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APPENDIXES



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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. APPENDIXES

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. This page in the original is blank.

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. A Conference Agenda and Attendees TEACHER SUPPLY, DEMAND, AND QUALITY CONFERENCE ON POLICY ISSUES, MODELS, AND DATA BASES March 21–23, 1991 National Academy of Sciences/National Research Council 2001 Wisconsin Avenue, N.W. — Green Building Washington, D.C. AGENDA Thursday, March 21, 1991 Green Building, Room 130 6:00 Reception   7:00 Dinner   8:00 Opening Session Lee Shulman, Chair   Welcome Emerson Elliott   After dinner speaker Albert Shanker Friday, March 22, 1991 Green Building, Room 130 8:30 a.m. Continental breakfast   9:00 Opening remarks Lee Shulman, Chair 9:05 The OERI Perspective on Teacher Supply, Demand, and Quality Christopher Cross

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. 9:20 What NCES Hopes to Learn from the Conference Paul Planchon 9:30 Paper: On the Problem of Improving Teacher Quality While Balancing Supply and Demand Mary Kennedy   Invited discussants James Stedman Arthur Wise   Open discussion   12:00 Luncheon   1:00 p.m. Paper: The State of the Art in Projecting Teacher Supply, Demand, and Quality Stephen Barro   Invited discussants Gus Haggstrom Ronald Kutscher   Open discussion   3:15 Paper: National Data Bases Relevant to Teacher Supply, Demand, and Quality Models and Projection Methods Ross Brewer Stephen Coelen James Wilson   Invited discussants Alan Fechter Thomas Hilton   Open discussion   5:30 Adjourn   Saturday, March 23, 1991 Green Building, Room 130 8:30 a.m. Continental breakfast   9:00 Opening remarks Lee Shulman, Chair 9:05 Panel on State Data  

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases.   Who Will Teach? Richard Murnane   Developing a Regional Database for the Northeast: Problems, Products, and Prospects James Wilson   A Cooperative Project to Develop Teacher Supply/Demand Information in the Southern States Robert Stoltz   State Data on Teacher Quality, Supply, and Demand Rolf Blank   Open discussion   11:30 Summary of Conference Findings Lee Shulman 12:00 noon Roundtable Discussion of Important Next Steps   12:55 p.m. Last Word Dorothy Gilford 1:00 Adjourn Buffet Luncheon   ATTENDEES Chair Lee S. Shulman. Charles E. Ducommun Professor of Education, School of Education, Stanford University Speakers and Discussants Stephen M. Barro, President, SMB Economic Research, Inc. Rolf K. Blank, Project Director, Science/Mathematics Indicators Project, Council of Chief State School Officers W. Ross Brewer, Director of Planning and Policy Development, Vermont Department of Education Stephen P. Coelen, Director, Massachusetts Institute for Social and Economic Research (MISER) and Data and Decision Analysis, Inc. Christopher T. Cross, Assistant Secretary, Office of Educational Research and Improvement Emerson J. Elliott. Acting Commissioner, National Center for Education Statistics

