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Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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V
DATA BASES

Part V addresses issues entailed in developing and maintaining data bases supportive of TSDQ modeling and research. Relevant data bases at the state, regional, and national levels are identified, reviewed, analyzed, and compared in terms of quality, comprehensiveness, timeliness, and inclusion of longitudinal information. Supplementary material describing relevant national data bases is contained in Appendix B, and the identification of variables relevant to TSDQ models and research contained in these national data bases is reported in Appendix C.

In addition, the paper by Murnane and the discussion by Grissmer illustrate the use of state data bases to investigate important TSDQ problems. The information contained in teacher data bases is also considered in relation to the information needs of policy makers concerned with teacher work force issues. Finally, the adequacy of existing data bases and the need for improved or additional data bases is examined.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×
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Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

State Data on Teacher Supply, Equity, and Qualifications

ROLF K. BLANK

State departments of education have been active in building and improving management information systems. An important part of most state systems is a data file of teachers and other professional educators in public elementary and secondary schools. States typically collect and report information on characteristics of teachers in response to a state law requiring such data. But state information systems are designed for a multitude of purposes, such as reporting data to federal agencies, monitoring the quality of teachers hired by local districts, and analyzing teacher supply and demand in the state.

The Council of Chief State School Officers (CCSSO), the national association of state superintendents and commissioners, has analyzed the types of data on teachers that are available from state education information systems. The CCSSO State Science/Mathematics Indicators Project has developed a system of indicators of the condition of science and mathematics education that are partly based on state-collected data, along with information about teacher characteristics (Blank, 1986; Blank and Dalkilic, 1990). The CCSSO Education Data Improvement Project has worked with states and the National Center for Education Statistics to standardize the definitions for state-collected teacher data and to improve the quality of state data (CCSSO, 1988). Information gathered from states through these two projects form the basis for this paper.

The characteristics of teachers that are included in state information systems vary widely, and the definitions used to collect these data also vary among states (Blank and Espenshade, 1988; CCSSO, 1988). The uses of state data on teachers for producing statistics on teacher supply and demand

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

also vary widely (National Research Council, 1987). As of 1987, 34 states had developed state reports on teacher shortages or supply and demand, while 26 of them indicated that such a report is provided annually (Blank and Espenshade, 1988). If state data are to be considered as a source for national analyses of the teaching force or for projections of teacher supply and demand, it is important to consider the availability, quality, and utility of state teacher data.

This paper addresses the following four questions concerning state data on elementary and secondary teachers in public schools:

  1. What is the breadth of teacher data are available from state information systems?

  2. What is the quality and timeliness of the state data?

  3. To what extent are state data on teachers linked through one or more data files so that analyses can be conducted?

  4. How can state data be used for national-level analyses?

BREADTH OF STATE DATA ON TEACHERS

CCSSO conducted a survey of all state departments of education in 1987 to determine the availability of state-collected data on a variety of possible indicators of science and mathematics education (Blank and Espenshade, 1988). The results were used to select a small set of indicators that were subsequently developed as state-by-state and national indicators (Blank and Dalkilic, 1990). In 1988 the CCSSO Education Data Improvement Project collected information from state departments of education on the definitions used to collect and report on characteristics of education staff (CCSSO, 1988). The information was used to consider revisions in procedures and data elements reported to the National Center for Education Statistics in the Common Core of Data.

Information on availability of state data from the two CCSSO projects was integrated to produce a summary of the number of states that collect data on elements of teacher supply, demand, and quality. The delineation of these three categories of teacher data and the identification of desired variables for measuring supply, demand, and quality followed the analysis and recommendations of two National Research Council reports (1987, 1990). The results of the NRC survey on ''availability of state data on public school professional personnel'' (1991) were used to check the results of the CCSSO surveys.

Table 1 lists the number of states that collect data on demographic characteristics of teachers, measures of teacher quality, and elements of teacher supply and demand. CCSSO surveys indicate that 49 states have an automated data file on the characteristics of teachers (and other professional

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

TABLE 1 State Data on Teachers: Number of States that Collect Data on Elements of Teacher Supply, Demand, and Qualifications as of Spring 1987 (N = 50 states and District of Columbia)

 

Number of States

Teacher Assignment by:

 

District

49

School

48

Grade level of assignment

43

Subject of current assignment

48

Demographic Characteristics of Teachers:

 

Date of birth (age)

40

Sex

47

Race/Ethnicity

44

Teacher Qualifications:

 

Education attainment (degree status)

47

Academic major (bachelor's degree)

40

Certification type

41

Subject/field of certification

40

Years of professional experience:

40

Years of teaching experience

25

Years in current district

36

Years in other district

23

Years in current assignment

13

Teacher Demand:

 

Pupil-teacher ratio

37

Pupil-teacher ratio by subject

22

Enrollment projections

36

Emergency/provisional certificates

41

Positions vacant or withdrawn, or filled with non-certified teacher or substitute

31

Teacher salary (contract or base)

47

Teacher Supply:

 

New college graduates in education

33

New graduates with non-education majors and certified to teach

20

In-migration of teachers from other states

27

Re-entrants into teaching

21

Entrants from other occupations

17

Continuing teachers/teacher attrition

32

Continuing teachers in new subject/field

27

Teachers retiring

37

New Hires:

Occupation prior year

10

Location of occupation prior year

7

 

SOURCE: State Science/Mathematics Indicators Project, 1987 (unpublished data). Council of Chief State School Officers, Washington, D.C.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

education personnel). All of the variables listed in Table 1 are generally not included on the same data file. There are three kinds of data files on teachers in most states: (a) current teacher file (including demographics, district and school, current assignments, education attainment, contract salary, and experience); (b) certification file (including type of certification and subjects or fields of certification); and (c) state retirement system file (including year of birth and year entering the system). Most states can link the files through the teacher social security number or other identifier.

The items listed in Table 1 under Teacher Demand and Teacher Supply may be from several data sources at the state level, including student membership counts by grade and higher education data, as well as from computations or analyses of data on the current teacher file, such as identifying the source of new hires. In 1987, 34 states reported having developed estimates of teacher shortages or supply and demand, but states were not asked to identify which of the data elements were used in these estimates. Thus, the list of elements by number of states in Table 1 shows the potential of state data for analyses of teacher supply, demand, and quality.

Over 40 states collect data on teachers' demographic characteristics, education attainment, and certification status. Cross-tabulations of data on combinations of these variables are difficult to accomplish in some of the states. For example, the Science/Math Indicators Project found that only 30 of the 40 states could report the number of teachers assigned in mathematics and science by their certification status in the subject(s) they were teaching.

QUALITY AND TIMELINESS OF TEACHER DATA

State departments of education collect data on characteristics of teachers for their current teacher file through several different data collection approaches. One approach is an annual survey form developed by the state for each individual teacher in the state. This form asks for new or updated demographic information, current teaching assignments and student enrollments by school period, and information on education attainment and salary. This type of form is used by about 20 states, including California, New York, Connecticut, Ohio, Minnesota, South Carolina, Alabama, and Virginia.

A second approach is a district or school form that lists teachers on the current state file and asks the district or school to update the existing data or provide new data on teacher demographics, subject and grade assignments, and education attainment and salary. Over half the states use this method of collecting data on teachers.

A third approach is a student-based computerized information system in which information about teacher assignments and teacher characteristics is linked to a statewide data base on each student. Student records and schedules and teacher data are relayed on computer files from the school to the

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

district to the state. This system is in place in Florida and Hawaii and is being developed in Georgia, Texas, North Carolina, and other states.

Certification information is collected and maintained in a separate certification file in most states. A majority of states have the current teacher file and certification file linked by social security number and annually check the certification status of each teacher. Some states have only recently computerized their certification file and some states do not cross-check assignments and certification.

There are a variety of issues in assessing the quality of state data on teachers. One issue is the response rate, i.e., the proportion of teachers in classrooms for which data are reported to the state. The Science/Math Indicators Project asked states to report on response rates and data editing procedures. About one-third of the states collect data directly from teachers, and response rates for those states were from 98 to 100 percent. However, both these states and states that collect data through schools and districts must depend on lists of current teachers and new teachers that are provided by schools or school districts.

A second issue is the reliability of the data that are reported by teachers and stored on state files. States reporting data on science and mathematics indicators were asked to report on data editing procedures in 1989–90. The large majority of states use computer edits, logic checks, and external validity comparisons. However, there are states that use few editing procedures and the quality of the data could be questioned.

Another issue is the comparability of teacher data between states due to differing definitions of teacher characteristics. The Education Data Improvement Project analyzed state definitions for 22 data elements concerning professional staff and found some variation for all the data elements. For example, variation in the definition of teacher age was very slight. The definitions and categories of types of teaching certificates are different in almost every state, although all but three states' definitions could be placed in three categories: regular/standard, probationary, and temporary/provisional/emergency. CCSSO worked with groups of state representatives to develop consensus definitions for data elements recommended to the Common Core of Data. Then, differences in definitions were reported to states so that data collection could be standardized, or, in some cases, a crosswalk could be designed for analyzing a state's data in relation to the consensus definition.

Another criterion of completeness of teacher data is the response rate for each requested data element. The 1989–90 data reported on science and mathematics indicators also provides information on this question. A total of 34 states reported on the age of teachers assigned in high school science and mathematics. Among these states, I percent of data on teacher age was missing. One state had 8 percent missing data on teacher age. A total of 32

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

states reported data on the race/ethnicity of science and mathematics teachers, and a total of I percent of these data were missing. One state (Hawaii) had 5 percent missing data on race/ethnicity.

All of the states with teacher files collect or update the data at least annually. Most states ask that data be reported early in the fall of each school year. The National Center for Education Statistics requests that Common Core of Data on education staff be reported as of October 1. States enter, edit, and clean the data between October and March. Some states do not have the data ready until June. For the CCSSO Science/Mathematics Indicators Project, 43 states reported data on teacher characteristics for the preceding school year by the end of July.

LINKED TEACHER DATA FROM STATES

Table 1 reports the number of states that collect and file data on elements of teacher quality, demand, and supply. An important question for any researcher or policy analyst is whether these data can be linked together for purposes of analysis. For many analyses, the ideal situation is to analyze teacher characteristics at the teacher level, such as teacher assignment by teacher age. Some analyses could be with data aggregated at the school, district, or state level. For example, in analyzing teacher demand and supply, it is useful to compare the number of new hires in a state who are first-time teachers with the previous year's number of college graduates who were certified to teach. If possible, however, it is most desirable to have data on teachers that can be analyzed at the teacher level.

To gain some perspective on the availability of state data on teachers that are linked at the teacher level, Table 2 shows a state-by-state listing of teacher characteristics and the extent to which the data are linked. The first column indicates that 44 states have data on teacher assignments by field or subject (e.g., subject=science, field=biology). Of these states, 36 states can analyze assignment by age, 40 states assignment by sex, and 33 states assignment by race/ethnicity. The second column shows that 30 states can determine the number of teachers assigned to a subject/field that are certified in the subject/field. The third column indicates that 35 states have data on student enrollments in secondary-level courses and 19 states (with links between teacher and student data) can analyze the proportion of students in a course taught by a teacher certified in their assigned field.

EXAMPLE OF USES OF STATE DATA FOR NATIONAL ANALYSES

The CCSSO Science/Mathematics Indicators Project gave high priority to developing three types of indicators of teacher work force: (a) supply,

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

TABLE 2 State Data Linked to Individual Teachers (Fall 1989)

State

Teacher Assignment by Field/Subject by Age (A). Sex (S), Race/Ethnicity (R)

Teacher Assignment by Certification Field

Secondary Course Enrollment

Alabama

Field by A,S,R

Yes

Yes, link

Alaska

No

No

No

Arizona

Subject by S,R

No

No

Arkansas

Field by A,S,R

Yes

Yes, link

California

Field by A,S,R

Yes

Yes, link

Colorado

Subject by A,S,R

Yes

No

Connecticut

Field by A,S,R

Yes

Yes, link

Delaware

Field by A,S,R

Yes

Yes

District of Columbia

No

No

Yes

Florida

Field

No

Yes, link

Georgia

No

No

No

Hawaii

Field by A,S,R

No

Yes, link

Idaho

Field by A,S,R

Yes

Yes

Illinois

Field by A,S,R

Yes

Yes, 5 yrs.

