PDF & Related InfoThe sample for the Survey on Professional Development and Training in U.S. Public Schools, 1999-2000, consisted of 5,253 full- and part-time teachers in regular public elementary, middle, and high schools in the 50 states and the District of Columbia. To select the sample of teachers, a sample of 2,209 public schools was first selected from the 1999-2000 Schools and Staffing Survey (SASS) universe file. The 1999-2000 SASS universe file was created from the 1997-98 NCES Common Core of Data (CCD) Public School Universe File. The sample for this study was designed to minimize the overlap with other large NCES studies being conducted concurrently. The sampling frame contained 81,405 regular public schools. Excluded from the sampling frame were special education, vocational, and alternative/other schools, schools in the territories, Department of Defense and Bureau of Indian Affairs schools, and schools with a high grade lower than one or ungraded, or that taught only adult education. The frame contained 49,691 regular elementary schools, 15,204 regular middle schools, and 16,510 regular high/combined schools. A school was defined as an elementary school if the lowest grade was less than or equal to grade 3 and the highest grade was less than or equal to grade 8. A middle school was defined as having a lowest grade greater than or equal to grade 4 and a highest grade less than or equal to grade 9. A school was considered a high school if its lowest grade was greater than or equal to grade 9 and the highest grade was greater than or equal to grade 10. Combined schools were defined as having a lowest grade less than or equal to grade 3 and a highest grade greater than or equal to grade 9 or the lowest grade is in grades 4 through 8 and the highest grade is in grades 10 through 12. High schools and combined schools were combined into one category for sampling.
The public school sampling frame was stratified by instructional level (elementary, middle, and high school/combined), locale (city, urban fringe, town, and rural), and school size (less than 300, 300 to 499, 500 to 999, 1,000 to 1,499, and 1,500 or more). Within the primary strata, schools were also sorted by percent minority enrollment in the school (less than 5 percent minority, 5 to 19 percent minority, 20 to 49 percent minority, and 50 percent or more minority) to produce additional implicit stratification. A sample of 2,209 schools was then selected from the sorted frame with conditional probabilities that accounted for the selection of the schools for the other NCES studies. The conditional probabilities were designed to minimize the overlap with the samples selected for the other studies while at the same time ensuring that the overall probabilities of selection were proportionate to size, where the measure of size was the estimated number of full-time-equivalent (FTE) teachers in the school (see Chowdhury, Chu, and Kaufman forthcoming). The sample contained 511 elementary schools, 855 middle schools, and 843 high/combined schools.
Each sampled school was asked to send a list of its teachers, from which a teacher sampling frame was prepared. The teacher sampling frame was designed to represent all public school teachers who taught one or more regularly scheduled classes in any of grades 1 through 12. This included special education teachers, general elementary teachers, vocational/technical education teachers, principals and all other staff members who taught at least one regularly scheduled class per week, itinerant, co-op, traveling, and satellite teachers who taught at the sampled school for more than 50 percent of their total teaching hours, current long-term substitute teachers, and other teachers who taught students in grades 1 through 12. Schools were instructed to omit the following from the teacher list: teachers" aides, student teachers, day care aides, unpaid volunteers, teachers who taught only kindergarten or preschool students, short-term substitutes, itinerant teachers who taught more than half of their teaching hours at another school, and principals and all other staff members who did not teach a regularly scheduled class at the school. Schools were then instructed to indicate the primary subject taught for each teacher on the list, using the following categories: (1) self-contained, for teachers who teach all or most academic subjects in a self-contained classroom setting (including most elementary school teachers), (2) math teachers, (3) science teachers, (4) English teachers, (5) social studies teachers, and (6) other, for teachers of all other subjects. Schools were then asked to indicate the total years of teaching experience for each teacher on the list, using the categories of 3 or fewer years, or 4 or more years teaching experience, counting the current academic year as 1 full year.
Within selected schools, eligible teachers were selected for the survey at rates that depended on instructional level, years of teaching experience (3 years or less versus 4 years or more), and subject taught. On average, 2.8 teachers (and no more than 4 teachers) were selected per school. A total of 5,253 teachers were selected. The sample contained 1,222 elementary school teachers, 2,033 middle school teachers, and 1,998 high school/combined teachers.
