This is a collection of statistical projects where I used Microsoft Excel. The definition of each project was given by ProfessionAI, while the statistical analysis part was done by me. More specifically: - customer_complaints_assignment is an example of Introduction to Data Analytics where, given a dataset with complaints of customers of financial companies, tasks about filtering, counting and basic analytics were done; - trades_on_exchanges is a project for Advanced Data Analytics where statistical analysis about trading operations where done; - progetto_finale_inferenza is a project about Statistica Inference where, from a toy dataset about the population of a city, inference analysis was made.

This text aims to foster the reflection and criticism in the process of developing research projects in clinical nutrition. We present aspects regarding the evidence, validity, and reliability of results of studies in this field. Appropriate study planning is critical, from defining the design and type of experiment, going through the ethical aspects, population choice, and calculation of sample size, to the assessment of the feasibility of the risks involved in study execution. Once the information is collected, the next stages correspond to the description of the results, statistical analyses, verification of the consistency of these results, and ultimately their correct interpretation.

Crowdfunding has become one of the main sources of initial capital for small businesses and start-up companies that are looking to launch their first products. Websites like Kickstarter and Indiegogo provide a platform for millions of creators to present their innovative ideas to the public. This is a win-win situation where creators could accumulate initial fund while the public get access to cutting-edge prototypical products that are not available in the market yet.

At any given point, Indiegogo has around 10,000 live campaigns while Kickstarter has 6,000. It has become increasingly difficult for projects to stand out of the crowd. Of course, advertisements via various channels are by far the most important factor to a successful campaign. However, for creators with a smaller budget, this leaves them wonder,

"How do we increase the probability of success of our campaign starting from the very moment we create our project on these websites?"

Data Sources

All of my raw data are scraped from Kickstarter.com.

1. First 4000 live projects that are currently campaigning on Kickstarter (live.csv)

   • Last updated: 2016-10-29 5pm PDT
   • amt.pledged: amount pledged (float)
   • blurb: project blurb (string)
   • by: project creator (string)
   • country: abbreviated country code (string of length 2)
   • currency: currency type of amt.pledged (string of length 3)
   • end.time: campaign end time (string "YYYY-MM-DDThh:mm:ss-TZD")
   • location: mostly city (string)
   • pecentage.funded: unit % (int)
   • state: mostly US states (string of length 2) and others (string)
   • title: project title (string)
   • type: type of location (string: County/Island/LocalAdmin/Suburb/Town/Zip)
   • url: project url after domain (string)

2. Top 4000 most backed projects ever on Kickstarter (most_backed.csv)

   • Last updated: 2016-10-30 10pm PDT
   • amt.pledged
   • blurb
   • by
   • category: project category (string)
   • currency
   • goal: original pledge goal (float)
   • location
   • num.backers: total number of backers (int)
   • num.backers.tier: number of backers corresponds to the pledge amount in pledge.tier (int[len(pledge.tier)])
   • pledge.tier: pledge tiers in USD (float[])
   • title
   • url

See more at http://datapolymath.paperplane.io/

This data package was created 2025-01-27 13:30:01 by NPSTORET and includes selected project, location, and result data. Data contained in the San Francisco Bay Area Network NPSTORET back-end file (SFAN_NPSTORET_BACKEND_20250121.ACCDB) were filtered to include: Organization: - SFAN: San Francisco Bay Area Network Project: - SFAN_I&M: SFAN Long-Term Water Quality Monitoring Program - SFAN_I&M_EXTRA: SFAN Long-Term Water Quality Monitoring Program, Extra Sampling - SFAN_WQ: SFAN Pilot Monitoring for Freshwater Quality Protocol Station: - Include Trip QC And All Station Visit Results Activity Start Date (>=1/1/1901 and <=9/30/2024) Value Status: - Accepted or Certified (exported as Final) or Verified (exported as Final) or Final The data package is organized into five data tables: - Projects.csv - describes the purpose and background of the monitoring efforts - Locations.csv - documents the attributes of the monitoring locations/stations - Results.csv - contains the field measurements, observations, and/or lab analyses for each sample/event/data grouping - HUC.csv - enumerates the domain of allowed values for 8-digit and 12-digit hydrologic unit codes utilized by the Locations datatable - Characteristics.csv - enumerates the domain of characteristics available in NPSTORET to identify what was sampled, measured or observed in Results Period of record for filtered data is 2003-06-18 to 2024-09-25. This data package is a snapshot in time of multiple National Park Service projects. The most current data for these projects, which may be more or less extensive than that in this data package, can be found on the Water Quality Portal at: https://www.waterqualitydata.us/data/Result/search?project=SFAN_I&M https://www.waterqualitydata.us/data/Result/search?project=SFAN_I&M_EXTRA https://www.waterqualitydata.us/data/Result/search?project=SFAN_WQ

