Education Quality Improvement Programme Impact Evaluation Endline Survey 2018
Tanzania, 2018
Oxford Policy Management Ltd
Identification
IDNO
TZA_2018_EQUIPIE-EL_v01_M
Title
Education Quality Improvement Programme Impact Evaluation Endline Survey 2018
Countries
| Name | Code |
|---|---|
| Tanzania | TZA |
Study notes
Education Quality Improvement Programme in Tanzania (EQUIP-T) is a six-year (2014-20) Government of Tanzania programme, funded by the United Kingdom Department for International Development (DFID), which seeks to improve the quality of primary education and to improve pupil learning outcomes, especially for girls. The programme focuses on strengthening professional capacity and performance of teachers, school leadership and management, systems which support district management of education, and community participation in education. Initially, the programme was intended to run for four years, with activities targeted at seven of the most educationally disadvantaged regions in Tanzania. In 2017 the programme was extended for a further two years, and the extension introduced some new sub-components to the seven regions, and introduced a reduced package of interventions to two new regions.
The independent Impact Evaluation (IE) of EQUIP-T is a five-year study funded by DFID. It is designed to: i) generate evidence on the impact of EQUIP-T on primary pupil learning outcomes, including any differential effects for boys and girls; ii) examine perceptions of effectiveness of different EQUIP-T components; iii) provide evidence on the fiscal affordability of scaling up EQUIP-T post-endline; and iv) communicate evidence generated by the impact evaluation to policy-makers and key education stakeholders. The evaluation uses a quasi-experimental approach to quantitative estimation of impact that combines propensity score matching (PSM) with difference-indifferences (DID).
The research priorities for the quantitative endline IE are captured in a comprehensive endline evaluation matrix (see Annex C in the 'EQUIP-Tanzania Impact Evaluation. Endline Quantitative Technical Report, Volume I: Results and Discussion' under Reports and policy notes). The matrix sets out evaluation questions linked to the programme theory of change. It asks questions related to the expected results at each stage along the results chain (from the receipt of inputs to delivery of outputs, and contributions to outcomes and impact) under each of the programme's components. The aim is to establish: (i) whether changes have happened as expected; (ii) why they happened or did not happen (i.e. whether key assumptions in the theory of change hold or not); (iii) whether there are any important unanticipated changes; and (iv) what links there are between the components in driving changes.
The main IE research areas are:
-Impact of EQUIP-T on standard 3 pupil learning in Kiswahili and mathematics.
-Impact of EQUIP-T on teacher absence from school and from classrooms.
-Impact of EQUIP-T on selected aspects of school leadership and management.
The IE uses a mixed methods approach that includes:
-A quantitative survey of 100 government primary schools in 17 programme treatment districts and 100 schools in 8 control districts in 2014, 2016 and 2018 covering:
*Standard three pupils and their parents/caregivers;
*Teachers who teach standards 1-3 Kiswahili;
*Teachers who teach standards 1-3 mathematics;
*Schools;
*Head teachers; and
*Standard two lesson observations in Kiswahili and mathematics.
-Qualitative fieldwork in a few treatment schools that overlap with a sub-set of the quantitative survey schools, in 2014, 2016 and 2019, consisting of key informant interviews (KIIs) and focus group discussions (FGDs) with head teachers, teachers, pupils, parents, school committee (SC) members, PTP members, region, district and ward education officials and EQUIP-T programme staff.
The endline data available in the World Bank Microdata Catalog are from the EQUIP-T IE quantitative endline survey conducted in 2018. The endline qualitative research will take place in mid-2019 with results available in early 2020.
The independent Impact Evaluation (IE) of EQUIP-T is a five-year study funded by DFID. It is designed to: i) generate evidence on the impact of EQUIP-T on primary pupil learning outcomes, including any differential effects for boys and girls; ii) examine perceptions of effectiveness of different EQUIP-T components; iii) provide evidence on the fiscal affordability of scaling up EQUIP-T post-endline; and iv) communicate evidence generated by the impact evaluation to policy-makers and key education stakeholders. The evaluation uses a quasi-experimental approach to quantitative estimation of impact that combines propensity score matching (PSM) with difference-indifferences (DID).
The research priorities for the quantitative endline IE are captured in a comprehensive endline evaluation matrix (see Annex C in the 'EQUIP-Tanzania Impact Evaluation. Endline Quantitative Technical Report, Volume I: Results and Discussion' under Reports and policy notes). The matrix sets out evaluation questions linked to the programme theory of change. It asks questions related to the expected results at each stage along the results chain (from the receipt of inputs to delivery of outputs, and contributions to outcomes and impact) under each of the programme's components. The aim is to establish: (i) whether changes have happened as expected; (ii) why they happened or did not happen (i.e. whether key assumptions in the theory of change hold or not); (iii) whether there are any important unanticipated changes; and (iv) what links there are between the components in driving changes.
The main IE research areas are:
-Impact of EQUIP-T on standard 3 pupil learning in Kiswahili and mathematics.
-Impact of EQUIP-T on teacher absence from school and from classrooms.
-Impact of EQUIP-T on selected aspects of school leadership and management.
The IE uses a mixed methods approach that includes:
-A quantitative survey of 100 government primary schools in 17 programme treatment districts and 100 schools in 8 control districts in 2014, 2016 and 2018 covering:
*Standard three pupils and their parents/caregivers;
*Teachers who teach standards 1-3 Kiswahili;
*Teachers who teach standards 1-3 mathematics;
*Schools;
*Head teachers; and
*Standard two lesson observations in Kiswahili and mathematics.
-Qualitative fieldwork in a few treatment schools that overlap with a sub-set of the quantitative survey schools, in 2014, 2016 and 2019, consisting of key informant interviews (KIIs) and focus group discussions (FGDs) with head teachers, teachers, pupils, parents, school committee (SC) members, PTP members, region, district and ward education officials and EQUIP-T programme staff.
The endline data available in the World Bank Microdata Catalog are from the EQUIP-T IE quantitative endline survey conducted in 2018. The endline qualitative research will take place in mid-2019 with results available in early 2020.
Kind of data
Sample survey data [ssd]
Version
Version
Version 2.1: Edited, anonymous dataset for public distribution.
Version date
2019-07-02
Version notes
Version 2.1 consists of six edited and anonymised datasets (at school, teacher, pupil, lesson and training session level) with the responses to a small number of questions removed; these were removed for confidentiality purposes or because they were not needed for the analysis. Some of the datasets also contain selected constructed indicators prefixed by n_. These constructed indicators are included to save data users time as they require complex reshaping and extraction of data from multiple sources (but they could be generated by data users if preferred).
