SCHOOL DISTRICT DATA BOOK USER'S GUIDE Acknowledgements 1. School District Data Book 1.1. General 1.1.1. Overview 1.1.1.1. Background and Development 1.1.1.2. Contents of the School District Data Book 1.1.1.3. Slide Show 1.1.2. Summary of Operations 1.1.3. Copyrights and Redistribution 1.2. Step-by-Step Examples of Using SDDB 1.2.1. Basic data for the United States 1.2.2. Basic data for one school district 1.2.3. A comparative display of two selected districts 1.2.4. A printed profile 1.2.5. A profile in a DOS file for word processing 1.2.6. A summary for all counties (districts) in a state 1.2.7. A report with one line for each state from the Census CD 2.0. Profiles and Tables 2.1. Select Geography and Subject Matter 2.1.1. Select Geography 2.1.1.1. Select a State 2.1.1.2. Select Type of Geography 2.1.1.2.1. Select a School District 2.1.1.2.2. Select a County 2.1.1.3. Select Another Area 2.1.2. Select Type of Display 2.1.2.1. Select Type of Profile 2.1.2.2. Select Type of Table 2.1.2.2.1. Select an Enrollment Category 2.1.2.2.2. Select a Grade/Age Category 2.1.2.2.3. Select a Specific Table 2.1.2.3 Select a Table or Display 2.1.3. Comparison Area 2.1.3.1. Use Default Comparison Geography 2.1.3.2. Select My Own Comparison Geography 2.1.4. Display and Automatic Spreadsheet File Extraction 2.1.4.1. Display Structure and Associated ASCII File 2.1.4.2. Automatic Spreadsheet File Extraction 2.2. Glossary 2.3. Index 3.0. Database Operations 3.1. Select a Database 3.2. Extract Data 3.2.1. Select a Database 3.2.2. Extract (E) or Master (M) 3.2.3. Enter File Name [for Extract only] 3.2.4. Enter Output File Name 3.2.5. Selected (S) or All (A) Fields 3.2.6. Enter Output Field Names 3.2.7. Enter Selection Expression 3.2.8. Enter Output Format: DBF, ASC or PRN 3.2.9. Extracting Data from Census CD-ROM Database 3.3. Report 3.3.1. Select a Database 3.3.2. Extract (E) or Master (M) 3.3.3. Enter File Name [for Extract only] 3.3.4. Enter Output File Name 3.3.5. Selected (S) or All (A) Fields 3.3.6. Enter Output Field Names 3.3.7. Enter Selection Expression 3.3.8. Using Data from Census CD-ROM Extracted Database 3.4. Statistics 3.4.1. Select a Database 3.4.2. Extract (E) or Master (M) 3.4.3. Enter File Name [for Extract only] 3.4.4. Enter Expression for Statistics 3.4.5. Enter Selection Expression 4.0. Maps 4.1. Select Type of Map 4.2. Select a State 4.3. Select Type of Subject Matter 4.3.1. Standard - Top 100 Items Database 4.3.2. Custom - Pregenerated Custom Census File 4.3.3. User Supplied File Name 4.4. Select Type of Interval 4.4.1. Equal Number 4.4.2. Equal Value 4.5. Map Spreadsheet Operations 4.5.1. Edit 4.5.2. Insert 4.5.3. Delete 4.5.4. Format 4.5.5. Math 4.5.6. Select 4.5.7. Deselect 4.6. Map Interpretation 5.0. Database Content 5.1. Top 100 Items Database 5.2. Common Core of Data Database 5.3. School District Finances Database 5.4. 1990 Census School District Special Tabulation Database A. Appendices A.1. Installation A.1.1. Computer Configuration and System Requirements A.1.2. Installation Processing A.1.3. SDDB Configuration File A.1.4. Performance Enhancement A.2. Codes and Reference Lists A.2.1. State FIPS Codes A.3. Sample Profiles A.3.1. Profile 001 - General Characteristics - Summary A.3.2. Profile 002 - General Characteristics - Detailed A.3.3. Profile 101 - Financial - Summary A.3.4. Profile 102 - Financial - Detailed A.3.5. Profile 105 - Administrative - Summary A.3.6. Profile 106 - Administrative - Detailed A.4. Census Data Accuracy A.4.1. Sample Design A.4.2. Confidentiality A.4.3. Errors in the Data A.4.4. Standard Errors Computation A.4.5. Estimation Procedure A.5. Census Collection and Processing Procedures A.5.1. Enumeration and Residence Rules A.5.2. Data Collection Procedures A.5.3. Processing Procedures A.5.4. Features Unique to the School District Special Tabulation Acknowledgements The School District Data Book (SDDB) is an information resource of the National Center for Education Statistics, U.S. Department of Education. It is the product of a multi-year effort involving several hundred people. From the National Center for Education Statistics (NCES): Emerson J. Elliott, Commissioner of the National Center for Education Statistics. Roger A. Herriot, Associate Commissioner for Standards and Methodology, whose farsighted vision conceived the School District Data Book and who continued to provide leadership for its development until his untimely death in April 1994. Theodore H. Drews, overall project coordinator. Marilyn McMillen Sam Peng Tai Phan Lee Hoffman John Sietsema From the Census Mapping Project: Each of the state coordinators who prepared maps for the school districts in their state. Ramsay Selden, Council of Chief State School Officers From the U.S. Bureau of the Census: Population Division: Arthur J. Norton Jane H. Ingold Paul M. Siegel Christophre Gaebler John Wells Nancy L. Sweet Decennial Management Division: Thomas C. Walsh Donald R. Dalzell Dennis W. Stoudt Jess D. Thompson Geography Division: Robert W. Marx Joel Sobel Catherine McCully Data User Services Division: Forrest B. Williams Larry W. Carbaugh Governments Division: Larry MacDonald Field Division Gail Leithauser David McCormack Sharon Neugebauer From the MESA Group: Warren G. Glimpse George Grier Eunice S. Grier Carl A. Friedman Suzanne L. Grier Others include: Steve Boal, Iowa Department of Education Janet Nickel, Wichita State University 1.0. School District Data Book This section provides general information regarding the role, scope and uses of the School District Data Book. 1.1. General -- School District Data Book -- Version 1.0. The School District Data Book is an information resource of the National Center for Education Statistics (NCES), U.S. Department of Education. For information about the School District Data Book project, contact: National Center for Education Statistics U.S. Department of Education 555 New Jersey Avenue, NW, Room 408 Washington, DC 20208 The School District Data Book has been developed by The MESA Group using data supplied by the Census Bureau and NCES. For assistance with use of the Data Book, contact: The MESA Group School District Data Book Telephone: (703) 418-4002 P.O. Box 816 Fax: (703) 418-4017 Alexandria, VA 22313-0816 For information on other topics about the system, press the F2 function key for the table of contents. With the highlight bar on the topic of interest (in the table of contents) press Enter and you will be transferred to that section. 1.1.1. Overview The School District Data Book is an electronic library containing so- cial, economic and administrative data for each of the 15,274 public school districts in the United States. Perhaps most notably, the School District Data Book contains the most comprehensive demographic database ever developed for the nation's children. The School District Data Book is contained on a set of 44 CD-ROM's. Using a conventional microcomputer equipped with a CD-ROM reader, imme- diate access is provided to data for every school district, county and state and the United States as a whole. This immense database of approximately 20 gigabytes, 20 billion charac- ters, of data (after reduction by data compression techniques) provides up to 200,000 data items for each school district or county. The map ping features enables users to view maps of all individual school dis- tricts in the nation for the first time. The School District Data Book enables users to: o Examine demographics, operations and finances of any school district. - Assess special needs of the children and households served. - Plan for types of growth or decline in student membership. o Compare characteristics of one school district to any other. o Locate districts within a region having certain characteristics. o Draw a thematic map to examine geographic distributions. o Extract data in a form that can be manipulated and used with your own data. o Use reference features as a handy electronic library. The School District Data Book has been developed under the sponsorship of the U.S. Department of Education National Center for Education Sta- tistics (NCES). The principal interest of NCES in developing the School District Data Book is to provide an effective way for the Department and Congress to access, analyze and interpret data from the 1990 Census School District Special Tabulation. However, since this information can benefit state and local education agencies as well as researchers, policy analysts and administrators in a variety of other organizations, NCES implemented a program to meet these broader needs. 1.1.1.1. Background and Development Census Mapping Project Development of the School District Data Book started in 1988 with the Census Mapping Project. Under this initiative, sponsored by the Nation- al Center for Education Statistics and coordinated by the Council of Chief State School Officers, all states participated in a program to develop school district maps. The maps, the first complete set ever to be developed for the nation, were the critical first step in the devel- opment of the database. A public school district is an area whose public schools are administra- tively affiliated with a local education agency recognized by the state education agency as responsible for implementing the state's elementary and secondary public education program. Through the Census Mapping Project, 15,274 school districts were mapped. School districts delineated by the Census Mapping Project are usually the same as those referenced in the NCES Common Core of Data Program. Accordingly, the Census Mapping Project used names and codes from the 1989-90 Common Core of Data as a means of identification. Most areas of the U.S. are covered by one or more school districts. However, there are parts of some states that are not covered by any school district. These 60 areas are referred to as "balance of county" areas and treated as "pseudo" school districts in the SDDB. As a re- sult, all areas of the U.S. are accounted for through the Census Mapping Project. Paper maps developed by individual states were sent to the U.S. Bureau of the Census. The Census Bureau digitized the maps and transferred the resulting data into the Census Bureau's TIGER System. The TIGER (Topo logically Integrated Geographic Encoding and Referencing) System is used by the Census Bureau as a way of tabulating address-oriented data. Once the school district maps were a part of the TIGER system, each of the nation's 6.5 million census blocks could be uniquely associated with their respective school districts. MESA Group and SDDB Development In 1992, the National Center for Education Statistics contracted with The MESA Group of Alexandria, Virginia to develop the School District Data Book (SDDB). It would be MESA's responsibility to assemble the raw data into the databases that became a part of the SDDB and to design and develop the software to meet the goals of the Department of Education for utility and ease of use. 1990 Census School District Special Tabulation In 1993, under the sponsorship of NCES, the Census Bureau produced the 1990 Census School District Special Tabulation files that comprise approximately 95 percent of the SDDB's data. MESA and Census Bureau staff worked together to develop data compression techniques to transfer the data files from a mainframe computing environment into microcomputer databases. The Census Bureau delivered the school district special tabulation files to MESA on approximately 200 high density magnetic tape reels. MESA transformed the census special tabulation data into a database structure suitable for CD-ROM and microcomputer use. Additional Statistical Data Sources In 1993-94, The MESA Group, with support from NCES, acquired two non- decennial census data files and integrated these into the SDDB CD-ROM framework. Described in more detail below, these files include: o administrative data from the NCES 1989-90 Common Core of Data (data on teachers, schools and students) and o financial data from the 1989-90 Survey of School District Finances produced by the Census Bureau for the Department of Education. School District Boundary Files In 1994, also under sponsorship of NCES, the Census Bureau provided The MESA Group with the TIGER/Line files for the U.S. The TIGER/Line files are a product of the Census Bureau and are themselves contained on CD- ROM. They contain data describing attributes of all street and non- street (such as rivers) features of the entire U.S. The 1994 TIGER/Line files used in development of the SDDB are referred to as "Version 5," are the only TIGER/Line files that contain coding for the school districts resulting from the Census Mapping Project. Other versions of TIGER/Line files have been released earlier. Using the TIGER/Line files, The MESA Group developed boundary files for all school districts in the U.S. These boundary files are used by the SDDB software to draw maps of school districts. The boundary files are an integral part of the SDDB CD-ROM series. SDDB Software During the period 1992-94, The MESA Group developed the software to be distributed on the SDDB CD-ROM. The SDDB software design is critical to meeting the goals of NCES to have the data not only easily accessible but also highly usable -- by users with varying interests and technical backgrounds. The main features of the SDDB include: o Profiles and Tables - select geography through menu-driven operations - select prestructured profiles providing highlight data, or - select data for tabular display from the school district special tabulation files o Database Operations - extract data from SDDB databases for use in other applications - locate districts/counties/states meeting any specified criteria in the SDDB database - prepare reports showing data across geographic areas - obtain basic distributional statistics for SDDB data o Maps - display map outlines for U.S. by State State by District State by County - display thematic maps showing subject matter in the SDDB for states, districts and counties o Electronic Index, Glossary and Reference - the reference manual is electronic and may be queried for information to help answer any immediate application question or to lookup information on any topic by keyword. - the electronic glossary functions like the reference manual providing online definitions - the electronic index, a subject matter index, functions like the reference manual and facilitates access to data by topic. 1.1.1.2. Contents of the School District Data Book The School District Data Book is distributed only on CD-ROM. The 44- volume CD-ROM set includes a U.S. by State volume and state by school district and county volumes. Basic content of each CD-ROM includes: o SDDB software and reference files o For all districts, counties, states and the U.S.: - "Top 100" database of key demographic items - Administrative database (no county data) - Financial database (no county data) o Boundary files for maps - U.S. by State - State by county (all states) The U.S. by State CD-ROM contains, in addition to the basic content: o U.S. and State 1990 Census school district special tabulation data. Each of the State CD-ROM contain, in addition to the basic content: o State, district and county 1990 Census school district special tabulation data for that state. Several states require two or more CD-ROM, while in other cases two or more states are contained on one CD-ROM. 1990 Census School District Special Tabulation The 1990 Census School District Special Tabulation data are provided for each school district, county, state and the U.S. This section provides an overview of the types of data tabulated in the special tabulation. Section 5 provides an expanded description of the record types described below and references the source of additional electronic documentation. Issues concerning processing and data accuracy are described in the appendices. For the 1990 Census school district special tabulation, data are organized by 7 types of tabulation records: Data Items 1 - Characteristics of All Households 981 2 - Characteristics of All Persons 5,688 3 - Characteristics of Households with Children 808 4 - Characteristics of Parents with Children 3,187 5 - Children's Households Characteristics 808 6 - Children's Parents Characteristics 2,813 7 - Children's Own Characteristics 2,271 Roughly 70 percent of the data items in each record correspond to the Census Bureau subject matter tables used in the 1990 Census Summary Tape File 3. The additional tables follow similar numbering/reference nomen- clature but have been defined by NCES to meet more specific types of data uses; e.g., dropout population and at-risk populations. For record types 3 through 7, tabulation categories are further detailed by type of enrollment: 1 - Total Enrolled & Not Enrolled 2 - Total Enrolled (Public & Private) 3 - Enrolled in Public School 4 - Enrolled in Private School 5 - Not Enrolled For each type of enrollment category, as applicable for a school dis- trict age/grade coverage, in record types 3 through 7, the data are fur- ther broken down by the following age/grade categories: 1- Total Relevant 2- Pre-Kindergarten 3- Kindergarten 4- Grade 1- 4 5- Grade 5- 8 6- Grade 9-12 7- Age 0- 2 years 8- Age 3- 4 years 9- Age 5-13 years 10- Age 14-17 years 11- Age 18-19 years 12- Age 3-19 years 13- Age 5-17 years Top 100 Database The "Top 100" database was developed to provide a compact file of key data items to be provided on each CD-ROM for each district, county, state and the U.S. These data have been drawn mainly from the Census school district special tabulation. They include: Persons by Sex Persons by Type of Household Persons by Urban/Rural Status Persons by Race/Ethnic Origin Labor Force Status Educational Attainment Families Households with Children Housing Units by Tenure/Occupancy Occupied Housing Units by Urban/Rural Status Economic Characteristics Median Gross Rent Median Housing Value Per Capita Income in 1989 Median Household Income Public Assistance Income in 1989 Poverty Status, Income in 1989 Dropouts At-Risk Pre-School Age Children At Risk School Age Children Attributes of Children Sex Urban/Rural Status Race/Ethnicity Age Household Type Poverty Status Enrolled in School Sex Race/Ethnicity Enrolled in Public School Sex Race/Ethnicity Administrative (Common Core of Data) Students Teachers Schools Financial (Census of Governments) Total Revenue Local Revenue State Revenue Federal Revenue Total Expenditures Current Expenditures Instruction Expenditures Support Expenditures Financial Data The financial data, from the 1989-90 Survey of School District Finances, includes data on the following subjects (complete detail not shown). Section 5 provides an expanded description of these data. Total Revenue by Sources Total Local Revenue Taxes by category Parent Government Contribution Local Intergovernmental School Lunch and other charges by category Interest Earnings Other Total State Revenue Direct from State State Revenue on Behalf of LEA Total Federal Aid Federal Aid Through State Direct Federal Aid Total Expenditures by Function Current For Instructional Programs Instruction by category Support Services by category Noninstructional Current Spending by category Capital Outlay Expenditure by category Payments to Other LEA's & Governments Interest on Debt Long-Term Debt Issued Long-Term Debt Retired Long-Term Debt Outstanding, End Yr Short-Term Debt Outstanding, Beg Yr Assets at End of Year Sinking Fund Bond Fund Administrative Data The administrative data have been derived from the 1989-90 Common Core of Data - School Level File. Using the school level data, school dis- trict level aggregates were prepared for schools, teachers and students cross-classified by: Urban/Rural classifications Race/ethnic origin Enrollment size Type of school Free lunch eligibility Section 5 provides an expanded description of these data. 1.1.1.3. Slide Show The CD-ROM contains a slide show that may be useful in obtaining an overview of the School District Data Book project. All of the slide show is contained in the directory SDDBSHOW. You may view the slide show by entering the following commands: D:\>E: (change drives to your CD-ROM drive) E:\>CD SDDBSHOW (change directory and move into the SDDBSHOW directory) E:\>SHOW SDDB (start the slide show) After the show has started, use the following commands: Esc ... stop show PgDn ... go to next slide PgUP ... go to previous slide The show has automatic timing on the slide display time. This timing may be suitable and not require you to use the PgDn/PgUp keys. As appropriate use the spacebar to pause on one display. Press Enter to restart display after space bar. 1.1.2. Summary of Operations 1.1.2.1. Main Menu This section reviews options available from the Main Menu. From the Main Menu you may choose one of the following operations: 1 - Profiles and Tables 2 - Database Operations 3 - Maps 4 - Quit (exit) Profiles and Tables enables you to retrieve data and have it presented in a preorganized format. Detailed information is presented in section 2. Database Operations enables you to extract data from the master database, locate areas with certain characteristics, prepare custom structured reports and obtain basic distributional statistics. Detailed information is presented in section 3. Maps enables you to draw thematic maps for the geography and subject matter that you select. Detailed information is presented in section 4. 1.1.2.2. Menu Operations Most of the School District Data Book is operated by making a selection from a menu. A menu is presented on the display screen with choices from which you may select. A selection is made from the current menu and the system determines the next course of action. When a menu is displayed, the number of choices available varies. To move between alternative choices, press the up or down arrow. As the arrow key is pressed, the highlight bar moves in the direction indicated. To choose an option from the menu, press Enter when the highlight bar is on the selection of choice. Mouse Usage. There is no mouse functionality in the system. Previous Menu. In general, you may press the Esc key and control will be returned to the previous menu. 1.2. Step-by-Step Examples of Using SDDB The next few sections provide examples of how you can use SDDB to perform various types of applications. 1.2.1. Basic data for the United States Follow these steps: 1 - Start SDDB 2 - Enter to select Profiles and Tables 3 - Enter to select United States (this defines geographic scope) 4 - Enter to select a District, State or the U.S. 5 - Enter to select the United States (this defines specific geography) 6 - Enter to select Proceed ... 7 - Enter to select Profiles 8 - Enter to select General Characteristics--Summary 9 - Enter to select Use default ... View the display. The form of this display is always of a geographic comparative nature. The general characteristics profile offers you a "primary" area and two "comparison" areas. In this simple display, the primary and comparison areas are all the same. Ready for a more interesting example? Proceed to 1.3.2. Note: if you followed these steps and did not get the U.S. profile, the system has not been fully installed. Refer to installation in the appendix (A.1.) for more information. Make a mistake? Anyplace along the way, press Esc to go to the previous menu. 1.2.2. Basic data for New York City school district Follow these steps: 1 - Start SDDB 2 - Enter to select Profiles and Tables 3 - Arrow (use directional arrow) down to New York Enter to select New York (this defines geographic scope) 4 - Enter to select a District, State or the U.S. 5 - Press the F3 Key Key in (without quotes) "New Y" and press Enter Enter to select New York City PSD (this defines specific geography) 6 - Enter to select Proceed ... 7 - Enter to select Profiles 8 - Enter to select General Characteristics--Summary 9 - Enter to select Use default ... View the display. The form of this display is always of a geographic comparative nature. The general characteristics profile offers you a "primary" area and two "comparison" areas. This display shows use of the default comparison area geography--if you select a district (or county) the default comparison areas will always be the respective state and the U.S. For a comparative display depicting two or more districts, Proceed to section 1.3.3. 1.2.3. A comparative display of two selected districts Follow these steps (only step 9 and remaining steps differ from the previous example): 1 - Start SDDB 2 - Enter to select Profiles and Tables 3 - Arrow (use directional arrow) down to New York Enter to select New York (this defines geographic scope) 4 - Enter to select a District, State or the U.S. 5 - Press the F3 Key Key in (without quotes) "New Y" and press Enter Enter to select New York City PSD (this defines specific geography) 6 - Enter to select Proceed ... 7 - Enter to select Profiles 8 - Enter to select General Characteristics--Summary 9 - Enter to select Select my Own ... 10 - Press the F3 Key Key in (without quotes) "Ill" and press Enter Enter to select Illinois 11 - Press the F3 Key Key in (without quotes) "City of C" and press Enter Enter to select City of Chicago PSD 12 - Press the F3 Key Key in (without quotes) "Cal" and press Enter Enter to select California 13 - Press the F3 Key Key in (without quotes) "Los Angeles U" and press Enter Enter to select Los Angeles Unified View the display. 1.2.4. Printing a profile To print the profile displayed in the forgoing example (or any other such display) verify that your printer is connected and turned on and press the F10 key. The contents of the display will then be printed. 1.2.5. Profile in a DOS file for Word Processing If you want to modify the profile you just displayed, as you exit SDDB to DOS, you can retrieve the file named "NEWFILE.DAT" that is located in the SDDB directory. This file contains a verbatim display of what you viewed on the last session screen display. This file is re-written each time SDDB performs a display. So, if you want to retain a particular display, exit to DOS after the display and rename the print file. 1.2.6. A summary for all counties (districts) in a state This example shows you how to create your own booklet to take out of the SDDB library! This example is a variation of 1.3.2. Follow these steps: 1 - Start SDDB 2 - Enter to select Profiles and Tables 3 - Arrow (use directional arrow) down to Arizona Enter to select Arizona (this defines geographic scope) 4 - Arrow down to the 4th option 5 - Enter to select All counties in the state 6 - Enter to select Proceed ... 7 - Enter to select Profiles 8 - Enter to select General Characteristics--Summary 9 - Enter to select Use default ... View the display. Now as you press page down and browse through the display, you will see that the summary profile has been created for each county in Arizona. If you press F10 now, and the printer is connected, a page will be printed for each county. 1.2.7. A report with one line for each state from the Census CD The example in 1.3.6. presents the data in a "table-oriented" manner. To obtain a report or list-oriented display, use Database Operations. To run the following example, remember that you must have the U.S. by State CD-ROM as the active CD-ROM. You might want a simple display of data by states to appear as follows: Record # NAME P008001 1 United States 29477923 2 Alabama 446664 3 Alaska 75307 ... 