Comprehensive YouTube channel statistics for Cement Craft Ideas - DIY Projects, featuring 758,000 subscribers and 209,665,371 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in US. Track 253 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Problem Statement

Kickstarter is a community of more than 10 million people comprising of creative, tech enthusiasts who help in bringing creative project to life. Till now, more than $3 billion dollars have been contributed by the members in fueling creative projects. The projects can be literally anything – a device, a game, an app, a film etc.

Kickstarter works on all or nothing basis i.e if a project doesn’t meet it goal, the project owner gets nothing. For example: if a projects’s goal is $500. Even if it gets funded till $499, the project won’t be a success.

Recently, Kickstarter released its public data repository to allow researchers and enthusiasts like us to help them solve a problem. Will a project get fully funded ?

In this challenge, you have to predict if a project will get successfully funded or not.

Data Description

There are three files given to download: train.csv, test.csv and sample_submission.csv The train data consists of sample projects from the May 2009 to May 2015. The test data consists of projects from June 2015 to March 2017.

Sample dataset associated with report of same name. Past river restoration projects in a variety of programs across all of Reclamation’s regions were evaluated to ascertain the best method of presenting this data. At the end of this project, this dataset was presented to the Enterprise Asset Registry team to be incorporated to the Fish Structures Asset Class layer. Therefore, all data associated with this spreadsheet lives within the Enterprise Asset Registry's geospatial Fish Structures Asset Class layer.

This survey, the sixth in the Bureau of Justice Statistics' program on Law Enforcement and Administrative Statistics (LEMAS), presents information on law enforcement agencies in the United States: state police, county police, special police (state and local), municipal police, and sheriff's departments. Variables include size of the population served by the police or sheriff's department, levels of employment and spending, various functions of the department, average salary levels for uniformed officers, policies and programs, and other matters related to management and personnel.This survey, the sixth in the Bureau of Justice Statistics' program on Law Enforcement and Administrative Statistics (LEMAS), presents information on law enforcement agencies in the United States: state police, county police, special police (state and local), municipal police, and sheriff's departments. Variables include size of the population served by the police or sheriff's department, levels of employment and spending, various functions of the department, average salary levels for uniformed officers, policies and programs, and other matters related to management and personnel.

Hypothesis: The reliability can be adopted to quantitatively measure the sustainability of mega-projects.

Presentation: This dataset shows two scenario based examples to establish an initial reliability assessment of megaproject sustainability. Data were gathered from the author’s assumption with regard to assumed differences between scenarios A and B. There are two sheets in this Microsoft Excel file, including a comparison between two scenarios by using a Fault Tree Analysis model, and a correlation analysis between reliability and unavailability.

Notable findings: It has been found from this exploratory experiment that the reliability can be used to quantitatively measure megaproject sustainability, and there is a negative correlation between reliability and unavailability among 11 related events in association with sustainability goals in the life-cycle of megaproject.

Interpretation: Results from data analysis by using the two sheets can be useful to inform decision making on megaproject sustainability. For example, the reliability to achieve sustainability goals can be enhanced by decrease the unavailability or the failure at individual work stages in megaproject delivery.