Scope
Topics
| Topic |
|---|
| Education |
| Primary education |
| Impact evaluation |
| Girls’ Education |
Keywords
| metadata.study_desc.study_info.keywords.keyword |
|---|
| Primary education |
| Education quality |
| Pupil learning |
| Student learning |
| Pupil learning assessment |
| Early Grade Reading Assessment |
| EGRA |
| Early Grade Mathematics Assessment |
| EGMA |
| Impact evaluation |
| Mixed methods evaluation |
| Teaching practices |
| Pedagogy |
| Teacher motivation |
| Teacher absenteeism |
| Classroom absenteeism |
| School leadership and management |
| Teacher support |
| District education management |
| Instructional time |
| Community participation |
| In-service training |
Coverage
Geographic coverage
The survey is representative of 17 EQUIP-T programme treatment districts. The survey is NOT representative of the 8 control districts. For more details see the section on Representativeness in 'EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume I: Results and Discussion' and 'EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume II: Methods and Technical Annexes' under Reports and policy notes.
----------------------------------------------------------------------
The 17 treatment districts are:
----------------------------------------------------------------------
-Dodoma Region: Bahi DC, Chamwino DC, Kongwa DC, Mpwapwa DC
-Kigoma Region: Kakonko DC, Kibondo DC
-Shinyanga Region: Kishapu DC, Shinyanga DC
-Simiyu Region: Bariadi DC, Bariadi TC, Itilima DC, Maswa DC, Meatu DC
-Tabora Region: Igunga DC, Nzega DC, Sikonge DC, Uyui DC
----------------------------------------------------------------------
The 8 control districts are:
----------------------------------------------------------------------
-Arusha Region: Ngorongoro DC
-Mwanza Region: Misungwi DC
-Pwani Region: Rufiji DC
-Rukwa Region: Nkasi DC
-Ruvuma Region: Tunduru DC
-Singida Region: Ikungi DC, Singida DC
-Tanga Region: Kilindi DC
----------------------------------------------------------------------
The 17 treatment districts are:
----------------------------------------------------------------------
-Dodoma Region: Bahi DC, Chamwino DC, Kongwa DC, Mpwapwa DC
-Kigoma Region: Kakonko DC, Kibondo DC
-Shinyanga Region: Kishapu DC, Shinyanga DC
-Simiyu Region: Bariadi DC, Bariadi TC, Itilima DC, Maswa DC, Meatu DC
-Tabora Region: Igunga DC, Nzega DC, Sikonge DC, Uyui DC
----------------------------------------------------------------------
The 8 control districts are:
----------------------------------------------------------------------
-Arusha Region: Ngorongoro DC
-Mwanza Region: Misungwi DC
-Pwani Region: Rufiji DC
-Rukwa Region: Nkasi DC
-Ruvuma Region: Tunduru DC
-Singida Region: Ikungi DC, Singida DC
-Tanga Region: Kilindi DC
Unit of analysis
- School
- Teacher
- Pupil
- Lesson (not sampled)
- EQUIP-T training session
- Teacher
- Pupil
- Lesson (not sampled)
- EQUIP-T training session
Producers and sponsors
Authoring entity
| Name |
|---|
| Oxford Policy Management Ltd |
Funding agencies
| Name | Abbreviation |
|---|---|
| Department for International Development UK | DFID |
Sampling
Sampling procedure
Because the EQUIP-T regions and districts were purposively selected (see 'EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume I: Results and Discussion' under Reports and policy notes), the IE sampling strategy used propensity score matching (PSM) to: (i) match eligible control districts to the pre-selected and eligible EQUIP-T districts (see below), and (ii) match schools from the control districts to a sample of randomly selected treatment schools in the treatment districts. The same schools are surveyed for each round of the IE (panel of schools) and a cross section of standard 3 pupils and Standard 1-3 teachers will be interviewed at each round of the survey (no pupil panel or teacher panel).
------------------------------------------------
Identifying districts eligible for matching
------------------------------------------------
Eligible control and treatment districts were those not participating in any other education programme or project that may confound the measurement of EQUIP-T impact. To generate the list of eligible control and treatment districts, all districts that are contaminated because of other education programmes or projects or may be affected by programme spill-over were excluded as follows:
-All districts located in Lindi and Mara regions as these are part of the EQUIP-T programme but implementation started later in these two regions (the IE does not cover these two regions);
-Districts that will receive partial EQUIP-T programme treatment or will be subject to potential EQUIP-T programme spillovers;
-Districts that are receiving other education programmes/projects that aim to influence the same outcomes as the EQUIP-T programme and would confound measurement of EQUIP-T impact;
-Districts that were part of pre-test 1 (two districts); and
-Districts that were part of pre-test 2 (one district).
-------------------
Sampling frame
-------------------
To be able to select an appropriate sample of pupils and teachers within schools and districts, the sampling frame consisted of information at three levels:
-District;
-School; and
-Within school.
The sampling frame data at the district and school levels was compiled from the following sources: the 2002 and 2012 Tanzania Population Censuses, Education Management Information System (EMIS) data from the Ministry of Education and Vocational Training (MoEVT) and the Prime Minister's Office for Regional and Local Government (PMO-RALG), and the UWEZO 2011 student learning assessment survey. For within school level sampling, the frames were constructed upon arrival at the selected schools and was used to sample pupils and teachers on the day of the school visit.
-------------------
Sampling stages
-------------------
Stage 1: Selection of control districts
--------------------------------------------
Because the treatment districts were known, the first step was to find sufficiently similar control districts that could serve as the counterfactual. PSM was used to match eligible control districts to the pre-selected, eligible treatment districts using the following matching variables: Population density, proportion of male headed households, household size, number of children per household, proportion of households that speak an ethnic language at home, and district level averages for household assets, infrastructure, education spending, parental education, school remoteness, pupil learning levels and pupil drop out.
Stage 2: Selection of treatment schools
-------------------------------------------------
In the second stage, schools in the treatment districts were selected using stratified systematic random sampling. The schools were selected using a probability proportional to size approach, where the measure of school size was the standard two enrolment of pupils. This means that schools with more pupils had a higher probability of being selected into the sample. To obtain a representative sample of programme treatment schools, the sample was implicitly stratified along four dimensions:
-District;
-PSLE scores for Kiswahili;
-PSLE scores for mathematics; and
-Total number of teachers per school.