Indiana

Field by A,S,R

No

Yes

Iowa

Field by A,S,R

No

Yes, link

Kansas

Field by A,S,R

No

Yes

Kentucky

Field by A,S,R

Yes

Yes, link

Louisiana

Field

No

Yes

Maine

Field by A,S,R

No

No

Maryland

Subject by A,S,R

Yes

No

Massachusetts

Field

No

No

Michigan

Field by A,S,R

No

No

Minnesota

Field by A,S

Yes

Yes, link

Mississippi

Field by A,S,R

Yes

Yes, link

Missouri

Field by A,S,R

Yes

Yes, link

Montana

Field by A,S,R

Yes

Yes

Nebraska

No

No

Yes

Nevada

Field by A,S,R

Yes

Yes, link

New Hampshire

Field by S

No

No

New Jersey

Field by A,S,R

Yes

No

New Mexico

Field by A,S,R

Yes

Yes

New York

Field by A,S

Yes

Yes, link

North Carolina

Field by A,S,R

Yes

Yes, link

North Dakota

Field by A,S,R

Yes

Yes, link

Ohio

Field by A,S,R

Yes

Yes, link

Oklahoma

Field by A,S,R

Yes

Yes

Oregon

Field by A,S

Yes

No

Pennsylvania

Field by A,S,R

Yes

Yes

Rhode Island

Field by A,S,R

Yes

No

South Carolina

Field by A,S,R

Yes

Yes, link

South Dakota

Field by A,S

Yes

No

Tennessee

Field by A,S

Yes

Yes

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

State

Teacher Assignment by Field/Subject by Age (A), Sex (S), Race/Ethnicity (R)

Teacher Assignment by Certification Field

Secondary Course Enrollment

Texas

Field by S,R

No

Yes, link

Utah

Field by A,S,R

Yes

No

Vermont

No

No

No

Virginia

Field by A,S,R

Yes

Yes, link

Washington

No

No

No

West Virginia

No

No

No

Wisconsin

Field by A,S,R

No

Yes, 3 yrs.

Wyoming

Field

Yes

Yes

Total

Field/subject = 44

Yes = 30

Yes = 35

 

Age = 36

 

 

 

Sex = 40

 

 

 

Race = 33

 

 

 

SOURCE: State Science/Mathematics Indicators Project, 1990 (unpublished data), Council of Chief State School Officers, Washington, D.C.

(b) equity, and (c) qualifications. Another priority area for state indicators of science and mathematics is school conditions that affect teaching and learning.

The CCSSO plan for state-by-state indicators of science and mathematics is based on cross-sectional data that can be compared by state and tracked over time. Some desirable indicators of teacher quality that require more complex data or qualitative measurement were not selected, such as state-by-state projections of teacher supply and demand and quality of instruction in the classroom. Other possible indicators of teacher quality, such as degree level and years of experience, were not selected because there is less evidence of a relationship to outcomes in science and mathematics.

States reported data on teachers using a common reporting system designed by the Science/Mathematics Indicators Project. CCSSO also conducted state-by-state analyses of the Schools and Staffing Survey of NCES. Some of the indicators are summarized in the following sections. A full report, entitled, State Indicators of Science and Mathematics Education: 1990 is available (Blank and Dalkilic).

Indicators of Current Teacher Supply

States reported data on the total number of teachers assigned to teach science, math, and computer science in grades 9–12 as of October 1, 1989.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

The state teacher numbers are universe counts based on data collected through state information systems.

Proportion of Teachers with Primary and Secondary Assignments

The CCSSO state data reporting plan requested the number of teachers with primary and secondary assignments in each of eight subjects. The operational definition of primary assignment is a teacher assigned to one subject for 50 percent or more of teaching periods; and secondary assignment is a teacher assigned to one subject less than 50 percent of teaching periods. The state data show that 89 percent of high school teachers of mathematics have their primary assignment in mathematics. Only slightly over half (53 percent) of all teachers of chemistry have their primary assignment in chemistry, and three-fourths of all teachers of physics have their primary assignment in another field (24 percent in physics). In many schools, physics is taught by a teacher with primary assignment in chemistry or earth science.

States vary in the proportion of teachers with primary assignments in science and math. For example, teachers of mathematics in Connecticut (95 percent) and Illinois (96 percent) are almost all teaching mathematics as their primary assignment, while California (68 percent primary assignment) and Utah (69 percent primary assignment) have about one-third of teachers of mathematics who have their primary assignment in another subject. Higher numbers of teachers with secondary assignments are probably due to population growth (such as in California) as well as increases in state course requirements. States with more small, rural districts, such as Arkansas, Oklahoma, and North Dakota had fewer teachers with primary assignments in any of the science fields, while states with a greater proportion of urban and suburban districts, such as Connecticut, New York, and Pennsylvania, have more teachers with primary assignments in the science fields.

Age of Science and Mathematics Teachers

Although the state science and mathematics indicators do not include detailed projections of teacher supply and demand, the age distributions of current science and mathematics teachers provide useful information on possible shortage fields as teachers near retirement age. The average percentage of teachers over age 50 is 20 percent in mathematics, 20 percent in biology, 23 percent in chemistry, and 23 percent in physics. The average percent of teachers under age 30 is 13 percent in mathematics, 12 percent in biology, 12 percent in chemistry, and 11 percent in physics.

The age distributions of mathematics and science teachers vary widely by state in all fields. The percentage of mathematics teachers over age 50

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

varies from 29 percent in Minnesota to 10 percent in Kentucky, compared with 10 percent under 30 in Minnesota and 19 percent under 30 in Kentucky. In chemistry, the percentage over 50 varies from 45 percent in Minnesota to 10 percent in Nevada, compared with 9 percent under 30 in Minnesota and 13 percent under 30 in Nevada.

The age distribution for mathematics and science teachers can be compared with the age statistics for all high school teachers as reported to the project from state data systems. A total of 21 percent of all high school teachers are over 50, and 10 percent are under 30. Only the fields of chemistry and physics have higher percentages of teachers over 50 than the average for high school teachers. There are slightly higher percentages of teachers under 30 in mathematics and science than the average for high school teachers.

One way of analyzing teacher age statistics by state is to note that states that have had flat or declining populations, particularly northeastern and midwestern states, have higher proportions of older science and mathematics teachers (e.g., Connecticut, Delaware, Illinois, Iowa, Minnesota, New York, Rhode Island, and Wisconsin). Many of the teachers over 50 in these states were hired in the 1960s, when school enrollments were increasing. These states may experience a shortage of such teachers in a few years as this group of teachers reaches retirement age.

Indicators of Equity in the Teaching Force

States reported data on two indicators of equity among current teachers in science and mathematics: sex and race/ethnicity. The distribution of science and mathematics teachers by sex and race/ethnicity provides a basis for states and the nation to compare the characteristics of the current teaching force with goals of improving the match between students and teachers in terms of sex and race/ethnic characteristics.

Gender of Science and Mathematics Teachers

The average percentage of female teachers by subject is 45 percent in mathematics, 37 percent in biology, 34 percent in chemistry, and 22 percent in physics. By comparison, 50 percent of all high school teachers are female, and 50 percent are male, as reported to the project from state data systems.

State-by-state statistics on the gender of mathematics and science teachers show that the distributions vary widely. In mathematics, the percent of female teachers varies from 21 percent in Minnesota to 66 percent in Virginia. (The data on all high school teachers shows 41 percent female in Minnesota and 62 percent female in Virginia.) Region is associated with

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

the sex distribution of science and mathematics teachers (and high school teachers in general). Thirteen states have more female than male mathematics teachers, and eight of these states are in the Southeast. In biology, the percentage of female teachers varies from 15 percent in Iowa to 63 percent in Alabama. Four states have more female than male biology teachers (Alabama, Mississippi, South Carolina, Virginia). Chemistry and physics have fewer female teachers in most states, but five states have more female than male chemistry teachers (all but Hawaii in the Southeast). No state has a majority of physics teachers that are female.

Race/Ethnicity of Science and Mathematics Teachers

The second indicator of equity in the science and mathematics teaching force is the race/ethnicity of current teachers. Nationally, 30 percent of elementary and secondary students are minorities, and 70 percent are non-Hispanic whites (NCES, 1989).

As of the 1989–90 school year, state data on the race/ethnicity of science and mathematics teachers (grades 9–12) show the following percentages of minority teachers by subject: 11 percent in mathematics, 10 percent in biology, 7 percent in chemistry, and 5 percent in physics. By comparison, the statistics for all high school teachers show 11 percent minority and 89 percent white.

The proportion of minority science and mathematics teachers in each state can be compared with the proportion of minority students. Among the 32 states that reported teacher race/ethnicity by field and student race/ethnicity, only 11 states had over 10 percent minority teachers in any of the 3 fields. Of the 19 states with more than 20 percent minority students, only 5 states have even half as many minority teachers in mathematics, biology, or chemistry as the proportion of minority students (Virginia, Alabama, South Carolina, Mississippi, Hawaii). The states with the highest proportions of minority teachers (among science and mathematics as well as all high school teachers) are in the southeastern states and Hawaii. There is relatively little variation among mathematics, biology, and chemistry in the percentage of minority teachers, although chemistry has slightly fewer minorities in most states. The state data show that, except for Hawaii, no state has representation of minority teachers similar to the racial/ethnic background of students.

Indicators of Teacher Qualifications in Subject Area

A state-by-state indicator of teacher qualifications is the proportion of science and mathematics teachers who are not state certified in their field, i.e., teaching "out of field." State-collected data on teacher assignments by certification status as of October 1, 1989, were reported to CCSSO.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

The proportion of teachers who are teaching out of field is a useful policy indicator because it is a quantifiable measure of the proportion of teachers in a district or state that do not meet basic qualifications (Shavelson et al., 1989). Certification has often been used as a working definition of qualified in the analysis of current teacher shortages in science, math, and other subjects (Darling-Hammond and Hudson, 1989; Oakes, 1990).

A major advantage of using state data on teacher assignments and certification is that the data can be computed from state administrative records and computerized data files, thereby alleviating the need for special surveys of teachers that require teachers' self-report of certification status. Since certification standards for each teaching field differ by state, it is important to report state-by-state information on state certification standards (see Blank and Dalkilic, 1990).

Thirty states reported results of cross-tabulating state data on teacher assignments by teacher certification status. The percentages of teachers assigned in high school mathematics and science that are not state certified are as follows: 9 percent of mathematics teachers, 8 percent of biology teachers, 8 percent of chemistry teachers, and 12 percent of physics teachers. These total percentages are based on 30 states (including four large states—California, New York, Illinois, and Pennsylvania) but do not include Florida and Texas (which are expected to report the data in the next reporting cycle).

In mathematics, the percentage of teachers not state certified varies by state from 52 percent in South Dakota and 31 percent in Colorado to 0 percent in Connecticut and North Dakota. In biology, the percentage out of field varies from 34 percent in Arkansas to 0 percent in several states, with the median state at 3 percent out of field. States with more than 15 percent of teachers out of field in chemistry and physics are Arkansas, California, Illinois, Mississippi, and South Dakota. Alabama. Delaware, and New York have more than 15 percent out of field in physics. The data show that some of the states with substantial numbers of science and mathematics teachers out of field have many small, rural districts (and thus many small high schools), such as South Dakota, Illinois, and Mississippi. States experiencing population growth such as California have high demand for teachers, and have more teachers out of field.

According to data from the Schools and Staffing Survey for 1987–88, less than 5 percent of teachers with primary assignments in science and math are assigned out of field (Bobbitt and McMillen, 1990). The state-by-state data from the CCSSO Project on certification status by teachers with primary and secondary assignments reveals that, in many states, a large proportion of chemistry and physics teaching is done by teachers with a secondary assignment in these fields (total for 28 states: chemistry—40 percent secondary assignment, physics—61 percent secondary assignment;

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

Blank and Dalkilic, 1990). Teachers with secondary assignments are less likely to be certified in the secondary field, particularly teachers of chemistry and physics.

Two-thirds of the states certify science teachers through ''broad-field'' certification as well as in specific fields of biology, chemistry, physics, etc. States reported teaching assignments by certification according to broad-field versus specific-field certification. Individual states may be able to increase the number of certified science teachers in more classrooms with a broad-field policy. However, as a group, states with broad-field science certification do not currently have lower percentages of science teachers out of field than states with only specific-field science certification (Blank, 1990).

School Conditions—Number of Teachers and Schools per State

National surveys have analyzed the proportion of schools that offer advanced science and mathematics courses (Weiss, 1987; Neuchatz and Covalt, 1988; Oakes, 1990). Neuchatz and Covalt found that 83 percent of high schools in the nation offer physics, and these schools include 96 percent of students. However, only 66 percent of schools offer physics each year.