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Alan Fechter, Executive Director, Office of Scientific and Engineering Personnel, National Academy of Sciences/National Research Council Gus W. Haggstrom, Senior Statistician, Economics Department, The RAND Corporation Thomas L. Hilton, Senior Research Scientist, Educational Testing Service Mary M. Kennedy, Professor of Education and Director, National Center for Research on Teacher Education, Michigan State University Ronald E. Kutscher, Associate Commissioner, Office of Employment Projections. Bureau of Labor Statistics Richard J. Murnane, Professor, Graduate School of Education, Harvard University Paul Planchon, Associate Commissioner, Elementary/Secondary Education Statistics Division, National Center for Education Statistics James B. Stedman, Specialist in Social Legislation, Education and Public Welfare Division. Congressional Research Service, Library of Congress Robert P. Stoltz, Vice President and Director for Education Policy, Southern Regional Education Board James M. Wilson, Senior Project Analyst, Massachusetts Institute for Social and Economic Research (MISER) and Data and Decision Analysis, Inc. Arthur E. Wise, President, National Council for Accreditation of Teacher Education Invited Participants Susan Ahmed, Mathematical Statistician, Statistical Standards and Methodology Division, National Center for Education Statistics Nabeel Alsalam, Chief, Indicators and Reports Branch, Data Development Division, National Center for Education Statistics Elizabeth Ashburn, Schools and School Professionals Division. Office of Research, Office of Educational Research and Improvement Sharon Bobbitt, Statistician, Elementary/Secondary Education Statistics Division, National Center for Education Statistics Teresa Bunsen, Project Officer, Division of Personnel Preparation, Office of Special Education Programs John G. Chapman, Program Analyst, Office of Planning, Budget, and Evaluation, U.S. Department of Education Joseph Conaty, Education Research Specialist, Office of Research, Office of Educational Research and Improvement Lynne Cook, Director, National Clearinghouse for Professions in Special Education C. Emily Feistritzer, President, Feistritzer Publications and Director, National Center for Education Information

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. David Florio, Director of Policy Studies, Office of the Assistant Secretary, Office of Educational Research and Improvement Debra Gerald, Mathematical Statistician, Statistical Standards and Methodology Division, National Center for Education Statistics Milton Goldberg, Director, Office of Research, Office of Educational Research and Improvement Jewell Gould, Director of Research, American Federation of Teachers Jeanne E. Griffith, Associate Commissioner, Data Development Division, National Center for Education Statistics David W. Grissmer, Deputy Director, Defense Manpower Research Center, The RAND Corporation Ron Hall, Acting Associate Commissioner, Postsecondary Education Statistics Division, National Center for Education Statistics Suzann R. Harrison, Executive Secretary, Georgia Professional Standards Commission David Haselkorn, President, Recruiting New Teachers, Inc. Terrence L. Hibpshman, Manager, Research Branch, Division of Research, Kentucky Department of Education Edward Hurley, Research Specialist, National Education Association Joseph S. Johnston, Jr., Vice President for Programs, Association of American Colleges Daniel Levine, Study Director, Committee on Postsecondary Education and Training for the Workplace, National Academy of Sciences / National Research Council Paul Lauritzen, Professor of Special Education, University of Wisconsin David Mandel, Vice President for Policy Development, National Board of Professional Teachers Marilyn M. McMillen, Statistician, Elementary/Secondary Education Statistics Division, National Center for Education Statistics Jeffrey Owings, Chief, Longitudinal and Household Studies Branch, Elementary/Secondary Education Statistics Division, National Center for Education Statistics John Ralph, Chief, Policy and Review Branch, Data Development Division, National Center for Education Statistics Mary Rollefson, Acting Chief, Special Surveys and Analysis Branch, National Center for Education Statistics Paul M. Siegel, Senior Education Analyst, Population Division, Bureau of the Census John J. Stiglmeier. Director, Information Center on Education, New York State Education Department Peter Stowe, Statistician, Postsecondary Education Statistics Division, National Center for Education Statistics

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Miron L. Straf, Director, Committee on National Statistics, National Academy of Sciences/National Research Council Larry E. Suter, Program Director, Assessments and Indicators, Office of Studies and Program Assessment, Directorate for Education and Human Resources, National Science Foundation Bayla White, Senior Budget Examiner, Office of Management and Budget Staff Dorothy M. Gilford, Project Director, Conference on Teacher Supply, Demand, and Quality Laura Lathrop, Research Assistant Jane Phillips, Administrative Assistant Consultant: Erling E. Boe, Professor, Graduate School of Education, University of Pennsylvania