Stage 3: Selection of control schools
----------------------------------------------
As in stage one, a non-random PSM approach was used to match eligible control schools to the sample of treatment schools. The matching variables were similar to the ones used as stratification criteria: Standard two enrolment, PSLE scores for Kiswahili and mathematics, and the total number of teachers per school.
The endline survey was conducted for the same schools as the baseline and midline surveys (a panel of schools). However, the IE does not have a panel of pupils or teachers as a pupil only attends standard three once (unless repeating) and there is high teacher turnover. Thus, the IE sample is a repeated cross-section of pupils and teachers in a panel of schools.
Stage 4: Selection of pupils and teachers within schools
-------------------------------------------------------------------
Pupils were sampled within schools using systematic random sampling based on school registers. The within-school sampling was assisted by selection tables automatically generated within the computer assisted survey instruments. Per school, 15 standard 3 pupils were sampled. The parents of these 15 sampled pupils were then interviewed using the poverty scorecard instrument.
For the teacher interviews, as at midline, all teachers of Standards 1-3 who teach Kiswhaili or maths were interviewed to boost the sample size as many schools are small (as opposed to baseline where up to three teachers were sampled within each school for the interviews).
Lesson observations were not randomly sampled. Instead, one maths and one Kiswahili Standard 2 lessons were selected within each school using convenience sampling to be observed on the day of the survey.
-------------------------
Replacement sample
-------------------------
At baseline, if a selected school could not be surveyed it was replaced. In the process of sampling, the impact evaluation team drew a replacement sample of schools, which was used for this purpose (reserve list) and the use of this list was carefully controlled. Five out of the 200 original baseline sample schools were replaced during the fieldwork. At midline and endline, all of the 200 schools surveyed at baseline were visited again (no replacements).
---------------
Sample sizes
---------------
The actual sample sizes at endline are:
-200 schools (100 treatment and 100 control).
-2,999 standard 3 pupils assessed in both Kiswahili and mathematics.
-2,992 poverty scorecards were administered to the assessed pupils' parent(s).
-889 teachers who teach standards 1 to 3 Kiswahili and/or mathematics interviewed.
-196 standard 2 Kiswahili and mathematics lessons observed (treatment schools only).
-99 teacher group interviews were conducted (treatment schools only).
Note that the lesson observation and the small group teacher interview were only conducted in treatment schools, because the information generated could not be used in the impact modelling and so collecting information in control schools was not necessary.
-------------------------
Representativeness
-------------------------
The results from the treatment schools are representative of government primary schools in the 17 EQUIP-T programme treatment districts. However, the results from the schools in the 8 control districts are NOT representative because these districts were not randomly sampled but matched to the 17 treatment districts using propensity score matching.
------------------------------------------------
Identifying districts eligible for matching
------------------------------------------------
Eligible control and treatment districts were those not participating in any other education programme or project that may confound the measurement of EQUIP-T impact. To generate the list of eligible control and treatment districts, all districts that are contaminated because of other education programmes or projects or may be affected by programme spill-over were excluded as follows:
-All districts located in Lindi and Mara regions as these are part of the EQUIP-T programme but implementation started later in these two regions (the IE does not cover these two regions);
-Districts that will receive partial EQUIP-T programme treatment or will be subject to potential EQUIP-T programme spillovers;
-Districts that are receiving other education programmes/projects that aim to influence the same outcomes as the EQUIP-T programme and would confound measurement of EQUIP-T impact;
-Districts that were part of pre-test 1 (two districts); and
-Districts that were part of pre-test 2 (one district).
-------------------
Sampling frame
-------------------
To be able to select an appropriate sample of pupils and teachers within schools and districts, the sampling frame consisted of information at three levels:
-District;
-School; and
-Within school.
The sampling frame data at the district and school levels was compiled from the following sources: the 2002 and 2012 Tanzania Population Censuses, Education Management Information System (EMIS) data from the Ministry of Education and Vocational Training (MoEVT) and the Prime Minister's Office for Regional and Local Government (PMO-RALG), and the UWEZO 2011 student learning assessment survey. For within school level sampling, the frames were constructed upon arrival at the selected schools and was used to sample pupils and teachers on the day of the school visit.
-------------------
Sampling stages
-------------------
Stage 1: Selection of control districts
--------------------------------------------
Because the treatment districts were known, the first step was to find sufficiently similar control districts that could serve as the counterfactual. PSM was used to match eligible control districts to the pre-selected, eligible treatment districts using the following matching variables: Population density, proportion of male headed households, household size, number of children per household, proportion of households that speak an ethnic language at home, and district level averages for household assets, infrastructure, education spending, parental education, school remoteness, pupil learning levels and pupil drop out.
Stage 2: Selection of treatment schools
-------------------------------------------------
In the second stage, schools in the treatment districts were selected using stratified systematic random sampling. The schools were selected using a probability proportional to size approach, where the measure of school size was the standard two enrolment of pupils. This means that schools with more pupils had a higher probability of being selected into the sample. To obtain a representative sample of programme treatment schools, the sample was implicitly stratified along four dimensions:
-District;
-PSLE scores for Kiswahili;
-PSLE scores for mathematics; and
-Total number of teachers per school.
Stage 3: Selection of control schools
----------------------------------------------
As in stage one, a non-random PSM approach was used to match eligible control schools to the sample of treatment schools. The matching variables were similar to the ones used as stratification criteria: Standard two enrolment, PSLE scores for Kiswahili and mathematics, and the total number of teachers per school.
The endline survey was conducted for the same schools as the baseline and midline surveys (a panel of schools). However, the IE does not have a panel of pupils or teachers as a pupil only attends standard three once (unless repeating) and there is high teacher turnover. Thus, the IE sample is a repeated cross-section of pupils and teachers in a panel of schools.
Stage 4: Selection of pupils and teachers within schools
-------------------------------------------------------------------
Pupils were sampled within schools using systematic random sampling based on school registers. The within-school sampling was assisted by selection tables automatically generated within the computer assisted survey instruments. Per school, 15 standard 3 pupils were sampled. The parents of these 15 sampled pupils were then interviewed using the poverty scorecard instrument.
For the teacher interviews, as at midline, all teachers of Standards 1-3 who teach Kiswhaili or maths were interviewed to boost the sample size as many schools are small (as opposed to baseline where up to three teachers were sampled within each school for the interviews).
Lesson observations were not randomly sampled. Instead, one maths and one Kiswahili Standard 2 lessons were selected within each school using convenience sampling to be observed on the day of the survey.