The number of science and mathematics teachers in each teaching field can be compared with the number of high schools in a state to approximate the proportion of schools that are able to offer science and mathematics courses in each field. Accordingly, a state-level indicator of course coverage in science and mathematics is the ratio of high schools in a state to the number of teachers assigned in each teaching field.

This school/teacher ratio is particularly applicable to analyzing school conditions in each state for teaching chemistry and physics. In many states the number of teachers is close to the number of schools, and in states that have fewer teachers than schools it is likely that some schools are not offering chemistry or physics. However, one caveat in using the school/teacher ratio to identify shortages of teachers in a state is that chemistry and physics teachers may be shared among several schools, and this cooperative arrangement is not accounted for in the school/teacher ratio. Also, this ratio may understate the problem of shortages in states that have large high schools with more than one physics or chemistry teacher and small schools with none (the state average would show each school having a teacher).

The school/teacher ratios in chemistry and physics reveal that:

  • 11 of 41 reporting states have more high schools than chemistry teachers,

  • 28 of 41 reporting states have more high schools than physics teachers, and

  • the number of high schools in Illinois, Iowa, Michigan, Mississippi, New Hampshire, Oklahoma, and Utah is more than twice the number of physics teachers.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

Several of the states with more high schools than physics teachers reported few or no teachers teaching out of field, such as Idaho, Nevada, North Dakota, Ohio, and Utah. In these states, a state policy may prevent assignment of noncertified teachers to shortage fields, or school districts do not offer a course if there is not a certified teacher.

SUMMARY

This paper has summarized the breadth of available state data on teacher supply, equity, and qualifications. A high proportion of states collect and store data on many of the desired data elements. The quality and timeliness of state data are good in many states, while other states have fewer quality controls and edits. State teacher data are typically stored on several different files, which makes analyses more problematic. State-by-state comparisons depend upon development of a consensus definition used by states or development of a cross-walk for comparing data among states. The example of analyses with state data on science and mathematics teachers shows that some states cannot report cross-tabulated data even though the data are collected. The science/mathematics indicators illustrate how state data can be used to assess conditions of teacher supply, equity, and qualifications among the states and in the nation.

REFERENCES

Blank, Rolf K. 1986 Science and Mathematics Indicators: Conceptual Framework for a State-Based Network. Washington, D.C.: CCSSO, State Education Assessment Center.

1990 Preliminary Report on State Indicators of Science and Mathematics Education: Course Enrollments and Teachers. Washington, D.C.: CCSSO, State Education Assessment Center.

Blank, Rolf K., and M. Dalkilic 1990 State Indicators of Science and Mathematics Education: 1990. Washington, D.C.: CCSSO, State Education Assessment Center.

Blank, Rolf K., and P. Espenshade 1988 Survey of States on Availability of Data on Science and Mathematics Education. Washington, D.C.: CCSSO, State Education Assessment Center.

Bobbitt, Sharon A., and Marilyn M. McMillen 1990 Teacher Training, Certification, and Assignment: A Presentation to the American Educational Research Association. Washington, D.C.: National Center for Education Statistics, U.S. Department of Education.


Council of Chief State School Officers 1988 Results of the Shuttle to Verify Staffing Data Elements. Washington, D.C.: CCSSO, State Education Assessment Center.


Darling-Hammond, Linda, and Lisa Hudson 1989 Teachers and Teaching Indicators for Monitoring Mathematics and Science Education: A Sourcebook. Santa Monica, California: The RAND Corporation.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

Gilford, Dorothy M., and Ellen Tenenbaum, eds. 1990 Precollege Science and Mathematics Teachers: Monitoring Supply, Demand, and Quality. Panel on Statistics on Supply and Demand for Precollege Science and Mathematics Teachers, Committee on National Statistics. Washington, D.C.:National Academy Press.


National Center for Education Statistics 1989 Digest of Education Statistics. Washington, D.C.: U.S. Department of Education.

National Research Council, Committee on National Statistics 1987 Toward Understanding Teacher Supply and Demand: Priorities for Research and Development. Interim Report. Panel on Statistics on Supply and Demand of Precollege Science and Mathematics Teachers. Washington, D.C.:National Academy Press.

Neuschatz, M., and M. Covalt 1988 1986–87 Nationwide Survey of Secondary School Teachers of Physics . New York: American Institute of Physics.


Oakes, J. 1990 Multiplying Inequalities: The Effects of Race, Social Class, and Tracking on Opportunities to Learn Mathematics and Science. Santa Monica, California: The RAND Corporation.


Shavelson, Richard, L. McDonnell, and J. Oakes., eds. 1989 Indicators for Monitoring Mathematics and Science Education: A Sourcebook. Santa Monica, California: The RAND Corporation.


Weiss, Iris R. 1987 Report of the 1985–86 National Survey of Science and Mathematics Education. Research Triangle Park, North Carolina: Research Triangle Institute.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

Developing a Regional Data Base on Educators in the Northeast: Problems, Products, and Prospects

JAMES M. WILSON III AND DAVID QUINBY

INTRODUCTION

In 1988, seven states in the Northeast agreed to explore the benefits of configuring their region as a common market for educators.1 In response to this possibility, the project reported here was initiated, in part, to collect data on educators in the region, to compile statistics on their migration within the region, to project educator supply and demand, and to develop policy simulation software.

The Massachusetts Institute for Social and Economic Research (MISER), having completed the study on educator supply and demand in Massachusetts in 1987, was selected to undertake work toward these objectives. Success of the project hinged on its endorsement by the education commissioners of the seven states in the region. Through this endorsement, the commissioners provided much-needed access to the sundry organizations and agencies that could provide relevant data.

Across the 7 states, more than 25 agencies and organizations were asked to provide data. They included certification bureaus, management information divisions of departments of education, departments of public health, teacher retirement boards, and private service bureaus. This information for the region was gathered on over 140 magnetic tapes. In many ways there was an archeological aspect to this task. Documentation for many of these files was neglected or lost, and key people in the development of data systems were retired or transferred. Considerable tenacity and cooperation were required to recover a data base going back 20 years. In all cases, agency staff were extremely cooperative in supplying information, despite their considerable burden of day-to-day operations.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

Our initial objective in the data collection process was to assemble event history records2 for educators located in each state. By educator we mean classroom teacher, instructional support staff (e.g., librarian), and school administrator. Close to 1 million individual records were so constructed. States vary in whom they track but, in general, the work force cohort consists of full-time elementary and secondary public school personnel. We compiled an event history of the work force from annual staff censuses and retirement system ledgers. And we extracted a complementary history of the reserve pool (i.e., those available to work) from certification data bases.

Figure 1 provides an example of an event history file, showing how the certification and staff files for the state of Connecticut were integrated to create a master record for each educator. Appendix A is an inventory of data collected over the region, and Appendix B indicates the range of years available in state archives. As can be seen, some states have detailed personnel records going back to 1968; others have records of considerably shorter duration. We admitted all available years rather than start at (or censor before) a common year.

DATA COLLECTION AND DATA PROCESSING

State administrative records are primarily used for specific accounting purposes. In many states, the educator data are rarely, if ever, examined in a longitudinal fashion. To estimate variables in our models, we had to transform the data into event history form (see Figure 1). To do this, we merged information over years and over data bases. This cross-year and cross-data-base merging created redundancy in the data, which provided opportunities to observe its reliability. Numerous correctable errors were discovered in this process, such as inconsistent data on gender, race, date of birth, and social security number.

We observed that data quality is often diminished by data collection and maintenance procedures. For example, some states record only the initial date of certification, and dates of subsequent certification or renewal are never entered. Therefore, information career development over time is lost. Other states record only the latest effective date of certificate renewal, which obliterates the original issue date, eliminating the possibility of modeling the time-dependent process from initial certification to entry into the teaching work force. The process of periodically purging data from files also degrades quality. Certification files seem particularly vulnerable to purging as individuals with expired credentials, those retired, and the deceased are often removed without archiving. This is simply lost history—of little administrative use but important for research, which may affect policy. An important matter for states to consider is the manifold uses for and potential

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

Figure 1 Connecticut Data Files.

of administrative records. Therefore, a broader perspective should guide data collection and maintenance procedures at the state level.

Assembling data from numerous states and agencies requires considerable documentation. Too often documentation was inadequate, particularly for items such as teaching assignment and certification endorsement codes, which change over time. When old code books were lost, codes were redefined on a "best guess" basis. In some cases, this was impossible because all personnel familiar with the changes had left state employment. A similar difficulty occurred when data bases migrated from one computer

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

system to another, which inevitably altered the data structure. Old data was archived as is, and it fell on us to link and align old to new data. Such changes were often the occasion for states to abandon some data items and add others, creating discontinuities in the data. There were also exceptional problems. For example, one state in the region simply lost a year of data in the time series it provided.

Diversity of data collection among the states creates many deficiencies to reconcile and align. Some states had 22 years of data; others had but 10 years. Six states could identify what their teachers were teaching; one could not. Although one state could identify the quality of students in each class and whether the classroom had a video cassette recorder or computer, the other states had no record of what courses students were taking. The diversity of data collected throughout the region is reported in Appendices A and B.

There were also considerable differences among the states as to the number and type of certification and teaching assignment categories. To solve this problem, a regional certification working group established a classification framework, which was used to aggregate these categories. The final categories agreed on can be seen in Appendix C.

Descriptive statistics revealed additional problems with the data. For example, one state had significant underreporting on its staff census in a particular year. This surfaced as a low retention rate that year and a high reentry rate the next. As is customary, we resorted to imputation and collected additional data to validate results.

Data problems also emerged when we attempted enrollment projections. The cohort-survival ratios were quite small for certain grades. We therefore examined all the district grade data for potential problems. We discovered one district that put the total of its fourth, fifth, and sixth graders into fourth grade. After fixing this, the cohort-survival ratios fell into line and the accuracy of the enrollment projections improved.

Another concern in using administrative records for policy research on this scale is the timeliness of reports. Given available resources, we were constrained to assemble event history files one state at a time. When all were complete, some files were two years old. An important next step in improving timeliness is to standardize both the updating of the data files, as well as the production of statistics, model estimation, and software calibration. We believe it will take at least two production cycles before the turnaround time between data acquisition and data products becomes reasonably short.

A final issue is that the statistics generated from administrative records reveal the influence of policy changes. Thus, it is critical to recover the policy history of a state in order to understand patterns in the data. Changes in certification requirements, certificate types, job classification, fiscal en-

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

vironment, and salary enhancements must be well understood if statistics are to be correctly interpreted. For example, in one state we observed the retention rate of educators ages 20 to 24 drop from 84 percent in one year to 58 percent the next. An initial reaction to this might be that there was a problem with the data. However, this startling change in retention was the result of capping community property taxes leading to massive reductions in force throughout the state's public education system.

UTILITY OF ADMINISTRATIVE DATA FOR EDUCATOR SUPPLY, DEMAND, AND QUALITY MODELS

A regional data base offers numerous opportunities to answer questions about educator work force dynamics. In certain respects, a regional data base is superior to national data bases in that it can provide detail at the district and, in many cases, at the school level. Furthermore, the long time series on individual educators allows actual measurement of trends over time, such as rates of certification-to-entrance, retention, attrition, reentry, reassignment, and interdistrict and interstate transfer. It also provides an opportunity to study such trends by specialization, a type of analysis particularly useful in areas such as special education, which has great diversity among its several specializations.

Existing data are limited in that they provide little indication of educator quality. Data are generally available about experience and educational attainment. Yet these are dubious indices of quality that require ad hoc scaling to fit a model. Another indication of quality is whether or not a person is working within her or his field of certification and, if so, what type of certificate they hold. Teacher test scores (such as Scholastic Aptitude Test and core battery scores) would be very useful, as would college grade point average (GPA).3

Lack of data also restricts the modeling of demand. There are virtually no data on curriculum, class size, and student body composition.4 To project enrollment by course, past participation rates by grade are required. Since the states estimate these rates, their reliability is obviously limited by the quality of the estimates.

Another weakness in this regional data base is that, currently, no data are collected on the number of graduates from programs leading to certification. A national data base collected by NCES, the Integrated Postsecondary Education Data System (IPEDS), does tally the number of graduates by postsecondary institution. In an earlier study on the Commonwealth of Massachusetts (Coelen and Wilson, 1987), a census of all programs leading to certification was undertaken. This microdata allowed the tracking of individuals into the education work force throughout the state. Five years of data provided enough information to identify trends over time in this

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

yield. To undertake such a project in the Northeast region would have required a census of the approximately 110 postsecondary programs leading to certification. Although this data component is important for numerous policy reasons, it was not incorporated into the MISER regional data base due to cost considerations.

