survey results more from its
overview of institutional interactions than from its specifi-cation of the impact such interactions actually have oninstitutional program planning and development. Neverthe-
less. this overview does reveal those organizations andagencies with which institutions wish to (or must ) interact.
This in turn informs state education planners of theenvironment in which institutions pursue their programdevelopment policies
INSTITUTIONAL PROGRAMDEVELOPMENT ANALYSES
Within the Austin-San Antonio region (i.e., CASPR andAASPR are more than 40 diverse post-secondary educationinstitutions Seven representative institutions were chosenfor in-depth analy see of institutional policies with regard tothe consideration of supply/demand factors in program de-velopment Included were a public senior college South-west Texas State University (San Marcos): a private senior
college St. Mary's University (San Antonio): three publictvv o-y ear colleges Austin Community College (Austin), SanAntonio College (San Antonio), and St. Phillip's College(San Antonio). and two proprietary schools Durham'sBusiness College (Austin ) and Parish Draughon's BusinessCollege arid Technical Institute (San Antonio) Included asthe eighth institution in this analysis was the JamesConnally Campus of the Texas State Technical Institute(TSTI m Waco. Although TSTI is located outside theseleoed region, the importance of the state technical
institute concept led to its inclusion and comparison with
other post-secondary education institutionsEstablished by the voters of Austin in December 1972,
Austin Gm/mu/ray college began classes in the fall of1971. Approximately 50 percent of the college's programs
66
are academically oriented and 50 percent occupationallyorien ted.
San Antonio College (SAC), operated together with St.Phillip's College under the jurisdiction of the San AntonioUnion Junior College District, was opened in 1925 as TheUniversity [of Texas] Junior College. SAC became a part ofthe San Antonio Public School System in 1930, andadopted its present name in 1948. It provides both transferand occupational programs. but emphasizes traditionalacademic areas and student transfers into four-year collegesand universities. St. Phillip's; on the other hand, focusesmuch of its attention on students who do not plan tocontinue education beyond two years. Originally foundedin 1898 as a private Episcopalian institution, it opened itsdoors in 1927 as a junior college serving the blackcommunity of San Antonio and vicinity. St. Phillip's,whose association with SAC began in 1942, now empha-sizes vocational-technical training and academic programsof a technical nature.
St Mary's University is private coeducational institu-tion owned and operated by the Society of Mary (RomanCatholic Church). First offering instruction in 1852, theuniversity currently offers undergraduate degrees in theSchool of Arts and Sciences and the School of BusinessAdministration. Advanced degrees are offered in law;education, arts and sciences, and business administration.
Chartered as Southwest Texas State Normal School in1899,, Southwest Texas State University is a coeducationalstate liberal arts institution offering a range of academicprograms at both the undergraduate and graduate levels.The institution has a regional focus, and seeks to appeal tostudent needs unanswered by other CASPR-AASPR post-secondary education institutions.
Durham's Business College. Austin,. was organized in1936. Its Business School Division and Technical SchoolDivision offer (according to the 1973.74 catalog) 10
different course offerings, with completion time per
offering ranging from 910 hours to 1,720 hours. Thecollege's approximately 300 students are primarily fromAustin and its immediate vicinity.
Pansh Draughon's Business College and Technical Insti-tute. San Antonio. has been in continuous operation since1888 The focus of the institute is on job placement. andthus its program has a strong business and industrialorientation. Its more than 500 students are enrolled in 20different business and technical programs.
The focus of the Texas State Technical Institute (TSTI)has been on producing individuals for immediate entry intothe Texas labor market. Created in 1965 by the TexasLegislature as a division of Texas A&M University. it beganclasses on the Waco campus in January 1966. A separategoverning board was established for the institute in 1969.Over 2,000 students are currently enrolled at the JamesConnally Campus in more than 55 degree and certificate
programs The institute's responsibilities are to !rain bothstudents and teachers in highly technical and vocationalprogram at easrs well as to conduct manpower develop-ment and mill/anon research programs to identify trainingand retraining needs in Texas
Procedures Within institutionsfor Program Development
One aspect of program development examined in theseeight institutions was the level at which program develop-ment takes place
In Southwest Texas State University. St Mary's Univer-sity and the three selected two-year colleges. programdevelopment generally occurs at the departmental level.Department faculty at these schools are responsible foridentifying the need for new programs and for developingcourse materials. At Southwest Texas. for instance, thedevelopment of the allied health professions programoriginated from a suggestion by a Biology Departmentfaculty member, who also researched the need for thisprogram without benefit of institutional support funds. AtAustin Community College. the Dean of OccupationalEducation and Technology designates a program leader ineach department to whom is given the responsibility fordeveloping new programs. After work is completed at thedepartment level in these schools. the program proposalsmust he cleared by various faculty and administrative units.and finally approved by the president and governing board.
At both San Antonio College and St. Phdhp's College.community groups uo have an impact on the programdevelopment process. Suggestions are always channeled tothe appropriate collegiate departments for review anddevelopment. No fu. teal procedures exist to solicit programideas from the community. this is achieved throughinformal faculty and administrative contacts,
In the two selected proprietary schools. no formalprocedures exist for program development. At ParishDraughon's. tor instance. !Mort-nal contacts of the school'sstaff with business and industry representatives. coin !nullitygroups and other schools ate used to modify existingprograms, New program ideas are usually suggested by theschool's director. on the basis of his business and educa-tional contacts. Program development within both pro-prietary schools occurs primardy at the discretion of theadministrative heads of the two institutions
TST I differs trom the examined four-year and two-yearinstitutions in that its program development process rsprimarily based in a Len; ral admimstrative dike establishedspeak all) for that purpose All program requests. most ofwhich flow from the state's business and industrial Loin-rummy . are Lhanneled into the dike of TSTI'S Manager ofCurrkultan and Facilities. This manager requests the
76 7
Program Development
school's Department of Occupational and EducationalResearch to perform a preliminary investigation of the needfor each proposed program. The manager's recommenda-tion for further program development (or for rejection)then is forwarded to the General Manager for Instruction,Existing departments become involved in the process onlywhen a proposed new program relates to existing ones.