-------------------------
Replacement sample
-------------------------
At baseline, if a selected school could not be surveyed it was replaced. In the process of sampling, the impact evaluation team drew a replacement sample of schools, which was used for this purpose (reserve list) and the use of this list was carefully controlled. Five out of the 200 original baseline sample schools were replaced during the fieldwork. At midline and endline, all of the 200 schools surveyed at baseline were visited again (no replacements).
---------------
Sample sizes
---------------
The actual sample sizes at endline are:
-200 schools (100 treatment and 100 control).
-2,999 standard 3 pupils assessed in both Kiswahili and mathematics.
-2,992 poverty scorecards were administered to the assessed pupils' parent(s).
-889 teachers who teach standards 1 to 3 Kiswahili and/or mathematics interviewed.
-196 standard 2 Kiswahili and mathematics lessons observed (treatment schools only).
-99 teacher group interviews were conducted (treatment schools only).
Note that the lesson observation and the small group teacher interview were only conducted in treatment schools, because the information generated could not be used in the impact modelling and so collecting information in control schools was not necessary.
-------------------------
Representativeness
-------------------------
The results from the treatment schools are representative of government primary schools in the 17 EQUIP-T programme treatment districts. However, the results from the schools in the 8 control districts are NOT representative because these districts were not randomly sampled but matched to the 17 treatment districts using propensity score matching.
Response rate
-----------------
Unit response
-----------------
Actual sample sizes at endline are close to target sample sizes. See 'Sampling Procedure' section for actual sample sizes.
- All 200 schools surveyed at baseline and midline were also surveyed at endline (100% response rate).
- For tested Standard 3 pupils, response rate is 99.9% (target: 3,000).
- For parents of tested Standard 3 pupils, response rate is 99.7% (target: 3,000).
- For interviewed Standards 1-3 teachers, response rate is 99.2% (target: 906 which is all teachers at endline who are teaching maths and/or Kiswahili to Standards 1-3), including 11% of teachers who were absent or unavailable on the day of the survey and were later interviewed by phone.
- The target for lesson observations (not sampled) was 200, but under the new Standards 1 and 2 curriculum, maths and Kiswahili (either reading or writing) lessons often run sequentially without a break, and this enabled 95 maths lessons to be observed and 101 Kiswahili lessons, in total more than the target (but maths lessons less than the target of a 100).
- For the teacher group interview, response rate is 99% (target: 100).
------------------
Item response
-----------------
Item response rates were generally high.
Unit response
-----------------
Actual sample sizes at endline are close to target sample sizes. See 'Sampling Procedure' section for actual sample sizes.
- All 200 schools surveyed at baseline and midline were also surveyed at endline (100% response rate).
- For tested Standard 3 pupils, response rate is 99.9% (target: 3,000).
- For parents of tested Standard 3 pupils, response rate is 99.7% (target: 3,000).
- For interviewed Standards 1-3 teachers, response rate is 99.2% (target: 906 which is all teachers at endline who are teaching maths and/or Kiswahili to Standards 1-3), including 11% of teachers who were absent or unavailable on the day of the survey and were later interviewed by phone.
- The target for lesson observations (not sampled) was 200, but under the new Standards 1 and 2 curriculum, maths and Kiswahili (either reading or writing) lessons often run sequentially without a break, and this enabled 95 maths lessons to be observed and 101 Kiswahili lessons, in total more than the target (but maths lessons less than the target of a 100).
- For the teacher group interview, response rate is 99% (target: 100).
------------------
Item response
-----------------
Item response rates were generally high.
Weighting
The survey is only representative of the 17 EQUIP-T programme districts and therefore survey weights were only constructed for schools, pupils and teachers in the treatment group (not for the control group).
To obtain results that are representative of the EQUIP-T programme treatment areas, treatment estimates should be weighted using the provided survey weights that are normalised values of the inverse probabilities of selection into the sample for each unit of analysis. The relevant probabilities of selection differ depending on whether analysis is carried out at school, pupil or teacher level, and survey weights for each of these units of analysis are included in the datasets.
-----------------------------------------------
School weights (treatment group only)
-----------------------------------------------
The probability of being selected of each school depended on the total number of schools being selected and its size relative to the total number of enrolled pupils across all schools in the 17 programme districts. Formally, the probability of a given school being selected into the sample equals the total number of schools sampled multiplied by the ratio of the number of pupils in the given school and the total number of pupils in all schools in the relevant programme areas. The school weights are appropriately normalised inverses of these probabilities.
Note: Refer to the end of this section for the strata, weights and finite population correction factor variables included in the dataset.
--------------------------------------------
Pupil weights (treatment group only)
--------------------------------------------
15 standard 3 pupils were randomly sampled at each school. The probability of selection of a pupil in a given school equals the school weight (defined above) multiplied by the ratio of the number of pupils selected per school (15 in all schools except in the schools that had less than 15 pupils present on that day) and the total number of eligible pupils in the given school. The pupil weights are appropriately normalised inverses of these probabilities.
Note: Refer to the end of this section for the strata, weights and finite population correction factor variables included in the dataset.
------------------------------------------------
Teacher weights (treatment group only)
------------------------------------------------
The probability of selection of a teacher in a given school equals the school weight (defined above) multiplied by the ratio of the number of teachers that were selected for a given teacher instrument per school and the total number of teachers eligible for the given instrument. The teacher weights are appropriately normalised inverses of these probabilities.
NOTE:
-For data from the teacher interviews, the teacher interview weights should be used: weight_tchint. Since all teachers eligible for the interview in each school were interviewed, this means that the selection probability for each teacher is equal to one in this case.
-For data from the teacher roster, the teacher roster weights should be used: weight_teacherroster. Since all teachers in each school are included in the roster, this means that the selection probability for each teacher is equal to one in this case.
Note: Refer to the end of this section for the strata, weights and finite population correction factor variables included in the dataset.
--------------------------------------------------------------------
Stratification, clustering and finite population corrections
--------------------------------------------------------------------
The survey weights should be used within a survey set-up that takes into account stratification, clustered sampling and finite population corrections.
Stratification during sampling was used at the primary sampling level, that is, at school level, and not at the lower levels (pupil and teacher). For the estimation set-up, strata for schools are defined by districts and teacher-body size terciles. Although, during sampling, schools were implicitly stratified by primary school leaving examination (PSLE) scores as well, this is a continuous variable that cannot be used to define strata in the estimation set-up.