A letter, instruction sheet, and form for preparing the list of teachers was sent to the principal of each sampled school in early October 1999. The letter introduced the study, requested the principal"s cooperation to sample teachers, and asked the principal to prepare the list of teachers. Telephone follow up was conducted from November 1999 through March 2000 with principals who did not respond to the initial request for teacher lists. Of the 2,209 schools in the sample, 9 were found to be out of the scope of the survey (no longer in existence), for a total of 2,200 eligible schools. Teacher lists were provided by 1,890 schools, or 86 percent of the eligible schools. The weighted response rate1 to the teacher list collection was 88 percent.
Questionnaires were mailed to the teachers in two phases, so that data collection on the teacher questionnaire would not be delayed while the list collection phase was being completed. The first phase of questionnaires was mailed in mid-March 2000, and the second in late April 2000. Telephone followup was conducted from April through September 2000 with teachers who did not respond to the initial questionnaire mailing. Teachers were called at their schools until late June.
Beginning in late June, when schools were closed for the summer and teachers could no longer be reached in this way, teachers were called at home if a telephone number for them could be located. To facilitate telephone data collection with teachers at their home numbers, a shorter version of the questionnaire was developed that retained key items. This shorter version of the questionnaire was used until data collection ended in early September. All data presented in this report are based only on key items from this shorter questionnaire (see appendix B, survey instrument).
Of the 5,253 teachers selected for the sample, 377 were found to be out of the scope of the survey. This left a total of 4,876 eligible teachers in the sample. Completed questionnaires were received from 4,128 teachers, or 85 percent of the eligible teachers (table A-1). Of these 4,128 teachers, 3,347 completed the full questionnaire, and 781 completed the shorter version of the questionnaire. The weighted teacher response rate was 84 percent. The unweighted overall response rate was 73 percent (85.9 percent for the list collection multiplied by 84.7 percent for the teacher questionnaire). The weighted overall response rate was 75 percent (88.3 percent for the list collection multiplied by 84.5 percent for the teacher questionnaire). Unweighted item nonresponse rates ranged from 0 percent to 6 percent, with most under 1 percent and one item with a nonresponse of 6 percent. Because the item nonresponse was so low, imputation for item nonresponse was not implemented.
The responses were weighted to produce national estimates (see table A-1). The weights were designed to adjust for the variable probabilities of selection and differential nonresponse. The findings in this report are estimates based on the sample selected and, consequently, are subject to sampling variability.
The survey estimates are also subject to nonsampling errors that can arise because of nonobservation (nonresponse or noncoverage) errors, errors of reporting, and errors made in data collection. These errors can sometimes bias the data. Nonsampling errors may include such problems as misrecording of responses; incorrect editing, coding, and data entry; differences related to the particular time the survey was conducted; or errors in data preparation. While general sampling theory can be used in part to determine how to estimate the sampling variability of a statistic, nonsampling errors are not easy to measure and, for measurement purposes, usually require that an experiment be conducted as part of the data collection procedures or that data external to the study be used.
To minimize the potential for nonsampling errors, the questionnaire was pretested with respondents like those who completed the survey. During the design of the survey and the survey pretest, an effort was made to check for consistency of interpretation of questions and to eliminate ambiguous items. The questionnaire and instructions were extensively reviewed by the National Center for Education Statistics and the Office of the Secretary, U.S. Department of Education. Manual and machine editing of the questionnaire responses were conducted to check the data for accuracy and consistency. Cases with missing or inconsistent items were recontacted by telephone. Data were keyed with 100 percent verification.
An examination of the survey data on out-of-field teaching led to concerns over the stability of the estimates. In particular, when these data were compared to estimates from a larger ongoing survey, the magnitude of the differences was not entirely explicable and thus was a cause for concern. NCES anticipates releasing a report with out-of-field estimates from the larger survey in September 2001.