49 Washington 678646 50 West Virginia 301488 51 Wisconsin 686790 52 Wyoming 88334 The above display has been prepared using the Database Operation-Report. Each line provides the name of the area and a single data item. P008001 is table 008, item 001 from the total relevant children record. P008 is total relevant children by race. Item 001 is White. Though not shown on the header line above, this report has been prepared for total relevant children enrolled in public school. To prepare the above report, follow these steps (assuming the U.S. by State CD-ROM is active): 1 - Start SDDB Step 1 - extract the data from the CD-ROM database 2 - Enter to select Profiles and Tables 3 - Enter to select United States 4 - Arrow down to the 5th option 5 - Enter to select All States 6 - Enter to select Proceed ... 7 - Arrow down one to Tables 8 - Enter to select Tables 9 - Arrow down one to Children's Own Characteristics 10 - Enter to select Children's Own Characteristics 11 - Enter to select the first option Total Relevant 12 - Arrow down three to Enrolled in Public School 13 - Enter to select Enrolled in Public School 14 - Enter to select Proceed ... 15 - Enter to accept default geography comparison 16 - After the table displays, press Esc Step 2 - prepare the report 17 - Enter Database Operations 18 - Arrow down one to Report 19 - Enter to select Report 20 - Arrow down four to 1990 Census ... 21 - Enter to select 1990 Census database 22 - Enter at the prompt for file name to see the data on screen 23 - Enter NAME at the prompt for the first output field 24 - Enter P008001 at the prompt for the second output field 25 - Enter to terminate the field selection list 26 - Enter to use no selection expression The report now appears. At this stage, you may return to the top of Step 2 above and repeat the report with different options (e.g., different field order, different fields, redirect the display to a file or use a selection criteria to qualify areas for your report. Note that the underlying file that is being used as the source for the data, extract.dat, will be overwritten the next time you perform a sequence of steps like Step 1 (or use the Database Operations- Extract. 1.1.3. Copyrights and Redistribution All statistical, geographic and text data files contained on the SDDB CD-ROM are regarded as public domain and may be used in any manner. All files, including software, contained in the SDDB CD-ROM directory "SDDB," may also be used in any manner. Certain software in the SDDB CD-ROM directory "IMAGE3A," is copyrighted and is being distributed by the Department of Education under a restricted licensing agreement. The files named IMAGE2.EXE and IMAGE2.OVR are copyrighted products of Warren G. Glimpse and are licensed for use only with a SDDB CD-ROM issued by the Department of Education. The files named DRIVERS.EXE and files named in the manner *.SYS are copyrighted products of Graphic Software Systems, Inc. and are licensed for use only with a SDDB CD-ROM issued by the Department of Education. 2.0. Profiles and Tables A profile is defined as an organized data presentation where all of the descriptive information and items displayed are predetermined. A table is defined as an organized data presentation where the describe information and items are predetermined IN MODULAR SECTIONS. The modular sections, selected by the user, are organized into the composite display in the manner specified by the user. Tables are only found as a request option for Census data. 2.1. Select Geography and Subject Matter 2.1.1. Select Geography 2.1.1.1. Select a State The selection made from this menu sets the geographic scope for the data request--the U.S. or a state. UNITED STATES -- ALL STATES ALABAMA ALASKA ARIZONA ARKANSAS CALIFORNIA COLORADO CONNECTICUT DELAWARE DISTRICT OF COLUMBIA FLORIDA GEORGIA HAWAII ... 2.1.1.2. Select Type of Geography The selection made from this menu specifies what type of geography is being requested and the scope of the selection (i.e., one area or all areas). School District, State or U.S. All School Districts County All Counties All States [U.S. & State Summaries] Option 1 should be used to retrieve data for one district, county or state. Option 2 will result in all districts being selected for the state specified in the previous menu. This option is state-specific and should not be used to select all districts in the U.S. Option 3 is used to select one county. Option 4 operates like Option 2 but for counties. Option 5 selects the U.S. and all states. 2.1.1.2.1. Select a School District If option 1 is selected from Select Type of Geography menu, the Select a School District menu appears. The following example, assuming Arizona was selected previously, shows districts listed in alphabetical order. 00000 ARIZONA 00450 AGUA FRIA UNION HS DIST 216 00480 AGUILA SCHOOL DISTRICT 63 00520 AJO UNIFIED DIST 15 00600 ALHAMBRA SCHOOL DISTRICT 68 00630 ALPINE ELEMENTARY DIST 7 04770 ALTAR VALLEY DIST #51 00680 AMPHITHEATER UNIFIED DIST #10 00720 ANTELOPE UNION HS DIST 50 00790 APACHE JUNCTION UNIF DIST 43 00750 APACHE SCHOOL DISTRICT #42 00840 ARLINGTON SCHOOL DISTRICT 47 00870 ASH CREEK SCHOOL DISTRICT #53 00910 ASH FORK UNIF DIST 31 ... 2.1.1.2.2. Select a County If option 3 is selected from Select Type of Geography menu, the Select a School County menu appears. The following example, assuming Arizona was selected previously, shows counties listed in alphabetical order. 000 ARIZONA -- ALL COUNTIES 001 APACHE, AZ 003 COCHISE, AZ 005 COCONINO, AZ 007 GILA, AZ 009 GRAHAM, AZ 011 GREENLEE, AZ 012 LA PAZ, AZ 013 MARICOPA, AZ 015 MOHAVE, AZ 017 NAVAJO, AZ 019 PIMA, AZ 021 PINAL, AZ 023 SANTA CRUZ, AZ 025 YAVAPAI, AZ 027 YUMA, AZ 2.1.1.3. Select Another Area This feature allows you to "stack" requests for multiple geographic areas. Proceed to display using selected geography Change existing geographic selection Select additional geographic area - same state Select additional geographic area - different state Review geographic selections Option 1--Accepting the first option will result in control passing to the next step of display (no additional geography can be added to the request list). Option 2--Using the second option results in the selection that was most recently made being replaced with a new selection. Option 3--Use this option to select another area from the same state. Option 4--Use this option to select another area from a different state. Option 5--Present a summary on the screen of the active requests to this point. 2.1.2. Select Type of Display Profiles and tables are two types of preorganized data presentation formats. A profile is defined as an organized data presentation where all of the descriptive information and items displayed are predetermined. Financial (F-33) and administrative (Common Core of Data) data are only presented in the form of profiles--no tables. A table is defined as an organized data presentation where the describe information and items are predetermined IN MODULAR SECTIONS. The modular sections, selected by the user, are organized into the composite display in the manner specified by the user. Tables contain only Census data. TABLES ARE ONLY AVAILABLE FOR THE GEOGRAPHY COVERED IN THE CENSUS SPECIAL TABULATION DATABASE ON THE ACTIVE CD-ROM. EXAMPLE: NO SCHOOL DISTRICT OR COUNTY LEVEL DATA ARE AVAILABLE ON THE U.S. BY STATE CD-ROM. 2.1.2.1. Select Type of Profile The following profiles are available for all areas at for each CD-ROM: Integrated Census, Administrative and Financial Data: 001 General Characteristics - Summary 002 General Characteristics - Detailed Financial Data Only (no county data): 101 Financial Profile - Summary D 102 Financial Profile - Detailed D Administrative Data Only (no county data): 105 Administrative Profile - Summary D 106 Administrative Profile - Detailed D The following CENSUS profiles are available on each CD-ROM for the geography included on that CD for the special tabulation database: C01 Demographic Profile - Households D C02 Demographic Profile - Persons/Parents D C03 Demographic Profile - Children's Own Characteristics D C04 Economic Profile - Households D C05 Economic Profile - Persons/Parents D Iteration possibilities differ for the "C" profiles as described below. C01 Demographic Profile - Households D Iterated for: All Households Households with Children Children's Households (latter category available for:) Total Enrolled & Not Enrolled Total Enrolled (Public & Private) Enrolled in Public School Enrolled in Private School Not Enrolled (crossed for each of:) Total Relevant Pre-Kindergarten Kindergarten Grade 1- 4 Grade 5- 8 Grade 9-12 Age 0- 2 years Age 3- 4 years Age 5-13 years Age 14-17 years Age 18-19 years Age 3-19 years Age 5-17 years C02 Demographic Profile - Persons/Parents D Iterated for: All Persons Parents with Children (latter category available for:) Total Enrolled & Not Enrolled Total Enrolled (Public & Private) Enrolled in Public School Enrolled in Private School Not Enrolled (crossed for each of:) Total Relevant Pre-Kindergarten Kindergarten Grade 1- 4 Grade 5- 8 Grade 9-12 Age 0- 2 years Age 3- 4 years Age 5-13 years Age 14-17 years Age 18-19 years Age 3-19 years Age 5-17 years C03 Demographic Profile - Children's Own Characteristics D Iterated for: Total Enrolled & Not Enrolled Total Enrolled (Public & Private) Enrolled in Public School Enrolled in Private School Not Enrolled (crossed for each of:) Total Relevant Pre-Kindergarten Kindergarten Grade 1- 4 Grade 5- 8 Grade 9-12 Age 0- 2 years Age 3- 4 years Age 5-13 years Age 14-17 years Age 18-19 years Age 3-19 years Age 5-17 years C04 Economic Profile - Households D Iterated for: All Households Households with Children Children's Households (latter category available for:) Total Enrolled & Not Enrolled Total Enrolled (Public & Private) Enrolled in Public School Enrolled in Private School Not Enrolled (crossed for each of:) Total Relevant Pre-Kindergarten Kindergarten Grade 1- 4 Grade 5- 8 Grade 9-12 Age 0- 2 years Age 3- 4 years Age 5-13 years Age 14-17 years Age 18-19 years Age 3-19 years Age 5-17 years C05 Economic Profile - Persons/Parents D Iterated for: All Persons Parents with Children (latter category available for:) Total Enrolled & Not Enrolled Total Enrolled (Public & Private) Enrolled in Public School Enrolled in Private School Not Enrolled (crossed for each of:) Total Relevant Pre-Kindergarten Kindergarten Grade 1- 4 Grade 5- 8 Grade 9-12 Age 0- 2 years Age 3- 4 years Age 5-13 years Age 14-17 years Age 18-19 years Age 3-19 years Age 5-17 years 2.1.2.2. Select Type of Table The following types of tables are available: Children's Households Characteristics CH Children's Own Characteristics CO Children's Parents Characteristics CP Characteristics of All Households HT Characteristics of Households with Children HC Characteristics of All Persons PS Characteristics of Parents with Children PR Each type of table corresponds to a type of tabulation universe. For each of the following types of tables, there is also potentially an iteration by enrollment and age/grade category. Children's Households Characteristics CH Children's Own Characteristics CO Children's Parents Characteristics CP Characteristics of Households with Children HC Characteristics of Parents with Children PR For districts with smaller enrollment sizes, there may have been insufficient observations upon which to base an estimate. In such cases, the data record is not contained within the database. 2.1.2.2.1. Select an Enrollment Category The following enrollment categories are available: Total Enrolled & Not Enrolled 1 Total Enrolled (Public & Private) 2 Enrolled in Public School 3 Enrolled in Private School 4 Not Enrolled 5 The availability of this iteration means that all of the children's characteristics are equally available for those enrolled in public school as well as those enrolled in public school. 2.1.2.2.2. Select a Grade/Age Category The following age/grade categories are available: Total Relevant F Pre-Kindergarten A Kindergarten B Grade 1- 4 C Grade 5- 8 D Grade 9-12 E Age 0- 2 years 1 Age 3- 4 years 2 Age 5-13 years 3 Age 14-17 years 4 Age 18-19 years 5 Age 3-19 years 6 Age 5-17 years 7 Note that the age/grade iteration for a particular grade is subject to that age/grade being relevant to the particular school district. As an example, since secondary grades are 9-12, a secondary school district will not have an age/grade iteration for grades PK, K, 1-4 or 5-8. 2.1.2.2.3. Select a Specific Table The menu options for selecting a specific table are too large to usefully replicate here. Instead, the first few tables available for the "Characteristics of All Households" are reviewed as an example. When the "Select a Specific Table" menu first appears, the top of the menu options appear as follows. TYPE TABLE ITEM HT P004 Families (1) [1] HT P004 Universe: Families HT P005 Households (1) [1] HT P005 Universe: Households HT P016 Persons in Household (7) [7] HT P016 Universe: Households HT P019 Household Type (4) [4] HT P019 Universe: Households HT P019B Household Type (4) by Poverty Status in 1989 HT P019B of Householder (2) [8] HT P019B Universe: Households ... Tables available in the above example are P004, P005, P016, P019, and P019B. As shown in the brackets, Table P004 contains one item whereas Table P019B contains eight items. By looking at the references to P019B, it is seen that these data are tabulations of households ("Universe: Households"). The bale contains the number of households by household type (4 categories) by poverty status of householder in 1989 (2 categories). Suppose that you want to see the subject referred to as P019B. To select this table, move the highlight bar to the line with P019B and press Enter. The selection process will proceed with the next step (see next subsection). Suppose that you do not know whether or not you want this table. For example, what the 4 household types are. To view the more detailed description of the tables, press the function key F5. The menu will now appear as shown below. TYPE TABLE ITEM HT P004 Families (1) [1] HT P004 Universe: Families HT P004 001 Total HT P005 Households (1) [1] HT P005 Universe: Households HT P005 001 Total HT P016 Persons in Household (7) [7] HT P016 Universe: Households HT P016 001 1 person HT P016 002 2 persons HT P016 003 3 persons HT P016 004 4 persons HT P016 005 5 persons HT P016 006 6 persons HT P016 007 7 or more persons HT P019 Household Type (4) [4] HT P019 Universe: Households HT P019 Family households: HT P019 001 Married-couple family HT P019 Other family HT P019 002 Male householder, no wife present HT P019 003 Female householder, no husband present HT P019 004 Nonfamily households HT P019B Household Type (4) by Poverty Status in 1989 HT P019B of Householder (2) [8] HT P019B Universe: Households HT P019B Family households: HT P019B Married-couple family: HT P019B 001 Income in 1989 above poverty level HT P019B 002 Income in 1989 below poverty level HT P019B Other family: HT P019B Male householder, no wife present: HT P019B 003 Income in 1989 above poverty level HT P019B 004 Income in 1989 below poverty level HT P019B Female householder, no husband present: HT P019B 005 Income in 1989 above poverty level HT P019B 006 Income in 1989 below poverty level HT P019B Nonfamily households: HT P019B 007 Income in 1989 above poverty level HT P019B 008 Income in 1989 below poverty level ... Note that the only difference in the above display, versus the previous one, is that the item descriptions are now shown. It can be seen that the types of households are: Family households Married-couple family Other family Nonfamily households If this categorization meets the data need, move the highlight bar onto any "P019B line" and press enter. The selection process will then proceed as described in the next subsection. 2.1.2.3. Select a Table or Display This feature allows you to "stack" requests for multiple tables. Proceed to display using selected table(s) Change existing table selection Select another table with same categories Select another table with different categories Review table selections Option 1--Accepting the first option will result in control passing to the next step of display (no additional tables could be added to the request list). Option 2--Using the second option results in the selection that was most recently made being replaced with a new selection. Option 3--If you are using the same type of tabulation (e.g., all households) and same iterations (e.g., total relevant and enrolled in public school, the third option will minimize the numbers of additional menu responses required to add more tables. Option 4--Like option 3 except you will pass through the type of table and iteration menus so other table types and iterations may be selected. Option 5--Present a summary on the screen of the active requests to this point. 2.1.3. Comparison Area All tables and profiles provide a comparison area display. This menu allows the user to accept the selection of "default" geography or to make your own selections. 2.1.3.1. Use Default Comparison Geography For Tables, the default geography is the U.S. if the primary geographic area is a state or the U.S. If the primary geographic area is a district, the default comparison area is the corresponding state. For profiles, two comparative geographic areas are presented. The default is for the first comparison area to be the state and the second comparison area to be the U.S. summary. 2.1.3.2. Select My Own Comparison Geography Tables. If you choose to select your own geography, the following succession of menus will occur: Select a State for Comparison Area Select Comparison Area Type of Geography Select an Area Profiles. Select a State (for Comparison Area 1) Select Comparison Area Select a State (for Comparison Area 2) Select Comparison Area Note that for profiles other than those beginning with "C" (Census CD-ROM database), the comparison area may be in any state or the U.S. 2.1.4. Display and Automatic Spreadsheet File Extraction 2.1.4.1. Display Structure and Associated ASCII File An Example - The U.S. by State CD-ROM is loaded and all states are selected. - Table display is selected with the following options: - Children's Own Characteristics - Enrolled in Public School - Total Relevant Children - Table 118 is selected The table is displayed as follows (the U.S. and all states are displayed; this example shows only the first two areas): School District Data Book 1990 Census School District Tabulations AREA 1: United States [00900 00000] AREA 2: United States [00900 00000] AREA 1 AREA 2 CO -P118 Poverty Status in 1989 (2) by Sex (2) [4] Universe: Children for whom poverty status is determined FOR: Enrolled in Public School AND: Total Relevant Income in 1989 above poverty level: 001 Male 16592979 16592979 002 Female 15439888 15439888 Income in 1989 below poverty level: 003 Male 3682090 3682090 004 Female 3576546 3576546 School District Data Book 1990 Census School District Tabulations AREA 1: Alabama [01901 00000] AREA 2: United States [00900 00000] AREA 1 AREA 2 CO -P118 Poverty Status in 1989 (2) by Sex (2) [4] Universe: Children for whom poverty status is determined FOR: Enrolled in Public School AND: Total Relevant Income in 1989 above poverty level: 001 Male 271295 16592979 002 Female 252906 15439888 Income in 1989 below poverty level: 003 Male 87153 3682090 004 Female 84709 3576546 The entirety of this file is also written into the file named newfile.dat which may be further manipulated with a word processor of choice. This enables you to tie in other data, add commentary and/or headers and stubs. The file named newfile.dat is overwritten each time a data display occurs. 2.1.4.2. Automatic Spreadsheet File Extraction Note that there are two ways to extract data from the Census CD-ROM files. Use of the "automatic" mode, described in this section, requires no knowledge of file specifications which are used in the "database" mode. The advantage of the "automatic" mode is that less technical knowledge, and your time, is required to perform an extraction. The advantage of the "database" mode (discussed further in section 5.4.) is that you may specify your subject matter and geographic specifications in a DOS file which can be reused at other times without having to re-enter the specifications each time through a menu. Again, this discussion is relevant only to the Census CD-ROM files and not the other databases. The extracted data output files from either method are similar. In both cases the files named "extract.dat" and "extract.dct" are created in the same format/file structure. These files are used for the automatic interface to the mapping operation (see section 4.3.2.) when you specify the source of data is from "pregenerated" files. While more elaborate data extraction processing is provided under database operations, the automatic spreadsheet file extraction occurs as the table display (above) is being generated. This feature enables users an ability to load data directly into a spreadsheet program of choice with minimal technical knowledge and learning time. An Example Note that this example makes use of the U.S. by State CD-ROM. The hypothetical objective would be to obtain a file, via the "automatic spreadsheet extraction," which includes a data record for each state. Because of this, it is important to select "U.S. by State" from the type of geography menu. Follow these steps: Profiles and Tables Select Geography and Subject Matter Select a State ... while on United States -- All States Select Type of Geography arrow-down to All States [U.S. and State Summaries] Select Another Areas? Select Type of Display arrow-down to Table Select Type of Table arrow to Children's Own Characteristics Select an Enrollment Category arrow to Total Enrolled (Public and Private) Select Grade/Age Category ... while on Total Relevant Select a Specific Table F3 P118 ... with CO P118 highlighted Processing now proceeds. As the processing takes place, the screen summarizes which geography is being processed and that two files, extract.prn and extract.dct, are being created. A third file, extract.dat, is also generated. Portions of these files are shown below. The rightmost portions of the data records are truncated for purposes of depicting the data here. extract.dct GEOCODE (SSCCCDDDDD) AREA NAME (A30) P118 70 2F001 9 <-- male above poverty level P118 70 2F002 9 <-- female above poverty level P118 70 2F003 9 <-- male below poverty level P118 70 2F004 9 <-- female below poverty level extract.prn "00","900","00000","United States ",19341333,18102535, "01","901","00000","Alabama ",309322,289201,8974 "02","902","00000","Alaska ",53454,49130,6158,5 "04","904","00000","Arizona ",274593,255890,6895 ... "54","954","00000","West Virginia ",130635,120521,4034 "55","955","00000","Wisconsin ",413094,386441,6352 "56","956","00000","Wyoming ",44554,41466,6444,6 extract.dat 00 900 00000 United States 19341333 18102535 3871923 01 901 00000 Alabama 309322 289201 89748 02 902 00000 Alaska 53454 49130 6158 04 904 00000 Arizona 274593 255890 68954 ... 54 954 00000 West Virginia 130635 120521 40348 55 955 00000 Wisconsin 413094 386441 63526 56 956 00000 Wyoming 44554 41466 6444 These files are now permanent, until overwritten, and control is returned to the main menu. 2.2. Glossary The Glossary is activated by pressing the F8 function key. At the top of the glossary is a list of terms described/defined within the glossary. To view the text associated with any glossary term, position the highlight bar on that term and press Enter. To view another term, press the F2 function key. 2.3. Index The Index is an index of Census tables. The index is useful for locating specific subject matter. The index is organized alphabetically by major subject matter groupings. The Index is activated by pressing the F9 function key. 3.0. Database Operations 3.1. Using Database Operations Database operations enable you to extract data, prepare a report or obtain basic statistics. Each of these operations are described below. 3.2. Extract Data The Extract Data feature enables you to pull-out selected data from the master database files. The purpose of this feature is to enable you to (1) take data out of the SDDB for use with other application software or (2) create a sub-file for use within SDDB Database Operations. The reason that you might want to extract data for further use within SDDB Database Operations is that processing time can often be minimized. As an example, if your applications involve certain data items in the Top 100 Items database, but only for the state of Arizona, you may find it easier to first create an extract file for Arizona before proceeding with other applications. Within SDDB, extract files may only be used with other database operations and mapping operations. If you create an extract once for Arizona, your subsequent processing involves only 244 records as opposed to processing the master database of more than 18,000 records. 3.2.1. Select a Database Select a database to instruct the system from which file you wish to extract data. Available databases include: Top 100 Items Common Core of Data School District Finances 1990 Census School District Special Tabulation See Section 5 for more detail on the content of these databases. To reach Section 5, press F2 now to go to the Table of Contents. Then PgDn or Arrow-Down to Section 5.0 and press Enter, Alternatively, you may press PgDn repeatedly until you reach that section. Sections 3.2.2 through 3.2.8. apply to extracting data from all databases other than the Census CD-ROM database. For information on this feature, see section 3.2.9. 3.2.2. Extract (E) or Master (M) The first time you work with Database Operations-Extract, enter M to select Master file. This will enable you to select a portion of the Master file for further processing. If you have already selected a portion of the Master file, enter E to make further selections of data from that file. 3.2.3. Enter File Name [for Extract only] If you specified Extract (E) in the previous step, you will be prompted for the DOS file name of the file that you created when you previously extracted data from the Master file. 3.2.4. Enter Output File Name Enter the DOS file name for the new file to be created. This name must be a valid DOS file name. 3.2.5. Selected (S) or All (A) Fields This operation allows you to select one, several or all fields from the file from which data are being extracted. By selecting only the fields that will be used, the extraction processing time will be minimized and the extracted file will process faster in subsequent applications. 3.2.6. Enter Output Field Names If the Selected Fields option was selected in the previous step, you will be prompted for the fields to be selected. Field names for the Top 100, CCD and F33 file are listed in an earlier section. Use PgUp to view the listings of the names and descriptions. You are prompted for one field at a time. The field must be spelled exactly as specified in the database or the prompt will appear again. Caution: if you press enter with no value specified, the field selection will terminate and the output will be produced as you have specified to that point. 3.2.7. Enter Selection Expression The selection option permits you to select only those records meeting certain criteria. Some examples follow for applications using the Top 100 database. Specification Result ST='04' Only records for the state of Arizona will be selected. ST='04' .and. CTY='000' Only records for the state of Arizona will be selected and also only if the county FIPS code (CTY) is 000 which means that only district records are extracted. D058>1000 Only those records with D058 (total relevant children) greater than 1,000 will be selected. D018>25000 Only those records with D018 (median household income) greater than $25,000 will be selected. CTY='000' .and. DIST='00000' Only state records will be selected. ST='36' .and. DIST='20850' Only the New York City PSD will be selected. 