The Best Management Practices Statistical Estimator (BMPSE) version 1.2.0 was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021). The BMPSE was assembled by using a Microsoft Access® database application to facilitate calculation of BMP performance statistics. Granato (2014) developed quantitative methods to estimate values of the trapezoidal-distribution statistics, correlation coefficients, and the minimum irreducible concentration (MIC) from available data. Granato (2014) developed the BMPSE to hold and process data from the International Stormwater Best Management Practices Database (BMPDB, www.bmpdatabase.org). Version 1.0 of the BMPSE contained a subset of the data from the 2012 version of the BMPDB; the current version of the BMPSE (1.2.0) contains a subset of the data from the December 2019 version of the BMPDB. Selected data from the BMPDB were screened for import into the BMPSE in consultation with Jane Clary, the data manager for the BMPDB. Modifications included identifying water quality constituents, making measurement units consistent, identifying paired inflow and outflow values, and converting BMPDB water quality values set as half the detection limit back to the detection limit. Total polycyclic aromatic hydrocarbons (PAH) values were added to the BMPSE from BMPDB data; they were calculated from individual PAH measurements at sites with enough data to calculate totals. The BMPSE tool can sort and rank the data, calculate plotting positions, calculate initial estimates, and calculate potential correlations to facilitate the distribution-fitting process (Granato, 2014). For water-quality ratio analysis the BMPSE generates the input files and the list of filenames for each constituent within the Graphical User Interface (GUI). The BMPSE calculates the Spearman’s rho (ρ) and Kendall’s tau (τ) correlation coefficients with their respective 95-percent confidence limits and the probability that each correlation coefficient value is not significantly different from zero by using standard methods (Granato, 2014). If the 95-percent confidence limit values are of the same sign, then the correlation coefficient is statistically different from zero. For hydrograph extension, the BMPSE calculates ρ and τ between the inflow volume and the hydrograph-extension values (Granato, 2014). For volume reduction, the BMPSE calculates ρ and τ between the inflow volume and the ratio of outflow to inflow volumes (Granato, 2014). For water-quality treatment, the BMPSE calculates ρ and τ between the inflow concentrations and the ratio of outflow to inflow concentrations (Granato, 2014; 2020). The BMPSE also calculates ρ between the inflow and the outflow concentrations when a water-quality treatment analysis is done. The current version (1.2.0) of the BMPSE also has the option to calculate urban-runoff quality statistics from inflows to BMPs by using computer code developed for the Highway Runoff Database (Granato and Cazenas, 2009;Granato, 2019). Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods, book 4, chap. C3, 112 p., CD-ROM https://pubs.usgs.gov/tm/04/c03 Granato, G.E., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014–5037, 37 p., http://dx.doi.org/10.3133/sir20145037. Granato, G.E., 2019, Highway-Runoff Database (HRDB) Version 1.1.0: U.S. Geological Survey data release, https://doi.org/10.5066/P94VL32J. Granato, G.E., and Cazenas, P.A., 2009, Highway-Runoff Database (HRDB Version 1.0)--A data warehouse and preprocessor for the stochastic empirical loading and dilution model: Washington, D.C., U.S. Department of Transportation, Federal Highway Administration, FHWA-HEP-09-004, 57 p. https://pubs.usgs.gov/sir/2009/5269/disc_content_100a_web/FHWA-HEP-09-004.pdf Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the stochastic empirical loading and dilution model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136

Abstract

The Southern Africa Consortium for Monitoring Educational Quality (SACMEQ) is a consortium of Ministries of Education and Culture located in the Southern Africa subregion. This consortium works in close partnership with the International Institute for Educational Planning (IIEP). SACMEQ’s main aim is to undertake co-operative educational policy research in order to generate information that can be used by decision-makers to plan the quality of education. SACMEQ’s programme of educational policy research has four features which have optimized its contributions to the field of educational planning: (1) it provides research-based policy advice concerning high-priority educational quality issues that have been identified by key decision-makers in Southern Africa, (2) it functions as a co-operative venture based on a strong network of Ministries of Education and Culture, (3) it combines research and training components that are linked with institutional capacity building, and its future directions are defined by participating ministries. In each participating country, a National Research Co-ordinator is responsible for implementing SACMEQ’s projects.

The SACMEQ I Project commenced in 1995 and was completed in 1999. The SACMEQ I main data collection was implemented in seven SACMEQ Ministries of Education (Kenya, Mauritius, Malawi, Namibia, Zambia, Zanzibar, and Zimbabwe). The study provided "agendas for government action" concerning: educational inputs to schools, benchmark standards for educational provision, equity in the allocation of educational resources, and the reading literacy performance of Grade 6 learners. The data collection for this project included information gathered from around 20,000 learners; 3,000 teachers; and 1,000 school principals.

This co-operative sub-regional educational research project collected data in order to guide decisionmaking in these countries with respect to questions around high priority policy issues. These included: • What are the baseline data for selected inputs to primary schools? • How do the conditions of primary schooling compare with the Ministry of Education and Culture’s own bench-mark standards? • Have educational inputs to schools been allocated in an equitable fashion? • What is the basic literacy level among pupils in upper primary school? • Which educational inputs to primary schools have most impact on pupil reading achievement at the upper primary level?