Although many elements of educator supply and demand can be modeled with existing administrative records, some basic questions cannot be answered. From administrative records there are ample data to estimate retention and attrition rates. We do know who was hired and when. We do have the two primary components of the reserve pool: individuals certified but never employed in education and experienced educators who left the system and may return. We know the entry rates from these groups, but we cannot easily tell if the number hired are under conditions of supply constraints or demand constraints. This is because we do not know the number of applicants denied due to demand constraints (not enough jobs) nor the number of positions unfilled due to supply constraints (no suitable applicants). Again, all we can observe from administrative records is hiring. Strong assumptions have to be made in modeling to identify available teacher supply.

Given these limitations, it is important to consider how a major national data base, the School and Staffing Survey (SASS) collected by NCES, can address some of these problems. SASS does provide data (a) on whether positions have or have not been filled, (b) on positions filled by individuals with emergency certificates, and (c) on positions withdrawn. Unfortunately, such constraints vary over districts and may occur for numerous reasons. We need to explore the representativeness of SASS data for estimating general supply and demand conditions when aggregated at the state level.

It may be problematic that factors influencing supply and demand are closely tied to geography and attributes of districts (urban, rural, socioeconomic status). For example, SASS provides information that may indicate, generally, what kind of districts suffered shortages in particular years of the survey, but it will not illuminate the range of supply and demand conditions within a state.

Furthermore, we do not know the reason for shortage if it occurs; that is, why the reserve pool is unresponsive to increased demand. To understand this necessitates an understanding of how the reserve pool is distributed geographically, and the competing job opportunities or life opportunities in local markets. SASS can begin to tell us about the propensity of individuals to reenter teaching with its Teacher Follow-Up Survey, but not the propensity for initial entry. The best work to date on this aspect of the reserve pool is Manski's analysis using the National Longitudinal Survey 1972 (Manski, 1987). This work sheds light on the causes of initial entry, but a complementary data base describing the size and composition of the

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

reserve pool is needed before projections can be made. Here again, the linking of administrative records and national survey data may be fruitful.

In examining administrative records, one can observe the dynamics of education at the district, state, and regional levels. However, we need district-level data that go beyond salary differentials and urban/rural classification. Other data that could be linked are the decennial census data that identify, in a general way, the socioeconomic status of a district. Although not encountered in the Northeast, problems may arise in other states, where school districts are not coterminous with municipality boundaries. This creates difficult problems in allocating census5 and other data such as births (used in enrollment projections) to school districts.

Interstate migration statistics are an important result of the Northeast regional study. While the magnitude of migration between states is known, the causes are as yet undetermined. For example, educator migration may be primarily driven by market forces acting on the educator's spouse. Furthermore, we should consider the change in real rather than nominal wages and factor in changes in retirement benefits and other incentives. The former are not available at the substate level and the latter are often difficult to quantify.

PROSPECTS FOR THE NORTHEAST REGIONAL DATA BASE

The northeastern states are tentatively committed to nurturing their nascent regional data base. With seed money spent and groundwork done, the cost of updates will be relatively low. With standardization, systematization, and software enhancement, the cost to benefit ratio will drop steadily.

A side issue is the improvement of the data collection by participating states. This ranges from adding some items, to improved archiving, to undertaking a census for the first time. This process of continuous improvement is a particularly difficult to sustain given the vicissitudes of the fiscal status of states.

In the course of the study, it was interesting to observe that education managers were quite uncertain about what data mattered and what did not. This arises, perhaps, from the culture of education, which has been largely bereft of accountability in a traditional management sense, and from the formidable problems in measuring the quality of education. If we are moving to a new period of accountability, then we are also entering a period of discovery regarding what information matters in educational management information systems (MIS). The regional common market forum exposed relative strengths and weaknesses in each state's MIS operation. The next steps are to identify useful data products, improve data quality, standardize data and reporting, and seek synergies in how administrative data are collected and organized.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

REFERENCES

Coelen, Stephen, and James Wilson 1987 Report on the Status of Teacher Supply and Demand in Massachusetts . Amherst: Massachusetts Institute for Social and Economic Research, University of Massachusetts.


Manski, Charles F. 1987 Academic ability, earnings, and the decision to become a teacher: Evidence from the National Longitudinal Study of the High School Class of 1972. In David A. Wise, ed., Public Sector Payrolls. Chicago: University of Chicago Press.

NOTES

1.  

The project was funded by a number of organizations: the National Science Foundation, the National Center for Educational Statistics, the Council of Chief State School Officers, and the Regional Laboratory for Educational Improvement of the Northeast and Islands. The regional laboratory also provided support for the numerous committees that managed the Northeast Common Market Study. The Bank of New England generously provided access to their computing facilities.

2.  

Event history files, or panel data, are data that comprise time-independent and time-varying data on individuals.

3.  

New York records college GPA in its certification master file. Some states require certification applicants to pass prescribed tests. For example, Maine requires qualifying scores on the Core Battery of the National Teacher Examination.

4.  

Except in New York's personnel master file.

5.  

This difficulty is being addressed by NCES in creating a special extract from the decennial census that will provide summary data for tracts coterminous with school districts.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

APPENDIX A Northeast Common Market Data Inventory

 

CT

MA

ME

NH

NY

RI

VT

Personal

Social security number

y

y

y

y

y

y

y

Sex

y

y

y

y

y

y

y

Race

y

n

y

n

n

y

n

Birth year

y

y

y

y

y

y

y

Name

y

y

y

y

y

y

y

Marital status

n

y

n

n

y

n

y

Certification

Effective date

y

y

y

y

y

y

y

Date of first certification

n

y

y

y

n

y

n

Expiration year

y

n

y

y

y

y

y

Sponsor institution

y

n

n

n

y

n

n

Certificate type

y

n

y

y

y

y

y

Endorsed subject area

y

y

y

y

y

y

y

Grade level

y

y

y

y

y

y

y

Education

Educational attainment

y

n

y

y

y

y

y

Alma mater

y

n

y

n

y

y

n

State of alma mater

y

y

y

n

n

n

n

Graduation year

y

n

y

n

y

y

n

Work History

Town, district, or region

y

y

y

y

y

y

y

County

n

n

n

y

y

n

y

School

y

n

y

y

y

y

n

Type of school

n

n

y

y

n

n

y

Number of students taught

n

n

n

n

y

n

n

Position title

n

n

y

y

y

y

y

Grade level(s) taught

y

n

y

y

y

y

y

Occupation last year

n

n

y

n

y

n

n

Location last year

n

n

y

n

y

n

n

Years of experience

y

n

y

n

y

y

y

Work load (FTE)

y

y

y

y

y

n

y

Salary

y

y

y

y

y

y

y

Assignments

y

n

y

y

y

y

y

Enrollments

School

n

y

n

y

y

y

y

Year

y

y

y

y

y

y

y

County

y

y

n

y

y

y

y

Town

y

y

y

y

y

y

y

Grades

y

y

y

y

y

y

y

Percent minority

n

y

n

n

y

n

n

Dropout rate

n

y

n

n

y

n

n

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

APPENDIX B Years of Data by State and Data Base

State

Databases

Years

CT

Births

74–87

 

Enrollments

77–87

 

Certifications

72–88

 

Active teachers

77–87

MA

Births

68–88

 

Enrollments

68–88

 

Certifications

72–88

 

Retirement board data

73–87

ME

Births

74–87

 

Enrollments

79–87

 

Certifications

73–89

 

Active teachers

74–88

NH

Births

79–88

 

Enrollments

84–88

 

Certifications

72–89

 

Retirement board data

68–88

 

Active teachers

88–89

NY

Births

60–88

 

Enrollments

67–88

 

Certifications

82–88

 

Active teachers

68–89

RI

Births

70–87

 

Enrollments

75–88

 

Certifications

84–90

 

Retirement board data

79–88

 

Active teachers

89–90

VT

Births

74–87

 

Enrollments

77–87

 

Certifications

74–88

 

Active teachers

79–87

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

APPENDIX C Categories Suggested by Certification Working Group

General Categories

Vocational Education

1 Agriculture

41 Agricultural occupations

2 Art

42 Health occupations

3 Business

43 Office occupations

4 Early childhood

44 Technology subjects

5 Elementary

45 Trade subjects

6 English

46 Other

7 English: second language

 

8 Health

Special Education

9 Home economics

47 Blind

10 Librarian & media specialist

48 Deaf

11 Mathematics

49 Early childhood

12 Marketing

50 Elementary

13 Music

51 Generic

14 Physical education

52 Intensive

15 Reading

53 Secondary

16 Technology education

54 Speech & hearing

17 Other

55 Other

75 Humanities

 

76 Bilingual Spanish

Administrators

77 Bilingual other

56 Principal

 

57 Assistant principal

Science

58 Elementary principal

18 Biology

59 Secondary principal

19 Chemistry

60 Teaching principal

20 Earth/space science

61 School business administrator

21 General science

62 Special education director

22 Physics

63 Superintendent

23 Other

64 Assistant superintendent

Social Science

65 Associate superintendent

24 Anthropology

66 Supervisor/director

25 Economics

67 Vocational education director

26 Geography

68 Other

27 History

 

28 Political science

Support Staff

29 Psychology

69 Guidance

30 Social studies

70 Nurse/teacher

31 Sociology

71 School nurse

32 Other

72 School psychologist

 

73 Social worker

Languages

74 Other

33 Asian

 

34 Classical

 

35 French

 

36 German

 

37 Italian

 

38 Russian

 

39 Spanish

 

40 Other

 

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

Overview and Discussion of National Data Bases Relevant to Teacher Supply, Demand, and Quality

THOMAS L. HILTON

The amount of data available for educational research has grown exponentially during the last 40 years and much of it is relevant to research on teacher supply, demand, and quality. A number of such data files are reviewed in a recently published book by members of the research staff at the Educational Testing Service (Hilton et al., 1992); these files are listed here in Table 1. This paper will review and comment on several of these national data bases that have tended to be neglected in research on teacher supply, demand, and quality.

The first is the Longitudinal Study of American Youth (LSAY). This is an NSF-sponsored study being conducted by the Public Opinion Laboratory at Northern Illinois University, under the direction of Jon Miller. The study started with two national cohorts four years ago, a cohort of approximately 3,000 seventh graders and a cohort of 3,000 tenth graders. Thus, when the two cohorts are linked, complete data from the seventh grade to the twelfth grade are available, and the study is continuing.

The primary focus of LSAY is on the development of competence in mathematics and science, although the data base is comprehensive enough to permit a broader range of investigations of educational development. The study is unique in that annual student and teacher data are being obtained in both the fall and the spring. It thus avoids the problem of High School and Beyond (HS&B) in having measurements only every two years (Sebring et al., 1987). Also, fall and spring data make it possible to measure change and attributes that change to a specific teacher and that may obviate the problem of disentangling cumulative change from kindergarten to twelfth grade that has plagued so many studies of teacher effects. What a

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

student knows as a high school senior represents the impact of 12 years of school (and nonschool) influences. Even HS&B does not solve the problem, for a student can be exposed to several teachers in a two-year period.

In LSAY, student data can be linked to particular teachers, textbooks, and syllabi, a capability that is unusual. The measurement of student behavior and characteristics relevant to science and mathematics is exceptionally broad. However, the measurement of teacher characteristics and behavior is less so, no doubt because the focus of the study is not primarily on teachers. But many questions concerning teacher effects can be addressed by means of this data base.

I emphasize the opportunity to study the dynamics of teacher quality

TABLE 1 Selected National Data Bases (in order of modal or initial age of subjects)

Data Base

Modal Age

Research Triangle Institute National Science Survey

6–12

National Assessment of Education Progress (NAEP)

9–17

Longitudinal Study of American Youth (LSAY)

13–19

National Longitudinal Surveys of Labor Market Experience

14–?

International Math Survey (U.S. Component)

14–18

National Education Longitudinal Study (NELS)

14–?

Project TALENT

15–30

Metropolitan Achievement Tests

16

Iowa Tests of Education

16

1980 High School and Beyond (HS&B)

16–?

Armed Services Vocational Aptitude Battery (ASVAB)

17

American College Testing Service

18

National Longitudinal Study of the High School Class of 1972

18–?

College Entrance Examination Board Admissions Testing Program (Scholastic Aptitude Test, Achievement texts, Advanced Placement Examinations)

18

High School Equivalency Test

19

Cooperative Institutional Research Program (CIRP)

19

Integrated Postsecondary Education Data System (IPEDS)

19–15

Current Population Surveys

All

Graduate Record Examinations

22

National Teacher Examinations

22

Recent College Graduate Survey

22

Graduate Enrollment Survey

26

Survey of Graduate Science Students and Postdoctorates

26

Survey of Earned Doctorates

26

 

SOURCE: Hilton et al. (1992).