Clearly the most prevalent program developmentpractice in these selected institutions is to delegate thisresponsibility to whichever faculty member or adminis-trator expresses the most interest in the new program. As aconsequence, the task is often performed by individualsfamiliar with the program area but unfamiliar with sourcesof and methods for developing supply/demand info' matronThis problem is further compounded by the timeconsuming nature of such activities,
It might be useful for each post-secondary educationinstitution to have a single office or position responsible forinvestigating new program requests. reviewing relevantsupply /demand factors. evaluating existing programs. andfunctioning as a liaison between the institution and thestate's educatior agencies on program development matters.The following sections describe how significant supply /,remand considerations are currently used in thus process,
institutional Interaction in Program Development
In examining institutional program developmentpolicies. the project participants sought to determine theextent to which post-secondary education in,titunonscommunicate with other such institutions in the process ofdeveloping programs. To what extent. for instance. doschools take into account the existing supply of similarpost-secondary programs in that geographic area)
The Regional Post-Secondary Education Surveyresponses. as noted above. indicated that institutionaladministrators in the ('ASPR and AASPR generally believedthey maintained considerable contact with other institu-tional representatives on progi am development matters.The eight institutional analyses reveal, however. thateffective interaction among these selected institutions islimited. This can be seen more dearly by noting Luirentpractices within these eight schools.
An important (and consideration in the pro-gram development activities of Southwest I exas StateUniversity has been its geographic location between TheUniversity of Texas at Austin and the several San Antonioinstitutions, In developing its allied health program. forinstance. Southwest Texas concentrated on providingprogram not provided elsewhere in the area It consultedwith area two-year colleges to determine if the latter'sgraduates might enroll in the program. Special contacts tin
f'ow-Secondary Pthwati(ol
this case were also made with St Phillip's College (SanAntonio). LI Cenno College (Dallas), and Tyler JuniorCollege foi these schools had experience in operatingrelated programs. This development work was all done byone protessoi however, and its success depended solelyupon his ability to perceive the steps that needed to betaken. Lacking. for example. were analytic techniques thatpiovided a clear Indication of institutional and programservice areas as all aid in making program developmentdecisions
St Mary's University is a member of an institutionalconsortium called United Colleges of San Antonio As aresult, it must consider the program offerings of Our Ladyof the Lake. Oblate. and Incarnate Word Colleges indeveloping its programs. This appears to be the extent of itsserious contacts with other schools, although the develop-ment of The Univorsity of Texas at San Antonio may resultin fume interaction with this institution.
Most schools review other institutions' programs toprevent duplication. St. Phillip's College and San AntonioCollege. on the other hand; look to other schools to seewhat program, they should be offering. There is no
apparent hesitation about duplhAting programs offered in(fillet institutions In tact, these two colleges often
duplicate each other's program: even though the San
Antonio Union Junior College District governs bothschools. This district also maintains a liaison committeewith The University of Texas at San Antonio to insuretransfeiability of student credits. particularly for the
district's large numbe- of Mexican-American students.The tw o selected proprietary schools are primarily
concerned about program development at other proprietaryschools. although Durham's Business College (Austin) doesstay informed about Austin Community College programofferings. Parish Draughon's stays informed about otherinstitutions' pi ograms through the !ex.'s Association ofP1oprietaiy Sch9ols.
A significant lack of contact was observed between TS-IIand public two-year colleges TSTI officials claim that thesecolleges have dit fering educational philosophies, and blamesthem for the limited communication. By law. TSTI mustreceive the approval of these two-year colleges whenotteong special off-campus lost! uctional programs in their
districts In its program development process. TSTI
specifically identifies those educational institutions, in andout of loos, public and private. which offer training inproposed new areas However, the assumption among TSTI
officials is that even it many schools offer the program.there must still be a need for more naming if industry isrequesting it. These officials believe that industry does not
try to overproduce trained manpower. since those busi-nesses will ultimately be requested by TSTI to assist inplacing graduates in jobs TSTI officials say there is no way
68
for them to know if industry goes to several schools
requesting the same programs. or if other schools aredeveloping similar programs simultaneously.
Impact of Student Demand on Program Development
Of particular interest in these eight institutional analyseswas the manner in which student demand for educationalprograms has been incorporated into the program development process.
In the case of the allied health piogram at SouthwestTexas State University, demonstration of student interest inthe proposed program was clearly a major concern of thefaculty member developing the proposal The professorinitial interest in the program was directly sparked by thefact that students at Southwest Texas were enrolling inpre-professional health courses. but were unable to com-plete work for a health degree. They had to transfer tofinish their training. To support his proposal, the professorvisited hospitals in Dallas. Houston, and the Austin -SanAntonio region to determine if their personnel would takeadvantage of the naming program to obtain licenses Healso consulted allied health educators in the state to see ifthey would be interested in enrolling in such a program Noattempt was made to quantify the number of studentslikely to enter the program in the future. however. It w a:simply a case of getting a "feel" for potential studentinterest In giving his approval of the program, the Dean ofthe College of Professional Schools. Southwest Texas StateUniversity. also shiwed a desire to respond to studentinterests but for different reasons With academic enroll-ments dropping, he realized it was essential to initiate highdemand programs to maintain state funding levels. Herealized as well that high demand programs were nowessentially job - oriented programs. especially at SouthwestTexas. given its large number of older-than-average stu-dents.
At St Mary's University. San Antonio College (SAC).and St. Phillip', College, student interest in new programs is
conveyed primarily through informal faculty- student
contacts At St Phillip's, for instance, students can petitionthe school to institute new courses or programs To plan forthe futin,:, St. Phillip's counselors recruiting in local highschools do try to obtain information on student interestsHowever. no formal survey instrument is used for thispurpose. nor are formal estimates of potential programenrollments made. At SAC, students will occasionally beasked to sign statements indicating their interests in
proposed programs. But here again no enrollment pro-jections are made to estimate future demand SA(' official,at times also look at other schools to see if their programsare attracting sufficient students before instituting a newprogram
78
Xustin Community College (ACC) has used the result, of.tudy done by the Austin Independent School District,
w Inch dealt with student /parent career interests, to ascer-tain information on student demand According to ACCofficials, however, student demand has been a minimalfa, tur in the college's program development process.
The proprietary schools studied do not attempt tode,elop estimates of potential enrollment in programs, i.e.-,of student demand. Information on students is collected,but it is neither aggregated nor used in the programdevelopment process.