Clustering is only relevant for pupil and teacher level data, as schools were the primary sampling units within the eligible programme treatment districts. School pupil data is also hierarchical in nature with pupils clustered within schools. Hence, for pupil and teacher estimates, clustering is set at the school level.
Because large proportions of the total eligible population were sampled in many schools at the teacher and pupil levels, the estimation set-up should also account for the finite population correction (FPC) factor. This FPC factor is the square root of the ratio of the population from which the sample is drawn minus the size of the sample and the population from which the sample is drawn minus one. In the case of school level data, the FPC factor is constant across all schools, as the sample of schools was drawn from a constant population of all eligible schools in the programme treatment areas. However, for teacher and pupil level data, the FPC factor changes depending on the school, as population sizes and, in the case of teacher level data, sample sizes vary as well.
-------------------------------------------------------------------------------------------------
Stratification, weight, finite population correction and treatment status variables
-------------------------------------------------------------------------------------------------
In the EQUIP-T IE endline datasets the stratification, weight, FPC and treatment status variables are as follows:
-The strata variable is: strata
-The school weights variable is: weight_school
-The school finite population correction factor is: fpc_school
-The pupil weight variable is: weight_pupil
-The pupil finite population correction factor is: fpc_pupil
-The teacher interview weight variable is: weight_tchint
-The teacher interview finite population correction factor is: fpc_tchint
-The teacher roster weight variable is: weight_teacherroster
-The teacher roster finite population correction factor is: fpc_teacherroster
-The treatment status variable is: treatment where 0=control school and 1=treatment school.
NOTE that for lesson-level estimates, the school weights are applied given that lessons were not sampled within each school.
To obtain results that are representative of the EQUIP-T programme treatment areas, treatment estimates should be weighted using the provided survey weights that are normalised values of the inverse probabilities of selection into the sample for each unit of analysis. The relevant probabilities of selection differ depending on whether analysis is carried out at school, pupil or teacher level, and survey weights for each of these units of analysis are included in the datasets.
-----------------------------------------------
School weights (treatment group only)
-----------------------------------------------
The probability of being selected of each school depended on the total number of schools being selected and its size relative to the total number of enrolled pupils across all schools in the 17 programme districts. Formally, the probability of a given school being selected into the sample equals the total number of schools sampled multiplied by the ratio of the number of pupils in the given school and the total number of pupils in all schools in the relevant programme areas. The school weights are appropriately normalised inverses of these probabilities.
Note: Refer to the end of this section for the strata, weights and finite population correction factor variables included in the dataset.
--------------------------------------------
Pupil weights (treatment group only)
--------------------------------------------
15 standard 3 pupils were randomly sampled at each school. The probability of selection of a pupil in a given school equals the school weight (defined above) multiplied by the ratio of the number of pupils selected per school (15 in all schools except in the schools that had less than 15 pupils present on that day) and the total number of eligible pupils in the given school. The pupil weights are appropriately normalised inverses of these probabilities.
Note: Refer to the end of this section for the strata, weights and finite population correction factor variables included in the dataset.
------------------------------------------------
Teacher weights (treatment group only)
------------------------------------------------
The probability of selection of a teacher in a given school equals the school weight (defined above) multiplied by the ratio of the number of teachers that were selected for a given teacher instrument per school and the total number of teachers eligible for the given instrument. The teacher weights are appropriately normalised inverses of these probabilities.
NOTE:
-For data from the teacher interviews, the teacher interview weights should be used: weight_tchint. Since all teachers eligible for the interview in each school were interviewed, this means that the selection probability for each teacher is equal to one in this case.
-For data from the teacher roster, the teacher roster weights should be used: weight_teacherroster. Since all teachers in each school are included in the roster, this means that the selection probability for each teacher is equal to one in this case.
Note: Refer to the end of this section for the strata, weights and finite population correction factor variables included in the dataset.
--------------------------------------------------------------------
Stratification, clustering and finite population corrections
--------------------------------------------------------------------
The survey weights should be used within a survey set-up that takes into account stratification, clustered sampling and finite population corrections.
Stratification during sampling was used at the primary sampling level, that is, at school level, and not at the lower levels (pupil and teacher). For the estimation set-up, strata for schools are defined by districts and teacher-body size terciles. Although, during sampling, schools were implicitly stratified by primary school leaving examination (PSLE) scores as well, this is a continuous variable that cannot be used to define strata in the estimation set-up.
Clustering is only relevant for pupil and teacher level data, as schools were the primary sampling units within the eligible programme treatment districts. School pupil data is also hierarchical in nature with pupils clustered within schools. Hence, for pupil and teacher estimates, clustering is set at the school level.
Because large proportions of the total eligible population were sampled in many schools at the teacher and pupil levels, the estimation set-up should also account for the finite population correction (FPC) factor. This FPC factor is the square root of the ratio of the population from which the sample is drawn minus the size of the sample and the population from which the sample is drawn minus one. In the case of school level data, the FPC factor is constant across all schools, as the sample of schools was drawn from a constant population of all eligible schools in the programme treatment areas. However, for teacher and pupil level data, the FPC factor changes depending on the school, as population sizes and, in the case of teacher level data, sample sizes vary as well.
-------------------------------------------------------------------------------------------------
Stratification, weight, finite population correction and treatment status variables
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In the EQUIP-T IE endline datasets the stratification, weight, FPC and treatment status variables are as follows:
-The strata variable is: strata
-The school weights variable is: weight_school
-The school finite population correction factor is: fpc_school
-The pupil weight variable is: weight_pupil
-The pupil finite population correction factor is: fpc_pupil
-The teacher interview weight variable is: weight_tchint
-The teacher interview finite population correction factor is: fpc_tchint
-The teacher roster weight variable is: weight_teacherroster
-The teacher roster finite population correction factor is: fpc_teacherroster
-The treatment status variable is: treatment where 0=control school and 1=treatment school.
NOTE that for lesson-level estimates, the school weights are applied given that lessons were not sampled within each school.
Data Collection
Dates of collection
| Start | End | Cycle |
|---|---|---|
| 2018-04-16 | 2018-05-21 | Endline |
Mode of data collection
Computer Assisted Personal Interview [capi]
Data collection supervision
----------------------------------------------------
Quality control and data checking protocols
----------------------------------------------------
At the end of each working day, supervisors collected all interview files from their team members and uploaded them into a shared and organised Dropbox folder that was set up by the data manager. The data manager would receive all files from all nine teams and export them into Stata data files (a statistical programme) and then run daily checks on all files to make sure they are complete and identify potential errors. Several mechanisms were put in place in order to ensure high quality of the data collected during the survey. These are briefly summarised in turn below.