3.2.8. Enter Output Format: DBF, ASC or PRN If you enter: DBF ... the output file will be dBASE III structure ASC ... the output file will be fixed length field ASCII structure PRN ... the output file will be a comma-delimited structure NOTE: To use the output file as the SOURCE file in future other database operations, the output file must be a DBF file. Examples If an extraction is performed using: o the Top 100 items database o a selection expression of: ST='04' .and. DIST='00000' (this says select Arizona (state FIPS 04) and records where the district code is all zero (county records)) o the selected items are: st, cty, dist, name, d001 The ASC version of the resulting extracted file appears as: 0400000000ARIZONA 1368843 0400100000APACHE, AZ 15981 0400300000COCHISE, AZ 34546 0400500000COCONINO, AZ 29918 0400700000GILA, AZ 15438 0400900000GRAHAM, AZ 7930 0401100000GREENLEE, AZ 2809 0401200000LA PAZ, AZ 5348 0401300000MARICOPA, AZ 807560 0401500000MOHAVE, AZ 36801 0401700000NAVAJO, AZ 22189 0401900000PIMA, AZ 261792 0402100000PINAL, AZ 39154 0402300000SANTA CRUZ, AZ 8808 0402500000YAVAPAI, AZ 44778 0402700000YUMA, AZ 35791 The PRN version of the resulting extracted file appears as: "04","000","00000","ARIZONA ",1368843 "04","001","00000","APACHE, AZ ",15981 "04","003","00000","COCHISE, AZ ",34546 "04","005","00000","COCONINO, AZ ",29918 "04","007","00000","GILA, AZ ",15438 "04","009","00000","GRAHAM, AZ ",7930 "04","011","00000","GREENLEE, AZ ",2809 "04","012","00000","LA PAZ, AZ ",5348 "04","013","00000","MARICOPA, AZ ",807560 "04","015","00000","MOHAVE, AZ ",36801 "04","017","00000","NAVAJO, AZ ",22189 "04","019","00000","PIMA, AZ ",261792 "04","021","00000","PINAL, AZ ",39154 "04","023","00000","SANTA CRUZ, AZ ",8808 "04","025","00000","YAVAPAI, AZ ",44778 "04","027","00000","YUMA, AZ ",35791 3.2.9. Extracting Data from Census CD-ROM Database Note that there are two ways to extract data from the Census CD-ROM files. Use of the "database" mode, described in this section. The "automatic" mode is described in section 2.1.4.2. If in doubt as to your requirements, consider the automatic mode first. The database mode provides more flexibility but is more technical. It requires you to prepare DOS files to control which subject matter and geography are selected. Using these files, you may extract the same types of data for a number of states, districts, etc. in a single operation. Once extracted, they can be imported into spreadsheets for analysis and manipulation. The extracted data output files from either method are similar. In both cases the files named "extract.dat" and "extract.dct" are created in the same format/file structure. These files are used for the automatic interface to the mapping operation (see section 4.3.2.) when you specify the source of data is from "pregenerated" files. You are prompted for the names of two control files which are described in detail below. For use with the U.S. by State CD-ROM, a sample geographic specifications file named ALLSTATE.TXT (listing all states) may be used with corresponding sample subject matter specifications file named TABLEX.TXT. These are both ASCII files corresponding to the rules listed below. 3.2.9.1. Preparing the Geographic Specifications File You must first prepare a geographic specifications file to tell the system for which geography the extractions are to be made. This must be done outside SDDB using the DOS text editor or equivalent software. You can choose any name for the file, but it is suggested that you use the name GEO so it can be easily remembered. Each line (record) in the geographic specifications file corresponds to one geographic area. The content of the record is: Character Content 1- 2 State FIPS code (see Appendix A.2.) 3- 5 County FIPS code (for U.S. by State CD-ROM, the county code is "9" followed by the state FIPS code) 7-11 District Code (all zero, not blank, for state or county retrievals) Sample Geographic Specifications File 00900 00000 01901 00000 02902 00000 04904 00000 ... 54954 00000 55955 00000 56956 00000 3.2.9.2. Preparing the Subject Matter Specifications File The subject matter specifications allow you to select whole tables as opposed to individual items. The subject matter specifications file has one line in it for each table to be selected. You can choose any name for the file, but it is suggested that you use the name SUB so it can be easily remembered. The record content is as follows. Character Content 1- 5 Table number 10-11 Record number 13 Enrollment category 14 Age/Grade Category A more detailed description follows. o table number (characters 1-5 left-justified) o record number (characters 10-11) 10 Characteristics of All Households HT 2A Characteristics of All Persons PS 2B Characteristics of All Persons PT 30 Characteristics of Households with Children HC 40 Characteristics of Parents with Children PR 50 Children's Households Characteristics CH 60 Children's Parents Characteristics CP 70 Children's Own Characteristics CO o enrollment category (character 13) 1 Total Enrolled & Not Enrolled 2 Total Enrolled (Public & Private) 3 Enrolled in Public School 4 Enrolled in Private School 5 Not Enrolled o age/grade category (character 14) F Total Relevant A Pre-Kindergarten B Kindergarten C Grade 1- 4 D Grade 5- 8 E Grade 9-12 1 Age 0- 2 years 2 Age 3- 4 years 3 Age 5-13 years 4 Age 14-17 years 5 Age 18-19 years 6 Age 3-19 years 7 Age 5-17 years Sample Subject Matter Specifications File P008 70 3F This file contains one table extraction record. There can be as many table extraction records as you like bit remember, most application software packages have maximum size record lengths--so choose only the number of tables that you really need. In the above example, the line reads extract table P008 from record type 7 (children's own characteristics) for children enrolled in public school (3) for the total relevant population (F). The following file contains a table extraction record for table P008 total enrolled and enrolled in public school. P008 70 2F <-- total enrolled P008 70 3F <-- enrolled in public school 3.2.9.3. Census Extract Output File Files produced are (assuming default names): newfile.dct newfile.prn extract.dat extract.dct Sample Output from Census CD-ROM Extraction newfile.prn "00","900","00000","00","00",29477923,6404300,463505,1291510,2242982 "01","901","00000","00","00",446664,246209,4784,4097,1039 "02","902","00000","00","00",75307,4630,23115,3811,1097 "04","904","00000","00","00",449603,23977,55442,9647,80292 ... "54","954","00000","00","00",301488,11440,583,1378,493 "55","955","00000","00","00",686790,65388,9887,13464,10679 "56","956","00000","00","00",88334,742,2723,621,2534 newfile.dct GEOCODE,A5 LO,A2 HI,A2 CO_3FP008__001,i9 CO_3FP008__002,i9 CO_3FP008__003,i9 CO_3FP008__004,i9 CO_3FP008__005,i9 3.3. Report Report is used to prepare custom reports from any of the databases. While report can be used as a primitive "report generator," the primary reason for using the report feature is to obtain listings of data items ACROSS geographic areas. Here are some examples report applications: o obtain a listing of all district codes and names. o identify just those districts with certain characteristics. (e.g., percent at-risk, dropout rate above a certain level) Report can direct the display of data to the screen or to a file. 3.3.1. Select a Database Select a database to be used in the report processing. Available databases include: Top 100 Items Common Core of Data School District Finances 1990 Census School District Special Tabulation See Section 5 for more detail on the content of these databases. Sections 3.3.2 through 3.3.7. apply to report generation using all databases other than the Census CD-ROM database. For information on this feature, see section 3.3.8. 3.3.2. Extract (E) or Master (M) If you have not previously extracted data from a master file, enter M to select Master file. Using this option means that you will select a portion of the Master file. If you wish to prepare a report based on an extract file from a from a previously extracted file, enter E for this operation. 3.3.3. Enter File Name [for Extract only] If you specified Extract (E) in the previous step, you will be prompted for the DOS file name of the file that you created when you previously extracted data from the Master file. 3.3.4. Enter Output File Name Enter the DOS file name for the new report file to be created. This name must be a valid DOS file name. If you press Enter with no name, the report will be directed to the screen. 3.3.5. Selected (S) or All (A) Fields This operation allows you to select one, several or all fields from the file from which data are being extracted. This option allows you to select on the items that you desire to have displayed as well as the order (from left to right). 3.3.6. Enter Output Field Names If the Selected Fields option was selected in the previous step, you will be prompted for the fields to be selected. Field names for the Top 100, CCD and F33 file are listed in an earlier section. Use PgUp to view the listings of the names and descriptions. You are prompted for one field at a time. The field must be spelled exactly as specified in the database or the prompt will appear again. Caution: if you press enter with no value specified, the field selection will terminate and the output will be produced as you have specified to that point. 3.3.7. Enter Selection Expression The selection option permits you to select only those records meeting certain criteria. Some examples follow for applications using the Top 100 database. See section 3.2.7. for examples. 3.3.8. Using Data from Census CD-ROM Extracted Database You may create a report using a file that you extracted from the Census CD-ROM database in a previous step. Using this feature without previously extracting a file will result in an error. The extracted file used by SDDB is named "extract.dbf". Extract.dbf may have a maximum of 125 data fields. The names of data items available in the file extract.dbf correspond to the table/item names that you selected during the extract process. In addition, the file extract.dbf has the standard names included of code (the geographic code for the area) and name (the name of the area). 3.4. Statistics The statistics feature provides basic statistics. While the statistics provided are quite few (count, total and sum), the logical manipulation offered through this feature makes it a powerful tool. For example, you can process all district records in the U.S. (or a subset area) for the number of at-risk children in districts with a certain level of Federal aid per student. At the end of the process you are given the count of the districts that qualify, the sum of the "expression" used and the total for the "expression" used. USAGE CAVEATS: Note that the Top 100 database contains records for states, counties and districts. Unless you use the Selection feature to screen on the type of geography, there will be a double/triple counting. For example, if you only wanted district records in Arizona, the selection expression would be ST='04' .and. CTY='000'. This selection criteria tells the system to only process records with a state FIPS code of 04 (Arizona) and where the value of the county FIPS code is 000 (the county FIPS code is 000 for district records). The statistics expression is computed for each record meeting the selection criteria. 3.4.1. Select a Database Select a database to be used in the statistics processing. Available databases include: Top 100 Items Common Core of Data School District Finances 1990 Census School District Special Tabulation See Section 5 for more detail on the content of these databases. Sections 3.4.2. through 3.4.5. apply to statistics from all databases other than the Census CD-ROM database. For information on this feature, see section 3.4.9. 3.4.2. Extract (E) or Master (M) If you have not previously extracted data from a master file, enter M to select Master file. Using this option means that you will select a portion of the Master file. If you wish to prepare statistics based on an extract file from a from a previously extracted file, enter E for this operation. 3.4.3. Enter File Name [for Extract only] If you specified Extract (E) in the previous step, you will be prompted for the DOS file name of the file that you created when you previously extracted data from the Master file. 3.4.4. Enter Expression for Statistics The expression for statistics instructs the system as to what data mathematical combination of items in the selected database to process. Suppose that you want statistics on the value of X, where X = EXPRESSION At the prompt key in the EXPRESSION (just the EXPRESSION, not X=) and press Enter. As an example, suppose you wanted statistics on total relevant children from the Top 100 Items database. The expression value would be only the data item name. The data item name for total relevant children is D058 (the names of items are presented by database in section 5). As another example, suppose that you want the number of Hispanic children enrolled in private school from this database. By looking at the item list, it is determined that the specific item desired is not included. But, the item value can be derived by subtracting Hispanic children enrolled in public school (D100) from Hispanic children enrolled (D092). Thus the EXPRESSION that would be entered is: D092-D100 3.4.5. Enter Selection Expression The selection option permits you to select only those records meeting certain criteria. Some examples follow for applications using the Top 100 database. See section 3.2.7. for examples. 4.0. MAPS This section provides general information on use of the SDDB map feature. Sections that follow correspond to the operations of the system as you proceed through the mapping steps in a sequential manner. Maps available through the SDDB are generated dynamically to meet your requirements. The maps are referred to as thematic maps. 4.1. Select Type of Map Select from three types of maps: U.S. by State -- displays the U.S. with all states State by District -- displays a selected state with all districts State by County -- displays a selected state with all counties 4.2. Select a State This menu does not appear if you have selected the U.S. by state map. 4.3. Select Type of Subject Matter To prepare a thematic map, you must instruct the SDDB which subject item to display. In total, you may select from more than 200,000 subject matter items from the SDDB databases. Select the type of file containing the data that you want to select from. Standard - Top 100 Items Database Custom - Pregenerated Custom Census File User Supplied File Name 4.3.1. Using the Standard Top 100 Items Database These data are available for all districts, counties and states on each CD-ROM. The state by district maps are not available on the first version of the U.S. by state CD-ROM as not all of the source TIGER files had been provided by the time this CD-ROM was developed. 4.3.2. Using Pregenerated Custom Census File Use of this feature is limited to the Census database available on the installed CD-ROM. Pregenerated Custom Census Files are those which have been prepared as an automatic product from displaying tables using the Profiles and Tables feature. To prepare a file for use with the mapping operation, the geography option: All states (U.S. by state CD-ROM) All counties (State CD-ROM) All districts (State CD-ROM) must be selected. Note that the all counties and all districts option is not available on the U.S. by State CD-ROM (and vice-versa) since the corresponding data do not exist on those CD-ROM. Example Suppose that you desire to prepare a map of the U.S. by state depicting the number of children enrolled in public school who are below poverty level. The first step is to use Profiles and Tables to select the desired data item for all states. To perform this operation, the All States geographic selection is used. Tables is selected as the type of display. Within Tables, the following tabulation selection is made: - Children's Own Characteristics - Enrolled in Public School - Total Relevant Children - Table 118 is selected As the processing takes place, the screen summarizes which geography is being processed and that two files, extract.prn and extract.dct, are being created. A third file, extract.dat, is also generated. Portions of these files are shown below. The rightmost portions of the data records are truncated for purposes of depicting the data here. extract.dct GEOCODE (SSCCCDDDDD) AREA NAME (A30) P118 70 2F001 9 <-- male above poverty level P118 70 2F002 9 <-- female above poverty level P118 70 2F003 9 <-- male below poverty level P118 70 2F004 9 <-- female below poverty level extract.prn "00","900","00000","United States ",19341333,18102535, "01","901","00000","Alabama ",309322,289201,8974 "02","902","00000","Alaska ",53454,49130,6158,5 "04","904","00000","Arizona ",274593,255890,6895 ... "54","954","00000","West Virginia ",130635,120521,4034 "55","955","00000","Wisconsin ",413094,386441,6352 "56","956","00000","Wyoming ",44554,41466,6444,6 extract.dat 00 900 00000 United States 19341333 18102535 3871923 01 901 00000 Alabama 309322 289201 89748 02 902 00000 Alaska 53454 49130 6158 04 904 00000 Arizona 274593 255890 68954 ... 54 954 00000 West Virginia 130635 120521 40348 55 955 00000 Wisconsin 413094 386441 63526 56 956 00000 Wyoming 44554 41466 6444 These files are now permanent, until overwritten, and control is returned to the main menu. From the main menu, the maps option is selected. The options then selected are: - U.S. by state map - Custom pregenerated census file - Equal number intervals After some additional processing steps, a spreadsheet is displayed depicting the data shown in the file extract.dat. At the top of the spreadsheet, the item names P1180001 through P1180004 are shown. The column of data beneath the each name contains the data for each of the geographic areas (states in this case). To prepare the map, one final step is required--to add the male and females below poverty level (P1180003 and P1180004). To accomplish this task, spreadsheet operations are used. Refer to Section 3.5. for more information on summing the two fields and selecting the summed value for display. 4.3.3. Use Supplied File Name This option enables you to use the Map View feature to display data other than that from the School District Data Book. The requirement to use this option is that the data file format must follow the structural specifications of those used in the dBASE-structured file 4.4. Select Type of Interval Equal Number Equal Value The map display classifies the data value for each area into one of five intervals. Select the type of interval classification that you prefer. 4.4.1. Equal Number Equal Number results in the number of areas being divided equally into the five intervals. For example, if the map display is for a state by county and a state has 15 counties, under the Equal Number option, each interval will be assigned 3 counties. 4.4.2. Equal Value Equal Value results in the number of areas being divided into the five intervals based on the data values. The range for the data value for each interval is determined by dividing the range (maximum minus minimum value for all areas being mapped) by five. The intervals then contain areas based on the interval size: interval 1: minimum value to minimum + 20% of range interval 2: interval 1 maximum to minimum + 40% of interval range interval 3: interval 2 maximum to minimum + 60% of interval range interval 4: interval 3 maximum to minimum + 80% of interval range interval 5: interval 4 maximum to minimum +100% of interval range 4.5. Map Spreadsheet Operations Map View automatically selects and opens the correct boundary file and data file for the thematic map display. Since many users will desire the ability to make mathematical relationships among the data items, Map View is designed to pause at a fully loaded spreadsheet. When the spreadsheet is displayed, you may select an item and draw a map or mathematically manipulate the data before drawing a map. For example, if your data file contains children enrolled in public school and total children enrolled, you might prefer to map the percent of children enrolled in public school (rather than number of children). To facilitate such mapping flexibility, several spreadsheet operations are available. The spreadsheet is matrix where each line corresponds to a geographic area. Each column corresponds to a data item. In summary, if you do not want to manipulate the data, follow the instructions on the top line of the spreadsheet: 1 - Using arrows, position the highlight box on the topmost cell of the data column of interest. 2 - Press Enter and the spreadsheet menu appears at the bottom of the screen. 3 - Using the right arrow, move right to highlight the Select option and press Enter. 4 - Press D (for Draw) and the map is automatically drawn. When done viewing the map, press Esc. The map screen clears and a prompt is given asking whether or not to print the map. Note: if the map is to be printed, an HP Laserjet compatible printer (compatible with HP PCL) must be connected and operational on LPT1:. For most map applications, the printer must have 2 MB or more memory. Spreadsheet menu options are discussed below. 4.5.1. Edit Used to manually alter the value of a spreadsheet cell. Useful to alter title for legend name for item being mapped. 4.5.2. Insert Used to insert a column. If you are creating a new item to map, such as a percentage, insert a column first. 4.5.3. Delete Not used this version 4.5.4. Format Not used this version 4.5.5. Math To create a new item, move the highlight cell to the top of a newly inserted column and press Enter. Move the highlight to Math and press Enter. A data entry line appears at the bottom of the screen. Enter the right-hand side of an equation to assign a value to the cells of the present (new) column. This expression may be of a standard mathematical form and obeys standard manipulative hierarchy of operation rules. The four basic operators + - / and * are permissible as are parentheses for argument grouping. Constants and variables are permissible. Variables must be referenced through the use of the "V" notation in the topmost row of the screen/spreadsheet. As an example suppose that you want to map the percent V2 is of V5 (after inserting a column that has become V1). After pressing Enter from the Math option, you would enter the following on the data entry line: 100*(V2/V3) A zero value in V3 will result in an error and the computation will not be made. Under Map View operations it is not possible to save the results of mathematical operations into a new file. The underlying DOS file is not affected by math operations. 4.5.6. Select Select instructs the system to use this designated column as the active variable (item) to map. To use select, position the highlight box on the topmost cell of the column (item) to be selected and press Enter. Press the right-arrow key until the Select option is highlighted on the menu line. As the spreadsheet re-displays, a box will appear around the column header box indicating the item has been selected fo display. 4.5.7. Deselect An item previously selected may be deselected in an identical manner as selecting the item. Instead of choosing Select, choose Deselect. 4.6. Map Interpretation The map display consists of three windows--the map, the legend and the scale. The map is a display of the geography that you requested in an earlier step. Each are has a colorized cross-hatch pattern corresponding to one of the intervals shown in the legend. As the data values for each area are evaluated, the area is assigned to a data interval which is associated with a particular hatch pattern. The legend shows intervals with the hatch pattern representing that interval appearing in a small box. Beside each interval hatch pattern is the data range for that interval. If, for example, the range reads 5.00 - 10.00, then any geographic area which has a data value falling into that range will be displayed with that hatch pattern. At the top of the legend box is the name of the item being displayed. If you are planning to print a map, you may want to edit the item name (within spreadsheet) and choose a name meaningful for you application. The scale box shows the area measurements represented by the map. The scale bar shows the physical distance on the map screen equal to the stated measure. Note that, due to variation among printer size and orientation, the scale as printed in hardcopy form will typically not accurately represent distances. The scale is of most use on the map when shown on the display device. Interpretation--Perhaps the most typical use of a thematic map is to "see" which areas have relatively high or low values for the selected item. You can see areas at-a-glance where concentrations exist. 