The most sophisticated approach to predicting studentdemand has been implemented by Texas State TechnicalInstitute ITSTI). In addition to contacts with Veterans'Adminisnation offices and other TSTI campuses, the JamesConnally Campus in Waco relies extensively on "The HighSchool Career Interest and Information Survey" to predictpotential pi ogram enrollments. This career interest survey,.designed by TSTI's senior vice president. has been fundedby TSTI and the Texas Education Agency (TEA). Thesurvey was administered on a pilot project basis in differentparts of the state between 1070 and 1973, eventuallyreaching some 00,000 Texas high school students. thesurvey has been endorsed by TEA's 20 Regional EducationSelvv:e ('enters. which have recommended its annualadministration in the state's high schools. financial con-straints have prevented TEA from following up on thisproposal. Thus the data from the original surveys arerapidly becoming obsolete and les, valuable for planning1 STI. however. continues to administer the survey in highschools which it considers its "teeder schools."
In estimating potential enrollment for a program hornthe surveys. TSTI planners examine the interest responsesof the survey population for particul ir employment Late-gone,: as well as tin related job categories A projection isthen made of the po:mtial enrollment in the pi ogram basedupon Industry duster responses
Impact of Employer Demand on Program Development
Substantial differences exist among the eight selectedinstitutions with regard to the manner in which employerdemand impacts the program development and planningprocess. In some cases. industrial and business employerswere directly involved through advisory committees and thelike. in other situations. informal contact, between theschool and employers were predominant: and yet anotherapproach was to rely upon manpower projections publishedin trade and technical publications.
The development of the Southwest Texas allied healthprogram included a consideration of potential employerdemand for its graduates The professor developing theprogram traveled to schools. hospitals. and professionalassociation, to determine if manpower in this field was
tit)
79
Program Development
needed in texas He also used infonnation from nationalpublications discussing the shortage of allied health profes-sionals. The Dean of the College of Professional Schoolssupported the program idea in part because he believed thatnew federal legislation requiring increased licensing ofcertain health professionals would provide a market forgraduates. He also believed that hospitals in the stateneeded additional trained personnel in this area. OneCorpus Christi hospital had even contacted the s,:hoolrequesting such a program. However, as in the case ofstudent demand,. no attempt was made to quantify actualand future, demand for the program among employers. Inaddition, while the employers were consulted to ascertaintheir employment needs, health professional, were notformally involved in the piogram-development process itselfthrough the use of employer advisory committees This isconsistent with our finding that the development ofacademic programs, in both the four-year and the twoye,colleges,. does not generally involve the use of committee,and is thus less likely to include concern, about manpowerissues in the planning process.
Three of the examined schools St Phillip', College.Austin Comm umty College. and 1 STI do use manpowerstudies in the development of new progtams. At St. Phillip',College. after a new program is suggested. the director ofoccupational education (or his staff ) will contact the lexaEmployment Commission (TEC) to see a study has beendone in that occupational area within the past six month,St Phillip's will also have its staff conduct phone ofdoor -to -dour surveys of local industries to obtain estimate,of Industry manpower requirement,
At Austin Community College. the office of occupa-tional education and technology attempt, to asses,
employer demand for existing or proposed new programsthrough its own surveys. This IN not done. however. on asystematic basis
1Si! piovides the most thorough inswinional medi-amsni for developing manpower data to support newprogram pioposals. This stems directly twin 1S1 I's legisla-tive mandate. which was to provide programs 10 meet stalemanpower needs It also result, from the I S1 I mandate todo it:Neell on future vocational-technical education need,of the sue. Due to staff and financial limit,. ISMDepartment of Occupational and Educational Resealch hasnever been able to fully early out this latter part of themandate: instead, it has concentrated on pi oviding supportfor program development activities on the Connally Cam-pus.
When a new pi ogram is suggested at IS II. the Depart-ment of Occupational and Educattonal Research surveys ava,iety of sources to assess manpower needs at that timeand five years hence Consulted source, include 1 E('studies. U.S. Department of Labor and U S Department of
1'0,1-Secondary Education
Commere repoits. labour union publkations, piofe..sionaljournals. occasional council of government studies ofmanpowei needs, Chamber of Commeice reports, and,petal labor market analyses dune by groups like the Texashidustoal Desclopinent Group at Texas A&NI University.TSTI also LonduLts its own phone surveys of representativeemploy ers around the state to get their estimates ofmanpower requirements It is significant that TSTI concen-trates on assessing statewide need; while the junior collegesseek to identify more localized needs.
While all public vocational technical programs in Texasare lequired to have industry program advisory committees.these same three institutions St. Phillip's College.. ACC,and TSTI go beyond this formal requirement and actuallyimolve potential employers in the planning process. Assis-tame is sought in assessing need for the new programs andui dkneloping the couises for them, as well as in updatingand evaluating existing programs.
In contrast to this, the two selected proprietary schoolsmaintained no formal mechanisms for industry input intoprogram development or program operation. In developinga program; the directors of Durham's Business College willcontact certain industry people on an ad hoc basis. Theybelieve the school staff is sensitive to industry needs andwill respond quickly to changes in the economy. Thedirector of Parish Draughon's said the school staff keeps up
cm rent information on manpower needs through tradepublications and ,2oveminent documents, but that these arenot used in program development The school is not
concerned about min oving its use of supply /demand datain program development, it believes that its informal systemis working well and that the costs associated with moreextensive data collection and analysis would be too high.
Impact of Program Evaluation on Program Development
I he institutional program development process does not1, !inmate with the implementation of the program Rather.through the Lontinuing input of students, employers, andfaculty /administrators. the process of program evaluationLontinlICS d 01 irg the program's existence
the project's eight institutional analyses reveal that
%0Lational-teLluuLal plogrann, are evaluated much inurefrequently than are academic programs. This is largely dueto the fact that it is relatively easier to measure theaLblinement of pmogram objectives tua vocational technicalprograms than it is for academic programs.
The fundamental ,bjeLtive of a vocational - technicalprogram is to train individuals for employment. Themeasure of success of a vocational-teLlmiLal program isoften judged by the number of students who securetraining- related jobs up.m completion of the program In
addition to placement data, a program may be evaluated on
the basis in surreys of employer satisfaction with the
70
progiaes graduates and student assessments of the pro-gram's merit.