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

with LSAY because this is so rare. Also, if one is interested in the antecedents of career choice, including the career of teaching, the LSAY data file is invaluable. By contrast, budget and wage variables have a common metric and can be found in several data bases. The data are all in the public domain and may be obtained from Jon Miller at no cost.

The second study I would mention briefly is Project TALENT, which only a few of us old-timers can remember (Flanagan et al., 1960). This was the first major longitudinal study in this country. The data collection was begun in 1960 and involved some 400,000 high school students. The data collection required 15 hours of school time, and the students were followed up for 15 years. It is hard to believe what they required of the students. Students apparently were a lot more compliant in 1960 than they are now. That may not have been a good state of affairs educationally but it was good from the point of view of educational researchers.

Since the original Project TALENT subjects are now 45 to 50 years old, I can easily imagine research questions for which the data would be indispensable, since the file has not only exhaustive individual student data, but also teacher and school data. The TALENT data are also in the public domain, obtainable from the American Institutes for Research in Palo Alto.

The third data source is the Graduate Record Examination (GRE). This data file includes not only test scores—important in terms of today's discussion—but also background information on the GRE takers. The most valuable of this information for teacher supply and demand are the items on the major field of the students' undergraduate study and the major field of their intended graduate study. Although these data are not routinely made available, arrangements can be made with the GRE program directors to obtain permission of the GRE board to use the data for only the cost of retrieving it.

Furthermore, it is not difficult to retrieve the scores on the Scholastic Aptitude Test (SAT) of GRE takers, including responses to the Student Descriptive Questionnaire. This introduces the possibility of relating the intended undergraduate majors of SAT takers to their intended graduate school majors. The possibility of using such data to develop a system of predicting graduate school enrollments by major from SAT data has intrigued me for a number of years. As far as I know, no one has done it.

While on the subject of GRE scores, I should also mention a little-known fact—namely that GRE scores were retrieved for 340 members of the 1980 HS&B senior cohort. This introduces the possibility of yet another linkage between a major data base and the GRE scores. These data were retrieved as part of a study of the postsecondary educational pathways followed by GRE takers, which was carried out for the GRE board (Hilton and Schrader, 1987). Although the sample is small, other researchers might find these data useful. GRE scores are an excellent indicator of the cogni-

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

tive ability of students electing graduate school work in education relative to that of students in other fields. The worrisome fact is that the study just mentioned indicated that graduate students in education had no competition for bottom place.

With respect to national data bases, I will conclude with comments on the National Teacher Examinations (NTE). One might think that these files, which include several hundred thousand cases a year, would supply useful national data concerning the supply of teachers in this country. However, there are at least three problems in dealing with these data.

First, the teachers who take the exams are far from being representative of all teachers in the United States, even though the tests are taken throughout the country. Some states require the test for all entering teachers, while in other states the tests are voluntary, and state and district requirements keep changing. Also, the age level of individuals taking the test varies widely from one state to another. Thus, trend analyses are for most purposes out of the question.

The second problem encountered in using NTE data is that the format and content of the tests have changed over the years. Currently, the test is undergoing a thorough reexamination with the definite possibility that the NTE of the future will be a drastically different instrument.

Third, because of the history of lawsuits resulting from the way NTE scores have been used by some states, the NTE program direction has been somewhat gun shy—for good reason—about making NTE data available for research purposes. I think these reasons are sufficient for us to cross the NTE off our list of data files available for research on teachers. It's a pity. because it's a huge file of data.

I turn next to a consideration of selected annual and longitudinal data bases as diagramed in Figure 1. Notice the intersections of some of the commonly used tests. The top row, for the National Longitudinal Study (NLS) of the National Center for Education Statistics (NCES), indicates that SAT and ACT scores are available as part of the base year survey. Then moving down to 1979, the Department of Labor National Longitudinal Survey—different of course from NLS—is a very comprehensive file that is readily available. One of its unique features is the presence of scores on the Armed Services Vocational Aptitude Battery (ASVAB) (U.S. Department of Defense, 1976). This well-known test is taken by some 1,000,000 high school students, mostly juniors and seniors, every year.

Returning to the figure and going down to HS&B, you can see that SAT, ACT, and GRE scores are all available for HS&B subjects, as mentioned earlier.

Most of the large national cross-sectional or longitudinal data files with which I've been involved were designed with only a general notion of the uses that would be made of the data. This being the case, we tended to err

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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Figure 1 Selected annual and longitudinal data bases. Source: Hilton et al. (1992).

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

on the side of overinclusion. We tended to include every questionnaire or interview item that might conceivably be of interest.

This circumstance suggests the importance of developing a comprehensive model of teacher supply and demand that includes relevant psychological, sociological, and economic variables. Only if such a model were available could the relevance of a particular source of data be judged. Whenever we ask about the validity of a data source, we have to ask ''validity for what?'' This question is unanswerable without at least a preliminary conceptualization of the phenomenon in which we are interested. Not only would such a model help us to decide whether a particular set of data is relevant, but it would also suggest data that may be missing from a particular data file—data that might be critical to investigations to be conducted by means of the data.

With the assistance of an adequate model of teacher supply and demand, future researchers would be able to be much more discriminating in choosing data files than they could without such a model. Furthermore it would enable them to select only the relevant parts of the large data files. The HS&B data file, for example, has grown to a mammoth size. Copies of the instruments and code books alone occupy two linear feet of shelf space in my office. Selecting relevant parts of the HS&B data file would be much easier with a model to guide the process.

Implementing a comprehensive teacher supply and demand model will probably require data from many different sources. This introduces the problem of merging data from multiple sources into one file if for no other reason than to reduce the cost of data processing. As a battle-scarred veteran of numerous efforts to do this, let me make a few comments.

Sometimes merging is straightforward, as for example when we merged the GRE and SAT data files. With a little caution we were reasonably sure we were dealing with the same students, that is, students with the same social security number, the same birth date, the same gender and the same spelling of their last names. It is disconcerting, however, to discover how many students do not know their social security number or do not write it down accurately. Or, when asked to give their birth date, they give the correct month and day but record the current year. Last names tend to be reliable—unless the sample includes women of marriage age—but matching on first names is useless, given the frequent use of nicknames.

Also one must be cautious about accepting apparent matches. It is a fact that in 1985 there were 18 John Smiths with exactly the same birth date in the SAT files for that year.

Usually, however, we want to compare categories of individuals in one data base with categories of similar individuals in another data base. For example, we may wish to compare high school seniors in the NLS sample who say they intend to go to college and major in secondary school teach-

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

ing with similar students in the HS&B sample. This particular comparison is possible, but don't try it for prelaw students. The relevant questionnaire item was changed in 1980 in such a way that the comparison is now impossible. This is but one of a host of examples I could cite of minor changes by well-meaning questionnaire designers that have made certain desirable comparisons impossible.

Here and now I would like to make a fervent plea for noncreative questionnaire design. I have a vision of a group of gremlins sitting around a table gleefully asking themselves how they can make slight changes in a questionnaire item so that future comparisons will be impossible. Even the Senior and Sophomore Questionnaires of HS&B are not uniform. You cannot, for example, compute the transition probability from high school to first college for the senior cohort in such a way that it can be unambiguously compared to the probability for the sophomore cohort. There was some very creative item writing there. The moral is that one should make sure a proposed comparison is possible before committing oneself to making it. The research community needs to be vocal about the comparability in national surveys.

Finally, I would like to comment on the question of investigating teacher quality. As is widely recognized, this is a very complex topic. I know of no national data bases fully adequate for investigations of the characteristics of teachers related to effective teaching. I must explain, however, what I mean by this, for there are a number of well-done research studies that purport to accomplish this, including a study currently under way by Ronald Ferguson of the Kennedy School of Harvard University.

In that study, district-level data on teacher characteristics for the whole state of Texas are being related to gains in achievement by students in each district. Community data, such as mean parental income, are being taken into consideration, so Ferguson is able to covary community socioeconomic status out of any correlations. But even this promising work has shortcomings. I would agree that the ultimate measure of teacher effectiveness is the effect teachers have on their students. Before and after measurement is the best way of estimating this effect. But the measurement cannot be limited to change in scores on pencil and paper measures of achievement.

At the risk of being labeled hopelessly idealistic, let me propose that of equal, possibly more importance is the effect the teacher has on such elusive concepts as the student's ability to engage in higher-order thinking and creative problem solving. These qualities are hard enough to measure, but there are some even harder qualities that are important. Here I have in mind such attributes as appreciation of history, respect for scholarship, intellectual curiosity, tolerance, citizenship, and desire for lifelong learning.

Obviously, when I talk about quality, I have in mind something quite different from the economic models we have seen today. To me, a quality

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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teacher is one who has a beneficial effect on the kind of outcomes that I've just listed. In contrast with this view, teacher quality is often regarded as qualifications or credentials. These may measure how much bargaining power a teacher has, and may be a perfectly good use of the word quality. I would only ask that we use "quality sub one" and "quality sub two", and that we be very clear about which kind of quality we're talking about.

I would also mention an incipient revolution in education in this country and that is the introduction in our schools of computer technology. Both performance testing and much of classroom instruction will be computerized in the future. Many unsolved problems stand in the way of widespread implementation in the schools, but it's inevitably coming.

This development may be a boon to empirical researchers: the record left in computer memory is likely to be an important source of research data. Any comprehensive file of school data in the future will want to take this powerful trend into consideration.

I would like to end on a real blue-sky note, just in case anybody is complacent. While we're on the subject of computerized data collection in the future, I see no reason why data relevant to teacher quality and some other teacher supply and demand variables cannot be obtained on line from classrooms throughout the country. The technology of electronic networking is highly developed. Theoretically NCES could have a standing national sample of elementary and secondary schools that routinely transmit data through an electronic network to a central repository where it is aggregated and displayed in such a way that teacher behavior and student learning can be observed as it happens. Why do I have to call this a blue-sky concept when highly developed electronic networking technology exists? Because this concept assumes successful resolution of the inevitable political, fiscal, union, and data processing problems entailed in attempting to implement such an electronic data collection system.

REFERENCES

Flanagan, J.C., J.T. Dailey, M.F. Shaycoft, W.A. Gorham, D.B. Orr, and I. Goldberg 1960 Designing the Study. Technical Report to the U.S. Office of Education, Cooperative Research Project No. 566. Pittsburgh, Pennsylvania: University of Pittsburgh, Project TALENT Office.


Hilton, T.L., A.E. Beaton, V.E. Lee, J. Pollack, D.A. Rock, W.B. Schrader, S.S. Swinton. W.W. Turnbull, and S. Urahn 1992 Using National Data Bases in Educational Research. Hillsdale. New Jersey: Lawrence Erlbaum Associates.

Hilton, T.L., and W.B. Schrader 1987 Pathways to Graduate School: An Empirical Study Based on National Longitudinal Data. Final Report, GRE No. 82-21-R. Princeton, New Jersey: Educational Testing Service.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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Sebring, P., B. Campbell, M. Glusberg, B. Spencer, M. Singleton, and M. Turner 1987 High School and Beyond 1980 Senior Cohort Third Follow-up (1986): Data File User's Manual. Washington, D.C.: National Center for Education Statistics.


U.S.Department of Defense 1976 ASVAB Specimen Test. Washington, D.C.: U.S. Government Printing Office.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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Who Will Teach?

RICHARD J. MURNANE

The purpose of this paper is to demonstrate how the administrative records of state departments of education can be used to inform a number of questions related to teacher supply. I do this by providing a number of examples of analyses that my colleagues (Judith Singer, John Willett, James Kemple, and Randall Olsen) and I have conducted using data provided by the North Carolina and Michigan state departments of education. All of these examples are taken from our book Who Will Teach? Policies That Matter, published by the Harvard University Press in 1991.

WHO PREPARES TO TEACH

Virtually every state in the country maintains information on the number of new teaching licenses granted each year.1 Records on the number of licenses granted, the backgrounds of the licensees, and the distribution of teaching fields in which the licenses are awarded provide useful information in assessing how the supply of potential new teachers is changing. We illustrate this with several examples from North Carolina.