5.0. Databases 5.1. TOP 100 ITEMS DATABASE This database is available on each CD-ROM. It contains the data items listed below for each county, school district and state. ST State FIPS Code C 2 CTY County FIPS Code C 3 DIST District Code (15274 districts + 30 BOC) C 5 STCODE State's Own Code C 14 MSAC CMSA Code (2-digit part) from LEA C 2 MSA MSA Code " C 4 DISTCTY County FIPS Code (for district records) C 3 ZIP ZIP Code C 5 LOW_GRADE Low Grade C 2 HIGH_GRADE High Grade C 2 NAME Area Name C 30 D001 Occupied Housing Units H004 10 00 N 9 D002 Vacant Housing Units H004 10 00 N 9 D003 Occupied Housing Units - Urban - Inside Urbanized Area H005 10 00 N 9 D004 Occupied Housing Units - Urban - Outside Urbanized Area H005 10 00 N 9 D005 Occupied Housing Units - Rural - Farm H005 10 00 N 9 D006 Occupied Housing Units - Rural - Nonfarm H005 10 00 N 9 D007 Occupied Housing Units - Inside Metro - In Central City H006 10 00 N 9 D008 Occupied Housing Units - Inside Metro - Not in Central City - Urban H006 10 00 N 9 D009 Occupied Housing Units - Inside Metro - Not in Central City - Rural H006 10 00 N 9 D010 Occupied Housing Units - Outside Metro - Urban H006 10 00 N 9 D011 Occupied Housing Units - Outside Metro - Rural H006 10 00 N 9 D012 Occupied Housing Units - Owner Occupied H008 10 00 N 9 D013 Occupied Housing Units - Renter Occupied H008 10 00 N 9 D014 Median Gross Rent H043A 10 00 N 9 D015 Median Value H061A 10 00 N 9 D016 Families P004 10 00 N 9 D017 Households P005 10 00 N 9 D018 Median Household Income P080A 10 00 N 9 D019 Households with Public Assistance Income in 1989 P095 10 00 N 9 D020 Households without Public Assistance Income in 1989 P095 10 00 N 9 D021 Households with Children Under 18 Years P200 10 00 N 9 D022 Households with Children 5 to 17 Years P201 10 00 N 9 D023 Unweighted Sample Count of Housing Units H002 1A 00 N 9 D024 100-Percent Count of Housing Units H003 1A 00 N 9 D025 Unweighted Sample Count of Persons P002 2A 00 N 9 D026 100-Percent Count of Persons P003 2A 00 N 9 D027 Dropouts 16-19 NEIS & NHG - In Households P061 2A 00 N 9 D028 Dropouts 16-19 NEIS & NHG - In Group Quarters P061 2A 00 N 9 D029 Per Capita Income in 1989 P114A 2A 00 N 9 D030 At Risk Pre-School Age Children - Less than 4 years P300 2A 00 N 9 D031 At Risk Pre-School Age Children - 4 to 5 years of age P300 2A 00 N 9 D032 At Risk School Age Children (6-19 years) P304 2A 00 N 9 D033 Persons with Income in 1989 Above Poverty Level P117 2A 00 N 9 D034 Persons with Income in 1989 Below Poverty Level P117 2A 00 N 9 D035 Total Persons P001 2B 00 N 9 D036 Persons - Urban - Inside Urbanized Area P006 2B 00 N 9 D037 Persons - Urban - Outside Urbanized Area P006 2B 00 N 9 D038 Persons - Rural - Farm P006 2B 00 N 9 D039 Persons - Rural - Nonfarm P006 2B 00 N 9 D040 Persons - Male P007 2B 00 N 9 D041 Persons - Female P007 2B 00 N 9 D042 Persons - NonHispanic White P012 2B 00 N 9 D043 Persons - NonHispanic Black P012 2B 00 N 9 D044 Persons - NonHispanic American Indian, Eskimo, Aleut P012 2B 00 N 9 D045 Persons - NonHispanic Asian and Pacific Islander P012 2B 00 N 9 D046 Persons - NonHispanic Other Races P012 2B 00 N 9 D047 Persons - Hispanic P012 2B 00 N 9 D048 Persons in Group Quarters P040 2B 00 N 9 D049 Persons 16 Years and Over - In Labor Force P070 2B 00 N 9 D050 Persons 16 Years and Over - Civilian Employed P070 2B 00 N 9 D051 Persons 16 Years and Over - Civilian Unemployed P070 2B 00 N 9 D052 Persons 20 Years and Over by Educational Attainment - 12th Grade or less, no diploma P188 2B 00 N 9 D053 Persons 20 Years and Over by Educational Attainment - High school graduate P188 2B 00 N 9 D054 Persons 20 Years and Over by Educational Attainment - Some college, no bachelor or higher degree P188 2B 00 N 9 D055 Persons 20 Years and Over by Educational Attainment - Bachelor's or higher degree P188 2B 00 N 9 D056 Households with Relevant Children P005 30 1F N 9 D057 Persons (Parents Living with Relevant Children) P001 40 1F N 9 D058 Total Relevant Children P001 70 1F N 9 D059 Relevant Children - Urban - Inside Urbanized Area P006 70 1F N 9 D060 Relevant Children - Urban - Outside Urbanized Area P006 70 1F N 9 D061 Relevant Children - Rural - Farm P006 70 1F N 9 D062 Relevant Children - Rural - Nonfarm P006 70 1F N 9 D063 Relevant Children - Male P007 70 1F N 9 D064 Relevant Children - Female P007 70 1F N 9 D065 Relevant Children - NonHispanic White P012 70 1F N 9 D066 Relevant Children - NonHispanic Black P012 70 1F N 9 D067 Relevant Children - NonHispanic American Indian, Eskimo, Aleut P012 70 1F N 9 D068 Relevant Children - NonHispanic Asian and Pacific Islander P012 70 1F N 9 D069 Relevant Children - NonHispanic Other Races P012 70 1F N 9 D070 Relevant Children - Hispanic P012 70 1F N 9 D071 Relevant Children Age 3 Years P013A 70 1F N 9 D072 Relevant Children Age 4 Years P013A 70 1F N 9 D073 Relevant Children Age 5 Years P013A 70 1F N 9 D074 Relevant Children Ages 5-13 Years P013A 70 1F N 9 D075 Relevant Children Ages 14-17 Years P013A 70 1F N 9 D076 Relevant Children Ages 18-19 Years P013A 70 1F N 9 D077 Relevant Children in Family Households - Householder, Spouse, Grandchild, Other Relative, NonRelativeP017 70 1F N 9 D078 Relevant Children in Family Households - Child (natural, adopted, step) P017 70 1F N 9 D079 Relevant Children in Non-Family Households P017 70 1F N 9 D080 Relevant Children in Group Quarters P017 70 1F N 9 D081 Relevant Children by Poverty Status - Income Above Poverty Level P118 70 1F N 9 D082 Relevant Children by Poverty Status - Income Below Poverty Level P118 70 1F N 9 D083 Relevant Children Ages 14-17 in Households P017 70 14 N 9 D084 Relevant Children Ages 14-17 in Group Quarters P017 70 14 N 9 D085 Relevant Children Enrolled in School - Male P007 70 2F N 9 D086 Relevant Children Enrolled in School - Female P007 70 2F N 9 D087 Relevant Children Enrolled in School - NonHispanic White P012 70 2F N 9 D088 Relevant Children Enrolled in School - NonHispanic Black P012 70 2F N 9 D089 Relevant Children Enrolled in School - NonHispanic American Indian, Eskimo, Aleut P012 70 2F N 9 D090 Relevant Children Enrolled in School - NonHispanic Asian and Pacific Islander P012 70 2F N 9 D091 Relevant Children Enrolled in School - NonHispanic Other Races P012 70 2F N 9 D092 Relevant Children Enrolled in School - Hispanic P012 70 2F N 9 D093 Relevant Children Enrolled in Public School - Male P007 70 3F N 9 D094 Relevant Children Enrolled in Public School - Female P007 70 3F N 9 D095 Relevant Children Enrolled in Public School - NonHispanic White P012 70 3F N 9 D096 Relevant Children Enrolled in Public School - NonHispanic Black P012 70 3F N 9 D097 Relevant Children Enrolled in Public School - NonHispanic American Indian, Eskimo, Aleut P012 70 3F N 9 D098 Relevant Children Enrolled in Public School - NonHispanic Asian and Pacific Islander P012 70 3F N 9 D099 Relevant Children Enrolled in Public School - NonHispanic Other Races P012 70 3F N 9 D100 Relevant Children Enrolled in Public School - Hispanic P012 70 3F N 9 CCDMEM CCD Membership from File A N 7 STUDENTS CCD Students N 9 TEACHERS CCD Teachers N 9 SCHOOLS CCD Schools N 9 TOTREV F33 Total Revenue N 12 LOCREV F33 Local Revenue N 12 STREV F33 State Revenue N 12 FEDREV F33 Federal Revenue N 12 TOTEXP F33 Total Expenditures N 12 CIPEXP F33 Current Instructional Programs Expenditures N 12 INSEXP F33 Instruction Expenditures N 12 NICEXP F33 Current NonInstructructional Program Expenditures N 12 X Not Used N 12 STATUS Not Used C 1 5.2. COMMON CORE OF DATA DATABASE This database is available on each CD-ROM. It contains the data items listed below for each school district and state. DIST School District ID LAT Approx Latitude of District Center LNG Approx Longitude of District Center LATP Approx Latitude of Dist Center of Pop LNGP Approx Longitude of Dist Center of Pop TPOP Total Population (100%) of District AREA Area of District (Square kilometers) ST FIPS Code -- State STAGID State's own ID for Dist SCHOOLS Number of Schools in CCD Schools File TEACHERS FTE Classroom Teachers STUDENTS Sum of Students Reported in Schools FREELUNCH Sum of Free Lunch Eligible Students reported AMERIND Sum of Amer Ind/Alaska Ntv students reported API Sum of Asian/Pacific Island students reported HISPANIC Sum of Hispanic students reported BLACK Sum of Black, not Hispanic, students reported WHITE Sum of White, not Hispanic, students reported SIZE1 Schools with Enrollment <100 SIZE2 Schools with 100 <= Enrollment < 200 SIZE3 Schools with 200 <= Enrollment < 300 SIZE4 Schools with 300 <= Enrollment < 400 SIZE5 Schools with 500 <= Enrollment < 600 SIZE6 Schools with 400 <= Enrollment < 500 SIZE7 Schools with 600 <= Enrollment < 700 SIZE8 Schools with 700 <= Enrollment < 800 SIZE9 Schools with 800 <= Enrollment < 1000 SIZE10 Schools with 1000 <= Enrollment <1500 SIZE11 Schools with Enrollment >= 1500 SIZE12 Schools with Enrollment not reported ENROLL1 Students in schools reporting < 100 students ENROLL2 Students in schools reporting 100< students <200 ENROLL3 Students in schools reporting 200< students <300 ENROLL4 Students in schools reporting 300< students <400 ENROLL5 Students in schools reporting 400< students <500 ENROLL6 Students in schools reporting 500< students <600 ENROLL7 Students in schools reporting 600< students <700 ENROLL8 Students in schools reporting 700< students <800 ENROLL9 Students in schools reporting 800< students <1000 ENROLL10 Students in schools reporting 1000= 1500 students TYPE1 Number of regular schools TYPE2 Number of Special education schools TYPE3 Nuber of vocational schools TYPE4 Number of other/alternative schools TYPNRO1 Students reported in Regular Schools TYPNRO2 Students reported in Special Ed Schools TYPNRO3 Students reported in Vocational Schools TYPNRO4 Students reported in Other/Alternative Schools LOCAL1 Schools in Large Central City LOCAL2 Schools in Mid-Size Central City LOCAL3 Schools in Urban Fringe of Large City LOCAL4 Schools in Urban Fringe of Mid-Sized City LOCAL5 Schools in Large Town LOCAL6 Schools in Small Town LOCAL7 Schools in Rural Territory LOCNRO1 Students reported in Large Cntrl City Schools LOCNRO2 Students reported in Mid-Size Cntrl City Schools LOCNRO3 Students reported in Schools in Fringe of Large City LOCNRO4 Students reported in Schools in Fringe of Med City LOCNRO5 Students reported in Schools in Large Towns LOCNRO6 Students reported in Schools in Small Towns LOCNRO7 Students reported in Schools in Rural Territory LOCFTE1 Teachers reported in Large Central City Schools LOCFTE2 Teachers reported in Mid-Size Central City Schools LOCFTE3 Teachers reported in Schools in Fringe of Large City LOCFTE4 Teachers reported in Schools in Fringe of Med City LOCFTE5 Teachers reported in Schools in Large Town LOCFTE6 Teachers reported in Schools in Small Town LOCFTE7 Teachers reported in Schools in Rural Territory LNCH1 Schools w/ Free Lunch Eligible <5% LNCH2 Schools w/ 5 <= % Free Lunch Eligible <10 LNCH3 Schools w/ 10 <= % Free Lunch Eligible <15 LNCH4 Schools w/ 15 <= % Free Lunch Eligible <20 LNCH5 Schools w/ 20 <= % Free Lunch Eligible <25 LNCH6 Schools w/ 25 <= % Free Lunch Eligible <40 LNCH7 Schools w/ Free Lunch Eligible >= 40% LNCH8 Schools w/ Free Lunch Eligible > Students LNCH9 Schools w/Free Lunch or Students missing LNCHNRO1 Students reported in schools w/ Free Lunch <5% LNCHNRO2 Students reported in schools w/ 5<= %Free Lunch<10 LNCHNRO3 Students reported in schools w/10<= %Free Lunch <15 LNCHNRO4 Students reported in schools w/15<= %Free Lunch <20 LNCHNRO5 Students reported in schools w/20<= %Free Lunch <25 LNCHNRO6 Students reported in schools w/25<= %Free Lunch <40 LNCHNRO7 Students reported in schools w/ Free Lunch >= 40% LNCHNRO8 Students reported in schools w/FreeLunchElg>Students LNCHNRO9 Students reported in schools w/Free Lunch missing LNCHFTE1 Teachers reported in schools w/ Free Lunch <5% LNCHFTE2 Teachers reported in schools w/5<= %Free Lunch<10 LNCHFTE3 Teachers reported in schools w/10<= %Free Lunch<15 LNCHFTE4 Teachers reported in schools w/15<= %Free Lunch<20 LNCHFTE5 Teachers reported in schools w/20<= %Free Lunch<25 LNCHFTE6 Teachers reported in schools w/25<= %Free Lunch<40 LNCHFTE7 Teachers reported in schools w/ Free Lunch >= 40% LNCHFTE8 Teachers reported in schools w/FreeLunch> Students LNCHFTE9 Teachers in Schools w/FreeLunch or Stdts mssng RACE Studnts whose Race/Ethnicity is reported PCTBLK1 Schools reporting <5% Black PCTBLK2 Schools reporting 5 <= % Black < 10 PCTBLK3 Schools reporting 10 <= % Black < 20 PCTBLK4 Schools reporting 20 <= % Black < 35 PCTBLK5 Schools reporting 35 <= % Black < 65 PCTBLK6 Schools reporting 65 <= % Black < 80 PCTBLK7 Schools reporting 80 <= % Black < 90 PCTBLK8 Schools reporting 90 <= % Black < 95 PCTBLK9 Schools reporting % Black >= 95 PCTBLK10 Schools with % Black missing PBNRO1 Students reported in schools w/ <5% Black PBNRO2 Students reported in schools w/ 5 <= % Black < 10 PBNRO3 Students reported in schools w/10 <= % Black < 20 PBNRO4 Students reported in schools w/20 <= % Black < 35 PBNRO5 Students reported in schools w/35 <= % Black < 65 PBNRO6 Students reported in schools w/65 <= % Black < 80 PBNRO7 Students reported in schools w/80 <= % Black < 90 PBNRO8 Students reported in schools w/90 <= % Black < 95 PBNRO9 Students reported in schools w/ % Black >= 95 PBNRO10 Students reported in schools w/ % Black missing PBFTE1 Teachers reported in schools w/<5% Black PBFTE2 Teachers reported in schools w/ 5 <= % Black < 10 PBFTE3 Teachers reported in schools w/10 <= % Black < 20 PBFTE4 Teachers reported in schools w/20 <= % Black < 35 PBFTE5 Teachers reported in schools w/35 <= % Black < 65 PBFTE6 Teachers reported in schools w/65 <= % Black < 80 PBFTE7 Teachers reported in schools w/80 <= % Black < 90 PBFTE8 Teachers reported in schools w/90 <= % Black < 95 PBFTE9 Teachers reported in schools w/ % Black >= 95 PBFTE10 Teachers reported in schools w/% Black missing PCTWYT1 Schools reporting <5% White PCTWYT2 Schools reporting 5 <= % White < 10 PCTWYT3 Schools reporting 10 <= % White < 20 PCTWYT4 Schools reporting 20 <= % White < 35 PCTWYT5 Schools reporting 35 <= % White < 65 PCTWYT6 Schools reporting 65 <= % White < 80 PCTWYT7 Schools reporting 80 <= % White < 90 PCTWYT8 Schools reporting 90 <= % White < 95 PCTWYT9 Schools reporting % White >= 95 PCTWYT10 Schools with % White not reported PWNRO1 Students reported in schools w/<5% White PWNRO2 Students reported in schools w/ 5 <= % White < 10 PWNRO3 Students reported in schools w/10 <= % White < 20 PWNRO4 Students reported in schools w/20 <= % White < 35 PWNRO5 Students reported in schools w/35 <= % White < 65 PWNRO6 Students reported in schools w/65 <= % White < 80 PWNRO7 Students reported in schools w/80 <= % White < 90 PWNRO8 Students reported in schools w/90 <= % White < 95 PWNRO9 Students reported in schools w/ % White >= 95 PWNRO10 Students reported in schools w/% White missing PWFTE1 Teachers reported in schools w/<5% White PWFTE2 Teachers reported in schools w/ 5 <= % White < 10 PWFTE3 Teachers reported in schools w/10 <= % White < 20 PWFTE4 Teachers reported in schools w/20 <= % White < 35 PWFTE5 Teachers reported in schools w/35 <= % White < 65 PWFTE6 Teachers reported in schools w/65 <= % White < 80 PWFTE7 Teachers reported in schools w/80 <= % White < 90 PWFTE8 Teachers reported in schools w/90 <= % White < 95 PWFTE9 Teachers reported in schools w/ % White >=95 PWFTE10 Teachers reported in schools w/% White missing PCTHSP1 Schools reporting <5% Hispanic PCTHSP2 Schools reporting 5 <= % Hispanic < 10 PCTHSP3 Schools reporting 10 <= % Hispanic < 15 PCTHSP4 Schools reporting 15 <= % Hispanic < 25 PCTHSP5 Schools reporting 25 <= % Hispanic < 75 PCTHSP6 Schools reporting 75 <= % Hispanic < 90 PCTHSP7 Schools reporting % Hispanic >=90 PCTHSP8 Schools with % Hispanic not reported PHNRO1 Students reported in schools w/<5% Hispanic PHNRO2 Students reported in schools w/ 5<= %Hispanic <10 PHNRO3 Students reported in schools w/10<= %Hispanic <15 PHNRO4 Students reported in schools w/15<= %Hispanic <25 PHNRO5 Students reported in schools w/25<= %Hispanic <75 PHNRO6 Students reported in schools w/75<= %Hispanic <90 PHNRO7 Students reported in schools w/Hispanic >=90 % PHNRO8 Students reported in schools w/%Hispanic missing PHFTE1 Teachers reported in schools w/<5% Hispanic PHFTE2 Teachers reported in schools w/5<= % Hispanic <10 PHFTE3 Teachers reported in schools w/10<= % Hispanic <15 PHFTE4 Teachers reported in schools w/15<= % Hispanic <25 PHFTE5 Teachers reported in schools w/25<= % Hispanic <75 PHFTE6 Teachers reported in schools w/75<= % Hispanic <90 PHFTE7 Teachers reported in schools w/ Hispanic >= 90 % PHFTE8 Teachers reported in schools w/ % Hispanic missing PCTIND1 Schools reporting <5% Native American PCTIND2 Schools reporting 5<= % Native American <10 PCTIND3 Schools reporting 10<= % Native American <15 PCTIND4 Schools reporting 15<= % Native American <25 PCTIND5 Schools reporting 25<= % Native American <75 PCTIND6 Schools reporting 75<= % Native American <90 PCTIND7 Schools reporting Native American >= 90 % PCTIND8 Schools with % Native American missing PINRO1 Students reported in schools w/<5% Nat Amer PINRO2 Students reported in schools w/5<= % Nat Am <10 PINRO3 Students reported in schools w/10<= % Nat Am <15 PINRO4 Students reported in schools w/15<= % Nat Am <25 PINRO5 Students reported in schools w/25<= % Nat Am <75 PINRO6 Students reported in schools w/75<= % Nat Am <90 PINRO7 Students reported in schools w/Nat Amer >= 90 % PINRO8 Students reported in schools w/% Nat Am missing PIFTE1 Teachers reported in schools w/<5% Nat Amer PIFTE2 Teachers reported in schools w/5<= % Nat Amer <10 PIFTE3 Teachers reported in schools w/10<= % Nat Amer <15 PIFTE4 Teachers reported in schools w/15<= % Nat Amer <25 PIFTE5 Teachers reported in schools w/25<= % Nat Amer <75 PIFTE6 Teachers reported in schools w/75<= % Nat Amer <90 PIFTE7 Teachers reported in schools w/Nat Amer >= 90 % PIFTE8 Teachers reported in schools w/% Nat Amer missing PCTAPI1 Schools rptng <5% Asian/Pacific Is PCTAPI2 Schools rptng 5<= %Asian/Pacific Is <10 PCTAPI3 Schools rptng 10<= %Asian/Pacific Is <15 PCTAPI4 Schools rptng 15<= %Asian/Pacific Is <25 PCTAPI5 Schools rptng 25<= %Asian/Pacific Is <75 PCTAPI6 Schools rptng 75<= %Asian/Pacific Is <90 PCTAPI7 Schools rptng Asian/Pacific Island>= 90% PCTAPI8 Schools with % Asian/Pacific Island mssng PAPNRO1 Students reported in schools w/<5% Asian/PI PAPNRO2 Students reported in schools w/ 5 <= % A/PI < 10 PAPNRO3 Students reported in schools w/10 <= % A/PI < 15 PAPNRO4 Students reported in schools w/15 <= % A/PI < 25 PAPNRO5 Students reported in schools w/25 <= % A/PI < 75 PAPNRO6 Students reported in schools w/75 <= % A/PI < 90 PAPNRO7 Students reported in schools w/A/PI >= 90 % PAPNRO8 Students reported in schools w/% A/PI missing PAPFTE1 Teachers reported in schools w/<5% Asian/Pacif Is PAPFTE2 Teachers reported in schools w/5 <= % A/PI < 10 PAPFTE3 Teachers reported in schools w/10 <= % A/PI < 15 PAPFTE4 Teachers reported in schools w/15 <= % A/PI < 25 PAPFTE5 Teachers reported in schools w/25 <= % A/PI < 75 PAPFTE6 Teachers reported in schools w/75 <= % A/PI < 90 PAPFTE7 Teachers reported in schools wA/PI />= 90 % PAPFTE8 Teachers reported in schools w/% A/PI missing 5.3. SCHOOL DISTRICT FINANCES DATABASE This database is available on each CD-ROM. It contains the data items listed below for each school district and state. ID Census Code NAME Area Name STFIPS State FIPS CTY County FIPS R Not used C Not used Y Not used S Not used D Not used ST State FIPS DIST District Code W Not used F Not used F1 Local-Property Taxes F2 Local-Parent Govt F3 Local-General Sales F4 Local-Net Income Taxes F5 Local-All Other Taxes F6 Local-School Lunch Charges F7 Local-Tuition & Trans Charges F8 Local-All Other Charges F9 Local-Interest Earnings F10 Local-Misc Revenue F11 Revenue Direct from Fed Govt F12 Fed Rev on Behalf of SS F13 Revenue Direct from State Govt F14 Fed Aid Thru State-School Lunch F15 Fed Aid Thru State-All Other F16 State Revenue on Behalf of SS F17 Loc Inter Rev-Interschool Trans F18 Loc Inter Rev-from Cities/Ctys F19 Instruction Expenditure F20 Support Svc Exp Not in 45-9 F21 Food Svc Exp F22 All Other Curr Op Exp F23 Exp on Behalf of SS-Instr F24 Exp on Behalf of SS-Support F25 Exp on Behalf of SS-Other F26 Ret Fund Trans to Own Sys-Instr F27 Interschool Trans F28 Capital Outlay-Constr F29 Capital Outlay-New & Rep Eq F30 Capital Outlay-Land & Ex Struc F31 Interg Exp-Payments to St F32 Interg Exp-Payments to Loc F33 Interest on Debt F34 Tot Salaries for E-S F35 Salaries-Instr Only F36 Long-term Debt-Out Beg Year F37 Long-term Debt-Issued F38 Long-term Debt-Retired F39 Long-term Debt-Out End Year F40 Short-term Debt Out Beg Yr F41 Short-tern Debit Out End Yr F42 Sinking Fund-Total Assets F43 Bond Fund-Total Assets F44 Other Funds-Total Assets F45 Fall Membership F46 Support Svcs Exp-Pupils F47 Support Svcs Exp-Instr Staff F48 Support Svcs Exp-Gen Admin F49 Support Svcs Exp-Schl Admin F50 Support Svcs Exp-All Other F51 Schl Ret Fund Trans-Spt Svcs F52 Cen State Rev/NCES Loc Rev F53 Public Utility Taxes 5.4. 1990 CENSUS SCHOOL DISTRICT SPECIAL TABULATION The 1990 Census School District Special Tabulation contains data tabulated by the U.S. Bureau of the Census, under the sponsorship of the National Center for Education Statistics, U.S. Department of Education, from the 1990 Census basic record files. Users may access the table/item descriptions for the 1990 Census School District Special Tabulation in two ways using the SDDB. Using the Profiles and Tables feature of SDDB, the menus may be used to examine the table descriptions. The Census table documentation is also provided electronically in separate ASCII files on the CD-ROM. These files are named in the manner SDDB??.TXT where ?? corresponds to the 2-character abbreviation provided below. The files may be printed directly or manipulated with word processor software of choice. >> THESE FILES ARE LOCATED IN THE ROOT DIRECTORY OF THE CD-ROM. << In summary, there are 7 type of tabulation records (and hence a documentation file for each): Type of Tabulation Record Abbreviation # Characteristics of All Households HT 1 Characteristics of All Persons PS and PT 2A and 2B Characteristics of Households with Children HC 3 Characteristics of Parents with Children PR 4 Children's Households Characteristics CH 5 Children's Parents Characteristics CP 6 Children's Own Characteristics CO 7 Theses records are assigned abbreviations and numeric references, as shown to the right of the tabulation title, as a short-hand method of referring to a type of tabulation. Each of these record types is discussed in more detail below. Each file is more fully documented in a DOS file provided separately on the CD-ROM. You may print the documentation file either with DOS print or using a word processor of your choice. However, it is not recommended that you print these files unless you have a specific requirement. While some programming applications might be facilitated by having a hard copy listing available, the Data Book has been designed to minimize the requirement for a hard copy reference. Use of data compression. The census data are stored quite differently that the database structures described above. A data compression technique removes data fields having a zero value so that no space is used on the disk storage media. As a result, a special program must be used to read and interpret the records. The format of the documentation files are identical and described via example in section 5.4.1. 5.4.1. Characteristics of All Households HT 1 Record layout documentation is contained in the DOS file named SDDBHT.TXT located in the root directory of the CD-ROM. You can determine the table numbers containing the data that you want by using the Index feature (F9 key) under Profiles and Tables. This record contains 981 data fields. All fields have a maximum width of nine except items 168,169,260,261,262,263,264,265,266,267,268,269, 300,301,675,721 and 722. Example. The following example is based on the same tables used in the example used in section 2.1.2.2.3. "Select a Specific Table" for Profiles and Tables. These lines are taken directly from the file SDDBHT.TXT. For presentation purposes, 20 characters of each line have been removed so that the index number and field width will shown in this display. In this example, there are 21 data fields described by this documentation. These data fields are referenced in two ways. First, they are described are "item numbers" within "table numbers." Total families (item 001) is the only data item (field) shown in table P004. The combination of table and item references uniquely describe all data fields in the record. However, these references do provide positional or locational reference. Thus, to determine where a particular item is located in a record, the field index, shown to right of each item descriptor, is used. As an example, total families is the first data field in the HT record. The 21st field is the number of nonfamily households with income in 1989 below poverty level. The number to the right of the dash following the index number is the maximum field width. All data fields are numeric with a width of nine being the typical maximum. Total Households, Record Type 1 Table P004 Families (1) [1] Universe: Families 001 Total 1- 9 Table P005 Households (1) [1] Universe: Households 001 Total 2- 9 Table P016 Persons in Household (7) [7] Universe: Households 001 1 person 3- 9 002 2 persons 4- 9 003 3 persons 5- 9 004 4 persons 6- 9 005 5 persons 7- 9 006 6 persons 8- 9 007 7 or more persons 9- 9 Table P019 Household Type (4) [4] Universe: Households Family households: 001 Married-couple family 10- 9 Other family 002 Male householder, no wife present 11- 9 003 Female householder, no husband present12- 9 004 Nonfamily households 13- 9 Table P019B Household Type (4) by Poverty Status in 1989 of Householder (2) [8] Universe: Households Family households: Married-couple family: 001 Income in 1989 above poverty level 14- 9 002 Income in 1989 below poverty level 15- 9 Other family: Male householder, no wife present: 003 Income in 1989 above poverty level 16- 9 004 Income in 1989 below poverty level 17- 9 Female householder, no husband present: 005 Income in 1989 above poverty level 18- 9 006 Income in 1989 below poverty level 19- 9 Nonfamily households: 007 Income in 1989 above poverty level 20- 9 008 Income in 1989 below poverty level 21- 9 5.4.2. Characteristics of All Persons PS and PT 2A and 2B Record layout documentation is contained in the DOS files named SDDBPS.TXT and SDDBPT.TXT located in the root directory of the CD-ROM. You can determine the table numbers containing the data that you want by using the Index feature (F9 key) under Profiles and Tables. PS (Persons Supplemental) Record -- contains 2501 data fields. All fields have a maximum width of nine except items 1062,1063,1064, 1069,1070,1071,1072,1073,1074,1075,1076,1077 and 1078. PT (Persons Total) Record -- contains 3187 data fields. All fields have a maximum width of nine except 2564,2565,3118,3119, 3186,3187 which have maximum width of 18. 5.4.3. Characteristics of Households with Children HC 3 Record layout documentation is contained in the DOS file named SDDBHC.TXT located in the root directory of the CD-ROM. You can determine the table numbers containing the data that you want by using the Index feature (F9 key) under Profiles and Tables. Note: The record structure and documentation for this record is identical to the first 808 cells of record type 1. HC Record -- contains 808 data fields. All fields have a maximum width of nine except 168,169,260,261,262, 263,264,265,266,267,268,269,300,301,675,721,722 which have maximum width of 18. 5.4.4. Characteristics of Parents with Children PR 4 Record layout documentation is contained in the DOS file named SDDBPR.TXT located in the root directory of the CD-ROM. You can determine the table numbers containing the data that you want by using the Index feature (F9 key) under Profiles and Tables. Note: The record structure and documentation for this record is identical to record type 2B. PR Record -- contains 3187 data fields. All fields have a maximum width of nine except 2564,2565,3118,3119, 3186,3187 which have maximum width of 18. 5.4.5. Children's Households Characteristics CH 5 Record layout documentation is contained in the DOS file named SDDBCH.TXT located in the root directory of the CD-ROM. You can determine the table numbers containing the data that you want by using the Index feature (F9 key) under Profiles and Tables. CH Record -- contains 808 data fields. All fields have a maximum width of nine except 168,169,260,261,262, 263,264,265,266,267,268,269,300,301,675,721,722 which have maximum width of 18. 5.4.6. Children's Parents Characteristics CP 6 Record layout documentation is contained in the DOS file named SDDBCP.TXT located in the root directory of the CD-ROM. You can determine the table numbers containing the data that you want by using the Index feature (F9 key) under Profiles and Tables. CP Record -- contains 2813 data fields. All fields have a maximum width of nine except 2190,2191,2744,2745, 2812,2813 which have maximum width of 18. 5.4.7. Children's Own Characteristics CO 7 Record layout documentation is contained in the DOS file named SDDBCO.TXT located in the root directory of the CD-ROM. You can determine the table numbers containing the data that you want by using the Index feature (F9 key) under Profiles and Tables. CO Record -- contains 2271 data fields. All fields have a maximum width of nine. A.1. Installation Procedures The installation procedure is used to set up your computer for using the School District Data Book. The installation procedure needs to be used only once. The install program is located in the root directory of the CD-ROM and is named INSTALL.EXE. This program can be copied from the CD-ROM and run from any drive/directory. However, under normal circumstances you should not need to copy install.exe. A.1.1. Computer Configuration and System Requirements To install the School District Data Book (SDDB) you must have the following ready: 1 - The SDDB CD-ROM in the CD-ROM drive that you will use. (Note: no checks are made to verify that the CD-ROM drive is properly connected. This step must be performed in advance of the SDDB setup.) 