-Selection and supervision of enumerators
As discussed above, each enumerator was supervised at least once by the training team during the training, piloting and first week of data collection. This allowed a well-informed selection of enumerators and their allocation into roles matching individual strengths and weaknesses.
-CAPI built-in routing and validations
One important quality control means in CAPI surveys are the use of automatic routing and checking rules built into the CAPI questionnaires that flag simple errors during the interview, i.e. early enough for them be corrected during the interview. In each CAPI instrument, validations and checks were incorporated in the design in order to significantly reduce errors and inaccuracies during data collection. In addition to having automatic skip patterns built into the design to eliminate errors resulting from wrong skips, the CAPI validations also checked for missing fields, out of range values and simple inconsistencies within instruments.
-Secondary consistency checks and cleaning in Stata
The EL survey exploited another key advantage of CAPI surveys, the immediate availability of data, by running a range of secondary consistencies checks across all data on a daily basis in Stata. Data received from the field was exported to Stata the following day, and a range of do-files were run to assess consistency and completeness, and make corrections if necessary. The checks comprised the following: ID uniqueness and matching across instruments; completeness of observations: target sample size versus actual; and intra- and inter-instrument consistency and out of range checks. The data manager ran the checking do-file on a daily basis on the latest cleaned data. This would return a list of potential issues which the data manager would then investigate and undertake the necessary cleaning actions. Whenever any issue was flagged, effort to obtain an explanation was undertaken either by reviewing enumerator comments or phoning teams. On a daily basis, the data manager collated, shared and discussed all flagged errors with the supervisors in the field, who in turn discussed them with their team members. Throughout the fieldwork, occurrences of errors were monitored in order to keep an eye on the performance of data collectors and constantly provide them with feedback to improve.
-Monitoring fieldwork progress and performance indicators
In addition to the above checks that were specific to each instrument, the survey team built a dashboard that allowed for daily monitoring of the general progress of the fieldwork and specific indicators revealing the performance of teams and enumerators over time. For example, indicators included number of control/treatment schools completed, number of instruments completed within each school, average interviewing time of each instrument, time of the day when the pupil tests were conducted, number of pupils interviewed for the scorecard instead of their parents, number of teacher interviews conducted over the phone, number of pupils being replaced, etc. These indicators were constructed in a Stata do-file that ran on the latest cleaned dataset and was then uploaded onto the dashboard (that was created using the visual software, Power BI) that would break down each of the indicators by team, enumerator (where applicable) and week of data collection. This was reviewed on a daily basis by the fieldwork management team and used to feedback to weaker teams and to improve performance.
-Back-checking data
The quality assurance protocol involved visits by the fieldwork management team to the field as well as data back-checks. Two members of the fieldwork management team visited a number of schools and households across 8 of the 12 regions over a two-week period. The purpose was to verify that the school and household interviews were conducted properly, to collect any missing information from these schools or clarify certain issues that were flagged as errors during the daily checking process, and to hold debriefs and retraining sessions with the teams in the field.
-Integration of Analysis and Survey Team
Another central element of QA was the strong integration of the fieldwork management team and the members of the quantitative analysis team, including the overall IE project manager. Members of both teams were involved in the fieldwork preparation and implementation, and in the analysis process which followed.
Quality control and data checking protocols
----------------------------------------------------
At the end of each working day, supervisors collected all interview files from their team members and uploaded them into a shared and organised Dropbox folder that was set up by the data manager. The data manager would receive all files from all nine teams and export them into Stata data files (a statistical programme) and then run daily checks on all files to make sure they are complete and identify potential errors. Several mechanisms were put in place in order to ensure high quality of the data collected during the survey. These are briefly summarised in turn below.
-Selection and supervision of enumerators
As discussed above, each enumerator was supervised at least once by the training team during the training, piloting and first week of data collection. This allowed a well-informed selection of enumerators and their allocation into roles matching individual strengths and weaknesses.
-CAPI built-in routing and validations
One important quality control means in CAPI surveys are the use of automatic routing and checking rules built into the CAPI questionnaires that flag simple errors during the interview, i.e. early enough for them be corrected during the interview. In each CAPI instrument, validations and checks were incorporated in the design in order to significantly reduce errors and inaccuracies during data collection. In addition to having automatic skip patterns built into the design to eliminate errors resulting from wrong skips, the CAPI validations also checked for missing fields, out of range values and simple inconsistencies within instruments.
-Secondary consistency checks and cleaning in Stata
The EL survey exploited another key advantage of CAPI surveys, the immediate availability of data, by running a range of secondary consistencies checks across all data on a daily basis in Stata. Data received from the field was exported to Stata the following day, and a range of do-files were run to assess consistency and completeness, and make corrections if necessary. The checks comprised the following: ID uniqueness and matching across instruments; completeness of observations: target sample size versus actual; and intra- and inter-instrument consistency and out of range checks. The data manager ran the checking do-file on a daily basis on the latest cleaned data. This would return a list of potential issues which the data manager would then investigate and undertake the necessary cleaning actions. Whenever any issue was flagged, effort to obtain an explanation was undertaken either by reviewing enumerator comments or phoning teams. On a daily basis, the data manager collated, shared and discussed all flagged errors with the supervisors in the field, who in turn discussed them with their team members. Throughout the fieldwork, occurrences of errors were monitored in order to keep an eye on the performance of data collectors and constantly provide them with feedback to improve.
-Monitoring fieldwork progress and performance indicators
In addition to the above checks that were specific to each instrument, the survey team built a dashboard that allowed for daily monitoring of the general progress of the fieldwork and specific indicators revealing the performance of teams and enumerators over time. For example, indicators included number of control/treatment schools completed, number of instruments completed within each school, average interviewing time of each instrument, time of the day when the pupil tests were conducted, number of pupils interviewed for the scorecard instead of their parents, number of teacher interviews conducted over the phone, number of pupils being replaced, etc. These indicators were constructed in a Stata do-file that ran on the latest cleaned dataset and was then uploaded onto the dashboard (that was created using the visual software, Power BI) that would break down each of the indicators by team, enumerator (where applicable) and week of data collection. This was reviewed on a daily basis by the fieldwork management team and used to feedback to weaker teams and to improve performance.