Implication: This dataset file can be used to perform reliability analysis in other experiment to access megaproject sustainability.

Context

Building construction projects generate huge amounts of data that can be leveraged to understand improvements in efficiency, cost savings, etc. There are several digital apps on the market that helps construction project managers keep track of the details of the process.

Content

This is a simple data set from a number of construction sites generated from project management field apps that are used for quality, safety a and site management.

Essential there are two files in this data set: - Forms – generated from check list for quality/safety/site management - Tasks – which is an action item typically used for quality snags/defects or safety issues.

Acknowledgements

This data set was donated by Jason Rymer, a BIM Manager from Ireland who was keen to see more construction-related data online to be used to learn

Inspiration

The goal of this data set is to help construction industry professionals to learn how to code and process data.

Index of public-facing analysis projects that use Cambridge data

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This data package was created 2025-03-18 17:05:08 by NPSTORET and includes selected project, _location, and result data. Data contained in the Gulf Coast Network NPSTORET back-end file (GULN_NPSTORET_BE_20250311.ACCDB) were filtered to include: Station: - Include Trip QC And All Station Visit Results Value Status: - Accepted or Certified (exported as Final) or Final The data package is organized into five data tables: - Projects.csv - describes the purpose and background of the monitoring efforts - Locations.csv - documents the attributes of the monitoring locations/stations - Results.csv - contains the field measurements, observations, and/or lab analyses for each sample/event/data grouping - HUC.csv - enumerates the _domain of allowed values for 8-digit and 12-digit hydrologic unit codes utilized by the Locations data table - Characteristics.csv - enumerates the _domain of characteristics available in NPSTORET to identify what was sampled, measured or observed in Results Period of record for filtered data is 1976-05-05 to 2024-12-17. This data package is a snapshot in time of multiple National Park Service projects. The most current data for these projects, which may be more or less extensive than that in this data package, can be found on the Water Quality Portal at: https://www.waterqualitydata.us/data/Result/search?project=BITH_HAR&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET https://www.waterqualitydata.us/data/Result/search?project=BITH_WQ&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET https://www.waterqualitydata.us/data/Result/search?project=GUIS_WQ&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET https://www.waterqualitydata.us/data/Result/search?project=JELA_WQ&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET https://www.waterqualitydata.us/data/Result/search?project=NATR_L1&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET https://www.waterqualitydata.us/data/Result/search?project=NATR_WQ&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET https://www.waterqualitydata.us/data/Result/search?project=PAAL_WQ&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET https://www.waterqualitydata.us/data/Result/search?project=PAIS_WQ&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET https://www.waterqualitydata.us/data/Result/search?project=SAAN_WQ&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET https://www.waterqualitydata.us/data/Result/search?project=VICK_WQ&mimeType=csv&zip=yes&dataProfile=biological&providers=STORET

This is a sample of key infrastructure projects happening in Ontario. Projects will be added and updated on an ongoing basis.

The dataset includes:

• project name
• category
• geography
• status
• target completion date
• description
• result
• area
• region
• address
• postal code
• highway / transit line
• estimated budget
• funding sources

This study recruited first-year students studying BSc Marine Biology and BSc Biology at the University of Portsmouth. As part of a core first-year module designed to train students in a range of essential laboratory techniques, these students undertake a single-instance practical to gain familiarity with dissection techniques and spotted dogfish (Scyliorhinus canicula) morphology. Students work in pairs, receive a specimen for examination, and are asked to complete a workbook regarding anatomical features.

Convenience sampling was conducted to recruit students from this practical to the current study. This utilised the lead author’s previous experience with teaching this activity to ensure study design would not impact learning objectives. This study was undertaken in accordance with the University of Portsmouth Ethics Policy (No: ED182005). All participants were informed of the voluntary and anonymous nature of the study, and of their right to withdraw without any negative repercussions on achievement and progression.