2 - A hard disk on your computer with sufficient space to install files required to use the SDDB. A minimum of 30 MB hard disk space is required to use SDDB. If your applications will involve extensive file extractions from the CD-ROM databases, your system should have 50-to-60 MB of space available at minimum. In addition, performance of the SDDB can be substantially enhanced by moving certain CD-ROM files to hard disk. This performance enhancement requires an additional 50 MB of space. See performance enhancement section below. While these hard disk requirements may seem substantial, remember that the size of the CD-ROM database supported is approximately 20 gigabytes of space -- in a compressed structure. Other computer system features. The recommended basic computer configuration is as follows: 1 - PC operating with 386 or 486 CPU 2 - MS-DOS 3.1 or later operating system 3 - CD-ROM drive with ability to process ISO 9660 CD-ROM connected to computer with MSCDEX software 4 - Display device: Profiles and Tables <-- most devices Database Operations <-- most devices Maps <-- VGA Color suggested, not required, for all applications 5 - Printer device: Profiles and Tables <-- most devices Database Operations <-- most devices Maps <-- HP Laserjet compatible with 2.5 MB memory HP Laserjet compatible suggested for all applications 6 - Mouse device: not used. 7 - Hard disk device: Minimum space available: 30MB Suggested hard disk space for typical applications: 30 MB Suggested hard disk space for extensive use applications: 200MB Directories used: SDDB and IMAGE3A (created by install) A.1.2. Installation Processing The install procedure requests the following information: 1 - The CD-ROM drive letter referencing the drive that you will use. (Note: a check is made immediately to verify that the installation CD-ROM exists and that the necessary files can be read.) 2 - The hard disk drive letter referencing the drive where you want SDDB hard disk files to be located. A.1.3. SDDB Configuration File The file named sddb.cfg controls certain default features for operation of the system. When initially set up, assuming the hard disk is d: and the CD-ROM drive is e:, the sddb.cfg appears as follows (line numbers do not appear in the file: 1. on <-- not presently used 2. 01 <-- not presently used 3. e:\ <-- path for optional CD-ROM databases 4. X <-- not presently used 5. newfile.dat <-- default primary name for certain extract files 6. sddb.dbf <-- path\filename for Census online dictionary 7. ccdctla.dbf <-- not presently used 8. e:\us.dat <-- active Census master file path\name 9. usctl.dbf <-- active Census master index file path\name 10. newfile.dbf <-- not presently used 11. newfile.ndx <-- not presently used 12. dbgeo1.txt <-- not presently used 13. P101.txt <-- not presently used 14. f33.rpt <-- not presently used 15. bnd <-- not presently used 16. ovr <-- not presently used 17. vga <-- not presently used 18. hplaser <-- not presently used A.1.4. Performance Enhancement Processing speed of the system can be improved by moving several files from the CD-ROM default location to hard disk. If you have the additional 50 MB required to implement this suggested procedure, follow these steps: 1 - While in the SDDB hard disk directory, e.g., D:\SDDB, issue the DOS commands: d:\sddb\>copy e:\top100.dbf d:\sddb\>copy e:\ccdext?.dbf d:\sddb\>copy e:\f33.dbf where E: is the CD-ROM drive containing the SDDB CD-ROM. 2 - change line 3 of the sddb.cfg file to read: D:\SDDB\ >>>>> End of Installation Section <<<<< A.2. Codes and Reference Lists A.2.1. State FIPS Codes 00 United States 01 ALABAMA 02 ALASKA 04 ARIZONA 05 ARKANSAS 06 CALIFORNIA 08 COLORADO 09 CONNECTICUT 10 DELAWARE 11 DISTRICT OF COLUMBIA 12 FLORIDA 13 GEORGIA 15 HAWAII 16 IDAHO 17 ILLINOIS 18 INDIANA 19 IOWA 20 KANSAS 21 KENTUCKY 22 LOUISIANA 23 MAINE 24 MARYLAND 25 MASSACHUSETTS 26 MICHIGAN 27 MINNESOTA 28 MISSISSIPPI 29 MISSOURI 30 MONTANA 31 NEBRASKA 32 NEVADA 33 NEW HAMPSHIRE 34 NEW JERSEY 35 NEW MEXICO 36 NEW YORK 37 NORTH CAROLINA 38 NORTH DAKOTA 39 OHIO 40 OKLAHOMA 41 OREGON 42 PENNSYLVANIA 44 RHODE ISLAND 45 SOUTH CAROLINA 46 SOUTH DAKOTA 47 TENNESSEE 48 TEXAS 49 UTAH 50 VERMONT 51 VIRGINIA 53 WASHINGTON 54 WEST VIRGINIA 55 WISCONSIN 56 WYOMING A.3. Sample Profiles A.3.1. Profile 001 **** School District Data Book **** General Characteristics Profile-Summary (001) Primary Area.......MESA UNIFIED SCHOOL DISTRICT State ID: 070204 Comparison Area 1..ARIZONA Comparison Area 2..UNITED STATES Primary Area Area 1 Area 2 State-County-District Codes 04-000-04970 04-00000 00-00000 Metropolitan Area (MSA) Code 00-6200 00-0000 - County Code (Some Districts) 013 000 Zip Code (Some Districts) 85203 00000 Grade Range (Districts) PK-12 00-00 00-00 Total Persons 340,125 3,665,228 248,709,873 Percent Urban 98.37 87.50 75.21 Percent White 85.20 71.81 75.76 Percent Black 1.60 2.87 11.77 Percent Asian/Pacific Islander 1.33 1.40 2.81 Percent Hispanic 10.06 18.57 8.81 Percent in Poverty 9.78 15.40 12.76 Total Housing Units 167,690 1,659,430 102,263,678 Median Housing Value $ 85,116 79,680 78,500 Median Household Income $ 30,111 27,540 30,056 Per Capita Income in 1989 $ 13,449 13,461 14,420 Total Children 80,649 843,522 55,325,634 Enrolled 65,560 675,205 45,745,358 Percent Public of Those Enrolled 92.86 91.67 87.18 Percent Private of Those Enrolled 7.14 8.33 12.82 Percent Urban 97.72 85.24 72.82 Percent White 79.24 59.88 68.92 Percent Black 1.87 3.52 14.77 Percent Asian and Pacific Islander 1.57 1.42 3.10 Percent Hispanic 14.80 26.89 12.04 Percent in Poverty 12.39 21.38 17.84 Percent Ages 3- 5 At-Risk 7.05 14.65 12.54 Percent Ages 6-19 At-Risk 1.85 5.29 5.04 Students per Teacher 22 19 17 Total Revenue per Student $ 4,041 4,761 5,261 Federal Revenue per Student $ 163 392 308 Total Expenditure per Student $ 3,766 4,874 5,312 A.3.2. Profile 002 **** School District Data Book **** General Characteristics Profile-Detailed (002) Primary Area.......MESA UNIFIED SCHOOL DISTRICT State ID: 070204 Comparison Area 1..ARIZONA Comparison Area 2..UNITED STATES Primary Area Area 1 Area 2 State-County-District Codes 04-000-04970 04-00000 00-00000 Metropolitan Area (MSA) Code 00-6200 00-0000 - County Code (Some Districts) 013 000 Zip Code (Some Districts) 85203 00000 Grade Range (Districts) PK-12 00-00 00-00 Total Persons 340,125 3,665,228 248,709,873 Male 165,786 1,807,996 121,172,379 Female 174,339 1,857,232 127,537,494 Total Persons - 100-Percent Count 340,295 3,665,228 248,709,873 Unweighted Sample Count 42,935 468,178 38,607,515 Persons by Type of Household Persons in Households 337,861 3,585,308 242,050,161 Persons in Non-household Settings 2,264 79,920 6,659,712 Persons by Urban/Rural Status Urban - Inside Urbanized Areas 334,584 2,656,388 158,258,042 Urban - Outside Urbanized Areas 0 550,687 28,793,501 Rural - Farm 70 6,967 3,871,583 Rural - Nonfarm 5,471 451,186 57,786,747 Persons by Race/Ethnic Origin NonHispanic White 289,777 2,631,906 188,424,773 Black 5,433 105,371 29,284,596 American Indian, Eskimo, Aleut 5,869 192,202 1,866,807 Asian and Pacific Islander 4,528 51,274 6,994,302 Other Races 292 3,847 239,306 Hispanic 34,226 680,628 21,900,089 Labor Force Status (Persons 16 Years & Over) In Labor Force 164,940 1,753,478 125,182,378 Civilian Employed 155,548 1,603,896 115,681,202 Civilian Unemployed 8,686 123,902 7,792,248 Educational Attainment (Persons 20 Years & Over) 12th Grade or Less, No Diploma 38,390 549,012 42,600,296 High School Graduate 65,233 676,884 53,459,489 Some College, No Bachelor Degree 87,988 862,638 47,160,089 Bachelor or Higher Degree 44,367 487,037 34,293,949 Families 90,018 949,418 65,049,428 Parents Living with Children 70,908 742,519 51,984,201 Households 128,453 1,371,885 91,993,582 With Children 3-19 Years, NHSG 41,886 450,522 31,050,897 With Children Under 18 Years 46,690 494,059 33,989,004 With Children 5 to 17 Years 35,901 388,849 26,867,196 Total Housing Units 167,690 1,659,430 102,263,678 Occupied Housing Units 128,607 1,368,843 91,947,410 Owner Occupied 83,718 879,000 59,031,378 Renter Occupied 44,889 489,843 32,916,032 Vacant Housing Units 39,083 290,587 10,316,268 Total Housing Units-100-Pct Count 167,829 1,659,430 102,263,678 Unweighted Sample Count 21,206 216,958 16,326,603 Occupied Housing Units Urban - Inside Urbanized Area 127,125 1,023,467 59,243,029 Urban - Outside Urbanized Area 0 197,421 10,792,596 Rural - Farm 24 2,475 1,391,483 Rural - Nonfarm 1,458 145,480 20,520,302 Inside Metro In Central City 104,401 772,874 29,793,633 Not in Central City - Urban 22,724 288,436 32,332,671 Not in Central City - Rural 1,482 43,833 9,139,082 Outside Metro - Urban 0 162,393 7,937,968 Outside Metro - Rural 0 101,307 12,744,056 Economic Characteristics Median Gross Rent 474 438 447 Median Housing Value 85,116 79,680 78,500 Per Capita Income in 1989 13,449 13,461 14,420 Median Household Income 30,111 27,540 30,056 Public Assistance Income in 1989 Persons with Assistance 5,146 84,132 6,943,269 Persons without Assistance 123,307 1,287,753 85,050,313 Poverty Status, Income in 1989 With Income Above Poverty Level 303,784 3,020,037 210,234,995 With Income Below Poverty Level 33,277 564,362 31,742,864 Dropouts, Persons 16-19 Years, Not HS Graduates and Not Enrolled in School In Households 2,087 29,438 1,528,412 In Group Quarters 19 919 77,082 At-Risk Pre-School Age Children Less than 4 years 817 17,651 951,559 4 to 5 years of age 361 7,913 431,465 At Risk School Age Children 6 to 19 years of age 1,186 35,397 2,232,178 Total Children 80,649 843,522 55,325,634 (3-19 Years, Not High School Graduate) Male 41,447 436,474 28,562,469 Female 39,202 407,048 26,763,165 Children by Urban/Rural Status Urban - Inside Urbanized Area 78,811 587,694 33,726,276 Urban - Outside Urbanized Area 0 131,295 6,563,128 Rural - Farm 16 1,621 892,513 Rural - Nonfarm 1,822 122,912 14,143,717 Children by Race/Ethnicity NonHispanic White 63,909 505,091 38,131,162 Black 1,506 29,731 8,174,313 American Indian, Eskimo, Aleut 1,892 68,460 553,604 Asian and Pacific Islander 1,270 12,011 1,714,600 Other Races 138 1,382 92,780 Hispanic 11,934 226,847 6,659,175 Children by Age Age 3 Years 5,636 57,922 3,656,737 Age 4 Years 5,430 58,538 3,682,236 Age 5 Years 5,632 58,022 3,686,738 Age 5 to 13 Years 48,503 490,267 32,007,392 Age 14 to 17 Years 17,737 192,866 13,061,288 In Households 17,659 190,270 12,914,917 In Group Quarters 78 2,596 146,371 Age 18 to 19 Years 3,343 43,929 2,917,981 Children by Household Type In Family Households Child (natural, adopted, step) 75,171 760,705 50,150,370 Other (e.g., householder, spouse) 4,416 68,917 4,389,525 In Nonfamily Households 931 9,592 483,176 In Group Quarters 131 4,308 302,563 Children by Poverty Status Income Above Poverty Level 69,616 648,713 44,568,994 Income Below Poverty Level 9,990 180,331 9,869,682 Children Enrolled in School 65,560 675,205 45,745,358 Male 33,728 349,250 23,574,082 Female 31,832 325,955 22,171,276 By Race/Ethnicity NonHispanic White 52,265 412,254 31,799,914 Black 1,229 23,759 6,732,276 American Indian, Eskimo, Aleut 1,553 54,712 449,369 Asian and Pacific Islander 1,149 9,916 1,457,709 Other Races 114 1,030 73,480 Hispanic 9,250 173,534 5,232,610 Children Enrolled in Public School 60,882 618,961 39,880,220 Male 31,384 320,433 20,594,707 Female 29,498 298,528 19,285,513 By Race/Ethnicity NonHispanic White 48,201 369,412 27,095,956 Black 1,159 22,540 6,265,423 American Indian, Eskimo, Aleut 1,494 51,681 424,747 Asian and Pacific Islander 1,044 8,971 1,239,862 Other Races 110 918 63,453 Hispanic 8,874 165,439 4,790,779 Adminstrative (Common Core of Data) Students 61,636 607,615 39,809,102 Teachers 2,755 32,134 2,319,127 Schools 63 1,026 81,637 Financial (Census of Governments) Total Revenue 249,055 2,892,838 209,434,632 Local Revenue 114,586 1,458,750 97,589,934 State Revenue 124,446 1,196,109 99,578,091 Federal Revenue 10,023 237,979 12,266,607 Total Expenditures 232,107 2,961,338 211,456,830 Current Expenditures 205,126 2,375,739 190,031,006 Instruction Expenditures 127,919 1,386,502 113,348,615 Support Expenditures 69,202 891,448 64,938,830 A.3.3. Profile 101 **** School District Data Book **** District Financial Profile (101) Primary Area.......MESA UNIFIED SCHOOL DIST Comparison Area 1..ARIZONA Comparison Area 2..United States Total Primary Area Area 1 Area 2 ST-CCD##: 04-04970 04-00000 00-00000 Students 67,483 607,615 40,573,365 Total Revenue per Student $ 3,691 4,793 5,177 Local Taxes per Student 1,168 1,751 1,568 Parent Govt Contribution/Student 0 0 438 State Revenue per Student 1,844 1,969 2,446 Federal Revenue per Student 149 392 302 Total Expenditure per Student $ 3,439 4,874 5,280 Current Spending per Student 3,040 3,910 4,675 Instructional Expenditure/Student 1,896 2,282 2,785 Support Services Spending/Student 1,025 1,467 1,601 TOTAL REVENUE BY SOURCE (000's) $ 249,055 2,912,469 210,062,764 Percent Local 46.01 50.76 46.91 Percent from Property Tax 31.65 36.53 29.51 Percent Parent Government 0.00 0.00 8.47 Percent Local Intergovernmental 6.09 4.14 2.10 Percent Charges 3.38 2.54 2.89 Percent State Sources 49.97 41.07 47.25 Percent Federal Sources 4.02 8.17 5.84 TOTAL EXPENDITURES (000's) 232,107 2,961,338 214,215,801 Percent Current Instruction Program 84.93 76.92 83.07 Percent Instruction 55.11 46.82 52.75 Percent Support Services 29.81 30.10 30.31 Percent Current Noninstructional 3.45 3.30 5.48 Percent Capital Outlay 6.68 15.16 8.06 A.3.4. Profile 102 **** School District Data Book **** District Financial Profile (102) Primary Area.......MESA UNIFIED SCHOOL DIST Comparison Area 1..ARIZONA Comparison Area 2..United States Total Primary Area Area 1 Area 2 ST-CCD##: 04-04970 04-00000 00-00000 Students 67,483 607,615 40,573,365 Total Revenue per Student $ 3,691 4,793 5,177 Local Taxes per Student 1,168 1,751 1,568 Parent Govt Contribution/Student 0 0 438 State Revenue per Student 1,844 1,969 2,446 Federal Revenue per Student 149 392 302 Total Expenditure per Student $ 3,439 4,874 5,280 Current Spending per Student 3,040 3,910 4,675 Instructional Expenditure/Student 1,896 2,282 2,785 Support Services Spending/Student 1,025 1,467 1,601 TOTAL REVENUE BY SOURCE (000's) $ 249,055 2,912,469 210,062,764 Total Local Revenue 114,586 1,478,381 98,548,112 Taxes 78,833 1,063,853 63,071,397 Property Tax 78,833 1,063,853 61,991,641 General Sales Tax 0 0 480,610 Income Tax 0 0 531,492 Public Utility Tax 0 0 67,654 Other Tax 0 0 557,749 Parent Government Contribution 0 0 17,789,644 Local Intergovernmental 15,171 120,507 4,405,288 Interschool Transfer 340 19,631 1,515,927 Cities and Counties 14,831 100,876 2,889,361 Charges 8,425 74,064 6,062,738 School Lunch 5,425 48,142 3,425,419 Tuition & Transportation 125 906 956,571 Other Charges 2,875 25,016 1,680,748 Interest Earnings 3,184 60,555 3,715,353 Other 8,973 159,402 3,503,692 Total State Revenue 124,446 1,196,109 99,248,045 Direct from State 124,446 1,195,589 92,800,968 State Revenue on Behalf of LEA 0 520 6,447,077 Total Federal Aid 10,023 237,979 12,266,607 Federal Aid Through State 8,066 159,551 10,923,599 School Lunch 2,837 52,227 3,405,163 All Other 5,229 107,324 7,518,436 Direct Federal Aid 1,957 78,428 1,343,008 TOTAL EXPENDITURES (000's) 232,107 2,961,338 214,215,801 Current For Instructional Programs 197,121 2,277,950 177,944,675 Instruction 127,919 1,386,502 113,005,845 Direct Instruction 127,919 1,386,502 108,000,000 Retirement Fund Transfer to LEA 0 0 470,718 Expenditures on Behalf of LEA 0 0 4,535,127 Support Services 69,202 891,448 64,938,830 Pupil Support Services 0 0 6,487,772 Instructional Staff Support Svc 0 0 5,607,468 General Admin Support Services 5,157 96,291 4,602,537 School Admin Support Services 0 0 9,098,041 Other Support Services 33,746 420,393 32,882,439 Support Services - NEC 30,299 374,764 6,260,573 Noninstructional Current Spending 8,005 97,789 11,743,561 Food Service 8,005 97,789 7,614,989 Expenditures on Behalf of the LEA 0 0 88,767 Other 0 0 4,039,805 Capital Outlay Expenditure 15,511 449,070 17,275,179 Equipment 5,272 65,269 4,828,022 Construction 9,836 371,512 9,896,710 Land and Existing Structures 403 12,289 2,550,447 Payments to Other LEA's & Govts 0 0 3,749,350 Interschool Transfer 0 0 3,101,741 Payments to State Governments 8,005 97,789 11,743,561 Payments to Local Governments 0 0 305,642 Interest on Debt 11,470 136,529 3,503,036 Long-Term Debt Issued $ 5,000 315,591 8,397,806 Long-Term Debt Retired 15,305 164,352 4,215,116 Long-Term Debt Outstanding, End Yr 166,372 1,951,236 52,004,222 Short-Term Debt Outstanding, Beg Yr 0 0 2,680,489 Assets at End of Year 48,893 768,867 41,767,465 Sinking Fund 16,126 160,692 3,212,577 Bond Fund 5,070 266,322 9,926,480 Other 27,697 341,853 28,628,408 A.3.5. Profile 105 **** School District Data Book **** Administrative Profile - Summary (105) Primary Area.......MESA UNIFIED SCHOOL DISTRICT Comparison Area 1..ARIZONA Comparison Area 2..United States Total Primary Area Area 1 Area 2 State and District Codes . . . . . . . 04-04970 04-00000 00-00000 Number of Students 61,324 615,475 39,858,731 Percent Free Lunch Eligible 0.00 0.00 8.47 Percent Amer. Indian/Alaska Native 2.36 7.03 0.86 Percent Asian & Pacific Islander 1.60 1.55 2.95 Percent Hispanic 10.59 25.26 10.58 Percent Black, Not Hispanic 1.98 4.35 14.55 Percent White, Not Hispanic 89.73 68.19 61.70 in Schools by Enrollment Size Percent Under 100 Students 0.19 0.61 0.89 Percent 100 - 199 Students 0.00 1.37 3.31 Percent 200 - 299 Students 0.35 2.57 6.24 Percent 300 - 399 Students 0.55 5.52 10.33 Percent 400 - 499 Students 0.00 8.29 12.47 Percent 500 - 599 Students 0.96 13.25 12.73 Percent 600 - 699 Students 1.11 11.34 10.89 Percent 700 - 799 Students 10.01 11.44 8.52 Percent 800 - 999 Students 35.99 16.63 11.48 Percent 1,000 - 1,499 Students 27.09 11.88 13.10 Percent 1,500 or More Students 23.74 17.10 10.05 in Schools by Urban/Rural Category Percent Large Central City 0.00 23.82 13.30 Percent Mid-Size Central City 89.41 28.80 16.63 Percent Urban Fringe of Large City 10.59 13.74 17.52 Percent Urban Fringe of Midsz City 0.00 3.75 11.97 Percent Large Town 0.00 4.63 2.34 Percent Small Town 0.00 18.80 21.68 Percent Rural Territory 0.00 6.46 16.55 in Schools by Type of School Percent Regular Schools 99.48 98.82 99.05 Percent Special Education Schools 0.06 0.09 0.32 Percent Vocational Schools 0.00 0.21 0.28 Number of Schools 63 1,010 81,370 Percent Regular 92.06 95.64 96.24 Percent Special Education 3.17 1.29 1.56 Percent Vocational 0.00 0.59 0.86 by Urban/Rural Category Percent Large Central City 0.00 19.01 9.13 Percent Mid-Size Central City 88.89 25.05 14.12 Percent Urban Fringe of Large City 11.11 10.20 14.24 Percent Urban Fringe of Midsz City 0.00 3.17 9.87 Percent Large Town 0.00 3.86 2.20 Percent Small Town 0.00 23.07 22.84 Percent Rural Territory 0.00 15.64 27.58 Number of Teachers 2,755 30,922 2,235,169 in Schools by Urban/Rural Category Percent Large Central City 0.00 24.06 12.80 Percent Mid-Size Central City 90.02 27.43 16.18 Percent Urban Fringe of Large City 9.98 13.25 16.78 Percent Urban Fringe of Midsz City 0.00 3.56 11.92 Percent Large Town 0.00 4.48 2.27 Percent Small Town 0.00 19.65 21.97 Percent Rural Territory 0.00 7.55 18.05 A.3.6. Profile 106 **** School District Data Book **** Administrative Profile - Detailed (106) Primary Area.......MESA UNIFIED SCHOOL DISTRICT Comparison Area 1..ARIZONA Comparison Area 2..United States Total Primary Area Area 1 Area 2 State and District Codes . . . . . . . 04-04970 04-00000 00-00000 Total Population (100%) of District 340,097 4,869,347 262,421,785 Area of District (Square kilometers) 473 271,820 8,848,435 Number of Schools in CCD Schools Fil 63 1,010 81,370 Teachers (Full-Time Equivalence: FTE 2,755 30,922 2,235,169 Students Reported in Schools Total 61,324 615,475 39,858,731 Total - Free Lunch Eligible 0 0 3,374,471 American Indian & Alaska Native 1,449 43,246 341,575 Asian & Pacific Islander 984 9,511 1,177,609 Hispanic 6,497 155,458 4,218,093 Black, not Hispanic 1,212 26,760 5,798,934 White, not Hispanic 55,023 419,675 24,591,250 Number of Schools by Enrollment Size Less than 100 5 81 6,842 100 - 199 0 57 8,754 200 - 299 1 63 9,893 300 - 399 1 96 11,773 400 - 499 0 114 11,082 500 - 599 1 148 9,271 600 - 699 1 108 6,718 700 - 799 8 94 4,548 800 - 999 25 115 5,163 1,000 - 1,499 14 62 4,379 1,500 or More 7 53 2,019 Enrollment not reported 0 19 928 Number of Students in Schools by Enrollment Reported Size Schools with less than 100 Student 119 3,784 353,299 Schools with 100 - 199 Students 0 8,414 1,319,257 Schools with 200 - 299 Students 217 15,834 2,486,460 Schools with 300 - 399 Students 339 33,992 4,117,724 Schools with 400 - 499 Students 0 51,025 4,971,946 Schools with 500 - 599 Students 587 81,536 5,075,502 Schools with 600 - 699 Students 682 69,779 4,340,413 Schools with 700 - 799 Students 6,140 70,416 3,395,071 Schools with 800 - 999 Students 22,069 102,329 4,574,390 Schools with 1000 - 1499 Students 16,610 73,120 5,220,665 Schools with 1500 or more Students 14,561 105,246 4,004,004 Number of Schools by Type Regular schools 58 966 78,313 Special education schools 2 13 1,270 Vocational schools 0 6 696 Other/alternative schools 3 25 1,091 Number of Students by Type of School Regular Schools 61,003 608,196 39,481,695 Special Ed Schools 37 584 126,659 Vocational Schools 0 1,273 112,229 Other/Alternative Schools 284 5,422 138,148 Number of Schools by Urban/Rural Category Large Central City 0 192 7,432 Mid-Size Central City 56 253 11,493 Urban Fringe of Large City 7 103 11,591 Urban Fringe of Mid-Sized City 0 32 8,034 Large Town 0 39 1,794 Small Town 0 233 18,581 Rural Territory 0 158 22,445 Number of Students Reported in Schoo by Urban/Rural Category Large Central City 0 146,613 5,301,358 Mid-Size Central City 54,830 177,254 6,628,597 Fringe of Large City 6,494 84,555 6,984,702 Fringe of Med City 0 23,064 4,770,167 Large Towns 0 28,519 932,990 Small Towns 0 115,732 8,643,161 Rural Territory 0 39,738 6,597,756 Number of Teachers (FTE) in Schools by Urban/Rural Category Large Central City 0 7,441 286,171 Mid-Size Central City 2,480 8,483 361,747 Fringe of Large City 275 4,096 375,119 Fringe of Med City 0 1,100 266,455 Large Town 0 1,384 50,660 Small Town 0 6,075 491,003 Rural Territory 0 2,334 403,364 Number of Schools by Free Lunch Eligibility Less than 5 percent eligible 0 2 12,919 5 - 9 percent eligible 0 0 3,936 10 - 14 percent eligible 0 0 3,815 15 - 19 percent eligible 0 0 3,139 20 - 24 percent eligible 0 0 2,714 25 - 39 percent eligible 0 0 5,307 40 percent or more eligible 0 0 6,128 With Eligible > Reported Students 0 0 72 With Free Lunch or Students missin 63 1,008 43,340 Number of Students in Schools by Free Lunch Eligibility Less than 5 percent eligible 0 153 6,223,087 5 - 9 percent eligible 0 0 2,222,002 10 - 14 percent eligible 0 0 1,894,107 15 - 19 percent eligible 0 0 1,463,043 20 - 24 percent eligible 0 0 1,188,958 25 - 39 percent eligible 0 0 2,436,015 40 percent or more eligible 0 0 2,698,487 With Eligible > Reported Students 0 0 17,747 With Free Lunch or Students missin 61,324 615,322 21,715,285 Number of Teachers (FTE) in Schools by Free Lunch Eligibility Less than 5 percent eligible 0 2 362,447 5 - 9 percent eligible 0 0 126,922 10 - 14 percent eligible 0 0 108,721 15 - 19 percent eligible 0 0 84,682 20 - 24 percent eligible 0 0 68,563 25 - 39 percent eligible 0 0 144,434 40 percent or more eligible 0 0 164,646 With Eligible > Reported Students 0 0 1,746 With Free Lunch or Students missin 2,755 30,920 1,171,686 Number of Students with Race/Ethnicity reported 65,165 654,644 36,125,497 Number of Schools by Percent Black Less than 5 percent 61 742 43,557 5 - 9 percent 1 144 5,986 10 - 19 percent 0 62 6,219 20 - 34 percent 0 14 5,798 35 - 64 percent 0 5 5,596 65 - 79 percent 0 2 1,422 80 - 89 percent 0 1 801 90 - 94 percent 0 0 539 95 percent or more 0 0 1,841 With percent Black missing 1 40 9,611 Number of Students in Schools by Percent Black Less than 5 percent 59,653 454,366 19,292,869 5 - 9 percent 920 93,566 3,608,595 10 - 19 percent 0 44,474 3,900,924 20 - 34 percent 0 7,808 3,498,637 35 - 64 percent 0 2,499 3,223,606 65 - 79 percent 0 821 783,391 80 - 89 percent 0 560 474,547 90 - 94 percent 0 0 312,804 95 percent or more 0 0 1,043,781 With percent Black missing 751 11,381 3,719,577 Number of Teachers (FTE) in Schools by Percent Black Less than 5 percent 2,685 22,810 1,052,110 5 - 9 percent 39 4,675 189,887 10 - 19 percent 0 2,212 212,926 20 - 34 percent 0 486 199,775 35 - 64 percent 0 135 190,094 65 - 79 percent 0 44 46,933 80 - 89 percent 0 30 27,826 90 - 94 percent 0 0 18,500 95 percent or more 0 0 58,427 With percent Black missing 31 502 237,651 Number of Schools by Percent White Less than 5 percent 1 76 4,405 5 - 9 percent 0 24 1,185 10 - 19 percent 0 44 2,013 20 - 34 percent 0 60 3,222 35 - 64 percent 3 222 10,819 65 - 79 percent 13 169 8,707 80 - 89 percent 29 178 8,868 90 - 94 percent 12 150 8,274 95 percent or more 4 47 24,266 With percent White missing 1 40 9,611 Number of Students in Schools by Percent White Less than 5 percent 28 36,177 2,902,567 5 - 9 percent 0 13,122 766,152 10 - 19 percent 0 24,507 1,245,613 20 - 34 percent 0 31,675 1,952,829 35 - 64 percent 1,790 139,763 6,206,199 65 - 79 percent 13,025 105,317 4,865,521 80 - 89 percent 29,612 128,425 4,787,254 90 - 94 percent 12,706 103,452 4,169,100 95 percent or more 3,412 21,656 9,243,919 With percent White missing 751 11,381 3,719,577 Number of Teachers (FTE) in Schools by Percent White Less than 5 percent 0 2,042 158,706 5 - 9 percent 0 686 41,298 10 - 19 percent 0 1,291 65,474 20 - 34 percent 0 1,681 104,551 35 - 64 percent 79 7,071 340,267 65 - 79 percent 598 5,237 267,040 80 - 89 percent 1,340 6,124 262,448 90 - 94 percent 571 5,081 230,554 95 percent or more 134 1,143 525,225 With percent White missing 31 502 237,651 Number of Schools by Percent Hispani Less than 5 percent 13 199 51,104 5 - 9 percent 27 166 5,565 10 - 14 percent 11 110 2,931 15 - 24 percent 9 157 3,553 25 - 74 percent 2 268 6,687 75 - 89 percent 0 49 1,029 90 percent or more 0 21 890 With percent Hispanic missing 1 40 9,611 Number of Students by Percent Hispan Less than 5 percent 11,535 116,120 23,533,129 5 - 9 percent 27,812 120,381 3,088,956 10 - 14 percent 12,250 77,039 1,738,172 15 - 24 percent 7,225 95,881 2,189,155 25 - 74 percent 1,751 158,322 4,193,390 75 - 89 percent 0 23,854 721,814 90 percent or more 0 12,497 674,538 With percent Hispanic missing 751 11,381 3,719,577 Number of Teachers (FTE) by Percent Hispanic Less than 5 percent 503 6,070 1,342,259 5 - 9 percent 1,245 5,757 165,118 10 - 14 percent 568 3,759 91,977 15 - 24 percent 330 4,763 113,415 25 - 74 percent 76 8,231 213,900 75 - 89 percent 0 1,190 35,654 90 percent or more 0 601 34,281 With percent Hispanic missing 31 502 237,651 Number of Schools by Percent Native American Less than 5 percent 51 788 68,546 5 - 9 percent 7 61 1,157 10 - 14 percent 3 21 514 15 - 24 percent 0 21 536 25 - 74 percent 0 27 660 75 - 89 percent 0 4 72 90 percent or more 1 48 274 With percent Native American missi 1 40 9,611 Number of Students by Percent Native American Less than 5 percent 53,377 509,160 35,063,391 5 - 9 percent 4,627 