Program evaluation of academiL progiao;s is coin phLatedby the fact that preparation for employment is often onlyone of sevei al program objectives. The variety of satesupon which an academic program must he measured doesnot invalidate the use of placement data but rather suggeststhe need to employ a wide variety of evaluation techniques.
At the present time no systematic placement data arecollected for academic programs in CASPR and AASPR.Public institutions are required to collect such data forstate-funded and federally-funded vocational - technical edu-cation programs. But these data are frequently gatheredonly to satisfy reporting requirements and are seldom usedin the program development process.
Concerning other methods of program evaluation, ParishDraughon's and TSTI indicated they are beginning tosystematically solicit employer evaluations of their grad-uates in an effort to improve their programs Whethel ornot this information will he used by the institutionsremains an unanswered question.
Concluvions
Post-secondary' education at the institutional level. as atthe state level, benefits from knowledge about the impactof supply/demand factors (e.g. student demands, employerneeds, program offerings of other institutions) on theplanning process. This chapter has focused on an importantaspect of this planning process, namely, progran develop-ment. to seek to clarify institutional policies and practices.
Through an overview survey of all post-secondary
education institutions in the Austin-San Antonio region(i.e. CASPR and AASPR) and in-depth institutionalanalyses of eight institutions, project participants were ableto develop insight into the current institutional processesand a better appreLiation of their defiLiencies.Comlusionsbased upon these observations follow.
I. Substantial informal institutional interactions occurwithin the public and private collegiate scoot in the
selected region. The significance of this interaction is
difficult to evaluate, however. Cooperative planning withinthe proprietary school sector in CASPR and AASPR is lesscommon. Furthermore, interaction between these twosectors is virtually nun - existent between the proprietaryschools and the public two-year 'tonally- oriented col-leges. While efficiency is not alway Desirable educational
goal, it would appear that institutions must inure effec-tively incorporate other institutions' planning decisions intotheir own planning processes.
2. It might be useful for each post-secondary educationiutution to designate specific responsibility (to an officeor individual) for investigating the desirability and feasibil-ity of new and existing programs in light of various student,
80
employ er, and institutional supply 'demand considerationsIlls designee could also function as a liaison between theinstitution and the state agencies concerned with institu-tional planning activities.
3. in an effort to facilitate institutional-state agencyinteraction and to develop improved lines of communica-tion w ithin sub -state regions, an appropriate set of sub-stateregions 1-.g., the 24 state planning regions, which includeCASPR and AASPR) might assume a more active role asclearing houses and disseminators of information.
4. More effective student follow-up and placementprocedures need to be developed; further information onwhat happens to dropouts is also needed. This type ofinformation is particularly useful in planning vocational-technical programs and institutional policies. All publiccolleges and universities might he required to maintain suchinformation on all former students. it might also he
submitted, for example, with appropriations requests.5. Own the thrust of recent federal legislation (e.g.,
the 1973 Comprehensive Employment and Training Act)and the increased emphasis on occupation-related educa-
Program Development
lion, it is critical that vocational-technical programs heresponsive to employer, community,. and student concernsand needs, The advisory committee structure has beenestablished to provide such input into the planning process.However, the proiect's analyses support the concern of theTexas Advisory Council for Technical-Vocational Education (1974) that these committees are often ineffective inserving this function, and the guidelines for their establish,went and operation should he reviewed. The feasibility ofestablishing similar advisory committees for academic pro-grams might also be examined, is well as the relatr.eadvantages and disadvantages of regional or statewiderather than institution-focused, advisory councils.
The administration of a brief high school career interestsurvey, either on a sample or a complete survey basis, mightalso he useful for institutional and state-level planning. aswell as for later follow -up analyses. (The State of Min-nesota, for example, receives this and a variety of otherinformation from all high school juniors and uses itextensively in student follow-up and in planning analyses.)
71
81
CHAPTER VI
AREAS FOR FURTHER INVESTIGATION
The analytic approaches described in this report shouldcontribute to a more effective state and institutionalpost-secondary education planning effort. By no meanshowever, iaxe all issues and problems been resolved.
A summary listing of areas requiring further investiga-tion is complicated by their overlapping nature. Neverthe-less. it is helpful to classify them under three headings.( I ) vocational technical sector: (2) academic (collegiate)sector, and (3) authority of and coordination betweenstate postsecondary education agencies.
VOCATIONAL- TECHNICAL SECTOR
Supply and Demand
Further analy sec to assess both the need for varioustypes of post-secondary vocational-technical education andthe ability of the state and 'or local community toaccommodate existing and future demand are required. Forexample
To what extent have institutional service areas beendelineated and used in planning for and providingvocational-technical education?How has,: the planning and program responsibilities(with respect to the comprehensive Employment andTraining At ) of local manpower planners in Texasbeen incorporated into discussions of supply anddemand'To what extent is vocational-technical education inproprietary schools and in public post-secondaryInstitutions meeting the needs of Texas students andemployers" How can this best he assessed''
Data Resources
A more systematic review of information sources.availability. utility, and levels of aggregation would behelpful For example
What information is available on the degree to which.and the effectiveness with which. different socio-economic groups are being served by the state's
public and private vocational-technical programs"What role should the Texas Employment Comm.,
72
son. the Advisory Council on Technical-VocationalEducation; the Governor's Advisory Committee orPost-secondary Education Planning, and other stateagencies assume in the generation and/or collection ofvocationaltechnical education and manpower/laborinformation"
Funding
There is a need to assess the adequacy of funding forpostsecondary vocattonal technical education in Texas andto describe how the funds federal: state. local, andprivate are being expended
How much stateappropnated money supports post-secondary vocational-technical schools and programs"Who receives it" For what purposes is it spent'How might decreases or shifts in federal support (e.g..resulting from federal vocational education legislationin 1975) affect state vocational-technkal education'In what ways do federal funds and associatedreporting requirements influence state vocational-technical education policies and programs"
Planning and Coordination
Existing policies and procedures in the planning, admin.istration. and intrasect coordination of soca tional.
technical education should he clarified and. it necessary.reexamined For instance
What formal and informal planning processes existwithin the Texas Education Agency (TEAT''How does rEA assess the need for postsecondaryvocational-technical programs. and how are prioritiesestablished"How are post-secondary vocational technical institu-tions (public and proprietary ) encouraged P:and coordinate programs and facilities" Is unneces-sary duplicatio.1 of programs and facilities beingavoided'To sonic extent, local athisoo councils assess needsand plan programs. To whom are they accountable.'In what ways do they interact with the stateeducation agencies.'