A visual overview of the data collection methodology is provided in Appendix 1. Due to the size of the first-year student cohort (120 students), this practical class had three repeats over a three-day period in January 2020. All classes were delivered by the lead author and supported by the same technician and demonstrating assistant. Each morning, dissection kits and specimens were prepared by the technician to allow one station per student pair. The stations were labelled sequentially so that each dogfish had an individual identification number. Prior to the start of each class, measurements were collected by the lead author and two lab assistants for all dogfish (e.g. total length, fin length, etc). This represented a “ground-truthed” dataset with which to compare student-collected measurements.

Approximately 40 students were timetabled to attend each class. The first class was timetabled to contain only Marine Biology students, the second class contained students from both degree streams, and the third class only contained Biology students. Therefore, ‘pure’ classes were initially selected as Experimental Groups whilst mixed class was kept as a no-treatment Control Group. Although the authors recognise that this does not represent an ideal experimental design, limited institutional resources and timetabling requirements restricted full educator control over this arrangement. This study limitation is further considered in the Discussion. Additionally, there was one case of a student attending the wrong day, resulting in a single mixed pair in one of the Experimental Groups (see Results).

Both Control and Experimental Groups had the same taught material to ensure no unfairness in terms of their education and learning outcomes. This included a description of sexual dimorphism (i.e. where two sexes of the same species exhibit different characteristics), which has been shown to exist in dogfish for a range of anatomical features (Filiz and Taskavak, 2006). This information was used to justify why students were recording measurements from their specimens. However, the Experimental Group was also told that their worksheets would be collected at the end of class to contribute to a scientific study investigating dogfish sexual dimorphism (which is indeed being conducted by the lead author); this was the only orchestrated difference between the Control and Experimental Groups. The importance of collecting accurate scientific measurements was emphasised to all students, regardless of their grouping.

A two-sided worksheet was given to all pairs for in-class completion and return. Each worksheet asked the pairs to indicate the degree stream they were from (Marine, Biology, or Both if a mixed pair) and the day of the week their class occurred. This information was collected to try and explain any underlying differences between students; for example, differing experiences between degree streams or communication between students on differing days. No additional background characteristics or demographic data were collected due to logistical challenges of ensuring student privacy whilst also linking such information to ground-truthed measurement records. Additionally, given the relatively small class sizes, it is unlikely that sufficient sample sizes representative of different demographic groups would have been captured for statistical analysis. The front page of the worksheet was specific to dogfish measurements and group work; it contained a diagram of a dogfish and indicated eight sites along the body where measurements were to be recorded, along with details on the dogfish sex and ID number (Appendix 2). The back page was specific to individual student motivation; it contained six Likert-style questions (one set per student; Table 1) relating to individual perception of practical classes, their confidence in their own technical skills, and their opinions of involving students in RLRPs. In order to provide some context and further interrogate the impact of the experiments, open-text comments were collected from students on the survey about their perception of undertaking the measurements. As these were a brief, adjunct to the research they are not intended for rigorous qualitative analysis (LaDonna et al 2018). Rather these comments were categorised based upon a descriptive interpretation of their core focus (e.g. ‘confidence’). Each statement could have multiple foci, for example if a student talked about gaining confidence but also acknowledged the possible employment benefits of engaging in the activity. These different foci were then quantified to build an understanding of the range of different perceptions across the cohorts.

This data set records the statistical data of agricultural added value of sub projects in Qinghai Province from 1997 to 2005, and the data is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains eight data tables, each of which has the same structure. For example, there are three fields in the data table from 1997 to 1998 Field 1: Indicators Field 2: 1998 Field 3: 1997

BC Energy Regulator Engineering Project approvals may be issued, upon application, under the authority of Section 100 of the Drilling and Production Regulation or Section 97 of the Petroleum and Natural Gas Act, depending on project type. Projects grant the applicant operating latitude, under specific conditions, for the purpose of extracting oil and/or natural gas in the most efficient way that will result in maximization of resource recovery and benefit to the Crown, balanced with surface impact and socio-economic factors. Examples are ?Good Engineering Practice?, allowing increased well density in a poor quality reservoir, or ?Pressure Maintenance Water Flood? to allow injection of water into an oil pool to increase total oil recovery. Spatial data for approved projects are included. Data is updated nightly.