35,596 486,667 10 - 14 percent 2,541 10,642 181,539 15 - 24 percent 0 12,950 169,834 25 - 74 percent 0 13,216 169,726 75 - 89 percent 0 599 12,653 90 percent or more 28 21,931 55,344 With percent Native American missi 751 11,381 3,719,577 Number of Teachers (FTE) by Percent Native American Less than 5 percent 2,394 25,230 1,934,690 5 - 9 percent 218 1,897 26,663 10 - 14 percent 111 528 10,357 15 - 24 percent 0 700 10,206 25 - 74 percent 0 646 10,564 75 - 89 percent 0 43 898 90 percent or more 0 1,349 3,765 With percent Native American missi 31 502 237,651 Number of Schools by Percent Asian/Pacific Islander Less than 5 percent 62 948 63,543 5 - 9 percent 0 21 4,236 10 - 14 percent 0 1 1,668 15 - 24 percent 0 0 1,231 25 - 74 percent 0 0 937 75 - 89 percent 0 0 115 90 percent or more 0 0 29 With percent missing 1 40 9,611 Number of Students by Percent Asian/Pacific Islander Less than 5 percent 60,573 589,949 30,511,027 5 - 9 percent 0 13,348 2,775,956 10 - 14 percent 0 797 1,117,996 15 - 24 percent 0 0 921,723 25 - 74 percent 0 0 714,284 75 - 89 percent 0 0 78,574 90 percent or more 0 0 19,594 With percent missing 751 11,381 3,719,577 Number of Teachers (FTE) by Percent Asian/Pacific Islander Less than 5 percent 2,724 29,709 1,719,938 5 - 9 percent 0 659 142,130 10 - 14 percent 0 39 54,542 15 - 24 percent 0 0 43,624 25 - 74 percent 0 0 31,551 75 - 89 percent 0 0 4,128 90 percent or more 0 0 1,026 With percent missing 31 502 237,651 A.4. Accuracy of the Data Approximately 70 percent of the subject matter tables contained in the 1990 Census school district special tabulation are directly derived from the subject matter table specifications used in the 1990 Census Summary Tape File 3 released by the Census Bureau as a standard data product of the 1990 Census. As a result, much of the description regarding accuracy of the data, collection and processing procedures is the same for the special tabulation and the standard census products. To insure consistency, much of the information presented in sections A.4. and A.5. are taken directly from the 1990 Census STF3 documentation. The data contained in the SDDB are based on the 1990 census sample. The data are estimates of the actual figures that would have been obtained from a complete count. Estimates derived from a sample are expected to be different from the 100-percent figures because they are subject to sampling and nonsampling errors. Sampling error in data arises from the selection of persons and housing units to be included in the sample. Nonsampling error affects both sample and 100-percent data, and is introduced as a result of errors that may occur during the collection and processing phases of the census. Provided below is a detailed discussion of both types of errors and a description of the estimation procedures. A.4.1. Sample Design Every person and housing unit in the United States was asked certain basic demographic and housing questions (for example, race, age, marital status, housing value, or rent). A sample of these persons and housing units was asked more detailed questions about such items as income, occupation, and housing costs in addition to the basic demographic and housing information. The primary sampling unit for the 1990 census was the housing unit, including all occupants. For persons living in group quarters, the sampling unit was the person. Persons in group quarters were sampled at a 1-in-6 rate. The sample designation method depended on the data collection procedures. Approximately 95 percent of the population was enumerated by the mailback procedure. In these areas, the Bureau of the Census either purchased a commercial mailing list, which was updated by the United States Postal Service and Census Bureau field staff, or prepared a mailing list by canvassing and listing each address in the area prior to Census Day. These lists were computerized and the appropriate units were electronically designated as sample units. The questionnaires were either mailed or hand-delivered to the addresses with instructions to complete and mail back the form. Housing units in governmental units with a precensus (1988) estimated population of fewer than 2,500 persons were sampled at 1-in-2. Governmental units were defined for sampling purposes as all incorporated places, all counties, all county equivalents such as parishes in Louisiana, and all minor civil divisions in Connecticut, Maine, Massachusetts, Michigan, Minnesota, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Wisconsin. Housing units in census tracts and block numbering areas (BNA's) with a precensus housing unit count below 2,000 housing units were sampled at 1-in-6 for those portions not in small governmental units (governmental units with a population less than 2,500). Housing units within census tracts and BNA's with 2,000 or more housing units were sampled at 1-in-8 for those portions not in small governmental units. In list/enumerate areas (about 5 percent of the population), each enumerator was given a blank address register with designated sample lines. Beginning about Census Day, the enumerator systematically canvassed an assigned area and listed all housing units in the address register in the order they were encountered. Completed questionnaires, including sample information for any housing unit listed on a designated sample line, were collected. For all governmental units with fewer than 2,500 persons in list/enumerate areas, a 1-in-2 sampling rate was used. All other list/enumerate areas were sampled at 1-in-6. Housing units in American Indian reservations, tribal jurisdiction statistical areas, and Alaska Native villages were sampled according to the same criteria as other governmental units, except the sampling rates were based on the size of the American Indian and Alaska Native population in those areas as measured in the 1980 census. Trust lands were sampled at the same rate as their associated American Indian reservations. Census designated places in Hawaii were sampled at the same rate as governmental units because the Census Bureau does not recognize incorporated places in Hawaii. The purpose of using variable sampling rates was to provide relatively more reliable estimates for small areas and decrease respondent burden in more densely populated areas while maintaining data reliability. When all sampling rates were taken into account across the Nation, approximately one out of every six housing units in the Nation was included in the 1990 census sample. A.4.2. Confidentiality To maintain the confidentiality required by law (Title 13, United States Code), the Bureau of the Census applies a confidentiality edit to the 1990 census data to assure that published data do not disclose information about specific individuals, households, or housing units. As a result, a small amount of uncertainty is introduced into the estimates of census characteristics. The sample itself provides adequate protection for most areas for which sample data are published since the resulting data are estimates of the actual counts; however, small areas require more protection. The edit is controlled so that the basic structure of the data is preserved. The confidentiality edit is implemented by selecting a small subset of individual households from the internal sample data files and blanking a subset of the data items on these household records. Responses to those data items were then imputed using the same imputation procedures that were used for nonresponse. A larger subset of households is selected for the confidentiality edit for small areas to provide greater protection for these areas. The editing process is implemented in such a way that the quality and usefulness of the data were preserved. A.4.3. Errors in the Data Since statistics in this data product are based on a sample, they may differ somewhat from 100-percent figures that would have been obtained if all housing units, persons within those housing units, and persons living in group quarters had been enumerated using the same questionnaires, instructions, enumerators, etc. The sample estimate also would differ from other samples of housing units, persons within those housing units, and persons living in group quarters. The deviation of a sample estimate from the average of all possible samples is called the sampling error. The standard error of a sample estimate is a measure of the variation among the estimates from all the possible samples and thus is a measure of the precision with which an estimate from a particular sample approximates the average result of all possible samples. The sample estimate and its estimated standard error permit the construction of interval estimates with prescribed confidence that the interval includes the average result of all possible samples. Described below is the method of calculating standard errors and confidence intervals for the data in this product. In addition to the variability which arises from the sampling procedures, both sample data and 100-percent data are subject to nonsampling error. Nonsampling error may be introduced during any of the various complex operations used to collect and process census data. For example, operations such as editing, reviewing, or handling questionnaires may introduce error into the data. A detailed discussion of the sources of nonsampling error is given in the section on "Control of Nonsampling Error" in this appendix. Nonsampling error may affect the data in two ways. Errors that are introduced randomly will increase the variability of the data and should therefore be reflected in the standard error. Errors that tend to be consistent in one direction will make both sample and 100-percent data biased in that direction. For example, if respondents consistently tend to under-report their income, then the resulting counts of households or families by income category will tend to be understated for the higher income categories and overstated for the lower income categories. Such biases are not reflected in the standard error. A.4.4. Calculation of Standard Errors Totals and Percentages--Tables A through C in this appendix contain the information necessary to calculate the standard errors of sample estimates in this data product. To calculate the standard error, it is necessary to know the basic standard error for the characteristic (given in table A or B) that would result under a simple random sample design (of persons, households, or housing units) and estimation technique; the design factor for the particular characteristic estimated (given in table C); and the number of persons or housing units in the tabulation area and the percent of these in the sample. The steps given below should be used to calculate the standard error of an estimate of a total or a percentage contained in this product. A percentage is defined here as a ratio of a numerator to a denominator where the numerator is a subset of the denominator. For example, the proportion of Black teachers is the ratio of Black teachers to all teachers. 1. Obtain the standard error from table A or B (or use the formula given below the table) for the estimated total or percentage, respectively. 2. Find the geographic area to which the estimate applies in the appropriate percent-in-sample table or appropriate matrix, and obtain the person or housing unit "percent-in-sample" figure for this area. Use the person "percent-in-sample" figure for person and family characteristics. Use the housing unit "percent-in-sample" figure for housing unit characteristics. 3. Use table C to obtain the design factor for the characteristic (for example, employment status, school enrollment) and the range that contains the percent- in-sample with which you are working. Multiply the basic standard error by this factor. The unadjusted standard errors of zero estimates or of very small estimated totals or percentages will approach zero. This is also the case for very large percentages or estimated totals that are close to the size of the tabulation areas to which they correspond. Nevertheless, these estimated totals and percentages still are subject to sampling and nonsampling variability, and an estimated standard error of zero (or a very small standard error) is not appropriate. For estimated percentages that are less than 2 or greater than 98, use the basic standard errors in table B that appear in the "2 or 98" row. For an estimated total that is less than 50 or within 50 of the total size of the tabulation area, use a basic standard error of 16. An illustration of the use of the tables is given in the section entitled "Use of Tables to Compute Standard Errors." Sums and Differences--The standard errors estimated from these tables are not directly applicable to sums of and differences between two sample estimates. To estimate the standard error of a sum or difference, the tables are to be used somewhat differently in the following three situations: 1. For the sum of or difference between a sample estimate and a 100-percent value, use the standard error of the sample estimate. The complete count value is not subject to sampling error. 2. For the sum of or difference between two sample estimates, the appropriate standard error is approximately the square root of the sum of the two individual standard errors squared; This method, however, will underestimate (overestimate) the standard error if the two items in a sum are highly positively (negatively) correlated or if the two items in a difference are highly negatively (positively) correlated. This method may also be used for the difference between (or sum of) sample estimates from two censuses or from a census sample and another survey. The standard error for estimates not based on the 1990 census sample must be obtained from an appropriate source outside of this appendix. For the differences between two estimates, one of which is a subclass of the other, use the tables directly where the calculated difference is the estimate of interest. For example, to determine the estimate of non-Black teachers, one may subtract the estimate of Black teachers from the estimate of total teachers. To determine the standard error of the estimate of non-Black teachers apply the above formula directly. Ratios--Frequently, the statistic of interest is the ratio of two variables, where the numerator is not a subset of the denominator. For example, the ratio of teachers to students in public elementary schools. The standard error of the ratio between two sample estimates is estimated as follows: 1. If the ratio is a proportion, then follow the procedure outlined for "Totals and Percentages." 2. If the ratio is not a proportion, then approximate the standard error. Medians--For the standard error of the median of a characteristic, it is necessary to examine the distribution from which the median is derived, as the size of the base and the distribution itself affect the standard error. An approximate method is given here. As the first step, compute one-half of the number on which the median is based (refer to this result as N/2). Treat N/2 as if it were an ordinary estimate and obtain its standard error as instructed above. Compute the desired confidence interval about N/2. Starting with the lowest value of the characteristic, cumulate the frequencies in each category of the characteristic until the sum equals or first exceeds the lower limit of the confidence interval about N/2. By linear interpolation, obtain a value of the characteristic corresponding to this sum. This is the lower limit of the confidence interval of the median. In a similar manner, continue cumulating frequencies until the sum equals or exceeds the count in excess of the upper limit of the interval about N/2. Interpolate as before to obtain the upper limit of the confidence interval for the estimated median. When interpolation is required in the upper open-ended interval of a distribution to obtain a confidence bound, use 1.5 times the lower limit of the open-ended confidence interval as the upper limit of the open-ended interval. Confidence Intervals A sample estimate and its estimated standard error may be used to construct confidence intervals about the estimate. These intervals are ranges that will contain the average value of the estimated characteristic that results over all possible samples, with a known probability. For example, if all possible samples that could result under the 1990 census sample design were independently selected and surveyed under the same conditions, and if the estimate and its estimated standard error were calculated for each of these samples, then: 1. Approximately 68 percent of the intervals from one estimated standard error below the estimate to one estimated standard error above the estimate would contain the average result from all possible samples; 2. Approximately 90 percent of the intervals from 1.645 times the estimated standard error below the estimate to 1.645 times the estimated standard error above the estimate would contain the average result from all possible samples. 3. Approximately 95 percent of the intervals from two estimated standard errors below the estimate to two estimated standard errors above the estimate would contain the average result from all possible samples. The intervals are referred to as 68 percent, 90 percent, and 95 percent confidence intervals, respectively. The average value of the estimated characteristic that could be derived from all possible samples is or is not contained in any particular computed interval. Thus, we cannot make the statement that the average value has a certain probability of falling between the limits of the calculated confidence interval. Rather, one can say with a specified probability of confidence that the calculated confidence interval includes the average estimate from all possible samples (approximately the 100-percent value). Confidence intervals also may be constructed for the ratio, sum of, or difference between two sample figures. This is done by first computing the ratio, sum, or difference, then obtaining the standard error of the ratio, sum, or difference (using the formulas given earlier), and finally forming a confidence interval for this estimated ratio, sum, or difference as above. One can then say with specified confidence that this interval includes the ratio, sum, or difference that would have been obtained by averaging the results from all possible samples. The estimated standard errors given in this appendix do not include all portions of the variability due to nonsampling error that may be present in the data. The standard errors reflect the effect of simple response variance, but not the effect of correlated errors introduced by enumerators, coders, or other field or processing personnel. Thus, the standard errors calculated represent a lower bound of the total error. As a result, confidence intervals formed using these estimated standard errors may not meet the stated levels of confidence (i.e., 68, 90, or 95 percent). Thus, some care must be exercised in the interpretation of the data in this data product based on the estimated standard errors. A standard sampling theory text should be helpful if the user needs more information about confidence intervals and non sampling errors. Use of Tables to Compute Standard Errors The following is a hypothetical example of how to compute a standard error of a total and a percentage. Suppose a particular data table shows that for City A 9,948 persons out of all 15,888 persons age 16 years and over were in the civilian labor force. The percent-in-sample table lists City A with a percent-in-sample of 16.0 percent (Persons column). The column in table C which includes 16.0 percent-in-sample shows the design factor to be 1.1 for "Employment status." The basic standard error for the estimated total 9,948 may be obtained from table A or from the formula given below table A. In order to avoid interpolation, the use of the formula will be demonstrated here. Suppose that the total population of City A was 21,220. The standard error of the estimated 9,948 persons 16 years and over who were in the civilian labor force is found by multiplying the basic standard error 163 by the design factor, 1.1 from table C. This yields an estimated standard error of 179 for the total number of persons 16 years and over in City A who were in the civilian labor force. The estimated percent of persons 16 years and over who were in the civilian labor force in City A is 62.6. From table B, the unadjusted standard error is found to be approximately 0.85 percentage points. The standard error for the estimated 62.6 percent of persons 16 years and over who were in the civilian labor force is 0.85 x 1.1 = 0.94 percentage points. A note of caution concerning numerical values is necessary. Standard errors of percentages derived in this manner are approximate. Calculations can be expressed to several decimal places, but to do so would indicate more precision in the data than is justifiable. Final results should contain no more than two decimal places when the estimated standard error is one percentage point (i.e., 1.00) or more. In the previous example, the standard error of the 9,948 persons 16 years and over in City A who were in the civilian labor force was found to be 179. The interval is 9,654 to 10,242 One can say, with about 90 percent confidence, that this interval includes the value that would have been obtained by averaging the results from all possible samples. The following is an illustration of the calculation of standard errors and confidence intervals when a difference between two sample estimates is obtained. For example, suppose the number of persons in City B age 16 years and over who were in the civilian labor force was 9,314 and the total number of persons 16 years and over was 16,666. Further suppose the population of City B was 25,225. Thus, the estimated percentage of persons 16 years and over who were in the civilian labor force is 55.9 percent. The unadjusted standard error determined using the formula provided at the bottom of table B is 0.86 percentage points. We find that City B had a percent-in-sample of 15.7. The range which includes 15.7 percent-in-sample in table C shows the design factor to be 1.1 for "Employment Status." Thus, the approximate standard error of the percentage (55.9 percent) is 0.86 x 1.1 = 0.95 percentage points. Now suppose that one wished to obtain the standard error of the difference between City A and City B of the percentages of persons who were 16 years and over and who were in the civilian labor force. The difference in the percentages of interest for the two cities is: 62.6-55.9=6.7 percent. Using the results of the previous example: 1.34 percentage points The 90 percent confidence interval for the difference is formed as before: 4.50 to 8.90 One can say with 90 percent confidence that the interval includes the difference that would have been obtained by averaging the results from all possible samples. For reasonably large samples, ratio estimates are normally distributed, particularly for the census population. Therefore, if we can calculate the standard error of a ratio estimate then we can form a confidence interval around the ratio. Suppose that one wished to obtain the standard error of the ratio of the estimate of persons who were 16 years and over and who were in the civilian labor force in City A to the estimate of persons who were 16 years and over and who were in the civilian labor force in City B. The ratio of the two estimates of interest is: 9948/9314 = 1.07 = .029 Using the results above, the 90 percent confidence interval for this ratio would be: 1.02 to 1.12 A.4.5. Estimation Procedure The estimates which appear in this publication were obtained from an iterative ratio estimation procedure (iterative proportional fitting) resulting in the assignment of a weight to each sample person or housing unit record. For any given tabulation area, a characteristic total was estimated by summing the weights assigned to the persons or housing units possessing the characteristic in the tabulation area. Estimates of family or household characteristics were based on the weight assigned to the family member designated as householder. Each sample person or housing unit record was assigned exactly one weight to be used to produce estimates of all characteristics. For example, if the weight given to a sample person or housing unit had the value 6, all characteristics of that person or housing unit would be tabulated with the weight of 6. The estimation procedure, however, did assign weights varying from person to person or housing unit to housing unit. The estimation procedure used to assign the weights was performed in geographically defined "weighting areas." Weighting areas generally were formed of contiguous geographic units which agreed closely with census tabulation areas within counties. Weighting areas were required to have a minimum sample of 400 persons. Weighting areas never crossed State or county boundaries. In small counties with a sample count below 400 persons, the minimum required sample condition was relaxed to permit the entire county to become a weighting area. Within a weighting area, the ratio estimation procedure for persons was performed in four stages. For persons, the first stage applied 17 household-type groups. The second stage used two groups: sampling rate of 1-in-2; sampling rate less than 1-in-2. The third stage used the dichotomy householders/non householders. The fourth stage applied 180 aggregate age-sex-race-Hispanic origin categories. The stages were as follows: PERSONS STAGE I: TYPE OF HOUSEHOLD Group Persons in Housing Units With a Family With Own Children Under 18 1 2 persons in housing unit 2 3 persons in housing unit 3 4 persons in housing unit 4 5 to 7 persons in housing unit 5 8 or more persons in housing unit Persons in Housing Units With a Family Without Own Children Under 18 6- 10 2 through 8 or more persons in housing unit Persons in All Other Housing Units 11 1 person in housing unit 12-16 2 through 8 or more persons in housing unit Persons in Group Quarters 17 Persons in Group Quarters STAGE II: SAMPLING RATES 1 Sampling rate of 1-in-2 2 Sampling rate less than 1-in-2 STAGE III: HOUSEHOLDER/NON HOUSEHOLDER 1 Householder 2 Non householder STAGE IV: AGE/SEX/RACE/HISPANIC ORIGIN Group White Persons of Hispanic Origin Male 1 0 to 4 years 2 5 to 14 years 3 15 to 19 years 4 20 to 24 years 5 25 to 34 years 6 35 to 54 years 7 55 to 64 years 8 65 to 74 years 9 75 years and over Female 10-18 Same age categories as groups 1 through 9. Persons Not of Hispanic Origin 19-36 Same sex and age categories as groups 1 through 18. Black 37-72 Same age/sex/Hispanic origin categories as groups 1 through 36. Asian or Pacific Islander 73-108 Same age/sex/Hispanic origin categories as groups 1 through 36. American Indian, Eskimo, or Aleut 109-144 Same age/sex/Hispanic origin categories as groups 1 through 36. Other Race (includes those races not listed above) 145-180 Same age/sex/Hispanic origin categories as groups 1 through 36. Within a weighting area, the first step in the estimation procedure was to assign an initial weight to each sample person record. This weight was approximately equal to the inverse of the probability of selecting a person for the census sample. The next step in the estimation procedure, prior to iterative proportional fitting, was to combine categories in each of the four estimation stages, when needed to increase the reliability of the ratio estimation procedure. For each stage, any