In 1995 there were five fully active members of SACMEQ: Mauritius, Namibia, Zambia, Tanzania (Zanzibar), and Zimbabwe. These Ministries of Education and Culture participated in all phases of SACMEQ’s establishment and its initial educational policy research project. There are also four partially active members of SACMEQ: Kenya, Tanzania (Mainland), Malawi, and Swaziland. These Ministries of Education and Culture have made contributions to the preparation of the Project Plan for SACMEQ’s initial educational policy research project. Three other countries (Botswana, Lesotho, and South Africa) had observer status due to their involvement in SACMEQ related training workshops or their participation in some elements of the preparation of the first proposal for launching SACMEQ.

Geographic coverage

National Coverage

Analysis unit

• Pupils
• Schools
• Teachers

Universe

The target population for SACMEQ's Initial Project was defined as "all pupils at the Grade 6 level in 1995 who were attending registered government or non-government schools". Grade 6 was chosen because it was the grade level where the basics of reading literacy were expected to have been acquired.

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified two-stage sample design was used to select around 150 schools in each country. Pupils were then selected within these schools by drawing simple random samples. A more detailed explanation of the sampling process is available under the 'Sampling' section of the report provided as external resources.

All sample designs applied in SACMEQ'S initial project were selected so as to meet the standards set down by the International Association for the Evaluation of Education Achievement (Ross, 1991). These standards require sample estimates of important pupil population characteristics to be (a) adjusted by weighing procedures designed to remove the potential for bias that may arise from different probabilities of selection, and (b) have sampling errors for the main criterion variables that are of the same magnitude or smaller than a simple random sample of 400 pupils (thereby providing 95 percent confidence limits for sample estimates of population percentages of plus or minus 5 percentage points, and 95 percent confidence limits for sample estimates of population means of plus or minus one tenth of a pupil standard deviation unit).

The desired target population in Zambia was 'all pupils at the Grade 6 level in the eleventh month of the school year, 1995, who were attending registered government and grant-aided schools in the country'. The number of schools and pupils in the desired, excluded, and defined population have been presented in Table 2.2 of the Sample Report provided as external resources. From the defined target population a probability sample of schools (with probability proportional to the Grade 6 enrolment in each school) was drawn. This resulted in a planned national sample of 165 schools and 3,300 pupils. This sample design was designed to yield an equivalent sample size' (Ross and Wilson, 1994) of 400 pupils - based on an estimated intra-class correlation (rho) for pupil reading test scores of around 0.30. In fact, after the rho was calculated for the reading scores, it was found to be 0.3 1 - which was about the same as had been expected At the first stage of sampling, schools were selected with a probability proportional to the number of pupils who were members of the defined target population. To achieve this selection a 'random start - constant interval' procedure was applied (Ross, 1987). In several strata there were some schools with numbers of pupils in the defined target population that exceeded the size of the 'constant interval', and therefore each of these schools was randomly broken into smaller 'pseudo schools' before the commencement of the sampling. At the second stage of sampling, a simple random sample of 20 pupils was selected within each selected school. Sampling weights were used to adjust for the disproportionate allocation of the sample across districts and also to account for the small loss of student data due to absenteeism on the day of the data collection.

Mode of data collection

Face-to-face [f2f]

Research instrument

The data collection for SACMEQ's Initial Project took place in October 1995 and involved the administration of questionnaires to pupils, teachers, and school heads. The pupil questionnaire contained questions about the pupils' home backgrounds and their school life; the teacher questionnaire asked about classrooms, teaching practices, working conditions, and teacher housing; and the school head questionnaire collected information about teachers, enrolments, buildings, facilities, and management. A reading literacy test was also given to the pupils. The test was based on items that were selected after a trial-testing programme had been completed.

The SACMEQ Data Collection Instruments include the following documents: - SACMEQ Questionnaires - which are administered to pupils, teachers, and school heads. - SACMEQ Tests - which are administered to pupils and teachers (covering reading mathematics, and HIV-AIDS knowledge). - Other SACMEQ Data Collection Instruments - such as take-home pupil questionnaires, school context proformas, and within-school project management documents.

Cleaning operations

All of the team leaders for the data collectors returned the instruments to the Ministry Headquarters (for the attention of the NRC), during the second week after the test administration. Once the instruments were returned to the Headquarters, three data entry staff within the Statistical Section of the Ministry entered the data, using the Data Entry Manager (DEM) a software programme developed at the IIEP (Schleicher, 1995). This software was adapted specifically for the entry of SACMEQ data. The data entry took six weeks and the data were sent on diskette to IIEP in March, 1996. It must be mentioned that at the time of data entry, the earlier version of the DEM structure files was used, and this caused major problems in cleaning the data at a later stage and reconstituting the structure of the files as they were meant to be.

Response rate

The planned sample was designed to contain 165 schools allocated across provinces, as shown in the first column of figures in Table 2.3 of the Survey Report provided as external resources. The achieved sample of schools was 157. The response rates for the sample have been recorded in Table 2.3. The percentage response for schools was 95.2 percent and that of pupils was 77.5 percent. The non-responding pupils were those who were absent on the day of testing. By province, this absenteeism varied from 2 to 12 percent.

Sampling error estimates

In the survey report provided as external resources, standard errors were provided for all important variables. The calculation of these errors acknowledged that the sample was not a simple random sample - but rather a complex two-stage cluster sample that included weighting adjustments to compensate for variations in selection probabilities. The errors were