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. B National Data Bases Related to Teacher Supply, Demand, and Quality The American Freshman Association of American Colleges Curriculum Database Common Core of Data Current Population Survey Graduate Record Examination (GRE) High School and Beyond (HS&B) Integrated Postsecondary Education Data System (IPEDS) Longitudinal Study of American Youth (LSAY) National Assessment of Educational Progress (NAEP) National Education Longitudinal Study of 1988 (NELS-88) The National Longitudinal Study (NLS-72) National Surveys of Science and Mathematics Education The Private School Survey Schools and Staffing Survey (SASS) and Teacher Follow-up Survey (TFS) Status of the American Public School Teacher Surveys of Recent College Graduates THE AMERICAN FRESHMAN The American Freshman survey is conducted annually by the Cooperative Institutional Research Program (CIRP). of the University of California at Los Angeles (UCLA). CIRP and UCLA's Higher Education Research Institute survey all incoming freshmen in full-time study in a sample of These summaries rely heavily on descriptions in Precollege Science and Mathematics Teachers (Dorothy M. Gilford and Ellen Tenenbaum, Editors, National Academy Press, Washington, D.C., 1990) and on material provided by various sponsors of data bases.

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. colleges and universities. The data are stratified by type of college, public or private control, and selectivity. Longitudinal follow-up studies are conducted each summer to track students two and four years after college entry. Freshman surveys typically involve 300,000 students at 600 institutions; follow-ups are done with probability samples of 25,000 students from each cohort. The 40-question survey instrument solicits data on high school background, including SAT or ACT scores and grade point average, intended major and educational goals, career plans, financial arrangements, and attitudes. Personal data include race/ethnicity, sex, and parents' income and occupations. Data from The American Freshman can illuminate the beginning stage of the supply pipeline—choosing a major and a career plan. The survey includes education as one of the ''Probable Major Fields of Study.'' This broad category has a number of subcategories, including levels of education, subject matter, and special education. The longitudinal survey can provide some information about the stability of the early preference for teaching as an occupation. Questionnaire data, such as SAT scores and number of honors courses taken in high school, can be used to provide some measure of the qualifications aspects of quality. Contact: Alexander W. Astin Higher Education Research Institute Graduate school of Education University of California 405 Hilgard Avenue Los Angeles, California 90024-1521 310/825-1925 ASSOCIATION OF AMERICAN COLLEGES CURRICULUM DATABASE The Association of American Colleges (AAC) expanded what began as a small study in 1985–86 and 1987–88 to a large study that collected transcript data on all spring 1991 graduates from 200 institutions. These data provide the foundation for a curriculum database (CDB). To identify changes and trends, the AAC plans to collect and process such data every two years. Of the 200 participating institutions, 100 were "friends" of the AAC and 100 were a stratified probability sample of 1,360 B.A.-granting institutions, which can provide national estimates. Twenty-four strata were used based on classifying the sampling frame by: control (public/private): type (liberal arts/comprehensive/doctoral/research); and geographic location (east/ middle/west). The CDB does not use a questionnaire but takes electronic transcript and student record data. Student data collected include: major/school, gender, ethnicity, date of birth, GPA, scores on the SAT and ACT, advanced placement credits, trans-

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. fer credits, and transfer level (when applicable). In addition, data were collected on all course enrollments, including the year, term, campus, grade, and credits of each course. Each institution was asked to map its curriculum to clarify the field or discipline to which courses and majors belonged. The CDB will provide information on course background as well as student variables that will be useful in longitudinal analysis of teacher preparation. Contact: Joseph S. Johnston, Jr. Association of American Colleges 1818 R Street, N.W. Washington, D.C. 20009 202/387-3760 COMMON CORE OF DATA The Common Core of Data (CCD) is a comprehensive, annual, national statistical data base for all public elementary and secondary schools and school districts. CCD is conducted by the National Center for Education Statistics, and the data base contains data that are comparable across states. The CCD is comprised of five surveys that are sent to state departments of education. These five data sets can be used separately or in conjunction with one another in addressing issues of teacher supply and demand. The data collected by the CCD surveys include three categories of information: general descriptive information on schools and school districts and state-level information on students and staff, and finances. The information on students and staff includes enrollment by grade and race, full-time equivalent staff by major employment, and high school graduates and completers in the previous year. Contact: John Sietsema Elementary and Secondary Education Statistics Division National Center for Education Statistics 555 New Jersey Avenue, N.W. Washington, D.C. 20208-5651 202/219-1335 CURRENT POPULATION SURVEY The Current Population Survey (CPS) is a continuing cross-sectional survey of a sample of U.S. households. The Bureau of Labor Statistics provides the major funding for the survey. It is conducted monthly by the Census Bureau and collects data on labor force status. The CPS surveys people age 15 or older on their employment status in the week prior to the