Trends in the Number of New Teaching Licenses

Figure 1 describes the number of teaching licenses granted by the state of North Carolina in each year from 1975–1982.2 The total declined from more than 6,5000 in 1975 to less than 3.200 in 1982. This dramatic decline provides a strong signal that the supply of new potential entrants to the teaching profession in the state grew much more slowly in the early 1980s

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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Figure 1 Number of new teaching licenses granted by North Carolina in each year from 1975 to 1982.

than in the mid-1970s. Is this a problem? There are many reasons why the number might have fallen. Was it because the number of college graduates in the state declined? Was it because changes in regulations made it more difficult to obtain a teaching license? Was it because changes in the attractiveness of teaching relative to alternative occupations reduced the number of graduates interested in teaching? Learning the answers to these questions is necessary to assess the policy significance of the decline in the number of licensees. Information on the trend in the number of new licensees does not answer the questions. 3 But timely provision of the trend data does highlight a potentially disturbing pattern and focuses attention on the need to explore the reasons for the decline in the number of new licensees.

The Trend in the Racial Composition of the Pool of Licensees

In many states there is growing concern that the representation of minority group members in the nation's teaching force is declining at the same time that the proportion of students who are minority group members is growing. One part of a strategy to identify the causes of this trend is to track the proportion of new licensees who are minority group members.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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Figure 2 Percentage of new teaching licenses granted by North Carolina to black college graduates in each year from 1975 to 1982.

Figure 2 provides this information for North Carolina. In 1978, one out of five new licensees was black; by 1981, only one out of 10 was black. This dramatic decline raises the question of what happened in the late 1970s to cause the abrupt decline in the representation of black college graduates in the pool of new licensees. As my colleagues and I explain in our book, the answer concerns changes in the requirements for obtaining a teaching license in the state. The point I emphasize here is that the information on the racial distribution of new licensees is helpful in identifying a troubling trend.

The Trend in the Distribution of Teaching Fields

An obvious further use of state data on teacher licenses is to track the distribution of teaching fields chosen by new licensees. This may be of particular value as states attempt to stiffen licensing requirements and to eliminate the practice of out-of-field teaching, whereby teachers are asked to provide instruction in fields in which they are not licensed. If the number of new licensees in each field does not keep up with projected demand, it is unlikely that prohibitions against out-of-field teaching can be main-

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

tained. Identifying fields in which demand is growing faster than the number of new licensees may signal the need for policies to increase supply, such as scholarship aid or salary bonuses.

WHO ENTERS TEACHING

By coupling records on teacher licensing with records on who teaches in a state's schools, it is possible to learn a great deal about the influence that licensing requirements have on who teaches in our schools. For example, we found that college graduates who obtained provisional teaching licenses in North Carolina (which were only valid for two years) were much less likely to become teachers in the state than were graduates who obtained continuing licenses (which provided lifelong licensure). This raises the question of whether the provisional licensing program is worth the resources devoted to administering it. It also indicates that counts of the number of licensed teachers that do not distinguish between types of licensure are likely to be poor indicators of potential teacher supply.

A number of states, including California, Connecticut, and New Jersey, have introduced alternative routes to obtaining a teaching license. Typically these alternative route programs substitute a brief period of intensive training (often during the summer months) for the undergraduate teacher education required for conventional licensure. Debate surrounding the value of these programs is intense. Advocates argue that they attract to teaching academically talented college graduates who would not otherwise teach. Critics argue that the alternative route programs do not provide enough preparation to enable participants to learn to teach effectively. One part of a strategy for assessing the consequences of alternative route programs is to compare the proportion of graduates of these programs who enter teaching with the proportion of graduates of conventional programs. Other parts of an evaluation strategy include comparison of the types of school districts that graduates of different types of programs work in, and comparison of the lengths of time graduates of different programs stay in teaching. All of these analyses can be done with the data typically collected by state departments of education.

HOW LONG TEACHERS STAY IN TEACHING

The most critical determinant of the demand for new teachers is the length of time that teachers stay in the classroom. Consequently, learning about patterns of attrition is an important part of evaluating whether the supply of teachers is likely to prove adequate in the years to come. Information on teachers collected by many state departments of education can be linked to create longitudinal career profiles for teachers. This creates the

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

potential to use these records to investigate how long teachers stay in teaching, and the factors that predict the likelihood that teachers leave the profession.

Career Lengths Vary by Subject Specialty

Table 1 provides predicted median lengths of stay in teaching for teachers with particular subject specialties who began their careers in Michigan or North Carolina.4 The entries in the table illustrate that attrition rates differ quite markedly between the two states. They also show, however, considerable regularity in the role subject specialty plays in predicting length of stay in teaching. In both states elementary school teachers stay in teaching longer on average than do secondary school teachers. Among secondary

TABLE 1 Median Teaching Career Duration (in years) by Subject Specialty, Controlling for Age, Gender, and Race

Subject Specialty

Michigan

North Carolina

Elementary

5.9

11.0+

Secondary

3.9

7.8

Biology

4.5

5.9

Social Studies

4.0

8.8

English

3.7

6.4

Mathematics

3.6

11.0+

Chemistry/physics

2.2

5.6

school teachers, those teaching chemistry and physics tend to have particularly short teaching careers. As explained in Who Will Teach? , these patterns are consistent with the view that teachers with the best career options outside teaching are the most likely to leave it. The pattern displayed in the table illustrates the importance of paying attention to subject specialty in analyzing patterns of teacher attrition.

Salaries Affect the Likelihood of Attrition for Novice Teachers

One question often asked by taxpayers and school boards is whether salaries affect teachers' careers. It is possible to use state department of education records on teachers' annual salaries and careers to estimate statistical models (called hazard models) that address this question. Figure 3 illustrates the results of such model estimation. The curve labeled M shows the predicted hazard profile for a hypothetical Michigan teacher who started her career in 1972. There was a 3-in-10 chance that this teacher would

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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Figure 3 Risk of leaving teaching for three entering secondary school teachers in Michigan: in average, low, and high salary streams.

leave teaching at the end of her first year. If she did stay for a second year, there was approximately a 1-in-6 chance that she would leave at the end of the second year. The downward slope of the hazard curve means that the risk of leaving declines with each successive year of additional teaching experience.

The curve labeled H depicts the predicted hazard profile for a teacher paid $2000 above the median salary scale in the state in each year. The curve labeled L depicts the hazard profile for a teacher paid $2,000 below the median salary in each year. Comparison of the three hazard profiles reveals several patterns. First, salary makes an important difference in the risk that a beginning teacher leaves the profession. The risk that a first-year teacher paid $2,000 below the median salary leaves teaching is greater than 1 in 3. In contrast, the risk that a beginning teacher paid $2,000 above the median salary would leave teaching is less than 1 in 4. Second, the role of

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

salary in predicting the risk of leaving teaching declines as teachers gain experience. Among teachers with more than seven years of experience, salary played no role in predicting length of stay in teaching. Third, the differences in the risk profiles associated with different salary profiles cumulate so that teachers paid $2,000 above the median salary scale in the state had a median length of stay in teaching (3.8 years) that was almost two years longer than the median length of stay for teachers paid $2,000 below the median salary scale.

WHO RETURNS TO TEACHING

Many analysts predict that the number of college graduates obtaining teaching licenses over the next 15 years will be far smaller than the demand for teachers needed to replace the large number of teachers expected to retire over this period. Whether the high predicted retirement rate and the low rate of new licensure results in significant shortages is likely to depend critically on the number of teachers who return to the classroom after career interruptions. Analysis of longitudinal records of teachers' career profiles created from state department of education records can provide information about the likelihood that teachers who leave the profession subsequently return after a career interruption.

Many Teachers Return

Figure 4 depicts the results of analyses of the likelihood that teachers in North Carolina and Michigan who left the classroom returned after a career interruption. The figure illustrates that, in both states, 1 in 6 teachers returns after an interruption of one year. Among teachers who stay out of teaching for a second year, approximately 1 in 20 returns. The downward slope of the curves illustrates that the longer a teacher is out of the classroom, the less likely he or she is to return. Although the likelihood of a return declines the longer a teacher is out of the classroom, the probabilities cumulate so that, in both states, 1 out of every 4 teachers who leave teaching returns within 5 years. Thus, returning teachers have been an important source of teacher supply.

The Probability of a Return to Teaching Varies by Subject Specialty

Table 2 displays the results of analysis exploring the extent to which the likelihood that a teacher returns to the classroom depends on subject specialty. In both states, elementary school teachers are more likely to return than secondary school teachers. Among secondary school teachers,

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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Figure 4 Rate of reentry into teaching after termination of a first teaching spell plotted against the length of time teachers have been out of the classroom in Michigan and North Carolina.

TABLE 2 Predicted Probabilities of a Return to Teaching in North Carolina and Michigan Within 5 Years of Terminating a First Teaching Spell, by Subject Specialty

Subject Specialty

Michigan

North Carolina

Elementary

.29

.31

Secondary

.24

.24

Social Studies

.28

.29

Biology

.26

.22

English

.23

.25

Mathematics

.21

.21

Chemistry/physics

.16

.17

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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those who taught chemistry and physics are less likely to return to teaching after a career interruption than are teachers of other subjects. The differences in probabilities of return are quite large. Approximately 1 out of every 3 elementary school teachers who leave teaching returns to the classroom within 5 years. In contrast, only 1 in 6 chemistry and physics teachers returns within 5 years.

SUMMARY

The examples provided in this brief paper illustrate only some of the ways data collected routinely by state departments of education can be used to inform questions related to teacher supply. Precisely what can be done with the data from any given state depends on the structure of the administrative records, on their accuracy, and on the extent to which they are kept up to date. It is also important to point out that the task of creating from administrative records data bases suitable for research is typically extremely time-consuming and resource-intensive. More than 5 years of work preceded the publication of Who Will Teach?. Much of this data preparation task stemmed from the fact that the state administrative record systems in Michigan and North Carolina were not designed to support research of the type we have described. Redesign of state record keeping systems could dramatically reduce the resources needed to use these records to address questions about teacher supply; it could also dramatically reduce the time needed to respond to queries posed by policy makers.

NOTES

1.  

We use the term teaching license instead of the more common term, teaching certificate, to differentiate the credential a candidate for a teaching position must possess (a license) from the credential that the National Board for Professional Teaching Standards hopes will designate superior teachers in the future (board certification).

2.  

Excluded from the count of number of licenses are licenses granted in vocational education and counseling, fields that have quite different licensing requirements from the following included fields: elementary education, special education, mathematics, English, biology, social studies, foreign languages, art, music, physical education, business education, and chemistry/physics (which is treated as one field because of the small number of licenses granted in these areas).

3.  

See Murnane et al. (1991: chs. 2 and 3) for answers to these questions.

4.  

These predictions are based on multivariate hazard models that hold constant characteristics of teachers and school districts. For further information, see Murnane et al. (1991: ch. 5).

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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Discussion: Teacher Supply and Demand Research With State Data Bases

David W. Grissmer

Data bases derived from state administrative records have proven to be very useful for research in the general area of teacher supply and demand. There are several issues in this general area that merit further research attention. In addition, there are some interesting future supply and demand scenarios that merit analysis.

ISSUES FOR FURTHER RESEARCH

Several topics that merit further research are:

  • Specific supply and demand analysis for math and science teachers,

  • Supply and demand analysis for minority teachers,

  • Effects of early retirement offers on teacher demand,

  • Effects of the declining number of returning and migrating teachers on the demand for teachers, and

  • Supply and attrition of high-quality teachers.

Each of these topics is discussed in the following sections.

Supply and Demand for Math and Science Teachers

Several studies of attrition rates of mathematics and science teachers apparently reach different conclusions. Some report much higher attrition

Grissmer made a significant contribution to the conference with his remarks during the open discussion of the session. Those remarks have been formalized in this paper.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
×

rates for physics/chemistry and biology teachers compared with other teachers; others conclude just the opposite. There are several hypothesis concerning differences in methodology, time periods of measurement, and definition of attrition that might explain the differences in conclusions, although these questions certainly need further empirical exploration.

Research based on state data sources from Michigan (Murnane and Olsen, 1989b), North Carolina (Murnane and Schwinden, 1990) and Indiana (Grissmer and Kirby, 1991, 1992) shows that physics/chemistry teachers have significantly higher attrition rates than the average of all teachers, and that biology teachers have the next highest rates. However, all three studies find that attrition rates for mathematics teachers are much lower than those for science teachers, although still above the average for all teachers. These studies all use entering cohort survival as the basis for their conclusions. The entering cohorts studied in the analysis are different by state. The Michigan analysis used entering cohorts from 1972 to 1980 followed through 1984, North Carolina the 1976 and 1978 cohort followed through 1986, and Indiana the 1966 through 1985 cohorts followed through 1988. The findings apply whether the definition of attrition includes or does not include teachers who subsequently return to teaching. However, the results are much stronger when teachers who return are not counted as attrition, since mathematics and science teachers return at much reduced rates than other types of teachers (Murnane et al., 1989; Grissmer and Kirby, 1992).