8.)
%%hat kinds of ,00ldination e.isi bem seconalatschools and post seconalat. ..haul. ith tegatal 10vocational-technical calm anon planning'
I hi. addresses the possiple need to establish bettetctitetia tot plograin development and evaluation Forevimple
Ilov% ettetti e ate c mien' ocational technical pio-gum. in meeting state goals and student needs'is et tec meth's, measured' I. available data sufficient'
tlemble ale post-secondat wcationaltechnicalprograms to changing occupational needs in the state'Ilia n flemlnlit maintamed 'Ilo%s much plogiam ealuation should he undertakent) the vb,Isolv outh.11 for echnical-VocationalI dna:anon ' Does the ('Itincil have sufficient audio'Icy and iesouiccs to pettotni these tasks el tectRel '
C \I)I AIR' (COI II GIA11-1 SI CIOR
Sulyi,11 and I)enand
Requited hoe ale tuithet Awl% Ne. to ,pocy, the potentialdemand tot tiaditional academic education and thecapabilitv of colleges .mal nimetsities to Jahn.' to Litany.In this demand I it inNtalke
In %%hat deLlee should academic education heto,. Used on suppking the needs of the marketphase '
%%hi: hats been the late of emploabiht tot I c.a.colleg..t graduate.' %%hat piopot non telliallh In thestate %%hal ale ettiplo% ed 111 poNnwit
!elated to thee' C111,401011411 thrill -'%%ha' lia%e n.,tiHet1 in the ,Ii.na,telistics of the
lice and ollege age porliant In, in I evis ' I. thole:1CrI ,'A WO, :TM( P.111 Iiine Lidenh
I within:
!Tibia. !imam: t I independent po.tsecondalv edtkailoninstitution. I ard .tudent.111.1. loth matte! tit
OtteNti iii. ielLti dull: tilt' ,001(1111411filli it pubti,
flail' al)i),4pflat:d pablu. ,olleges ale also ,ontinningto surtace
Should the ( oorditiating Boatal anal its slatt and utthe state's it,vv I .`07 ( ,utnnission inane aonel.0audinate s'ate ,ippiotmation. tot pub!, college..m1/41 unier sides '
Sii 'lid independent collegial'. institutions Ill levisic,ctve gleatei state support' It so. %%amid gleater
state ,tipeit sion tit tli.;Ne schools he vpropriate '111,11 impact a llI lecent and potential changes intoderal edu,ation assi,tan,e 11.1%e on 1 c.a. college.and tinive,sitie.'
83
for Further lin ectrganon
Plunnunz amt Coordinatum
1,o.ting politic. and procedures related to the planningand intiasector coordination ut academic education shouldhe identified and It necessaiv reevitntned but eample
Ilam might independent colleges and universities inrevis he inure el tectively included Iv state planning.'%kiwi cliteria guide the establishment and,or e.pan-sion ut public colleges and110W mght the Coot dinating Bawd and its stall moreettectivel influence state higher education planning,gRen the piesent ielative autonomv institutioaal
Erahnilion
11n. Is no less impotrant than the evaluation atvocation-technical education, svitli similar questionsemerging I or eample
Is evsting data adequatelv used to evaluate therilettnrne.. tit collc,tate progiams'' Dow is it usedIlia Is et le( tiv enessIs the Coordinating Boaid statt equipped to ettec-mei% pet bum its pop attl eVaIllalltni tole" Should it%loolik.e. and of iesponsibilities he e.panded''
At 1 DORI 11 01 1ND COOR1)1\ A I BE 1W1-1
si All I'OS oNDARy LI)t ( HON GI \cif S
Plumung amt ('oorduration
111,111ded .imong the issues tacing the ( ool (lithium: Bomdand whet "agencies ale
110v. do the ( otudinating thud .tall the I e \a,ducatton .gencv the \Litt it the I `.02 ( ommission.
and representatives ,'t odic! invoked state tut:ant/anon, Inmate and structure the]: 110.,1 LLot1(1.1t1 edit-,1111 phoining a, mines ' 11hit me then iespectiatplanning ohie, ties' Are dies being ' %%hat
IS pc. al ,ntditi Won me ft tettlined%Vital 01/4 L Ms'
110%% ettectivelv has the Join, ( 'Immo 'ate apetatearIllse its oblet. Ilse. heen !call /1:d'1111.0 .li the advantages and disadvantages ot mole.etillah/ed 1)0,1 st...olitl.11% edit, inoll plailittlip and
snu, two in \,'Proprietary Cehools
1 lir, issue ale.' eticonpasses II A ,eltilicatinn tit e\,i'PU1)11041. .t haul. and the possible mime mole 01 the
.hate I 2.02 I onimission 1 al e \ample
,Ar.' present cettitnation anal nuptial' mg plocedutesapploptiate ' Should state involvement in 1)1 ogiain
lit:16)pillOtli Mid .R11111..01)11. politis be epandeal '1)oes !IA cutient!\ have sritticient atultoi it% totequi plop' 'oat v ..haul, to submit the student and
Po vt-Seaidary Education
program data necessary for effective statewide post-secondary education coordination?What roles should the Coordinating Board and thestate's 1202 Commission assume relative to the
proprietary school sector?
Community Colleges
This is basically a coordination issue, since both the
Coordinating Board and the State Board of Education/TEAexercise partial jurisdiction over this sector. For example:
74
Should all data and other information on communitycolleges and proprietary schools be collected andstored in a single location. then made accessible toboth these state agencies? If so, who should have theresponsibility for storage? Who would control theaccess to the information?What might be the responsibilities of the 1202
Commission with regard to community college
planning?
84
BIBLIOGRAPIFY
Adsisor. Council for Technical-VoLat anal Education11973) Fourth Annual Reps trt. Austin (November ).