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. school attributes, and from questionnaires completed by school district personnel about district variables. These different sources provide quite different sets of data. The SASS data base also contains information from questionnaires completed by one sample of teachers in 1988 and another sample in 1991. The contents of these two particular questionnaire sources are quite similar. Likewise, the National Assessment of Education Progress (NAEP) includes information from somewhat different versions of questionnaires for science and for mathematics teachers at the eighth grade level in 1990, each of which can be considered a particular source. Other data bases, such as CCD, amass similar data annually, each installment of which constitutes a particular source. The five tables of this appendix were developed to provide helpful guidance for researchers, modelers, data base managers, and others interested in identifying sources of national data about variables relevant to analyses of teacher supply and demand issues. However, data bases are too complex internally to serve as the optimal unit of analysis for the purpose of providing helpful guidance. On the other hand, particular sources within data bases are too numerous to serve as a manageable unit of analysis, and, in any event, many particular sources contain a great deal of overlapping content. In view of these considerations, an intermediate unit of analysis was devised—a unit more particular than a data base but broader than a particular source. This intermediate unit of analysis is a cluster of particular sources within a data base. To define such clusters, particular sources of data within data bases were analyzed to identify those sources that contained substantially overlapping information. For example, the teacher questionnaires of SASS for 1988 and 1991 were clustered together because there was a high degree of overlap of information collected. Similarly, the school questionnaires of SASS for 1988 and 1991 formed another cluster because of overlapping content. However, the differences between the teacher and school questionnaires were sufficiently great that they were treated as different clusters of particular sources. As a result of this type of analysis, the 15 national data bases were broken down into a total of 29 clusters of particular data sources. The particular sources contained within these 29 clusters are listed in the section preceding Table 1. Though useful, the strategy of consolidating particular data sources into 29 clusters was adopted at the cost of obscuring certain differences among sources included in a cluster. These differences are not revealed in the five tables of this appendix. For example, variations in similar questionnaires collected on two different occasions are not reported here, such as the differences between the teacher questionnaires of SASS for 1988 and 1991. Likewise, variations between similar questionnaires collected from different groups of teachers at a particular time (such as from eighth grade mathematics teachers and science teachers in 1990 for the NAEP) are not re-

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. ported here. Interested users must review questionnaire forms to determine the specific information contained in each data base. Although the rows of Tables 1 through 5 are termed variables and are intentionally very specific, different definitions of each variable may be used in different data bases, and sometimes for particular data sources within a data base. For example, the numbers of employed teachers are based on different operational definitions of a teacher and may be reported as either the number of full-time equivalent teachers, the number of full-time teachers only, or the number of full-time plus part-time teachers. Therefore, users of TSDQ data must review the original questionnaires and related documentation to determine specific definitions of TSDQ variables used. In addition, some variables included in the tables are not recorded as such in their designated data base sources. These are derived variables. For example, combinations of questionnaire items included in the teacher questionnaires of SASS provide a basis for computing the percentage of teachers who transfer from primary teaching assignments in special education to general education from one year to the next even though there is no particular questionnaire item about such cross-field transfer. Of course, data users can generate many other useful derived variables. Finally, some national data bases (such as SASS) have been structured to provide national estimates of TSDQ variables, while other data bases do not because they are not derived from national probability samples. For example, some teacher samples (e.g., from NELS:88, NAEP, and LSAY) have been formed by selecting teachers linked to national probability samples of students. Therefore, these teacher samples are not representative of the national teaching force. In another example, data on entering teachers from the Surveys of College Graduates are limited to graduates at the baccalaureate level. Since new teachers graduating at the masters level are not included, these surveys are not representative of all recently graduated new teachers. Therefore, users of data bases must determine the population represented in the TSDQ data. For the several reasons described above, entries in Tables 1 through 5 should be viewed only as promising leads to data base sources of TSDQ variables. Users of this information should review the specific contents of data elements in each relevant data base before concluding whether needed information about particular variables is either available or is not available. Data Sources Reported in Tables 1 through 5 Schools and Staffing Survey: Public School Teacher Questionnaire (1988 and 1991) (Sponsor: National Center for Education Statistics) Schools and Staffing Survey: Public School Questionnaire (1988 and 1991) (Sponsor: National Center for Education Statistics)

