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PMA2020 Uganda Round 6 Service Delivery Point Survey (2018)
No full textUganda Round 6 Service Delivery Point (SQ) survey used a two-stage cluster design with urban-rural and region as strata. The project used the same set of 110 enumeration areas (EAs) as those that were selected in the previous round and drawn by the Uganda Bureau of Statistics from its master sampling frame. In each EA, health facilities were listed and mapped. The final sample (and completion rates) included 342 health facilities. Data collection was conducted between April to May 2018. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/vfbp-bz42</a
PMA Uganda Phase 3 Service Delivery Point Survey (2022)
No full textPMA Uganda Phase 3 Service Delivery Point Baseline Survey includes 141 enumeration areas (EAs) selected using a multi-stage stratified cluster design with urban-rural and region strata. Of the 141 enumeration areas, 19 new enumeration areas were added for a CIFF (Children’s Investment Fund Foundation) sponsored study that are now part of the PMA Uganda cross-sectional sample. The results are representative at the national level and within urban/rural strata. The final sample included 382 facilities which completed the interview. Data collection was conducted between September and October 2022. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/f0vf-qd73</a
PMA2020 Kenya Round 5 Service Delivery Point Survey (2016)
No full textKenya Round 5 Service Delivery Point (SQ) survey used a two-stage cluster design with urban and rural and county as strata. A sample of 151 enumeration areas (EAs) was drawn by the Kenya Bureau of Statistics from its master sampling frame. The round 5 sample included the addition of two new counties, Kakamega and West Pokot, and a new set of enumeration areas was selected, adjacent to the areas enumerated in the first four survey rounds. Each EA was listed and mapped. Public facilities were included if a selected EA fell within the catchment area. Private facilities were included if they fell within the boundaries of the EA. Data collection was conducted between November and December, 2016. The final completed sample was 410 SDPs. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/vfbp-bz42</a
PMA2020 Nigeria (Oyo) Round 1 Household & Female Survey (2014)
No full textNigeria (Oyo) Round 1 Household and Female (HQFQ) survey a two-stage cluster design. A sample of 80 enumeration areas (EAs) was drawn from the National Population Commission’s master sampling frame. In each EA households and private health facilities were listed and mapped, with 35 households randomly selected. Households were surveyed and occupants enumerated. All eligible females age 15 to 49 were contacted and consented for interviews. The final sample included 2,590 households and, 1,875 de facto females. Data collection was conducted between November and December 2017. The Oyo state was not included in the national estimates since the Oyo data collection was done separately and its sampling method differed from those of other states. Thus, data for Oyo state is released separately in a different dataset. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/b88s-zx32</a
PMA Nigeria (Kano, Lagos) Phase 1 Client Exit Interview (2020)
No full textPMA Nigeria (Kano & Lagos) Phase 1 Client Exit Interview includes a total of 77 clusters of enumeration areas (EAs) from Kano and Lagos. The EAs were drawn using the stratified cluster design with urban-rural strata from the National Population Commission’s master sampling frame. In each cluster of EAs, households and private health facilities were listed and mapped. Each cluster of EAs serves as the primary sampling unit from which 35 households and up to 3 private health facilities were randomly selected. Public facilities were included if a selected EA fell within the catchment area. The results are state-level representative. The final sample included 548 (100%) clients from Kano and 467 (99.8%) clients from Lagos, who completed the interview. Data collection was conducted between December 2019 and January 2020. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/1hrp-p268</a
PMA2020 Democratic Republic of Congo (Kinshasa, Kongo Central) Round 7 Service Delivery Point Survey (2018)
No full textDemocratic Republic of Congo (Kinshasa) Round 7 Service Delivery Point (SQ) survey used a two-stage cluster design to draw a sample of 58 enumeration areas (EA) representative for the city of Kinshasa, using selection probabilities proportional to EA size. Each EA was listed and mapped. Private and public service delivery points (SDP) who provide services to the EA were interviewed. The final sample included 186 SDPs. Data collection was conducted between October and November 2018.Democratic Republic of Congo (Kongo Central) Round 7 Service Delivery Point (SQ) survey used a two stage cluster design to draw a representative sample for the province of Kongo Central. A total of 52 enumeration areas (EA) were randomly sampled using probabilities proportional to size. Each EA was listed and mapped. Private and public service delivery points (SDP) who provide services to the EA were interviewed. The final sample included 124 SDPs. Data collection was conducted between October and November 2018. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/vfbp-bz42</a
PMA2020 Ghana Round 1 Service Delivery Point Survey (2013)
No full textGhana Round 1 Service Delivery Point (SQ) survey used a two-stage cluster design with urban and rural, major ecological zones as the strata. A sample of 100 enumeration areas (EAs) was drawn by the Ghana Statistical Service from its master sampling frame. Each EA was listed and mapped. Public facilities were included if a selected EA fell within the catchment area. Private facilities were included if they fell within the boundaries of the EA. Data collection was conducted between September and October, 2013. The final completed sample in Ghana Round 1 was 138 SDPs. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/vfbp-bz42</a
PMA2020 Nigeria Round 5 Service Delivery Point Survey (2018)
No full textNigeria (National) Round 5 Service Delivery Point (SQ) survey used a three-stage sampling approach within a sample of seven states - Anambra, Kaduna, Kano, Lagos, Nasarawa, Rivers, Taraba. One state per zone was selected using probability proportional to size from among each of Nigeria’s six zones. The seventh state (Kaduna) was allocated to the northwest zone. A total of 274 clusters of enumeration areas (EAs) were drawn from the National Population Commission’s master sampling frame. In each cluster of EAs, households and private health facilities were listed and mapped; public facilities were included if a selected EA fell within the catchment area. Private facilities were included if they fell within the boundaries of the EA. The final sample included 689 SDPs. Data collection was conducted between April and May 2018 in all states. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/vfbp-bz42</a
PMA Nigeria (Kano, Lagos) Phase 4 Service Delivery Point Survey (2024)
No full textPMA Nigeria (Kano & Lagos) Phase 4 Service Delivery Point Survey includes 25 enumeration areas (EAs) in Kano and 52 EAs in Lagos. The EAs were drawn using the same stratified cluster design with urban-rural strata from the National Population Commission’s master sampling frame. The results are representative at the state level. The final sample included 68 facilities in Kano and 135 facilities in Lagos which completed the interview. Data collection was conducted between December 2023 and January 2024. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/f0vf-qd73</a
Data associated with the publication: Anti-forensics under scrutiny: assessing the effectiveness of digital obfuscation in the cloud
No full textTo spur research of digital forensics in the cloud, this data set comprises virtual machine (VM) images that include anti-forensic techniques. We provisioned VMs using Amazon's Elastic Compute Cloud (EC2); populated them with user activity; and applied various techniques to destroy, disrupt, and obfuscate the activity patterns. We then obtained the VM's disk storage as "evidence" in a common forensics format