11974) Fifth Annual Report (draft).Austin (September(.
AULT. David E. and Thomas E. JOHNSON. Jr. (1973)"Pr, whabilistic Models Appplied to Hospital Planning."Growth am! Change: :1 Imola! of Regional Develop-ment. sot. 4. no. 2 (April) 7-13.
BLALOCK. Hubert M., Jr. 119721 Social Statistics. SecondEdition. New York NIL-Grass-Hill Book Company.
BRADSHAW. B S. and POSTON. D.E. (1971) "TexasPopulation in 1970 I. Trends. 1950-1970." TexasIhisiness Renew. sot. xh, no. 5 Slav 1.
Carnegie Commission on Higher Education 11973) Prioritiesfor -letigui- Final Repc.rt ot the (arm !fie (ommissbm on
iceation, New Yor k Book
CompanyOrronich ot Higher Education. (1974) "Education
0111).): Decides to Push "1202' Panels" (March II).Coordinating Board. Texas College and University System
119bsa)Pnbry Paper .\o. 2 (state plan for junior collegedeselopment to 19s5). Austin.
I ) Policy Paper No. 3 (procedurestot r:Atutr. pubh). Junior colleges). Austin (April(.
111)70 ) Grodehrres. Pro( (lures. and Cn-terra Re ataig to Requirements for AdministretweChanges and Seit , Degree Programs. Austin 1Nosember 1.
11971) Memo:adorn describing the work.k(isities of the Coordinating Board st.itt (December).
(1913a) "A Statement of the Coordi-ruling Board's Responsibilit and Authority in Voca-rional-Fedinkal Fdtkation." Prepared at the request ofthe House Iducanori Committee. 1 ex.'s State I egisl.i-lure (Nose:I:her I I
119731)) "Institutions of Higher Educa-tion 11; Texas. 1972-73" (December).
DRAPER. N.R. and II SMITH (1966) Applied Regression:Mall sic. Ness York John Wiley Jill! Sons. Inc
GL1 \N' . Lynl.m ( 1973) "Pressures on Higher Education."APRA College and nuersity Journal. vol 12. no 4(September ).
HAINES. George H., Jr.. Leonard S. SIMON and MamasALEXIS (1972a) "An Analysts of Central City Neigh-borhood Food Trading Areas." Journal of RegionalScience. vol 12. no 1: 95-105.
(1972b) "Maximum Likelihood Estima-tion of Central City Food Trading Areas." Journal ofMarketing Research. vol. Ix (May ) 154-159.
HAMBURG. Morris 11970 )Statistical Analysis tor DecisionMaking. New York: Hai."tort. Brace and World. Inc.
HUFF. Dasid I 119731 "Hie Delineation of a NationalSystem of Planning Regions on the Basis of UrbanSpheres of Influence." Regional Snaky. col 7:323.329.
HUFF. David L. and L. BI UL 11960 ProgrammedScthititrit for Estimating Retail Sales l'c,titials.Lass reMe Center for Regional Studies. UnisersoKansas.
R Durkin 11959) Indiridual Choice Beharic.r. NewYork John Wiles and Sons. Inc
Ly ndon B. Johnson School of Puhlh. Affaus 119-2 ) Guideto Texas State Agencies. Fourth Edition. Austin I he
Universit ot Texas at Austin11974a) Post-secondars !AI:Hamm in
Texas Orgatri:ation and Issues. Phase I Repot I. AustinI he Unisersit of Texas at Austin I I ebruar
974b ) Texas Atlas .4 Hight r duc
1ustin The I nicersimy 01 TIA.1% .it .lust``(( 1ugust11974c) MAPPER Users Vanual. Austin
The Uniserso of Texas at Austin (Noscinher )National Commission on the Finarking ()I' Post :Secondary
Education INCITE 11 )73 Financing Post-See h.laryEducation in the United States. Washington. DC U.SGovernment Printing ()thee.
NIL Norman H.. Dale H. BENT. and Hadlai Hl LI 119701.Statistu al Package pg. the sfuaj lent t's Ness Nod,MLGraw -Hill Book Co
POSTON. Dudley L. Jr , James GRUND1 ACII. and (MimsCONWAY ( 1973) Prole noun ot the illeg -.1g Pc Pula-non ot Texas Counties Through 19s.i. Austin Popula-tion Research ('enter. The Urnsersit of Feu. at Austin(January)
7S
85
Post - Secondary Education
teas Education Agency (1973a) Directory of Schoolscertificated Older Texas Proprietary School Act andSchools Offering Courses Approved Jri Veterans
1March).( iiqb) "Status Report: leas Proprie-
tary School Act" (May).( I 97.3c) Guidelines and Minimum Stan-
dards Jri Operation of Texas Proprietary Schools (re-
vised) (October ).(1973d) "Texas Education Agency's
Authority and Responsibility in the Administration ofVocational Education in Local Education Agencies inthe State." Prepared at the request of the HouseEducation Committee. Texas State Legislature (Novem-ber 1).
(1973e) Texas State Plan for VocationalEducation. Fiscal Year 1974 (November)
Teas Education code (1972) Vemon's Texas CodesAnnotated: Education (Titles I. 2. 3). St. Paul- WestPublishing Company.
Texas Industrial Commission 11973) "Special Report on
76
Industrial Start-Up Training." Prepared for the HouseEducation Committee: Texas State Legislature(October ).
Texas Research League (1972) Texas Public SchoolFinance. A Majority of Exceptions. Austin (November).
THEIL, Henri (1971) Applied Economic Forecasting,Amsterdam. North Holland Publishing Company.
U.S. Bureau of the Census 11973) Census of Population:1970. Vol. 1. Characteristics of the Population. Part 45,Texas. Washington. D.C.: U.S. Government PrintingOffice.
U.S. Department of Health. Education, and Welfare (HEW)(1974) Digest of Educational Statistics: 1973, Wash-
ington. D.C.: U.S. Government Printing Office.U.S. House of Representatives 11971) Committee on
Education and Labor. Special Committee on Education.Higher Education Amendments of 197/ (Hearings onH.R. 32. H.R. 5191. H.R. 5192, H.R. 5193. and H.R.7248). Washington. D.C.. U.S. Government PrintingOt lice.