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Schools and Staffing Survey: Teacher Demand and Shortage Questionnaire for Public School Districts (1988 and 1991) (Sponsor: National Center for Education Statistics) Schools and Staffing Survey: Public School Administrator Questionnaire (1988 and 1991) (Sponsor: National Center for Education Statistics) Teacher Followup Survey: Questionnaire for Current Teachers (1989) (Sponsor: National Center for Education Statistics) Teacher Followup Survey: Questionnaire for Former Teachers (1989) (Sponsor: National Center for Education Statistics) Common Core of Data (1989–90) (Sponsor: National Center for Education Statistics) National Education Longitudinal Study of 1988: Teacher Questionnaire (Base Questionnaire of 1988 and Following), Questionnaire for History, English, Mathematics, and Science Versions of 1990) (Sponsor: National Center for Education Statistics) National Education Longitudinal Study of 1988: School Questionnaire (Base 1988 and 1990 Followup) (Sponsor: National Center for Education Statistics) National Assessment of Educational Progress: Teacher Questionnaire for Grade 3, Grade 7, and Grade 11 (1986) (Sponsor: National Center for Education Statistics) National Assessment of Educational Progress: Teacher Questionnaire for Grade 4 and Grade 8 (1988) (Sponsor: National Center for Education Statistics) National Assessment of Educational Progress: Teacher Questionnaires for Math Teachers and For Science Teachers, both for Grade 8 (1990) (Sponsor: National Center for Education Statistics) National Assessment of Educational Progress: Teacher Questionnaires for Grade 4, Mathematics Teachers and Writing Teachers, both for Grade 8 (1991–92) (Sponsor: National Center for Education Statistics) National Longitudinal Study (NLS-72) (1972, 1973–74, 1974–75, 1976–77, 1978–80, and 1986) (Sponsor: National Center for Education Statistics) High School and Beyond: Administrator and Teacher Surveys (1984) (Sponsor: National Center for Education Statistics) Current Population Survey (March, May, and October 1991 and January 1992) (Sponsor: Bureau of the Census) National Survey of Science and Mathematics Education: Teacher Questionnaire in (a) Elementary, (b) Elementary Mathematics, (c) Secondary, and (d) Secondary Mathematics (1985) (Sponsor: National Science Foundation)

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. National Surveys of Science and Mathematics Education: Principal Questionnaire (1985) (Sponsor: National Science Foundation) Status of the American Public School Teacher (1991) (Sponsor: National Education Association) American Freshman Survey (1990) (Sponsor: Cooperative Institutional Research Program. University of California, Los Angeles) American Freshman Followup Survey (1991) (Sponsor: Cooperative Institutional Research Program, University of California, Los Angeles) Association of American Colleges: Curriculum Data Base (1991) (Sponsor: Association of American Colleges) Integrated Postsecondary Education Data Base (1991) (Sponsor: National Center for Education Statistics) Survey of 1989–90 College Graduates (1991) (Sponsor: National Center for Education Statistics) Survey of 1985–86 College Graduates (1987) (Sponsor: National Center for Education Statistics) Graduate Record Examination (1991) (Sponsor: Educational Testing Service) Longitudinal Survey of American Youth: Teacher Questionnaires (1987, 89, and 90) (Sponsor: National Science Foundation) Longitudinal Survey of American Youth: Student Questionnaire (1987, 88, 89, and 90) (Sponsor: National Science Foundation) Longitudinal Survey of American Youth: Principal Questionnaire (1988–89) (Sponsor: National Science Foundation)