group that did not meet certain criteria for the unweighed sample count or for the ratio of the 100-percent to the initially weighted sample count, was combined, or collapsed, with another group in the same stage according to a specified collapsing pattern. At the fourth stage, an additional criterion concerning the number of complete count persons in each race/Hispanic origin category was applied. As the final step, the initial weights underwent four stages of ratio adjustment applying the grouping procedures described above. At the first stage, the ratio of the complete census count to the sum of the initial weights for each sample person was computed for each stage I group. The initial weight assigned to each person in a group was then multiplied by the stage I group ratio to produce an adjusted weight. In stage II, the stage I adjusted weights were again adjusted by the ratio of the complete census count to the sum of the stage I weights for sample persons in each stage II group. Next, at stage III, the stage II weights were adjusted by the ratio of the complete census count to the sum of the stage II weights for sample persons in each stage III group. Finally, at stage IV, the stage III weights were adjusted by the ratio of the complete census count to the sum of the stage III weights for sample persons in each stage IV group. The four stages of ratio adjustment were performed two times (two iterations) in the order given above. The weights obtained from the second iteration for stage IV were assigned to the sample person records. However, to avoid complications in rounding for tabulated data, only whole number weights were assigned. For example, if the final weight of the persons in a particular group was 7.25 then 1/4 of the sample persons in this group were randomly assigned a weight of 8, while the remaining 3/4 received a weight of 7. The ratio estimation procedure for housing units was essentially the same as that for persons, except that vacant units were treated differently. The occupied housing unit ratio estimation procedure was done in four stages, and the vacant housing unit ratio estimation procedure was done in a single stage. The first stage for occupied housing units applied 16 household type categories, while the second stage used the two sampling categories described above for persons. The third stage applied three units-in-structure categories; i.e. single units, multi-unit less than 10 and multi-unit 10 or more. The fourth stage could potentially use 200 tenure-race-Hispanic origin-value/rent groups. The stages for ratio estimation for housing units were as follows: OCCUPIED HOUSING UNITS STAGE I: TYPE OF HOUSEHOLD Group Housing Units With a Family With Own Children Under 18 1 2 persons in housing unit 2 3 persons in housing unit 3 4 persons in housing unit 4 5 to 7 persons in housing unit 5 8 or more persons in housing unit Housing Units With a Family Without Own Children Under 18 6-10 2 through 8 or more persons in housing unit All Other Housing Units 11 1 person in housing unit 12-16 2 through 8 or more persons in housing unit STAGE II: SAMPLING RATE CATEGORY 1 Sampling rate of 1-in-2 2 Sampling rate less than 1-in-2 STAGE III: UNITS IN STRUCTURE 1 Single unit structure 2 Multi-unit structure consisting of fewer than 10 individual units 3 Multi-unit structure consisting of 10 or more individual units STAGE IV: TENURE/RACE AND HISPANIC ORIGIN OF HOUSEHOLDER/VALUE OR RENT Group Owner White Householder Householder of Hispanic Origin Value 1 Less than $20,000 2 $20,000 to $39,999 3 $40,000 to $59,999 4 $60,000 to $79,999 5 $80,000 to $99,999 6 $100,000 to $149,999 7 $150,000 to $249,999 8 $250,000 to $299,999 9 $300,000 or more 10 Other1/ Householder Not of Hispanic Origin 11-20 Same value categories as groups 1 through 10 Black Householder 21-40 Same Hispanic origin/value categories as groups 1 through 20 Asian or Pacific Islander Householder 41-60 Same Hispanic origin/value categories as groups 1 through 20 American Indian, Eskimo, or Aleut Householder 61-80 Same Hispanic origin/value categories as groups 1 through 20 Householder of Other Race 81-100 Same Hispanic origin/value categories as groups 1 through 20 Renter White Householder Householder of Hispanic origin Rent 101 Less than $100 102 $100 to $199 103 $200 to $299 104 $300 to $399 105 $400 to $499 106 $500 to $599 107 $600 to $749 108 $750 to $999 109 $1,000 or more 110 No cash rent Householder Not of Hispanic Origin 111-120 Same rent categories as groups 101 through 110 Black Householder 121-140 Same Hispanic origin/rent categories as groups 101 through 120 Asian or Pacific Islander House holder 141-160 Same Hispanic origin/rent categories as groups 101 through 120 American Indian, Eskimo, or Aleut Householder 161-180 Same Hispanic origin/rent categories as groups 101 through 120 Householder of Other Race 181-200 Same Hispanic origin/rent categories as groups 101 through 120 Vacant Housing Units 1 Vacant for rent 2 Vacant for sale 3 Other vacant (1) Value of units in this category results from other factors besides housing value alone, for example, inclusion of more than 10 acres of land, or presence of a business establishment on the premises. The estimates produced by this procedure realize some of the gains in sampling efficiency that would have resulted if the population had been stratified into the ratio estimation groups before sampling, and if the sampling rate had been applied independently to each group. The net effect is a reduction in both the standard error and the possible bias of most estimated characteristics to levels below what would have resulted from simply using the initial, unadjusted weight. A by-product of this estimation procedure is that the estimates from the sample will, for the most part, be consistent with the complete count figures for the population and housing unit groups used in the estimation procedure. Control of Non sampling Error As mentioned earlier, both sample and 100-percent data are subject to non sampling error. This component of error could introduce serious bias into the data, and the total error could increase dramatically over that which would result purely from sampling. While it is impossible to completely eliminate nonsampling error from an operation as large and complex as the decennial census, the Bureau of the Census attempted to control the sources of such error during the collection and processing operations. Described below are the primary sources of nonsampling error and the programs instituted for control of this error. The success of these programs, however, was contingent upon how well the instructions actually were carried out during the census. As part of the 1990 census evaluation program, both the effects of these programs and the amount of error remaining after their application will be evaluated. Undercoverage--It is possible for some households or persons to be missed entirely by the census. The undercoverage of persons and housing units can introduce biases into the data. Several coverage improvement programs were implemented during the development of the census address list and census enumeration and processing to minimize undercoverage of the population and housing units. These programs were developed based on experience from the 1980 census and results from the 1990 census testing cycle. In developing and updating the census address list, the Census Bureau used a variety of specialized procedures in different parts of the country. In the large urban areas, the Census Bureau purchased and geocoded address lists. Concurrent with geocoding, the United States Postal Service (USPS) reviewed and updated this list. After the postal check, census enumerators conducted a dependent canvass and update operation. In the fall of 1989, local officials were given the opportunity to examine block counts of address listings (local review) and identify possible errors. Prior to mailout, the USPS conducted a final review. In small cities, suburban areas, and selected rural parts of the country, the Census Bureau created the address list through a listing operation. The USPS reviewed and updated this list, and the Census Bureau reconciled USPS corrections and updated through a field operation. In the fall of 1989, local officials participated in reviewing block counts of address listings. Prior to mailout, the USPS conducted a final review. The Census Bureau (rather than the USPS) conducted a listing operation in the fall of 1989 and delivered census questionnaires in selected rural and seasonal housing areas in March of 1990. In some inner-city public housing developments, whose addresses had been obtained via the purchased address list noted above, census questionnaires were also delivered by Census Bureau enumerators. Coverage improvement programs continued during and after mailout. A recheck of units initially classified as vacant or nonexistent improved further the coverage of persons and housing units. All local officials were given the opportunity to participate in a post-census local review, and census enumerators conducted an additional recanvass. In addition, efforts were made to improve the coverage of unique population groups, such as the homeless and parolees/probationers. Computer and clerical edits and telephone and personal visit followup also contributed to improved coverage. More extensive discussion of the programs implemented to improve coverage will be published by the Census Bureau when the evaluation of the coverage improvement program is completed. Respondent and Enumerator Error--The person answering the questionnaire or responding to the questions posed by an enumerator could serve as a source of error, although the questions were phrased as clearly as possible based on precensus tests, and detailed instructions for completing the questionnaire were provided to each household. In addition, respondents' answers were edited for completeness and consistency, and problems were followed up as necessary. The enumerator may misinterpret or otherwise incorrectly record information given by a respondent; may fail to collect some of the information for a person or household; or may collect data for households that were not designated as part of the sample. To control these problems, the work of enumerators was monitored carefully. Field staff were prepared for their tasks by using standardized training packages that included hands-on experience in using census materials. A sample of the households interviewed by enumerators for nonresponse were reinterviewed to control for the possibility of data for fabricated persons being submitted by enumerators. Also, the estimation procedure was designed to control for biases that would result from the collection of data from households not designated for the sample. Processing Error--The many phases involved in processing the census data represent potential sources for the introduction of nonsampling error. The processing of the census questionnaires includes the field editing, followup, and transmittal of completed questionnaires; the manual coding of write-in responses; and the electronic data processing. The various field, coding and computer operations undergo a number of quality control checks to insure their accurate application. Nonresponse--Nonresponse to particular questions on the census questionnaire allows for the introduction of bias into the data, since the characteristics of the nonrespondents have not been observed and may differ from those reported by respondents. As a result, any imputation procedure using respondent data may not completely reflect this difference either at the elemental level (individual person or housing unit) or on the average. Some protection against the introduction of large biases is afforded by minimizing nonresponse. In the census, nonresponse was reduced substantially during the field operations by the various edit and followup operations aimed at obtaining a response for every question. Characteristics for the nonresponses remaining after this operation were imputed by the computer by using reported data for a person or housing unit with similar characteristics. EDITING OF UNACCEPTABLE DATA The objective of the processing operation is to produce a set of data that describes the population as accurately and clearly as possible. To meet this objective, questionnaires were edited during field data collection operations for consistency, completeness, and acceptability. Questionnaires also were reviewed by census clerks for omissions, certain specific inconsistencies, and population coverage. For example, write-in entries such as "Don't know" or "NA" were considered unacceptable. For some district offices, the initial edit was automated; however, for the majority of the district offices, it was performed by clerks. As a result of this operation, a telephone or personal visit followup was made to obtain missing information. Potential coverage errors were included in the followup, as well as a sample of questionnaires with omissions and/or inconsistencies. Subsequent to field operations, remaining incomplete or inconsistent information on the questionnaires was assigned using imputation procedures during the final automated edit of the collected data. Imputations, or computer assignments of acceptable codes in place of unacceptable entries or blanks, are needed most often when an entry for a given item is lacking or when the information reported for a person or housing unit on that item is inconsistent with other information for that same person or housing unit. As in previous censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable codes in place of blanks or unacceptable entries enhances the usefulness of the data. Another way in which corrections were made during the computer editing process was through substitution; that is, the assignment of a full set of characteristics for a person or housing unit. When there was an indication that a housing unit was occupied but the questionnaire contained no information for the people within the household or the occupants were not listed on the questionnaire, a previously accepted household was selected as a substitute, and the full set of characteristics for the substitute was duplicated. The assignment of the full set of housing characteristics occurred when there was no housing information available. If the housing unit was determined to be occupied, the housing characteristics were assigned from a previously processed occupied unit. If the housing unit was vacant, the housing characteristics were assigned from a previously processed vacant unit. Table A. Unadjusted Standard Error for Estimated Totals \[Based on a 1-in-6 simple random sample\] Size of publication area 2/ Estimated Total 1/ 500 1,000 2,500 5,000 10,000 25,000 50,000 100,000 50 16 16 16 16 16 16 16 16 100 20 21 22 22 22 22 22 22 250 25 30 35 35 35 35 35 35 500 - 35 45 45 50 50 50 50 1,000 - - 55 65 65 70 70 70 2,500 - - - 80 95 110 110 110 5,000 - - - - 110 140 150 150 10,000 - - - - - 170 200 210 15,000 - - - - - 170 230 250 25,000 - - - - - - 250 310 75,000 - - - - - - - 310 100,000 - - - - - - - - 250,000 - - - - - - - - 500,000 - - - - - - - - 1,000,000 - - - - - - - - 5,000,000 - - - - - - - - 10,000,000 - - - - - - - - ------------------------------------------------------------------------- Estimated Total 1/ 250,000 500,000 1,000,000 5,000,000 10,000,000 25,000,000 50 16 16 16 16 16 16 100 22 22 22 22 22 22 250 35 35 35 35 35 35 500 50 50 50 50 50 50 1,000 70 70 70 70 70 70 2,500 110 110 110 110 110 110 5,000 160 160 160 160 160 160 10,000 220 220 220 220 220 220 15,000 270 270 270 270 270 270 25,000 340 350 350 350 350 350 75,000 510 570 590 610 610 610 100,000 550 630 670 700 700 710 250,000 - 790 970 1 090 1 100 1 100 500,000 - - 1 120 1 500 1 540 1 570 1,000,000 - - - 2 000 2 120 2 190 5,000,000 - - - - 3 540 4 470 10,000,000 - - - - - 5 480 (1) For estimated totals larger than 10,000,000, the standard error is somewhat larger than the table values. (2) The total count of persons in the area if the estimated total is a person characteristic, or the total count of housing units in the area if the estimated total is a housing unit characteristic. Table B. Unadjusted Standard Error in Percentage Points for Estimated Percentage Based on a 1-in-6 simple random sample Base of percentage1/ Estimated Percentage 500 750 1,000 1,500 2,500 5,000 7,500 10,000 2 or 98 1.4 1.1 1.0 0.8 0.6 0.4 0.4 0.3 5 or 95 2.2 1.8 1.5 1.3 1.0 0.7 0.6 0.5 10 or 90 3.0 2.4 2.1 1.7 1.3 0.9 0.8 0.7 15 or 85 3.6 2.9 2.5 2.1 1.6 1.1 0.9 0.8 20 or 80 4.0 3.3 2.8 2.3 1.8 1.3 1.0 0.9 25 or 75 4.3 3.5 3.1 2.5 1.9 1.4 1.1 1.0 30 or 70 4.6 3.7 3.2 2.6 2.0 1.4 1.2 1.0 35 or 65 4.8 3.9 3.4 2.8 2.1 1.5 1.2 1.1 50 5.0 4.1 3.5 2.9 2.2 1.6 1.3 1.1 -------------------------------------------------------------------------- Estimated Percentage 25,000 50,000 100,000 250,000 500,000 2 or 98 0.2 0.1 0.1 0.1 0.1 5 or 95 0.3 0.2 0.2 0.1 0.1 10 or 90 0.4 0.3 0.2 0.1 0.1 15 or 85 0.5 0.4 0.3 0.2 0.1 20 or 80 0.6 0.4 0.3 0.2 0.1 25 or 75 0.6 0.4 0.3 0.2 0.1 30 or 70 0.6 0.5 0.3 0.2 0.1 35 or 65 0.7 0.5 0.3 0.2 0.2 50 0.7 0.5 0.4 0.2 0.2 (1) For a percentage and/or base of percentage not shown in the table, the formula given below may be used to calculate the standard error. This table should only be used for proportions, that is, where the numerator is a subset of the denominator. A.5. Census Collection and Processing Procedures Approximately 70 percent of the subject matter tables contained in the 1990 Census school district special tabulation are directly derived from the subject matter table specifications used in the 1990 Census Summary Tape File 3 released by the Census Bureau as a standard data product of the 1990 Census. As a result, much of the description regarding accuracy of the data, collection and processing procedures is the same for the special tabulation and the standard census products. To insure consistency, much of the information presented in sections A.4. and A.5. are taken directly from the 1990 Census STF3 documentation. A.5.1. Census Enumeration and Residence Rules In accordance with census practice dating back to the first United States census in 1790, each person was to be enumerated as an inhabitant of his or her "usual residence" in the 1990 census. Usual residence is the place where the person lives and sleeps most of the time or considers to be his or her usual residence. This place is not necessarily the same as the person's legal residence or voting residence. In the vast majority of cases, however, the use of these different bases of classification would produce substantially the same statistics, although there might be appreciable differences for a few areas. The implementation of this practice has resulted in the establishment of rules for certain categories of persons whose usual place of residence is not immediately apparent. Furthermore, this practice means that persons were not always counted as residents of the place where they happened to be staying on Census Day (April 1, 1990). Enumeration Rules Each person whose usual residence was in the United States was to be included in the census, without regard to the person's legal status or citizenship. In a departure from earlier censuses, foreign diplomatic personnel participated voluntarily in the census, regardless of their residence on or off the premises of an embassy. As in previous censuses, persons in the United States specifically excluded from the census were foreign travelers who had not established a residence. Americans with a usual residence outside the United States were not enumerated in the 1990 census. United States military and Federal civilian employees, and their dependents overseas, are included in the population counts for States for purposes of Congressional apportionment, but are excluded from all other tabulations for States and their subdivisions. The counts of United States military and Federal civilian employees, and their dependents, were obtained from administrative records maintained by Federal departments and agencies. Other Americans living overseas, such as employees of international agencies and private businesses and students, were not enumerated, nor were their counts obtained from administrative sources. On the other hand, Americans temporarily overseas were to be enumerated at their usual residence in the United States. Residence Rules Each person included in the census was to be counted at his or her usual residence--the place where he or she lives and sleeps most of the time or the place where the person considers to be his or her usual home. If a person had no usual residence, the person was to be counted where he or she was staying on April 1, 1990. Persons temporarily away from their usual residence, whether in the United States or overseas, on a vacation or on a business trip, were counted at their usual residence. Persons who occupied more than one residence during the year were counted at the one they considered to be their usual residence. Persons who moved on or near Census Day were counted at the place they considered to be their usual residence. Persons in the Armed Forces-- Members of the Armed Forces were counted as residents of the area in which the installation was located, either on the installation or in the surrounding community. Family members of Armed Forces personnel were counted where they were living on Census Day (for example, with the Armed Forces person or at another location). Each Navy ship not deployed to the 6th or 7th Fleet was attributed to the municipality that the Department of the Navy designated as its homeport. If the homeport included more than one municipality, ships berthed there on Census Day were assigned by the Bureau of the Census to the municipality in which the land immediately adjacent to the dock or pier was actually located. Ships attributed to the homeport, but not physically present and not deployed to the 6th or 7th Fleet, were assigned to the municipality named on the Department of the Navy's homeport list. These rules also apply to Coast Guard vessels. Personnel assigned to each Navy and Coast Guard ship were given the opportunity to report a residence off the ship. Those who did report an off-ship residence in the communities surrounding the homeport were counted there; those who did not were counted as residents of the ship. Personnel on Navy ships deployed to the 6th or 7th Fleet on Census Day were considered to be part of the overseas population. Persons on Maritime Ships-- Persons aboard maritime ships who reported an off-ship residence were counted at that residence. Those who did not were counted as residents of the ship, and were attributed as follows: The port where the ship was docked on Census Day, if that port was in the United States or its territories. The port of departure if the ship was at sea, provided the port was in the United States or its territories. The port of destination in the United States or its territories, if the port of departure of a ship at sea was a foreign port. The overseas population if the ship was docked at a foreign port or at sea between foreign ports. (These persons were not included in the overseas population for apportionment purposes.) Persons Away at School-- College students were counted as residents of the area in which they were living while attending college, as they have been since the 1950 census. Children in boarding schools below the college level were counted at their parental home. Persons in Institutions-- Persons under formally authorized, supervised care or custody, such as in Federal or State prisons; local jails; Federal detention centers; juvenile institutions; nursing, convalescent, and rest homes for the aged and dependent; or homes, schools, hospitals, or wards for the physically handicapped, mentally retarded, or mentally ill, were counted at these places. Persons Away From Their Usual Residence on Census Day-- Migrant agricultural workers who did not report a usual residence elsewhere were counted as residents of the place where they were on Census Day. Persons in worker camps who did not report a usual residence elsewhere were counted as residents of the camp where they were on Census Day. In some parts of the country, natural disasters displaced significant numbers of households from their usual place of residence. If these persons reported a destroyed or damaged residence as their usual residence, they were counted at that location. Persons away from their usual residence were counted by means of interviews with other members of their families, resident managers, or neighbors. A.5.2. Census Data Collection Procedures The 1990 census was conducted primarily through self-enumeration. The questionnaire packet included general information about the 1990 census and an instruction guide explaining how to complete the questionnaire. Spanish- language questionnaires and instruction guides were available on request. Instruction guides also were available in 32 other languages. Enumeration of Housing Units Each housing unit in the country received one of two versions of the census questionnaire: A short-form questionnaire that contained a limited number of basic population and housing questions; these questions were asked of all persons and housing units and are often referred to as 100-percent questions. A long-form questionnaire that contained the 100- percent items and a number of additional questions; a sampling procedure was used to determine those housing units that were to receive the long-form questionnaire. Three sampling rates were employed. For slightly more than one-half of the country, one in every six housing units (about 17 percent) received the long-form or sample questionnaire. In functioning local governmental units (counties and incorporated places, and in some parts of the country, towns and townships) estimated to have fewer than 2,500 inhabitants, every other housing unit (50 percent) received the sample questionnaire in order to enhance the reliability of the sample data for these small areas. For census tracts and block numbering areas having more than 2,000 housing units in the Census Bureau's address files, one in every eight housing units (about 13 percent) received a sample questionnaire, providing reliable statistics for these areas while permitting the Census Bureau to stay within a limit of 17.7 million sample questionnaires, or a one-in-six sample, nationwide. The mail-out/mail-back procedure was used mainly in cities, suburban areas, towns, and rural areas where mailing addresses consisted of a house number and street name. In these areas, the Census Bureau developed mailing lists that included about 88.4 million addresses. The questionnaires were delivered through the mail and respondents were to return them by mail. Census questionnaires were delivered 1 week before Census Day (April 1, 1990) The update/leave/mail-back method was used mainly in densely populated rural areas where it was difficult to develop mailing lists because mailing addresses did not use house number and street name. The Census Bureau compiled lists of housing units in advance of the census. Enumerators delivered the questionnaires, asked respondents to return them by mail, and added housing units not on the mailing lists. This method was used mainly in the South and Midwest, and also included some high-rise, low-income urban areas. A variation of this method was used in urban areas having large numbers of boarded-up buildings. About 11 million