S)
APPENDIX A
DEFINITION OF POVERTY LEVEL
DEFINITION OF POVERTY LEVEL*
fee poverty statistics presented in this report are basedOn a definition originated by the Social Security Administram,) in 1964 and subsequently modified by a FederalInteragency Ct.nunission. The mile\ provides a range ofpoverty income cutoffs adjusted 1) such factors as fami!ysue. sin of the family head, number of children under IS
years old, and farm and nonfarm residence. At the core ofthis definition of povert), is J nutrition II} adequate foodplan ('economy plan) designed by the Department ofAgriculture for 'emergency or temporary use when fundsare low,' The index allows for difference, in the ,,,i ofMing bemeen tam and nonfarm families h setting thepkwertv thresholds for farm families at S5 perant of the
Appendt\ 11 "Definition. and 1 .p1 !nations of Sub)ei.t haraLteri.ti...- Chapter C "General Social .ind 1 ,onomii. ( tiara(teri.m..." Ci.ircus of I'npulattmt: 1970. Iol. I, (,harm remit( of thePopulation. lint 15. l'extrc IW ithineton. DC I. S (onetime:1iPrinting Ohm:, 197 1)
corresponding levels for nonfarm families. The povertyincome cutoffs are revised annually as reflected in theConsumer Price Index
"In 1969, the poserty thresholds ranged from SI .487for a female unrelated individual 65 years old and overliving on a fnm to 56.116 for a nonfarm family with a malehead and with seven or more persons. The average povertythreshold for a nonfarm family of four headed by a malewas S.%.745.
"Pkwerty thresholds are computed on a national basisonly. No attempt has been made to adjust these thresholdsfor regional, state. or other local variations in the cost ofliving (e\cept for the farm-nonfarm differential describedabove j.
77
87
APPENDIX B
STUDENT ALLOCATION MODEL
TECHNICAL SUMMARYOF STUDENT ALLOCATION MODEL
The basic proposition set forth in the Huff model is thathe probability P of a given alternative j being chosen ire iisonic specified set of choice alternatives n is proportion..! ,o
-. where u1
is the utility of the jth alternative. That is111
n
P = u') ) E l'.1
F1
(I)
For the purposes of this study uj is defined as theservices offered by a given institution of higher educationdivided by the difficulty of attending an institution at agiven distance from the students' county of origin. There-fore-
P = S-11 1
n
TitX
T11
Ti;1
1=1
(2)
where P ti = the probability that a student originating incounty r will select to attend a college oruniversity j:the services provided at school/ fur educationrepresented by the market share of all stu-dents attending college which was capturedby that type of institution:
T11
= the distance (difficulty factor) associatedwith the flow of students for a given type ofinstitution applied to a specific origin. r. anddestination ./. rcrionship:
A = a "friction" parameter that is to he estimatedempirically and associated with the distanceor difficulty factor:and
n = the number of schools.The expected number of students going from county Ito
a partszular school / is proportional to the site of thestudent age population in county i times the rate at whichstudents in county r attend college multiplied by the
S1 =
probability that a student originating at county i will begoing to school/. That is
I: P-- 13- (3)-II u 1
where F = the expected number of students that will be.4going from the ith county to the ith school.the probability of a student from county rgoing to school is and
B1 = the total number of students in county r whowill he going to college (College-Going Ratetimes I xi Student-Age Population).
The total expected number of college students receivedby a green school is derived by summing the expectedstudent flows from all counties I hat is
Pi) =
m
i=,
Eij (4)
where1-1 - = the total number of students going to school j
from all Texas counties:Eli = the expected number of students going from
county I to school j: andin = the number of Texas counties.
NATURE OF THE PARAMETERS
It has been shown in a number of studies that spatialmovements display a distancedecay function. i.e., move-ments decline increasingly with distance Therefore. inter-action models such as the one being employed in this studyraise distance to sonic power. This exponent is noted as X(lambda) in equation 121 above. This is simply a recognitionthat. in general. students will select to go to the nearestcollege if school services and sins are held constant.
If An denotes actual student flows from county r to aschool / and thethe expected flows. then a measure ofcorrespondence between the two is simply the differencesquared. The sum of squares for all paired %allies wou:,: he
in n
S = m L 1E,
- Aur1=1 1=1
7)
sa
(5)
fhe procedure for estunating X consists of a Fibonaccise irch over a defined Intel% at to find a value of X winchyields the lowest value of S. 1 e . the lowest sum of squares.The optimal value derived b) this successive approximationprocedure is then weighed and averaged for each institutiongrouping that was used
Ilaying derived an optimal value of lambda. the expectedstudent flows frinn each of the 254 counties to cash groupof schools can he derived.
The statistical measure that will he employed to deter-mine the accuracy of the model predictions is Theirsinequalitl coefficient.* Normally. Then coefficient is usedto measure the correspondence between actual and pre-dicted (hungry Willie this coefficient is being applied toabsolute %alms in this case. as opposed to changes. theinterpretation of the U coefficient Is somewhat differentbut still of considerable value.
For thls study. it is not unreasonable to measure theseriousness of the prediction win- h its square. wind) isthe mean- square - prediction err it for the set of all possiblem by n ofherxatiotis Thus
n
= > > (Elj .Aij)- (6)it
1=1 1=1
In onto to obtain a measure whkii has the samedimension .is the expected and actual student flows it B
'Henri I hell. .Ippitt d H. 0,r,w4z, I ore( as Iva: All1NIC:(L1(11 N"rfil
ilt111,11itt Ptibils111(1, ( °nip sits I. pp 21, ;2
70
Appendix. B
appropriate to take the square root of the meansquare-prediction error (RMS). This figure represents the averageWilt:lima (plus or minus) in the number of students thatare predicted to attend versus the actual attendance lot allpossible flows between counties and institutions.