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. TABLE 1 Supply of Teachers for Public Education: Dependent Variables and Data Sources Teacher Flow Variables Key to Data Sourcesa 1. Entering leachers hired into positions (inflow): Numbers of entering teachers disaggregated by   A. Recent college graduates hired, disaggregated by   1. Grade level 1 24 25   2. Major teaching field 1 24 25   3. Subject matter specialty 1 24 25   4. Total recent college graduates hired 1 24 25   B. Entering teachers hired from the reserve pool, disaggregated by   1. Grade level 1   2. Major teaching field 1   3. Subject matter specialty 1   4. Total leachers hired from the reserve pool 1   C. Total entering teachers hired into positions disaggregated by   1. Grade level 1 10 11   2. Major teaching field 1   3. Subject matter specialty 1 10 11   4. Total entering teachers hired 1 2 8 10 11 12 17 II. Retained teachers in public education classified by type of position shift from one year to the next (withinflow): Numbers of retained teachers disaggretated by   A. Stable retention/no transfer (same school and teaching assignment): Numbers of teachers disaggregated by   1. Grade level 1 5 8 10 11   2. Major teaching field 1 5   3. Subject matter specialty 1 5 10   4. Total teachers retained in same school and teaching assignment 1 5 8 10 11   B. Transfer retention within public education, disaggregated by type of transfer supply to different positions in public education: Numbers of teachers transferring to different positions disaggregated by   1. Different school (school migration), same primary assignment dissaggregated by   a. Grade level 1 5   b. Major teaching field 1 5   c. Subject matter specialty 1 5   d. Total migrating teachers 1 5   2. Same school, different primary assignment (reassignment) disaggretated by   a. Grade level 1 5   b. Major teaching field 1 5   c. Subject matter specially 1 5   d. Total teachers reassigned 1 5          

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Teacher Flow Variables Key to Data Sourcesa 3. Different school, different primary assignment disaggregated by   a. Grade level 1 5   b. Major teaching field 1 5   c. Subject matter specialty 1 5   d. Total teachers migrating and reassigned 1 5   C. School transfer supply within public education: Numbers of teachers transferring to a different school (school migration) whether or not changing primary teaching assignment (sum of categories II.B.1. and 3. above), disaggregated by   1. Grade level 1 5 8 10 11   2. Major teaching field 1 5   3. Subject matter specialty 1 5 10   4. Total migrating teachers 1 2 5 8 10 11   D. Total teachers retained in public education from one year to the next: Numbers of teachers retained disaggregated by   1. Grade level 1 5 8 10 11   2. Major teaching field 1 5   3. Subject matter specialty 1 5 10 12 13   4. Total teachers retained in public education 1 5 8 10 11 12 13 17   III. Total teachers employed in public education in any one year (sum of I.C. and II.D.): Numbers of teachers employed disaggregated by   A. Grade level 1 3   B. Major teaching field 1   C. Total teachers employed in public education 1 9 13   D. Total teachers employed in public education 1 2 3 8 9 13 17 27 29 IV. Attrition of teachers from public education from one year to the next (i.e., outflow): Numbers of exiting teachers disaggregated by   A. Grade level 6   B. Major teaching field 6   C. Subject matter specialty 6   D. Total exiting teachers 6   V. Reserve pool component: Former teachers unemployed since leaving teaching   A. Number of years out of work 16   B. Intention to look for work in next 12 months 16   aNumbers refer to preceding list of data source clusters.