housing units were enumerated using this method. The list/enumerate method (formerly called conventional or door-to-door enumeration) was used mainly in very remote and sparsely-settled areas. The United States Postal Service delivered unaddressed short-form questionnaires before Census Day. Starting a week before Census Day, enumerators canvassed these areas, checked that all housing units received a questionnaire, created a list of all housing units, completed long-form questionnaires, and picked up the completed short-form questionnaires. This method was used mainly in the West and Northeast to enumerate an estimated 6.5 million housing units. Followup Nonresponse Followup-- In areas where respondents were to mail back their questionnaires, an enumerator visited each address from which a questionnaire was not received. Coverage and Edit-Failure Followup-- In the mail-back areas, some households returned a questionnaire that did not meet specific quality standards because of incomplete or inconsistent information, or the respondent had indicated difficulty in deciding who was to be listed on the questionnaire. These households were contacted by telephone or by personal visit to obtain the missing information or to clarify who was to be enumerated in the household. In areas where an enumerator picked up the questionnaires, the enumerator checked the respondent-filled questionnaire for completeness and consistency. Special Enumeration Procedures Special procedures and questionnaires were used for the enumeration of persons in group quarters, such as college dormitories, nursing homes, prisons, military barracks, and ships. The questionnaires (Individual Census Reports, Military Census Reports, and Shipboard Census Reports) included the 100-percent population questions but did not include any housing questions. In all group quarters, all persons were asked the basic population questions; in most group quarters, additional questions were asked of a sample (one-in-six) of persons. Shelter and Street Night (S-Night) The Census Bureau collected data for various components of the homeless population at different stages in the 1990 census. "Shelter and Street Night" (S-Night) was a special census operation to count the population in four types of locations where homeless people are found. On the evening of March 20, 1990, and during the early morning hours of March 21, 1990, enumerators counted persons in pre-identified locations: Emergency shelters for the homeless population (public and private; permanent and temporary). Shelters with temporary lodging for runaway youths. Shelters for abused women and their children. Open locations in streets or other places not intended for habitation. Emergency shelters include all hotels and motels costing $12 or less (excluding taxes) per night regardless of whether persons living there considered themselves to be homeless, hotels and motels (regardless of cost) used entirely to shelter homeless persons, and pre-identified rooms in hotels and motels used for homeless persons and families. Enumeration in shelters usually occurred from 6 p.m. to midnight; street enumeration, from 2 a.m. to 4 a.m.; abandoned and boarded-up buildings from 4 a.m. to 8 a.m.; and shelters for abused women, from 6 p.m. on March 20 to noon on March 21. Other components, which some consider as part of the homeless population, were enumerated as part of regular census operations. These include persons doubled up with other families, as well as persons with no other usual home living in transient sites, such as commercial campgrounds, maternity homes for unwed mothers, and drug/alcohol abuse detoxification centers. In institutions, such as local jails and mental hospitals, the Census Bureau does not know who has a usual home elsewhere; therefore, even though some are literally homeless, these persons cannot be identified separately as a component of the homeless population. There is no generally agreed-upon definition of "the homeless," and there are limitations in the census count that prevent obtaining a total count of the homeless population under any definition. As such, the Census Bureau does not have a definition and will not provide a total count of "the homeless." Rather, the Census Bureau will provide counts and characteristics of persons found at the time of the census in selected types of living arrangements. These selected components can be used as building blocks to construct a count of homeless persons appropriate to particular purposes as long as the data limitations are taken into account. In preparation for "Shelter-and-Street-Night" enumeration, the regional census centers (RCC's) mailed a certified letter (Form D-33 (L)) to the highest elected official of each active functioning government of the United States (more than 39,000) requesting them to identify: All shelters with sleeping facilities (permanent and temporary, such as church basements, armories, public buildings, and so forth, that could be open on March 20). Hotels and motels used to house homeless persons and families. A list of outdoor locations where homeless persons tend to be at night. Places such as bus or train stations, subway stations, airports, hospital emergency rooms, and so forth, where homeless persons seek shelter at night. The specific addresses of abandoned or boarded-up buildings where homeless persons were thought to stay at night. The letter from the RCC's to the governmental units emphasized the importance of listing night-time congregating sites. The list of shelters was expanded using information from administrative records and informed local sources. The street sites were limited to the list provided by the jurisdictions. All governmental units were eligible for "Shelter and Street Night." For cities with 50,000 or more persons, the Census Bureau took additional steps to update the list of shelter and street locations if the local jurisdiction did not respond to the certified letter. Smaller cities and rural areas participated if the local jurisdiction provided the Census Bureau a list of shelters or open public places to visit or if shelters were identified through our inventory development, local knowledge update, or during the Special Place Prelist operation. The Census Bureau encouraged persons familiar with homeless persons and the homeless themselves to apply as enumerators. This recruiting effort was particularly successful in larger cities. For shelters, both long- and short-form Individual Census Reports (ICR's) were distributed. For street enumeration, only short-form ICR's were used. Persons in shelters and at street locations were asked the basic population questions. Additional questions about social and economic characteristics were asked of a sample of persons in shelters only. Enumerators were instructed not to ask who was homeless; rather, they were told to count all persons (including children) staying overnight at the shelters, and everyone they saw on the street except the police, other persons in uniform, and persons engaged in employment or obvious money-making activities other than begging and panhandling. At both shelter and street sites, persons found sleeping were not awakened to answer questions. Rather, the enumerator answered the sex and race questions by observation and estimated the person's age to the best of his or her ability. In shelters, administrative records and information from the shelter operator were used, when available, for persons who were already asleep. Less than 1 percent of shelters refused to participate in the census count at first. By the end of the census period, most of those eventually cooperated and the number of refusals had been reduced to a few. For the final refusals, head counts and population characteristics were obtained by enumerators standing outside such shelters and counting people as they left in the morning. The "street" count was restricted to persons who were visible when the enumerator came to the open, public locations that had been identified by local jurisdictions. Homeless persons who were well hidden, moving about, or in locations other than those identified by the local governments were likely missed. The number missed will never be known and there is no basis to make an estimate of the number missed from census data. The count of persons in open, public places was affected by many factors, including the extra efforts made to encourage people to go to shelters for "Shelter and Street Night," the weather (which was unusually cold in many parts of the country), the presence of the media, and distrust of the census. Expectations of the number of homeless persons on the street cannot be based on the number seen during the day because the night-time situation is normally very different as more homeless persons are in shelters or very well hidden. For both "Shelter-and-Street-Night" locations, the Census Bureau assumed that the usual home of those enumerated was in the block where they were found (shelter or street). The "Shelter-and-Street-Night" operation replaced and expanded the 1980 Mission Night (M-Night) and Casual Count operations. These two operations were aimed at counting the population who reported having no usual residence. M-Night was conducted a week after Census Day, in April 1980. Enumerators visited hotels, motels, and similar places costing $4 or less each night; missions, flophouses, local jails and similar places at which the average length of stay was 30 days or less; and nonshelter locations, such as bus depots, train stations, and all night movie theaters. Questions were asked of everyone, regardless of age. Enumerators conducted M-Night up to midnight on April 8, 1980, and returned the next morning to collect any forms completed after midnight. The Casual Count operation was conducted in May 1980 at additional nonshelter locations, such as street corners, pool halls, welfare and employment offices. This operation lasted for approximately 2 weeks. Casual Count was conducted during the day only in selected large central cities. Only persons who appeared to be at least 15 years of age were asked if they had been previously enumerated. A.5.3. Processing Procedures Respondents returned many census questionnaires by mail to 1 of over 344 census district offices or to one of six processing offices. In these offices, the questionnaires were "checked in" and edited for completeness and consistency of the responses. After this initial processing had been performed, all questionnaires were sent to the processing offices. In the processing offices, the household questionnaires were microfilmed and processed by the Film Optical Sensing Device for Input to Computers (FOSDIC). For most items on the questionnaire, the information supplied by the respondent was indicated by filling circles in predesignated positions. FOSDIC electronically "read" these filled circles from the microfilm copy of the questionnaire and transferred the information to computer tape. The computer tape did not include individual names, addresses, or handwritten responses. The data processing was performed in several stages. All questionnaires were microfilmed, "read" by FOSDIC, and transferred to computer disk. Selected written entries in the race question on both the short and long forms were keyed from the microfilm and coded using the data base developed from the 1980 census and subsequent content and operational tests. Keying of other written entries on the long forms occurred in the seven processing offices. The information (for example, income dollar amounts or homeowner shelter costs) on these keyed files was merged with the FOSDIC data or processed further through one of three automated coding programs. The codes for industry, occupation, place-of-birth, migration, place-of-work, ancestry, language, relationship, race, and Hispanic origin were merged with the FOSDIC data for editing, weighting, and tabulating operations at Census Bureau headquarters. All responses to the questions on Individual Census Reports (ICR's), Military Census Reports (MCR's), and Shipboard Census Reports (SCR's) were keyed, not processed by microfilm or FOSDIC. A.5.4. Features Unique to the School District Special Tabulation The 1990 Census School District Special Tabulation has many features that are unique to this set of data in comparison to the standard 1990 Census data products. Many of these features are discussed in this section. A.5.4.1. File A and File D During the early stages of developing this special tabulation different types of statistical files were planned. Of these various file types only those referred to as File A and File D were released for use. There is no mention to File A and File D elsewhere in the School District Data Book since all 1990 Census data contained in the SDDB is from File D. File A contains a very small set of items (approximately 20 items per district) and was prepared specifi- cally to meet requirements for usage in formula allocations of Federal aid. The data in File D were not developed to meet the formula allocation requirements but rather for purposes of general statistical analysis. References to File A and to File D are important since: (1) it was the File A data distributed to states for review and comment prior to finalizing these data for use in formula allocations and thus may be known to some users, and (2) it is possible that in some districts the total population data shown in the final File A data differs from the total population shown in the final File D data. The reason that the File A data can differ from the File D data is due to possible adjustments that were made to the File A data upon further examination by NCES. The review and revision pro- cess was closed and File A data finalized in the fall of 1993. A.5.4.2. Assigning Children to a Specific Grade Grades were assigned to every person reported age 3-19 years on April 1, 1990. Students had to be assigned to single grades of enrollment because of the considerable variation in grade ranges of U.S. school districts. Grade of enrollment was assumed to be the grade succeeding the highest grade completed reported on the 1990 Census. For the highest grade completed groups 1-4 and 5-8, a method for placing persons in specific grades within those ranges was developed using data collected in the Current Population Survey (CPS). The October supplement to the CPS asks respondents for the spe- cific single grade enrollment of each student in a household. Using the data from the age-grade distribution in the CPS, per- sons whose grade falls within the grade groups in the Census can be assigned to specific grades. First, persons were "aged" in the CPS distribution half a year from October to April assuming individuals' grades remained fixed. Half the students in a given grade in October will have the same age (in years) in April, and half will be one year older. Second, from this synthetic April age by grade of enrollment data, the single grade of enrollment distribution was estimated for the combinations of highest grade completed and age present in the Census. To reduce sampling variation, the average of CPS data for October of 1988, 1989 and 1990 (aged to April of 1989, 1990 and 1991) was used. The complete set of grade of enrollment distributions conditional on highest grade completed and age in April is referred to as a "grade assignment rule." Because the grade distribution varies by gender, ethnicity, and race, there are grade assignment rules for each of the following groups: Hispanic males, Hispanic females, non-Hispanic black males, non-Hispanic black females, non-black non-Hispanic males, and non-black non-Hispanic females. The weighted values of each not high school graduate student in the CPS between 3 and 19 years old were used to create these "grade assignment rules" for each group. Each student was assigned a grade using these rules and his/her highest grade completed, age, sex, and race as reported in the 1990 Census. This method of assigning students to grades using CPS age-grade distributions was examined using data from the 1980 Census, where the question on grade enrollment included possible responses for single specific grades. Grade assignment rules for black and non- black males and females were created from October 1979 CPS data, and were then applied to the 1980 census in the same way as the grade assignment rules were applied to the 1990 census. It is important to note that the chief reason people were as signed specific grades was to assign them to one school district. The grades on the boundaries of elementary and secondary school districts, usually 6, 7, or 8, are the most important for the purposes of school district assignment. Assigning a person to the "wrong" grade in the "right" district has no impact on the accuracy of most tabulations. The 1980 tests showed that there is no pattern of assigning more or less people to any grade using the rule when compared to the total enrollment for each grade as reported in the Census. A.5.4.3. School District Grade Span and "Augmented" Grade Span Each school district's grade range in the 1989-1990 Common Core of Data (CCD) Public Education Agency represents the lowest and highest grades with non-zero student counts in the schools oper- ated by the agency. Grades recognized for inclusion in the uni- verse of elementary and secondary agencies range from prekinder- garten (PK) through grade twelve (12). Where the CCD grade ranges of the school districts that serve a block do not include every grade from first through twelfth, then those district grade ranges have been augmented. It was presumed that each school district has one grade range that is the same throughout its territory. Upon reviewing the mapped school dis- tricts with their grade ranges, the Census Bureau found a few instances where the grade range for a given district was not consistent throughout the territory. A school district's grade span was only augmented, however, when the added grades did not cause that school district's range to overlap with the reported grades from other school districts for any piece of the school district. Augmentation does not add kindergarten or pre-kinder- garten to school districts that do not report those grades. The grade range of a school district reported as "elementary" could be augmented down to first grade and up no farther than eighth grade. The grade range of a school district reported as "secondary" could be augmented up to twelfth grade and down no farther than seventh grade. The grade range of a school district reported as "consolidated" was augmented up to twelfth grade and down to first grade. There are areas mapped as being covered by elementary districts but no secondary districts. The grade ranges for these elementary districts are augmented as indicated. A.5.4.4. Allocating Persons Living on Split Blocks Forty states have at least a few instances in which a school district boundary divides a census block. Connecticut, the District of Columbia, Florida, Georgia, Louisiana, Maine, Mary land, Massachusetts, Nevada, New Jersey, Rhode Island, and West Virginia have no split blocks. Some blocks have been split into five pieces, so that five different school districts or combina- tions of school districts serve the residents. The Census Bu reau's Geography Division worked with each state education agency to create a list of the blocks, or pieces of blocks, in each school district. For some school districts, the boundaries follow county or Minor Civil Division (MCD) lines that have already been coded in the Topologically Integrated Geographic Encoding and Reference System (TIGER). For other school districts, the states had to map the school district boundaries in order to produce the list of blocks. The boundaries of Census blocks tend to follow physical features or political boundaries (roads, railroads, county line, etc.). Where school district boundaries split census blocks, states had the option of supplying proportions to represent the population fractions on each side of the boundary, or letting a computer determine the geographic area within the block on each side of the boundary and splitting the population using that fraction. All of the housing units in a split block are distributed among the districts in proportion to these fractions. In addition, the sample (long form) housing unit records for the sample cases in a split block are distributed among the districts in proportion to these fractions. For further information regarding sample design, estimation, or confidentiality, please refer to the 1990 CP-5 Census report, Appendix C, Accuracy of the Data. The sample records of a block were allocated between parts of split blocks separately for each of four types of housing units (Households containing no persons age 5-17, Households with 5-17 above poverty, Households with persons age 5-17 below poverty, and group quarters). The school districts are allocated persons sequentially, beginning with any consolidated school district(s), followed by elementary, middle, and finally secondary school district(s). The first school district is assigned households until it achieves its quota (allocated percentage), followed by the other school districts until all persons are allocated to one district. The table presented below illustrates this assignment when a block has been split. The first line shows the allocation frac tions either supplied or calculated from the area of the block part. Subsequent lines show how people in different living situa tions are allocated. Total Housing Units Persons in: Housing Allocated to: Units SD #1 SD #2 SD #3 Allocation fraction .50 .30 .20 Households without 5-17 40 20 12 8 Households with 5-17 above poverty 27 14 8 5 Households with 5-17 below poverty 14 7 4 3 Group Quarters 1 1 0 0 A.5.4.5. Persons Not Covered by a School District There are some blocks that are not covered by any school dis- trict. In most cases, there are no students living in these blocks. This tabulation treats these areas as pseudo school districts, with grade ranges PK-12. They are reported as "balance of county" school districts for each county in which they occur. Students who live on these blocks are assigned to these "balance of county" school districts. Thus, each school age person of each county is assigned to one and only one school district. Balance of county records have a school district identification number of "81" followed by the FIPS county code CCC (e.g. 81CCC). A.5.4.6. Persons Assigned to Grade Not Served by School District on Block There are blocks covered only by an elementary school district and not a secondary school district (or vice versa). A student who is assigned a grade not in the range served by the school district(s) present is tabulated as relevant in one of the school districts that serve the student's block, with priority going to the elementary school(s) followed by any secondary school(s). A tabulation of all the students by grade in each school district will show how many students are assigned to a grade not in the range of the school district to which they are assigned. A.5.4.7. Overlapping Grade Ranges There are some rural school districts that do not serve every grade throughout their territory. In some cases, another school district may have a school close to an isolated group of blocks; so, the students who live in those blocks may attend high school, for example, in the closer school district. Both school districts might include these blocks in the mapped boundaries and it would appear from the maps that both school districts served the high school students on those "overlapped" blocks. If there is an overlap between a consolidated and an elementary school district, the students in elementary grades are assigned to the elementary school district. If there is an overlap between an elementary and a secondary school district, the students in secondary grades are assigned to the secondary school district. Finally, if there is an overlap between a consolidated and a secondary school dis trict, the students in secondary grades are assigned to the secondary school district. A.5.4.8. The Concept of Relevancy A person is "relevant" to a school district if he/she lives within the territory of the district and his/her assigned grade falls within the augmented grade range. Persons in the territory covered by no district are relevant to the "balance of county." Persons in the territory not covered by a school district whose grade range includes theirs are tallied as relevant in the school district with the next lower grade range. In the latter cases, it will be clear in the tabulations by specific grade that there are "relevant" students who were assigned a grade outside the grade range of the district. Most of 1990 census school district special tabulation data are shown in two ways--characteristics by the children's age category or their grade range. In fact, school districts differ consider ably as to the grade range or levels educated within each type, to the point that some districts may not offer a particular level at all [e.g., elementary and middle levels are blended into one type for some districts, or all levels are offered by one type [e.g., the concept of a consolidated school district offering all grades within that type]. For this reason, age categories may not be a particularly good presentation for some analysis purposes. Thus, data is also presented by the concept of grade relevancy in an effort to afford users the most flexible presentation in terms of anticipated analyses. Grade relevancy as a concept attempts to classify the actual grade span provided within each particular school district. For example, the data for a district which offers middle schooling for grades 6 through 9 are presented with that grade span, i.e., 6-9; while another district which defines middle as 5-8, contains a data presentation with that range. These grade relevancy data within the district level(s) were determined from administrative data gathered from each school district separate from and outside of the 1990 Census. The census data were then tallied as appro priate based on response data as directed by the appropriate administrative level and grade range information. Note the assignment to a particular grade range and thus level within a school district is based primarily on actual response data from the 1990 census. However, where persons were of the appropriate "school age" age and there was not a census response or the category contained more than one grade, a grade was as signed to that person. Such assignment was based on a person's age and other selected demographic characteristics. For example, a six year old could be assigned to either elementary or kinder garten since a proportion of persons with this age will be in kindergarten while others are already in first grade. Presentation of Relevant Data Data for relevant children are presented by complete records in the database. For each of record types 3 through 7 (see discus- sion in section 5), there is a record type iteration for each the following age/grade ranges: Total Relevant Pre-Kindergarten Kindergarten Grade 1- 4 Grade 5- 8 Grade 9-12 Age 0- 2 years Age 3- 4 years Age 5-13 years Age 14-17 years Age 18-19 years Age 3-19 years Age 5-17 years Each of the above record iterations is repeated for each of the following enrollment categories: Total Enrolled & Not Enrolled Total Enrolled (Public & Private) Enrolled in Public School Enrolled in Private School Not Enrolled