Kickstarter, the popular crowdfunding platform, has seen a significant number of projects fall short of their funding goals. As of January 2025, 376,698 projects failed to reach their targets, with the majority (246,351) achieving only 1-20 percent of their funding objectives. This failure rate underscores the challenges creators face in securing financial backing for their ideas, despite Kickstarter's global reach and billions in pledged funds. Crowdfunding's growing impact Since its launch in 2009, Kickstarter has become a major player in the crowdfunding industry. The number of projects hosted on the platform exceeded 651,000 projects, with pledges surpassing 8.5 billion U.S. dollars. Notably, the most successful project to date, "Surpise! Four Secret Novels by Brandon Sanderson", raised an impressive 41 million U.S. dollars in 2022. These figures highlight the platform's potential for creators to secure substantial funding for their projects. Success rates vary by category While many projects struggle to meet their funding goals, success rates differ significantly across categories. As of January 2025, comics boasted the highest success rate at 67.65 percent, followed by dance at 61.11 percent and theater at 59.72 percent. These statistics suggest that certain creative fields may resonate more strongly with Kickstarter's backer community, potentially offering better odds for project success in these areas.

This data package was created 2024-10-17 13:50:35 by NPSTORET and includes selected project, location, and result data. Data contained in Cumberland Piedmont Network NPSTORET back-end file (CUPN_NPSTORET_BE_2024_03_28.ACCDB) were filtered to include: Project: - ABLI_WQ: CUPN WQ Monitoring, ABLI - CARL_WQ: CUPN WQ Monitoring, CARL - CHCH_WQ: CUPN WQ Monitoring, CHCH - COWP_WQ: CUPN WQ Monitoring, COWP - CUGA_WQ: CUPN WQ Monitoring, CUGA - FODO_WQ: CUPN WQ Monitoring, FODO - GUCO_WQ: CUPN WQ Monitoring, GUCO - KIMO_WQ: CUPN WQ Monitoring, KIMO - LIRI_WQ: CUPN WQ Monitoring, LIRI - MACA_WQ: CUPN WQ Monitoring, MACA - NISI_WQ: CUPN WQ Monitoring, NISI - RUCA_WQ: CUPN WQ Monitoring, RUCA - SHIL_WQ: CUPN WQ Monitoring, SHIL - STRI_WQ: CUPN WQ Monitoring, STRI Station: - Include Trip QC And All Station Visit Results Value Status: - Accepted or Certified (exported as Final) or Final The data package is organized into five data tables: - Projects.csv - describes the purpose and background of the monitoring efforts - Locations.csv - documents the attributes of the monitoring locations/stations - Results.csv - contains the field measurements, observations, and/or lab analyses for each sample/event/data grouping - HUC.csv - enumerates the domain of allowed values for 8-digit and 12-digit hydrologic unit codes utilized by the Locations datatable - Characteristics.csv - enumerates the domain of characteristics available in NPSTORET to identify what was sampled, measured or observed in Results Period of record for filtered data is 2002-07-10 to 2023-09-27. This data package is a snapshot in time of multiple National Park Service projects. The most current data for these projects, which may be more or less extensive than that in this data package, can be found on the Water Quality Portal at: https://www.waterqualitydata.us/data/Result/search?project=ABLI_WQ https://www.waterqualitydata.us/data/Result/search?project=CARL_WQ https://www.waterqualitydata.us/data/Result/search?project=CHCH_WQ https://www.waterqualitydata.us/data/Result/search?project=COWP_WQ https://www.waterqualitydata.us/data/Result/search?project=CUGA_WQ https://www.waterqualitydata.us/data/Result/search?project=FODO_WQ https://www.waterqualitydata.us/data/Result/search?project=GUCO_WQ https://www.waterqualitydata.us/data/Result/search?project=KIMO_WQ https://www.waterqualitydata.us/data/Result/search?project=LIRI_WQ https://www.waterqualitydata.us/data/Result/search?project=MACA_WQ https://www.waterqualitydata.us/data/Result/search?project=NISI_WQ https://www.waterqualitydata.us/data/Result/search?project=RUCA_WQ https://www.waterqualitydata.us/data/Result/search?project=SHIL_WQ https://www.waterqualitydata.us/data/Result/search?project=STRI_WQ