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. TABLE 2 Supply of Teachers for Public Education: Independent Variables and Data Sources Predictor Variables Key to Data Sourcesa I. Year of     A. Most recent entry into teaching     1 14     B. Exit attrition (last year in teaching)     6 14   II. Employment Status     A. Type of position       1. Regular: Full time   1 8 10 11 15       2. Regular: Part time   1 8       3. Substitute   1 8 10 11 25       4. Itinerant   1     B. Percent of full time     1 2 5 14 24 25   III. Teacher characteristics: Demographic     A. Age     1 8 10 11 14 15 16         17 19 24 25     B. Gender     1 8 9 10 11 12 13         14 15 16 17 19 24 25   C. Race/ethnicity     1 2 8 10 11 12 13         14 15 16 17 19 24 25   D. Marital/family status     1 5 6 14 15 16 19         24 25     E. Number of dependent children     1 5 6 24 25     VI. Teacher characteristics: Qualifications     A. Degree(s) earned by       1. Subject matter         (1) Major 1 5 6 8 10 11 12         13 14 15 17 24 25 27       (2) Minor 1 5 6 8 11 12 14         15 25 27       2. Level   1 2 5 6 8 9 10         11 12 13 16 17 19 27     3. Year   1 5 6 14 15 19 24         25 27     B. Type of teaching certification held     1 3 5 6 8 10 11         12 13 15 17 19 24 25   C. Nonteaching experience     1 6 14     D. Prior teaching experience       1. Any prior teaching experience   1 24 25       2. Number of years taught         a. In public sector 1 15         b. In private sector 1 15         c. Both sectors combined 1 10 11 12 13 14 15         17 19 27       3. Last year taught   14 15       4. Grade level(s) taught   1 10 15       5. Major teaching field(s)   1 15       6. Subject matter specialty(s)   1     E. Teacher competence ratings     18     F. Inservice training     1 8 12 13 17 19 27   G. College GPA     24 25     H. Tested ability score(s)     —   V. Teaching practice variables       1 8 10 11 12 13 17         27  

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Predictor Variables Key to Data Sourcesa VI. Financial Variables     A. Teacher compensation (current year)       1. Annual base teaching salary   1 3 5 19 24 25 29     2. Benefits provided   3 17       3. Salary/bonus inducements   1 5 9 19       4. Supplemental earnings opportunities   1 5 19       5. Extended contract   18       6. Subsidized retraining   17 18     B. Total family income     1 5 6     C. Relative wages by occupation       1. Local   —       2. State   —       3. National   16     D. Former earned income     6 14 15     E. Income earned in year after leaving teaching     6     F. Local employment rates     —     G. District (LEA) financial variables     —     H. Retirement financial incentives     —   VII. School Variables     A. Urbanicity     2 5 9 18 19 24     B. Total enrollment     2 9 18     C. Enrollment trends     2     D. Distance from high school home to school of employment     25   VIII. School transfer variable (from prior year)     A. Different school, same district     1 5     B. Different school, different district within same state     1 5     C. Different school in contiguous slate     1 5     D. Different school in noncontiguous state     1 5   XI. Distance from residence to school of employment 19   X. Working conditions     A. Teaching load       1. Number of teaching contact hours per week   1 10 13 19 27       2. Average number of students per contact hour   1 10 13 19 27     B. Availability of teaching assistant(s)     2 13 19     C. Adequacy of       1. Instructional materials   1 11 12 13 27       2. Instructional equipment   1 5 12 19 27         3. Administrative support   1 5 8 27       4. Teacher authority over         a. Instruction and marking 1 5 8 10 11 12 13         27         b. Student conduct 1 5 8 10 11 13 27   D. School safety     1 5 8 9     E. Student variables       1. Academic performance   1 11 12 13 17 27       2. SES   2 9 18 29       3. Race/ethnicity   2 9 17 18 29     aNumbers refer to preceding list of data source clusters.