-Back-checking data
The quality assurance protocol involved visits by the fieldwork management team to the field as well as data back-checks. Two members of the fieldwork management team visited a number of schools and households across 8 of the 12 regions over a two-week period. The purpose was to verify that the school and household interviews were conducted properly, to collect any missing information from these schools or clarify certain issues that were flagged as errors during the daily checking process, and to hold debriefs and retraining sessions with the teams in the field.
-Integration of Analysis and Survey Team
Another central element of QA was the strong integration of the fieldwork management team and the members of the quantitative analysis team, including the overall IE project manager. Members of both teams were involved in the fieldwork preparation and implementation, and in the analysis process which followed.
Questionnaires
------------------
Questionnaires
------------------
The enumerators administered all of the instruments using Computer Assisted Personal Interviewing (CAPI). All instruments were translated into Kiswahili and administered to all respondents in Kiswahili.
The endline survey round uses a set of survey instruments that retain most of the baseline and midline questions but with some additions to take into account changes in programme context and design and focus of programme implementation. There were two overarching changes to the suite of instruments, compared to the midline set, as follows:
- TDNA instruments dropped: These were designed to measure teachers’ Kiswahili and maths subject knowledge, and were introduced at baseline because one of the original objectives of the early-grade teachers’ in-service training intervention was to strengthen subject knowledge. However, the final design of EQUIP-T’s in-service training chose not to focus on subject knowledge. At endline it made more sense to direct data collection efforts on instruments that are more directly relevant to the programme’s interventions, and so the decision was taken to drop the TDNA.
- New small-group interview with teachers (focused on in-service training): The early-grade teacher in-service training is central to the programme’s theory of change, and has absorbed a large share of the programme’s spending. For this reason, it merits particular focus in the impact evaluation. Attendance at in-service training is already captured in early-grade teacher interviews, but given the high level of teacher turnover that was found at midline, getting a picture of the delivery of in-service training at a school level over the duration of the programme is useful complementary data. By gathering a small group of teachers that have attended the different types of in-service training (3Rs curriculum, Kiswahili, maths and gender-responsive pedagogy), as well as the in-service training coordinator (INCO), this instrument captures the delivery of the various residential in-service training courses, as well as the school-based training sessions.
Apart from these two changes to the group of instruments, these are the main changes that have been made to the other midline instruments:
- Parents of tested standard 3 pupils interview (score card): addition of questions on their child’s pre-school attendance (including school readiness programme (SRP)); communication with
the school; awareness of the parent-teachers partnership (PTP); and corporal punishment.
- Standards 1-3 teacher interview: addition of specific questions on EQUIP-T in-service training modules completed since baseline; attendance at ward cluster reflection meetings and school performance management meetings (SPMMs); outstanding non-salary claims; removal of questions on receipt of salary.
- Standard 2 lesson observation: addition of observations related to gender-responsive pedagogy; use of maths learning materials (not textbooks); display of positive and safe learning campaign related materials.
- Head teacher interview and school records: addition of questions related to initiatives to support pupil welfare (e.g. health, hygiene, safety and child protection); initiatives to support
marginalised groups of pupils (girls, children with disabilities, pupils with learning difficulties, pupils that are vulnerable for other socio-economic reasons); new EQUIP-T interventions since midline (tablet-based SIS, business plans and income-generating activities (IGA), SPMMs, JUU clubs, pupil suggestion boxes); PTP grant spending patterns; more detail on head teacher’s attendance at in-service training; outstanding non-salary claims; removal of questions on receipt of salary, missing ages of baseline pupils, and information for sampling teachers for TDNA.
The revisions to the midline instruments were trialled during a pre-test held in February 2018.
Refer to the scope section for a description of information collected under each instrument.
Questionnaires
------------------
The enumerators administered all of the instruments using Computer Assisted Personal Interviewing (CAPI). All instruments were translated into Kiswahili and administered to all respondents in Kiswahili.
The endline survey round uses a set of survey instruments that retain most of the baseline and midline questions but with some additions to take into account changes in programme context and design and focus of programme implementation. There were two overarching changes to the suite of instruments, compared to the midline set, as follows:
- TDNA instruments dropped: These were designed to measure teachers’ Kiswahili and maths subject knowledge, and were introduced at baseline because one of the original objectives of the early-grade teachers’ in-service training intervention was to strengthen subject knowledge. However, the final design of EQUIP-T’s in-service training chose not to focus on subject knowledge. At endline it made more sense to direct data collection efforts on instruments that are more directly relevant to the programme’s interventions, and so the decision was taken to drop the TDNA.
- New small-group interview with teachers (focused on in-service training): The early-grade teacher in-service training is central to the programme’s theory of change, and has absorbed a large share of the programme’s spending. For this reason, it merits particular focus in the impact evaluation. Attendance at in-service training is already captured in early-grade teacher interviews, but given the high level of teacher turnover that was found at midline, getting a picture of the delivery of in-service training at a school level over the duration of the programme is useful complementary data. By gathering a small group of teachers that have attended the different types of in-service training (3Rs curriculum, Kiswahili, maths and gender-responsive pedagogy), as well as the in-service training coordinator (INCO), this instrument captures the delivery of the various residential in-service training courses, as well as the school-based training sessions.
Apart from these two changes to the group of instruments, these are the main changes that have been made to the other midline instruments:
- Parents of tested standard 3 pupils interview (score card): addition of questions on their child’s pre-school attendance (including school readiness programme (SRP)); communication with
the school; awareness of the parent-teachers partnership (PTP); and corporal punishment.
- Standards 1-3 teacher interview: addition of specific questions on EQUIP-T in-service training modules completed since baseline; attendance at ward cluster reflection meetings and school performance management meetings (SPMMs); outstanding non-salary claims; removal of questions on receipt of salary.
- Standard 2 lesson observation: addition of observations related to gender-responsive pedagogy; use of maths learning materials (not textbooks); display of positive and safe learning campaign related materials.
- Head teacher interview and school records: addition of questions related to initiatives to support pupil welfare (e.g. health, hygiene, safety and child protection); initiatives to support
marginalised groups of pupils (girls, children with disabilities, pupils with learning difficulties, pupils that are vulnerable for other socio-economic reasons); new EQUIP-T interventions since midline (tablet-based SIS, business plans and income-generating activities (IGA), SPMMs, JUU clubs, pupil suggestion boxes); PTP grant spending patterns; more detail on head teacher’s attendance at in-service training; outstanding non-salary claims; removal of questions on receipt of salary, missing ages of baseline pupils, and information for sampling teachers for TDNA.