A recent analysis of attrition (Bobbitt et al., 1991), using the national sample of teachers from the Schools and Staffing Surveys (SASS) and the Teacher Followup Survey (TFS), concluded that chemistry/physics, biology, and mathematics had attrition rates below the average for all teachers. This data was based on attrition in a single year (1987–88 to 1988–89) from the base of all mathematics and science teachers.

Three differences that might explain these conflicting results are the use of different samples, different time periods, and different definitions of attrition. A cohort sample measures early attrition for younger teachers, whereas a cross-sectional sample of all teachers measures attrition for all age groups. Therefore, the state measurements and national measurements are not comparable. However, further work on each data base can develop more comparable measurements. The state data can provide cross-sectional estimates that would be more comparable with the national measurements. The national data can also be used to generate cohort attrition for one and eventually more years, which could then be compared with cohort rates from the state samples. In these comparisons, it is critical to control for, as a minimum, age and gender since small differences in sample composition can cause large differences in attrition rates. Thus a multivariate analysis is needed to perform these comparisons.

The second explanation is that the attrition measurements were for dif-

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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ferent time periods. The state cohort data measures average attrition for cohorts during the late 1960s, the 1970s, and early 1980s. The national sample measures attrition from 1987–88 to 1988–89. Evidence from New York and Illinois (Grissmer and Kirby, 1992) seems to indicate that teacher attrition rates have fallen over time, with the exception of a period in the late 1970s in which reductions in the teaching force occurred. Data from Michigan (Murnane and Olsen, 1989b) seem counter to this trend of lower attrition rates over time, with their initial cohorts from the early 1970s having lower attrition than the cohorts from the late 1970s. However, this may be due to the choice of the late 1970s as the second time period when reductions in force were present. Other data that continue into the 1980s indicate much lower attrition in the 1980s than the early 1970s. If this is true, then comparing rates for different time periods probably cannot be done.

The definition of attrition can also present comparison problems. The national sample measures annual attrition without taking account of returning teachers, while state analyses measure cohort attrition in two ways—with and without taking account of returning teachers. Since about one-third of teachers return (Murnane et al., 1989; Grissmer and Kirby, 1991), comparing attrition without comparable treatment of returning teachers can make estimates very different. This is especially true when comparing attrition rates of science/math teachers with other teachers. Science and math teachers return much less frequently than other types of teachers (Murnane et al., 1989; Grissmer and Kirby, 1991). This means that annual attrition rates for science and mathematics teachers could be similar or even lower, but the lower return rates would make permanent attrition rates higher for science teachers. While this may explain part of the discrepancy, it is also true that the state measurements shows higher annual cohort attrition for science teachers.

Other explanations are possible, but I think less likely. The particular states used in the analysis may have quite different attrition patterns and may not be nationally representative. However, many of the other results from the states and national data are not in conflict. It is possible that the age and gender distribution of science teachers in the states studied is different than in the national distribution. This could be easily checked on the different data bases.

It is also possible that the classifications used for science and math teachers are very different. The state cohort data classified teachers on the basis of what they taught in the first year of teaching. The SASS and TFS classified teachers on the basis of their main assignment in the initial year of the survey—1987–88. These might be very different groups. The cohort classification probably corresponds to certification area since they are newly hired teachers. Once teachers have some experience, there may be many

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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science and math teachers not teaching in their main area, or conversely, teachers not having their main certification in science or math may be teaching these subjects. To the extent that (a) entering cohorts of science and math teachers teach other subjects or (b) individuals who were not initially certified in science and math are teaching these subjects, then the attrition rates for the state and national data could be different.

These discrepancies emphasize the need to perform attrition analysis within a common analytic context in which similar methodologies, similar controls, similar time periods, and similar definitions of attrition can be utilized. This common analysis needs to include data from several states and national data from the SASS and TFS. Otherwise these kinds of conflicting findings will arise over and over again, when common analysis could eliminate almost all of these conflicts.

There is a need to develop a complete supply and demand analysis for math and science teachers that includes identifying the sources of supply (returning, migrating, and inexperienced teachers), the paths into and out of teaching math and science, national return rates, and the effects of upcoming retirement eligibility and early retirement offers. We have not yet included these individual groups in supply and demand models to assess the potential for additional shortages.

This analysis is best done by combining data from several states, along with the simultaneous use of SASS and TFS data. SASS and TFS provide nationally representative data on math and science teachers, but sample sizes for newly entering teachers are rather small. In addition, supply and demand analysis is best done using long time series data whereby trends in key variables can be analyzed. Since at the present time SASS data will cover only a short time period, state data are best used to analyze these trends. The state data are based on very large sample sizes and on moderately long histories that allow better entering cohort analysis. Supply and demand analysis also needs to model the early stage of teaching careers most accurately since this is where attrition rates are high and more unpredictable.

Finally, more research is needed to discover the causes of the significant attrition differences among types of science and math teachers. One hypothesis is that it simply reflects differences in outside job opportunities (Murnane et al., 1989). The hypothesis is that mathematics teacher training may provide less job transferability than that of science training. This is especially true of those teaching lower levels of mathematics.

Another hypothesis is that laboratory teaching as opposed to classroom teaching is inherently harder, and there is greater sensitivity to the quality of equipment and facilities. Survey responses (Weiss and Boyd, 1990) from science and math teachers show differences in their sensitivities to working conditions. Science teachers—but not mathematics teachers—rate facilities

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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and equipment and ''time for hands-on instruction'' as key aspects of their dissatisfaction with teaching. Finally, differences in attrition rate by subject may reflect variation in gender proportions in each area and their sensitivities. Men are more sensitive to lack of administrative support and low salaries than women (Weiss and Boyd, 1990). This sensitivity would be heightened by higher outside opportunities. Any teaching specialty with more men—other things being equal—would probably have higher attrition in situations in which where higher outside wage opportunities exist.

Minority Teachers

Declining proportions of minority representation in the overall teacher force could occur at a time when minority student enrollments are rising. The cause of lower minority teacher proportions may be more attributable to lower minority college enrollments and choice of education as a career rather than lower proportions of minority certified applicants obtaining jobs (Murnane and Schwinden, 1989). The studies of state data have not generally focused on the question of differences in attrition rates among racial/ ethnic groups. Analyses of SASS and TFS generally show white and black rates to be similar, but lower attrition for Hispanic teachers (Bobbitt et al., 1991). More specific analysis of minority supply and demand is required.

Hispanic teachers especially merit analysis given the recent immigration trends. However, the sample sizes generally are small except in certain states. Two states stand out as having large enough populations of both black and Hispanic teachers to justify an analysis: New York and Texas. Fortunately, both of these states have fairly good data, and it has been edited and prepared for analysis (Texas by RAND and New York by the University of Massachusetts). Some joint analysis of these two larger states with the SASS and TFS data would provide a much clearer picture of minority teacher supply and demand behavior.

Teacher Early Retirement and Teacher Demand

The teaching force is unbalanced with respect to age and experience. Younger teachers—those under 35—are a smaller portion of the teaching force than at any time in the last 25 years, and half of all teachers are over 42, making them retirement eligible at age 55 within 13 years at the latest. An important supply and demand question is how soon these retirements will occur, and thus when replacement will be needed. Current retirement patterns show a strong tendency for teachers to stay until 62 or 65. If this is the case, then demand for new teachers will increase more slowly. Budget problems in states could make early retirement offers very attractive—in fact epidemic. Replacing older teachers with younger teachers significantly

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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reduces education costs, even with somewhat increased retirement costs. Retirement costs are not generally paid out of operating budgets, which makes early retirement even more attractive to educational administrators.

Research is needed on the precise patterns of teacher retirement and the effects of early retirement offers on the decision to leave teaching. Massive early retirement could increase demand for younger teachers significantly. The federal government could also provide states with research on the effects of different types of early retirement offers. Research is also needed on quality issues inherit in early retirement offers. Is the tradeoff of younger for older teachers likely to increase or decrease teacher quality?

This research would again best be done in the context of combining data from several states with SASS and TFS data to find whether retirement patterns are similar across states. However, the state data may be especially valuable here because early retirement plans will be state specific. The effect of early retirement plans on retirement age could be obtained using time-series state data and/or comparing retirement behavior across states. Alternately, future SASS and TFS questions or a separate survey of teachers with regard to early retirement plans and the factors behind retirement decisions might be important.

The Declining Reserve Pool

Perhaps the most ominous trends for future shortages is the fact that the supply of returning and migrating teachers will be declining in future years. We currently depend on these teachers to fill about 50 percent or more of vacancies in any year. If there are fewer returning and migrating teachers, then we will need more younger teachers. Returning and migrating teachers will decline because of simple demographics. Teachers who return to teaching leave teaching most often between 25–35 years of age. Teachers over age 40 leave teaching less frequently. So as the average age of the teaching population increases, there will be a smaller reserve pool of teachers. Teacher migration also peaks during the 25–35 age span. Since there are going to be fewer teachers in this age span as the teaching force ages, there will be fewer returning and migrating teachers to fill vacancies.

We need more research on the patterns of returning and migrating teachers to determine the precise decline in these pools over the next 5–15 years. Data available from several states could be readily utilized to explore these patterns and the subsequent decline in the reserve pool. SASS and TFS data will have only limited utility here because they will not capture the longer time period in which many teachers return. Estimating the changing return rates of teachers would enable better estimates to be made of the reserve pool, and the timing of much stronger demand for new teachers. If this occurs about the time of massive early retirements, a problem in supply

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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could result. So this research needs to be combined with the early retirement research to determine the relative timing of the two phenomena.

Supply and Attrition of High-Quality Teachers

Research has generally established the lower entrance rate and higher attrition rate of teacher graduates who score well on aptitude tests (Vance and Schlechty, 1982; Murnane et al., 1989; Manski, 1987; Murnane and Schwinden, 1989). The latter study however distinguishes between white and black applicants and shows that black applicants show the opposite effect, namely that higher NTE scores lead to increased chances of entry into teaching. There have been many programs within colleges to attract better students, and better induction programs into teaching may lower attrition rates (Hudson et al., 1991). But research is needed to discover differences in quality in teachers who stay and leave and the role of salary and working conditions in these decisions. There are several approaches that could be tried—case studies, surveys, and analysis of state data that include teacher test scores or teacher evaluations.

FUTURE RESEARCH DIRECTIONS

Much of the work of the last few years has focused on the collection, editing, and initial analysis of very large data bases. The SASS and TFS surveys have been designed and fielded. These surveys are an invaluable addition to the study of teacher supply and demand. The analysis of this data base is only beginning, and other waves of data will eventually be added to the 1987–88 and 1988–89 data.

SASS and TFS provide the only nationally representative data on teachers available for this purpose. The sample size of the survey is large and adequate for many purposes, although analysis of entering cohorts (or any age group such as retirees) within specialty categories can be a problem. The main drawback for analyzing teacher supply and demand issues is that it contains no time-series data, and therefore trends in key variables cannot be tracked.

There are three research centers that have invested in analysis of state data: Harvard University, the University of Massachusetts, and RAND. Harvard University has analyzed data from Michigan and North Carolina. The University of Massachusetts has data from eight New England states, and RAND has data from Indiana and Texas. A sizable investment has been made simply to assemble these data and prepare them for analysis. While some analysis has been done and published, this analysis has not yet tapped the potential of the data bases, partly because the initial costs of preparing the

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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data was underestimated by all, and fewer resources were then available for actual research.

State data has the advantage of large samples (Texas has 180,000 teachers and New York has 240,000). The combined state data bases that have been prepared for analysis contain the records of approximately I million teachers—about 40 percent of U.S. teachers. These large sample sizes are critical when studying the behavior of age subgroups of teachers such as minority and science and math teachers. Data drawn from surveys is simply too expensive to allow for the sample sizes necessary to study many subgroups. In addition, state data are the only available data from which time-series and long-term longitudinal data can be constructed. Such data, as opposed to data collected at a single point in time, are critical to assessments of teacher supply and demand.

State and SASS/TFS data are basically complementary, and joint analysis can enhance considerably the analysis of several educational policy issues. I will provide two examples in which joint analysis can considerably improve our understanding of issues. The first is the question of attrition of science and math teachers described above. As reported, several conflicting results arose from analysis of the several data bases. It will be difficult to determine if these differences are an artifact of the analysis method or the time period or represent real differences among states and national samples in teacher behavior. However, joint analysis of data will be able to address many of these issues and avoid many unnecessary conflicts.