The standard error is a measure of the variability of estimates due to sampling. It indicates the variability of a sample estimate that would be obtained from all possible samples of a given design and size. Standard errors are used as a measure of the precision expected from a particular sample. If all possible samples were surveyed under similar conditions, intervals of 1.96 standard errors below to 1.96 standard errors above a particular statistic would include the true population parameter being estimated in about 95 percent of the samples. This is a 95 percent confidence interval. For example, the estimated percentage of teachers who felt very well prepared for their overall teaching assignments is 61.1 percent, and the estimated standard error is 0.9 percent. The 95 percent confidence interval for the statistic extends from [61.1 - (0.9 times 1.96)] to [61.1 + (0.9 times 1.96)], or from 59.3 to 62.9 percent.
Estimates of standard errors were computed using a technique known as jackknife replication. As with any replication method, jackknife replication involves constructing a number of subsamples (replicates) from the full sample and computing the statistic of interest for each replicate. The mean square error of the replicate estimates around the full sample estimate provides an estimate of the variances of the statistics. To construct the replications, 50 stratified subsamples of the full sample were created and then dropped one at a time to define 50 jackknife replicates. A computer program (WesVar Complex Samples) was used to calculate the estimates of standard errors. WesVar is a stand-alone Windows application that computes sampling errors from complex samples for a wide variety of statistics (totals, percents, ratios, log-odds ratios, general functions of estimates in tables, linear regression parameters, and logistic regression parameters).
The test statistics used in the analysis were calculated using the jackknife variances and thus appropriately reflected the complex nature of the sample design. In particular, an adjusted chi-square test using Satterthwaite's approximation to the design effect was used in the analysis of the two-way tables. Finally, Bonferroni adjustments were made to control for multiple comparisons where appropriate. For example, for an "experiment-wise" comparison involving g pairwise comparisons, each difference was tested at the 0.05/g significance level to control for the fact that g differences were simultaneously tested.
Elementary school
-lowest grade less than or equal to grade 3 and highest grade less than or equal to grade 8.-lowest grade greater than or equal to grade 4 and highest grade less than or equal to grade 9.
Middle school-lowest grade greater than or equal to grade 9 and highest grade greater than or equal to grade 10.
High school-lowest grade less than or equal to grade 3 and highest grade greater than or equal to grade 9 or the lowest grade is in grades 4 through 8 and the highest grade is in grades 10 through 12.
Combined school
Less than 500 students
500 to 999 students
1,000 or more students
Central city
-a large or mid-size central city of a Metropolitan Statistical Area (MSA).-urban fringe is a place within an MSA of a central city, but not primarily its central city; large town is an incorporated place not within an MSA, with a population greater than or equal to 25,000.
Urban fringe/large town-small town is an incorporated place not within an MSA, with a population less than 25,000 and greater than or equal to 2,500; rural is a place with a population less than 2,500 and/or a population density of less than 1,000 per square mile, and defined as rural by the U.S. Bureau of the Census.
Small town/rural
5 percent or less
6 to 20 percent
21 to 50 percent
More than 50 percent
Less than 35 percent
35 to 49 percent
50 to 74 percent
75 percent or more
3 or fewer years
4 to 9 years
10 or more years
It is important to note that many of the school characteristics used for independent analyses may also be related to each other. For example, enrollment size and instructional level of schools are related, with middle and high schools typically being larger than elementary schools. Similarly, poverty concentration and minority enrollment are related, with schools with a high minority enrollment also more likely to have a high concentration of poverty. Other relationships between analysis variables may exist. Because of the relatively small sample size used in this study, it is difficult to separate the independent effects of these variables. Their existence, however, should be considered in the interpretation of the data presented in this report.
The survey was performed under contract with Westat. Westat's Project Director was Elizabeth Farris, and the Survey Manager was Basmat Parsad. Bernie Greene was the NCES Project Officer. The data were requested by Terry Dozier, Office of the Secretary, U.S. Department of Education.
This report was reviewed by the following individuals:
Jeff Owings, Elementary/Secondary and Libraries Studies Division For more information about the survey, contact Bernie Greene, Early Childhood, International, and Crosscutting Studies Division, National Center for Education Statistics, Office of Educational Research and Improvement, U.S. Department of Education, 1990 K Street, NW, Washington, DC 20006, e-mail: frss@ed.gov
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