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

99

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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75

77

78

80

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.

1

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

5

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