A second example is the analysis of early retirement behavior among teachers. The SASS data will be able to determine age-specific retirement rates that are nationally representative. However, since variation in pensions and early retirement offers exist at the state level, the sample sizes of retirement-eligible teachers within states that make such offers will be too small to determine how offers affect behavior. However, state data with its large sample sizes can track retirement rates over long time periods and reveal differences in retirement rates when offers are made. These data can also be used for interstate analysis of retirement rates and the relationship of these rates to differences in pension rules and amounts.

Research with data from several states is showing both similarities and differences in teacher supply and demand factors. There is similarity in age distributions and in some general attrition patterns among age groups and subject areas. There appear to be differences in (a) dependence on returning and migrating teachers to fill vacancies, (b) attrition levels, (c) capacity to generate new teachers, (d) proportions of minority teachers, and (e) early retirement programs and retirement patterns. These differences can arise from local or district policies, from state policies, and from differences in behavioral characteristics among teachers in different states.

Understanding these differences is important because teacher supply

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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and demand factors, and the potential for shortages, will vary by state. It is also important because some states and districts have better policies and practices that can be identified and exported. Certain questions can also be better addressed by certain state data than others partly because data are available from some states for longer periods. Also, some states have implemented programs such as early retirement offers, while others do not.

The critical point here is that the analysis of state and national data needs to be done within an integrated framework that involves the following:

  • Framing the same hypothesis as the basis for the research,

  • Estimating attrition and other factors with the same model specifications,

  • Using common definitions of variables across states,

  • Estimating for similar time periods whenever possible, and

  • Recognizing key differences across states that can explain differences in results.

If analyses of state and national data are done within this framework, then key national trends can be distinguished from within state trends. Trends that are supported by analyses of data from several states are more persuasive than from one state only. Moreover it is usually impossible to compare results derived from independent teacher supply and demand models because of different model specifications and variable definitions. This type of analysis would also allow us to identify key differences caused by particular state policies.

A research consortium also has significant cost advantages. Such analyses accomplished jointly would be less costly than if done independently and would result in a more interpretable set of findings. Exchange of models, ideas, hypotheses, and data handling techniques would benefit all research groups and would yield information of greater utility for education policy makers.

REFERENCES

Bobbitt, S.A., E. Faupel, and S. Burns 1991 Characteristics of Stayers, Movers, and Leavers: Results from the Teacher Followup Survey, 1988–89. NCES 91-128. Washington, D.C.: U.S. Department of Education, National Center for Education Statistics.


Grissmer, D. W., and S. N. Kirby 1991 Patterns of Attrition Among Indiana Teachers: An Executive Summary . R4167-LE. Santa Monica, California: RAND.

1992 Patterns of Attrition Among Indiana Teachers. R-4076-LE. Santa Monica, California: RAND.


Hudson. L., D.W. Grissmer, and S.N. Kirby 1991 Entering and Reentering Teachers in Indiana: The Role of the Beginning Teacher Internship Program. R-4048-LE. October. Santa Monica, California: RAND.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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Manski, C.F. 1987 Academic ability, earnings, and the decision to become a teacher: Evidence from the National Longitudinal Study of the High School Class of 1972. In D. A. Wise, ed., Public Sector Payrolls. Chicago, Illinois:University of Chicago Press.

Murnane, R.J., and R.J. Olsen 1989a Will there be enough teachers? American Economic Review Papers and Proceedings 79:242-246.

1989b The effects of salaries and opportunity costs on duration in teaching: Evidence from Michigan. Review of Economics and Statistics 11:347-352.

Murnane, R.J., and M. Schwinden 1989 Race, gender, and opportunity: Supply and demand for new teachers in North Carolina, 1975–1985. Educational Evaluation and Policy Analysts 11:93-108.

1990 The effects of salaries and opportunity costs on length of stay in teaching evidence from North Carolina. Journal of Human Resources 25:106-124.

Murnane. R.J., J.D. Singer, and J.B. Willet 1989 The influences of salaries and "opportunity costs" on teachers' career choices: Evidence from North Carolina. Harvard Educational Review 59:325-346.


Vance, V.S., and P.C. Schlechty 1982 The distribution of academic ability in the teaching force: Policy implications. Phi Delta Kappan 64:22-27.


Weiss, I.R., and S.E. Boyd 1990 Where Are They Now? Chapel Hill, North Carolina: Horizon Research, Inc.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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General Discussion

The general discussion focused primarily on data bases relevant to teacher supply, demand, and quality (TSDQ) organized at three different levels: state, regional, and national. The characteristics of such data bases established at each level were considered, including information sources, methods used, and their strengths and limitations. The following paragraphs summarize the discussion pertaining to the five state data bases, national data bases, the relationship between data bases and modeling, and funding for data base development and maintenance.

Existing state data bases relevant to TSDQ typically have been derived from state administrative records containing information about teachers, students, and schools. States rarely use sample surveys of teachers to obtain TSDQ data. Several factors cause development of a teacher data base within a particular state to be difficult, time-consuming, and expensive. These factors include the sheer volume of detailed information available from multiple in-state administrative records, accessibility of data from these sources, and differences in definitions and time periods covered.

Once assembled, data bases support the investigation of a wide range of important TSDQ issues because of three characteristic strengths. First, state records contain a wealth of detailed data, often much greater detail than can be gathered reasonably by sample surveys. Second, these records are maintained year-by-year, thereby permitting examination of trends over time and permitting cohort studies of teachers as their careers develop. Third, state records incorporate entire populations of teachers, students, and schools. In contrast, surveys are usually based on samples of these populations, and limited sample sizes often do not permit the disaggregation needed for de-

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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tailed analyses. Thus, at their best, state data bases provide opportunities for detailed, longitudinal analyses of TSDQ phenomena in teacher populations.

Unfortunately, state data bases are beset by a number of limitations. One is that teachers of a single state, or a combination of a few states, cannot provide national estimates. Furthermore, variations among states in definitions of variables as well as policies pertaining to teachers limit the generalizability of state-level findings. For example, definitions of second-year algebra and state policies pertaining to such factors as teacher certification, benefits packages, and collective bargaining vary a great deal among states. The conference participants discussed approaches to address the problem of differences in definitions. One is that cross-walks are sometimes successfully devised to bridge such differences. Another is that NCES is revising and developing, at the urging of many states, its handbook of definitions of teacher variables. Beginning with state and SASS definitions, the objective is to develop consensus definitions among the states and NCES and to use these in data base development. Of course, the definitions of many variables already are consistent, and much TSDQ research with data bases from different states has yielded consistent findings.

Another major problem is the quality of many state data bases relevant to TSDQ. In this context, quality includes the dimensions of data accuracy; breadth of variables covered; completeness of data for variables covered; timeliness of data; and difficulty in linking, and consistency within a state of, definitions across different sets of administrative records that contain the basic information used to create teacher data bases. It was generally agreed that most states will have to expand a great deal of effort to establish high-quality teacher data bases. However, this is a costly enterprise, which many states cannot afford.

A final limitation of state data bases is their inability to provide information on teachers migrating out of state. When a teacher discontinues teaching in the public school sector of a particular state, state records typically do not indicate whether the teacher left the profession, migrated to a school in another state, or transferred to a private school. Without this information, attrition studies lack precision and may yield misleading interpretations.

Turning next to the regional level, it was recognized that the only regional TSDQ data base currently operational has been developed for New York and the New England states by the Massachusetts Institute for Social and Economic Research (MISER).1 In cooperation with these states, MISER has taken the lead in assembling seven useful state data bases by extracting information available from multiple sources within each of the seven states constituting its region. However, MISER has not generated "original" teacher data through sample surveys or other means.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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With funding from federal, state, and private sources, MISER worked closely with the seven states of the northeastern region to improve and expand considerably the teacher data bases for each state, and has begun to create linkages among them for analytic purposes. These data bases probably go well beyond what the cooperating states would have developed on their own initiative. This regional interest and activity has had several beneficial results. First, it has stimulated these states to improve their collection and management of teacher data. As one commentator noted, this unlikely result suggests a new data base principle, namely, that "bad data begets good data," at least when comparisons and competition among states can occur. Second, the regionalization of the data has permitted the study of cross-state migration of teachers. Third, the generation of comparative state-by-state teacher data has stimulated intense interest among the chief state school officers and has led to policy changes such as regionalizing teacher credentialing.

With respect to the national level, a number of data bases useful for analyses of TSDQ issues now exist. Only one or two of these data sets are derived from data collected and reported by state or local education agencies. Most national data relevant to TSDQ have been generated by sample surveys with questionnaires—the prime example being the Schools and Staffing Survey (SASS), and its longitudinal component the Teacher Followup Survey (TFS), of the National Center for Education Statistics (NCES).

Since national and state data bases (including the one regional data base) differ greatly in their data sources and methods, their strengths and limitations also remarkably differ. For the most part, the strengths of national data bases relevant to TSDQ are the limitations of state data bases, and vice versa. At their best, national data bases, such as SASS, include a great deal of information about probability samples of teachers for each state and for the nation as a whole. In addition, the surveys define variables uniformly across all units sampled and use standardized procedures. Therefore, these surveys provide a national perspective of the teaching force, opportunity for state-by-state comparisons, and a basis for analysis of teacher migration among states.

National data bases, however, are as yet very limited in the extent to which they can support time-series analyses for teachers. In addition, practical limitations on the length of survey questionnaires do not permit the level of detailed information gathering that is typical of administrative records from which state data bases are derived. Finally, resources available for national surveys limit sample sizes, thereby either excluding many important cross-tabulations of data or producing estimates with large standard errors.

Since the best currently available state and national data bases are complementary in their respective patterns of strengths and limitations, there is

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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great advantage not only in having both types, but also in being able to link data bases at these two levels. From the perspective of NCES, there is considerable potential for linking SASS with state data bases, and it is important to work toward achieving this dual strategy.

The reciprocal relationship between state data bases and MISER's regional TSDQ model was also discussed. It is readily apparent that data limitations (both quality and availability) constrain the variety of analyses demonstrated to be important by the model. However, this very fact was helpful in identifying data gap problems with the quality of state data. When these limitations became apparent, some states were stimulated to improve their state data collection and management.

Conferees recognized the limitations of funding data base development. States need to make major improvements in their teacher data collection and management. This is expensive. Several participants argued that all state money available for teacher data base development should be devoted to improving within-state data, and that none of it should be allocated to development of regional data bases. Even if this principle were adopted, most states probably would not have sufficient resources to produce high-quality data bases. It was therefore suggested that NCES, or some other federal agency, provide states with capacity-building grants. While this might seem to exclude the funding of regional data bases, it was pointed out that resources available to MISER to develop data in the Northeast were devoted primarily to working with individual states to improve and expand their data, and that this was carried much further than would have occurred had there not been a regional presence.

NOTE

1.  

In addition, the Southern Region Education Board (SREB) has explored the feasibility of developing such a data base for the Southern region of the nation. In a report delivered at the conference, Lynn Cornett and Robert Stoltz, both of SREB, stated that 11 SREB states were interested in cooperating in an effort to create a regional TSDQ data base derived from the records of each state. A preliminary survey of these 11 interested states found that 7 had excellent data for this purpose, one had satisfactory data, and three did not seem to have the minimum essential data available in readily usable form. SREB staff prepared a cost analysis for the project, but it is unlikely that it will start in the near future. There are several reasons for the delay: lack of state funds due to budget shortfalls, lack of high-level strong advocates within state education departments, lack of an immediate crisis involving TSDQ issues, and lack of external pressure on state education departments to produce TSDQ data and analyses. In view of these constraints, SREB is exploring the feasibility of an incremental low-cost approach to initiating the development of a TSDQ data base and model for the southern region of the nation.

Suggested Citation:"PART V DATA BASES." National Research Council. 1992. Teacher Supply, Demand, and Quality: Policy Issues, Models, and Data Bases. Washington, DC: The National Academies Press. doi: 10.17226/2040.
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This book examines policy issues, projection models, and data bases pertaining to the supply of, demand for, and quality of teachers in the United States from kindergarten to twelfth grade.

It identifies additional data needed to clarify policy issues or for use in projection models, with a long-range view of contributing to the development of a teaching force of higher quality in the United States.

The book has major implications for the teacher work force and for statisticians and researchers involved in investigating, modeling, and projecting teacher supply, demand, and quality.

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