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. To reduce the energy burden on income-qualified households within New York State, NYSERDA offers the EmPower New York (EmPower) program, a retrofit program that provides cost-effective electric reduction measures (i.e., primarily lighting and refrigerator replacements), and cost-effective home performance measures (i.e., insulation air sealing, heating system repair and replacments, and health and safety measures) to income qualified homeowners and renters. Home assessments and implementation services are provided by Building Performance Institute (BPI) Goldstar contractors to reduce energy use for low income households. This data set includes energy efficiency projects completed since January 2018 for households with income up to 60% area (county) median income.

D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 54 percent of the Estimated Annual kWh Savings and 70 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: https://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-EmPower-New-York-Impact-Report.pdf.

This dataset includes the following data points for projects completed after January 1, 2018: Reporting Period, Project ID, Project County, Project City, Project ZIP, Gas Utility, Electric Utility, Project Completion Date, Total Project Cost (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Number of Units, Job Type, Type of Dwelling, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Modeled Energy Savings $ Estimate (USD).

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Abstract

The NPS is nationally-representative household survey which provides measures of poverty, agricultural yields, and other key development indicators. The NPS is an “integrated” household survey, in that it covers a broad range of topics in the same questionnaire – from education and health to crime, gender-based violence and a range of other sections – to allow analysis of the links between sectors and the determinants of development outcomes.

The National Panel Survey (NPS) was designed to meet three principle objectives.

The first, overarching goals was to monitor progress toward the goals set out in the National Strategy for Growth and Poverty Reduction (aka, the MKUKUTA goals) and other national development objectives (MDG, PAF, etc.). The NPS provides high-quality, annual data on a long list of MKUKUTA indicators that is both nationally representative and comparable over time. As such, the NPS is intended to provide a key benchmark for tracking progress on poverty reduction and a wide range of other development indicators.

The second goal of the NPS is to facilitate better understanding of the determinants of poverty reduction in Tanzania. The NPS will enable detailed study of poverty dynamics at two levels. In addition to tracking the evolution of aggregate poverty numbers at the national level in years between Household Budget Surveys, the NPS will enable analysis of the micro-level determinants of poverty reduction at the household level. Panel data will provide the basis for analyzing the causal determinants of income growth, increasing or decreasing yields, improvements in educational achievement, and changes in the quality of public service provision over time by linking changes in these outcomes to household and community characteristics.

A third objective of the NPS is to provide data to evaluate the impact of specific policies and programs. With its national coverage and long time frame, the NPS will provide an ideal platform to conduct rigorous impact evaluations of government and non-government development initiatives. To achieve this goal, the National Bureau of Statistics will need to work in close collaboration with the relevant line ministries to link administrative data on relevant projects to changes in development outcomes measured in the survey.

Geographic coverage

The survey covered all regions and all districts of Tanzania, both mainland and Zanzibar.

Kind of data

Sample survey data [ssd]

Sampling procedure

In order to monitor progress toward the MKUKUTA goals, it was vital that the NPS have a nationally-representative sample design. As such, in 2008/09 the NPS interviewed 3,280 households spanning all regions and all districts of Tanzania, both mainland and Zanzibar.