The revisions to the midline instruments were trialled during a pre-test held in February 2018.
Refer to the scope section for a description of information collected under each instrument.
Data collector(s)
| Name | Abbreviation |
|---|---|
| Oxford Policy Management Ltd | OPM |
Data Processing
Data editing
Given the data was electronically collected, it was continually checked, edited and processed throughout the survey cycle.
A first stage of data checking was done by the survey team which involved (i) checking of all IDs; (ii) checking for missing observations; (iii) checking for missing item responses where none should be missing; and (iv) first round of checks for inadmissible/out of range and inconsistent values. See section 'Supervision' for more details. Additional data processing activities were performed at the end of data collection in order to transform the collected cleaned data into a format that is ready for analysis. The aim of these activities was to produce reliable, consistent and fully-documented datasets that can be analysed throughout the survey and archived at the end in such a way that they can be used by other data users well into the future. Data processing activities involved:
- Computing and merging in the sampling weights,
- Reshaping datasets in order to produce data files for each unit of observation,
- Anonymising data by removing all variables that identify respondents such as names, address, GPS coordinates, etc.,
- Classifying non-response and coding them using a pre-determined classification scheme,
- Reviewing 'Other (specify)' responses by checking if any of the responses fall into existing response categories and can be recoded into the existing category or if there are multiple similar other responses that warrant the creation of a new response category (a decision to be made by the data analysts), and
- Properly naming and labelling the variables in each dataset.
The datasets were then sent to the analysis team where they were subjected to a second set of checking and cleaning activities. This included checking for out of range responses and inadmissible values not captured by the filters built into the CAPI software or the initial data checking process by the survey team.
A comprehensive data checking and analysis system was created including a logical folder structure, the development of a detailed data analysis guide and template syntax files (in Stata), to ensure data checking and cleaning activities were recorded, that all analysts used the same file and variable naming conventions, variable definitions, disaggregation variables and weighted estimates appropriately.
A first stage of data checking was done by the survey team which involved (i) checking of all IDs; (ii) checking for missing observations; (iii) checking for missing item responses where none should be missing; and (iv) first round of checks for inadmissible/out of range and inconsistent values. See section 'Supervision' for more details. Additional data processing activities were performed at the end of data collection in order to transform the collected cleaned data into a format that is ready for analysis. The aim of these activities was to produce reliable, consistent and fully-documented datasets that can be analysed throughout the survey and archived at the end in such a way that they can be used by other data users well into the future. Data processing activities involved:
- Computing and merging in the sampling weights,
- Reshaping datasets in order to produce data files for each unit of observation,
- Anonymising data by removing all variables that identify respondents such as names, address, GPS coordinates, etc.,
- Classifying non-response and coding them using a pre-determined classification scheme,
- Reviewing 'Other (specify)' responses by checking if any of the responses fall into existing response categories and can be recoded into the existing category or if there are multiple similar other responses that warrant the creation of a new response category (a decision to be made by the data analysts), and
- Properly naming and labelling the variables in each dataset.
The datasets were then sent to the analysis team where they were subjected to a second set of checking and cleaning activities. This included checking for out of range responses and inadmissible values not captured by the filters built into the CAPI software or the initial data checking process by the survey team.
A comprehensive data checking and analysis system was created including a logical folder structure, the development of a detailed data analysis guide and template syntax files (in Stata), to ensure data checking and cleaning activities were recorded, that all analysts used the same file and variable naming conventions, variable definitions, disaggregation variables and weighted estimates appropriately.
Data access
Access authorities
| Name | URI | |
|---|---|---|
| Oxford Policy Management Ltd | admin@opml.co.uk | http://www.opml.co.uk/ |
Access conditions
The datasets have been anonymised and are available as a Public Use Dataset. They are accessible to all for statistical and research purposes only, under the following terms and conditions:
1. The data and other materials will not be redistributed or sold to other individuals, institutions, or organisations without the written agreement of Oxford Policy Management Ltd.
2. The data will be used for statistical and scientific research purposes only. They will be used solely for reporting of aggregated information, and not for investigation of specific individuals or organisations.
3. No attempt will be made to re-identify respondents, and no use will be made of the identity of any person or establishment discovered inadvertently. Any such discovery would immediately be reported to Oxford Policy Management Ltd.
4. No attempt will be made to produce links among datasets provided by Oxford Policy Management Ltd, or among data from Oxford Policy Management Ltd and other datasets that could identify individuals or organisations.
5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from Oxford Policy Management Ltd will cite the source of data in accordance with the Citation Requirement provided with each dataset.
6. An electronic copy of all reports and publications based on the requested data will be sent to Oxford Policy Management Ltd.
The original collector of the data, Oxford Policy Management Ltd, and the relevant funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
1. The data and other materials will not be redistributed or sold to other individuals, institutions, or organisations without the written agreement of Oxford Policy Management Ltd.
2. The data will be used for statistical and scientific research purposes only. They will be used solely for reporting of aggregated information, and not for investigation of specific individuals or organisations.
3. No attempt will be made to re-identify respondents, and no use will be made of the identity of any person or establishment discovered inadvertently. Any such discovery would immediately be reported to Oxford Policy Management Ltd.
4. No attempt will be made to produce links among datasets provided by Oxford Policy Management Ltd, or among data from Oxford Policy Management Ltd and other datasets that could identify individuals or organisations.
5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from Oxford Policy Management Ltd will cite the source of data in accordance with the Citation Requirement provided with each dataset.
6. An electronic copy of all reports and publications based on the requested data will be sent to Oxford Policy Management Ltd.
The original collector of the data, Oxford Policy Management Ltd, and the relevant funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Citation requirements
Oxford Policy Management.Tanzania - Education Quality Improvement Programme Impact Evaluation Endline Survey 2018, Version 2.1 of the public use dataset (July 2019). Ref: TZA_2018_EQUIPIE-EL_v01_M. Downloaded from [url] on [date].
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorised distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Copyrights
(c) 2019, Oxford Policy Management Ltd.
Contacts
Contact(s)
| Name | URI | |
|---|---|---|
| Oxford Policy Management Ltd | admin@opml.co.uk | http://www.opml.co.uk/ |
Metadata production
IDNo
DDI_TZA_2018_EQUIPIE-EL_v01_M
Producers
| Name | Affiliation | Role |
|---|---|---|
| Harb, Jana | Oxford Policy Management Ltd | Data analyst |
Production date
2019-07-02
Version
Version 1 (July 2019)