Anodic! use for the RMS prediction error is to comparepredictions and "no-change extrapolations..' This measure-ment can he achieved by dividing the RMS prediction errorby the square root of the mean square successive differenceof the actual values. The result is the positive square root of
n
E E (E,_ A02(7)
The positive square root of this value yields theinequality coeffioent U. The range of U is zero to .)0. Theticlaims that where this inequality coefficient is significantlygreater than one. it indicates that predictions are worsethan those which would he made by a naive "no-changeextrapolation' of past data
80
APPENDIX C
RESPONSES TO REGIONALPOST-SECONDARY EDUCATION SURVEY
REGIONAL POST-SECONDARY EDUCATION SURVEY
RESPONDENTS
Public, Private Colleges and Universities
Austin Community CollegeConcordia Lutheran CollegeHuston-Tillotson CollegeIncarnate Word CollegeSt Edward's UniversitySt. Mary's UniversitySan Antonio CollegeSchreiner InstituteSouthwest Texas State UmversiiySouthwestern UniversityTexas Lutheran CollegeThe University of Texas at San AntonioTrinity University
Proprietary Schools.
Capitol City Trade and Technical School*CBM Education Center of San Antonio. Inc.Durham's Business College (Austin)Elkin's Institute in San Antonio. Inc.Hallmark Aero-TechJacki Nell Executive Secretary SchoolParish Draughon's Business College and Technical InstituteSan Antonio College of Medical and Dental Assistants. Inc.Texas Vocational School
*Although nu luded to the Ill ill-Out emu!! ( it) 1 rode and Fe,hniLJI S..hool %J. inathertently omitted t rom the institutional h.t to Que.tion
80 90
Appendix C
RESPONSES TO QUESTION I
Table 4: Number of institutional interactions, by type, between each responding institution and other institutions in theCASPR and AASPR region.
Responding Institution
1
Public/PrivateColleges andUniversities
3 4 5Type of
Interaction
ProprietarySchools
3 4 5
Pub he rn:ute
Austin Community College 4 I 4 4 7 0 0 0 0 0Concordia Lutheran College 0 0 0 10 2 0 0 0 0 0Huston-Tillotson College 7 0 3 14 14 0 0 0 0 0incarnate Word College 2 0 5 4 8 0 0 0 0 0St Edward's University 3 0 t) 10 15 0 0 0 0 0St. Mai>. 's Unnersity 3 0 7 9 8 0 0 0 0 0San Antonio College 14 8 9 8 11 4 2 I 0 1_Schreiner Institute 0 0 0 0 10 0 () 0 0 0Soutimest Texas State Universit), 15 2 14 15 15 1 2 _ 0 0 ')Southwestern Unn cr.!' y 7 0 0 11 8 0 0 0 0 0Texas Lutheran College 3 0 4 ( 14 0 0 0 0 1-The Cniversit of Texas at San Antonio 7 0 7 3 10 0 0 0 0 01 mu t Unnersit 8 0 12 14 15 0 0 0 0
Prilinerar
( apt tol City rr,tde and 1 echmcal School 0 0 0 0 0 0 0 0 3 4CHNI 1.docation Center of San Antonio, Inc 0 0 U 0 0 0 0 0Durham's Business ( oilege Austin) () 0 () 1 0 0 7 0
Institute in San Antonio. Inc. 0 0 0 0 0 0 0 0 6 7Hallmark Aero-Tech 0 0 0 7 0 0 0 0 9 4Jacki Nell Executive Secretary School 0 0 0 0 3 0 0 0 0 I
Parish Draughon's Business College and Technical Institute 0 0 0 0 2 1 0 4 4 1
San Antonio College of Medical and Dental Assistants, Inc. 0 0 0 0 0 () 0 11 10 13Texas Vocational School 0 0 0 I 0 0 0
81
91
Post-Secondary Education
Table B: Institutions in the CASPR-AASPR region with which responding institutions indicate interaction.
Institution IndicatedBy Respondents*
1
Public/PrivateRespondents
2 3 4 5Type of
Interaction 1
ProprietaryRespondents
2 3 4 5
Austin Community College 4 1 3 4 8 0 0 0 0 0Concordia Lutheran College 5 0 4 6 7 0 0 0 0 0Huston-Tillotson College 5 0 3 6 7 0 0 0 0 1
Incarnate Word College 6 I 6 8 9 0 0 0 0 0Our Lady of the Lake College 7 I 7 10 1 1 0 0 0 0 0St. Edward's University 6 1 5 7 6 0 0 0 0 1
St. Mar's University 6 I 6 8 9 0 0 0 0 0St Philip's College 4 0 4 6 9 0 0 0 1 0San Antonio College 3 I 5 6 9 0 0 0 0 0Southwest Texas State University 2 1 3 7 8 0 0 0 0 I
Southwestern University 4 0 3 5 7 0 0 0 0 0Schreiner Institute 2 0 2 3 6 0 0 0 0 0Texas Lutheran College 4 0 5 7 10 0 0 0 0 0The University of Texas at Austin 7 I 7 10 11 0 0 0 0 1
The University of Texas at San Antonio 3 I 5 6 10 0 0 0 0 0Trinity University 5 I 6 9 10 0 0 0 0 0
*Proprietary schools are not included because the number of respondents noting interaction with such institutions wasminimal.
82
92
.4ppendix C
RESPONSES TO QUESTION 2
Table C: Organizations and agencies in the CASPR-AASPR region with which responding institutions indicate interaL non.
Organization/ AgencyIdentified by Respondent
( ASPRAASPRCoordinating BoardTexas Education AgencyAssociation of Independent College an0 UniversitiesTexas Pubiic Junior College AssociationAdvisory Council for Technical-Vocational EducationState Legislatur:Governor \ OfficeTexas Employment Commi sion'Texas Industrial CommissionOISProprietary School Advisory Commission
1
67
III I
10
5
3
10
9
8
3
4s_
Public/PrivateRespondents
2 3 4 5
1_ 1 0 I
4 I 0 1
9 5 I 5
10 10 0 2
9 4 9 43 I 2 _ 1
1_ I I I
5 3 0 46 3 0 3
5 2 0 1
2 _ I 0 01 I 0 0I 0 0 0
6
0000
5
2 _
I
2 _
2_
00
00
Type ofInteraction
I
1
005
I
0I
0i
5
006
2
1
0
04
00
I
0I
I
00s
ProprietaryRespondents
3 4 S 6
0 0 0 0
0 0 0 00 0 0 07 0 1 00 0 0 00 () 0 00 0 0 00 0 0 I
0 0 0 02 0 0 0
0 0 0 0
0 0 0 03 0 0 0
83
93