The sample size of 3,280 households was calculated to be sufficient to produce national estimates of poverty, agricultural production and other key indicators. It will also be possible in the final analysis to produce disaggregated poverty rates for 4 different strata: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar. Alternatively, estimates of most key indicators can be produced at the zone level, as used for the Demographic and Health Survey (DHS) reports and other surveys. There are 7 of these zones in total on the mainland: North, Central, Eastern, South, Southern Highlands, West and Lake. As with any survey though, the confidence of the estimates declines as statistics are disaggregated into smaller zones.

Due to the limits of the sample size it is not possible to produce reliable statistics at the regional or district level.

The guiding principle in the choice of sample size, following standard practice for NBS surveys, was to produce estimates with a 95% confidence interval no larger than 5% of the mean for key indicators. In this case, household consumption and maize yields were used as the basis for those calculations.

The NPS was based on a stratified, multi-stage cluster sample design. The principle strata were Mainland versus Zanzibar, and within these, rural versus urban areas, with a special stratum set aside for Dar es Salaam. Within each stratum, clusters were chosen at random, with the probability of selection proportional to their population size. In urban areas a 'cluster' was defined as a census enumeration area (from the 2002 Population and Housing Census), while in rural areas an entire village was taken as a cluster. This primary motivation for using an entire village in rural areas was for consistency with the HBS 2007 sample which did likewise.

Based on the 2002 Population and Housing Census, rural residents comprise roughly 77% of the population, compared with 63% of the NPS sample. The NPS sample gives slighter greater weight to urban areas due to the higher levels of inequality in these areas, and added difficulty in estimating poverty rates and other statistics. Similarly, Zanzibar comprised roughly 3% of the Tanzanian population in the 2002 census, but constitutes nearly 15% of the NPS sample, so as to allow separate Zanzibar-specific estimates to be presented for most indicators.

Finally, although it has been stressed that the 2008/09 round is the first year of the NPS, the sample design for year 1 was deliberately linked to the 2007 HBS to facilitate comparison between the surveys. On mainland Tanzania, 200 of the 350 in the NPS were drawn from the 2007 HBS sample (this included all 140 rural HBS clusters). Within these 200 HBS clusters, a portion of the (8) households sampled for the NPS were taken from the sample of (24) HBS households in the cluster. (The number of HBS households sampled varied from cluster to cluster, in proportion to the share of the population, as measured through a comprehensive household listing, that had remained stationary in the cluster since the time of the HBS. This was done to ensure that the NPS sample remained nationally representative despite possible non-random attrition of HBS households.)

This design created a panel of approximately 1,200 HBS households - interviewed in both the HBS and NPS - within the total sample of 3,280 NPS households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The main survey instrument of the NPS was the household questionnaire. This was administered to all households in the sample.

General household information – including food consumption and other household expenditure, which is central to poverty measurement – was solicited from the household head or another knowledgeable adult member of the household. In addition, wherever possible, each individual member over 5 years of age was interviewed directly for sections on education, health, labour, and food eaten outside the home.

In addition to the household questionnaire, a separate 46-page agricultural questionnaire was administered to all households with any agricultural activities (including farming, fishing or livestock, or ownership of any shamba even if not under cultivation). The agricultural questionnaire included detailed sections on each plot and each crop under cultivation, as well as information on farm assets, extension services, use and marketing of farm by-products, etc. For a sample of roughly 25% of the farming households, enumerators used GPS devices to directly measure the size of all farming plots.

Finally, apart from the questionnaires administered to households, a separate community questionnaire collected information from village, kitongoji and/or mtaa leaders. The community questionnaire covered topics including local administration and governance and access to basic services.

In a number of places, the NPS questionnaires provide extra detail relevant to MKUKUTA progress that goes beyond the specific indicators outlined in the MKUKUTA monitoring framework. In such cases, additional tables and statistics have been presented – in the relevant sections of the report – as a way of providing a deeper understanding of the process at work underlying progress on the core indicators. Key examples here are the enormous detail available on smallholder farming activities, which go far beyond the basic MKUKUTA indicators on technology usage and food production, and the in depth questions in the NPS on genderbased violence.