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. TABLE 3 Demand for Teachers in Public Education: Dependent Variables and Data Sources Teacher Demand Variables Key to Data Sourcesa 1. Funded positions for teachers disaggregated by status from prior year: Numbers of funded full-time (FTE) positions by     A. Continued funded FTE positions from prior year: Numbers of continued FTE positions     —     B. Newly created and funded FTE positions for current year (i.e., new positions): Numbers of new FTE positions     —     C. Total funded FTE positions for current year: Numbers of total FTE positions disaggregated by             1. Grade level   —       2. Subject matter specialty   —       3. Total funded FTE positions   3   II. Funded positions for teachers disaggregated by filled vs shortage positions: Numbers of funded full-time equivalent (FTE) positions by     A. Positions filled by employed FTE teachers in current year, disaggregated by             1. Grade level   —       2. Subject matter specialty   —       3. Total employed FTE teachers   3 7   B. Teacher shortage for current year as measured by       1. Retained, but open, FTE positions for current year, disaggregated by         a. Grade level —         b. Subject matter specialty —         c. Total open FTE positions 3       2. Unfunded (i.e., discontinued) FTE positions for current year due to inadequate supply of applicants, disaggregated by           a. Grade level —         b. Subject matter specialty —         c. Total discontinued FTE positions 3       3. Total teacher shortage for current year, as measured by initially funded, but unfilled, FTE positions, disaggregated by           a. Grade level —         b. Subject matter specialty —         c. Total unfilled FTE positions 3   III. Unfunded (i.e., discontinued) positions for teachers from prior year: Numbers of discontinued FTE positions —   IV. Teacher shortage for current year as measured by: Degree of difficulty in filling funded, but open, positions. disaggregated by       A. Grade level   —       B. Subject matter specialty   18       C. Overall difficulty in filling positions   4 18 aNumbers refer to preceding list of data source clusters.

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. TABLE 4 Demand for Teachers in Public Education: Independent Variables and Data Sources Predictor Variables Key to Data Sourcesa I. Teachers required (i.e., needed) variables   A. Student enrollment variables     1. Student/population ratio       —       2. Student K-12 school enrollment       2 3     3. Public/private split of student enrollment       1 7     4. Public school student enrollment: Numbers of students disaggregated by                 a. Instructional variables                 (1) Level   2 3 7       (2) Subject matter   —           (3) Course   —         b. Student variables                 (1) Age   3           (2) Race/ethnicity   2 7         (3) Special needs                 (a) Handicap 2 7           (b) Limited English proficiency 2           (4) Free lunch eligible   2 3 7 B. Curriculum requirements         1. Elementary school       —       2. High school graduation       3     C. Work load variables         1. Planned teacher-pupil ratio       —       2. Teaching load: Number of teaching contact hours per week       —     D. Total teachers required (i.e. needed)         —     II. Financial variables   A. Exogenous financial variables         —     B. District (LEA) endogenous financial variable         —     III. Employment obligations to teachers retained from prior year —     IV. School variables   A. Urbanicity         2 7   B. Student enrollment         2 7   C. Number of teachers         2 7   V. LEA Variables   A. Urbanicity         7     B. Student enrollment         3 7   C. Number of teachers         3 7   D. Number of schools         7     aNumbers refer to preceding list of data source clusters.

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Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. TABLE 5 Student Interest In and Preparation for Teaching Careers Student Variables Key to Data Sourcesa I. Student interest in teaching careers           A. Educational level of interested persons               1. Students in seventh grade 28           2. High school seniors 28           3. College freshmen 20           4. College upperclassmen 21           5. College graduates 26         B. Demographic characteristics of interested persons               1. Age 20 21 22 26 28   2. Gender 20 21 22 26 28   3. Race/ethnicity 21 22 26     C. Tested ability scores of students interested in enrolling in teacher preparation programs   21 22 26     D. Grade point averages               1. High school 28           2. College 26         II. Teacher education enrollments disaggregated by           A. Subject matter specialty   22         B. Degree level   —         C. Total teacher education enrollments   22         III. Teacher education enrollments as a proportion of total higher education enrollments at the baccalaureate level 22         IV. Number of teacher education graduates disaggregated by           A. Grade level   23         B. Major teaching field   23         C. Subject matter specialty   23         D. Degree level   23         E. Total teacher education graduates   23 24 25     V. Year of graduation from teacher education program 8 14 15 24 25 VI. Sources of college funding 24 25       aNumbers refer to preceding list of data source clusters.