The paper discussed sources of data. Data is a set of values of qualitative or quantitative variables. Data is facts or figures from which conclusions can be drawn. Before one can present and interpret information, there has to be a process of gathering and sorting data. Just as trees are the raw material from which paper is produced, so too, can data be viewed as the raw material from which information is obtained. It is evident from the above discussion that primary data is an original and unique data, which is directly collected by the researcher from a source such as observations, surveys, questionnaires, case studies and interviews according to his requirements

Abstract

This survey provides information on household income and expenditure leading to measure the levels and changes of the living conditions of the people and to observe the consumption patterns .

Key objectives of the survey - To identify the income patterns in Urban, Rural and Estate Sectors & provinces. - To identify the income patterns by income levels. - Average consumption of food items and non food items - Expenditure patterns by sector and by income level.

Geographic coverage

National coverage.

Analysis unit

Household, Individuals

Universe

For this survey a sample of buildings and the occupants therein was drawn from the whole island

Kind of data

Sample survey data [ssd]

Sampling procedure

A two stage stratified random sample design was used in the survey. Urban, Rural and Estate sectors of the Districts were the domains for stratification. The sample frame was the list of buildings that were prepared for the Census of Population and Housing 2001.

Selection of Primary Sampling Units (PSU's) Primary sampling units are the census blocks prepared for the Census of Population and Housing - 2001. The sample frame, which is a collection of all census blocks in the domain, was used for the selection of primary sampling units. A sample of 500 primary sampling units was selected from the sampling frame for the survey.

Selection of Secondary Sampling Units (SSU's) Secondary Sampling Units are the housing units in the selected 500 primary sampling units (census blocks). From each primary sampling unit 10 housing units (SSU) were selected for the survey. The total sample size of 5000 housing units was selected and distributed among Districts in Sri Lanka.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionaires

       The survey schedule was designed to collect data by household and separate schedules were used for each household identified according to the definition of the household within the housing units selected for the survey. The survey schedule consists three main sections . 

       1. Demographic section 
       2. Expenditure
       3. Income

The Demographic characteristics and usual activities of the inmates belonging to the household were reported in the Demographic section of the schedule (and close relatives temporarily living away are also listed in this section). Expenditure section has two sub sections to report food and non-food consumption data separately. Expenditure incurred on their own decisions by boarders and servants are recorded in the sub section under the Main expenditure section. The income has seven sub sections categorized according to the main sources of income.

Sampling error estimates

The exact differences or sampling error ,varies depending on the particular sample selected and the variability is measured by the standard error of the estimate. There is about a 95% chance or level of confidence that an estimate based on a sample will differ by no more than 1.96 standard errors from the true population value because of sampling error. Analyses relating to the HIES are generally conducted at the 95% level of confidence .

          confidence interval =  Estimate value ± (standard error )*(1.96)

Data appraisal

http://www.statistics.gov.lk/HIES/HIES%202007/introduction%20%20HIES.pdf

By visiting the above website a description about the adjustments for non-response could be read in section 1.2 of the Final report.

Abstract

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Homabay county

Analysis unit

Households

Universe

Adolescent girls aged 15-19 years, parents and the community health volunteers

Sampling procedure

Quantitative Sampling

We estimated a sample size of 1,918 to detect a five percentage-point difference in the use of long term methods between baseline and endline time points at 80% power.As baseline, 23% of the adolescent girls reported that they were using long term methods in Homa Bay county. We sampled three sub counties—Ndhiwa, Homa Bay town and Kasipul for the endline survey. However, as fieldwork was interrupted due to the COVID-19 pandemic, we added one sub county—Karachuonyo sub county—when data collection resumed in September 2020. Sub counties and wards were purposively selected from sub counties that had been prioritized for the ITH program based on availability of ITH affiliated health facilities. The purposive selection of sub counties based on presence of ITH intervention affiliated health facilities meant that urban and peri-urban areas were oversampled due to the concentration of the health facilities in urban/peri-urban areas. In each ward, eight villages that formed the immediate catchment area for each ITH program affiliated health facilities were then selected for the study. We conducted a household listing of all households in each sampled village to identify households with an adolescent girl who met the study's inclusion criteria. Households were then randomly sampled from the list of households with eligible adolescents of age 15-19 years. To be eligible, an adolescent girl had to be aged 15-19 years, resident in the study area for at least six months preceding the study. Accordingly, students who stayed in boarding schools away from their parents were excluded from the study.

Qualitative Sampling

The qualitative component involved in-depth interviews (IDIs) with adolescent girls ages 15-19 years and focus group discussions (FGDs) with parents/adults and CHVs. We conducted IDIs with adolescent girls who had enrolled in the program but dropped out for various reasons, as well as girls who were enrolled and still using t-safe services. In addition, we conducted FGDs with CHVs and parents/adult caretakers of adolescents aged 15-19 years from the program areas. Participants were purposively selected from the villages included in the evaluation study. For the endline study, we conducted 17 IDIs with adolescents who had been enrolled in the ITH program and were receiving services or had dropped from the program. We also conducted two FGDs with CHVs and four FGDs with parents/adultcaretakers of adolescents aged 15-19 years.

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f] for quantitative data collection and Focus Group Discussions and In Depth Interviews for qualitative data collection

Research instrument

An interviewer-administered questionnaire was used to collect data from adolescent girls. The questionnaire included questions on socio-demographic and household characteristics; SRH knowledge and sources of information; sexual activity and relationships; contraceptive knowledge, access, choice and use; and exposure to family planning messages and contraceptive decision making. To assess adolescents’ exposure to the t-safe program we included a series of questions drawn from similar project evaluation surveys as well as t-safe project program monitoring indicators. The questions assessed whether adolescents had ever heard the t-safe program, whether they have ever been contacted by mobilizers, whether they participated in any community event organized by the t-safe mobilizers, whether they received information about SRH through t-safe affiliated organizations Facebook or website, and whether they received SMS or WhatsApp messages focused on SRH from tsafe. For those who responded positively, the survey asked further questions on the sources; from which site on internet or Facebook’ or ‘which person or organization sent you these messages’ and ‘how many times have you received information’. Adolescents were also asked whether they had ever registered to a t-safe or Triggerise platform using a mobile phone after discussing with a mobilizer, after discussing with their peers or family members or by themselves after hearing from some other places. The questionnaire was developed in English and then translated into Kiswahili. Data were collected on android tablets programmed using the Open Data Kit (ODK)-based SurveyCTO platform.

For the qualitative component ;Semi-structured interview guides were developed by experienced researchers in consultation with the program partners for the qualitative interviews (with adolescent girls) and FGDs (with parents/adult caretakers of adolescents and CHVs). The guides included probes to explore adolescents' exposure to the ITH program; their experiences with program's SRH services; their perceptions on quality of services; as well as challenges and barriers to access of SRH services. The guides also included probes on the community’s "support" for adolescents' sexual and reproductive health services and; their perspectives on the effects of the program. The guides were developed in English and then translated into Kiswahili for data collection. The guides were pre-tested during the pilot study.

Cleaning operations

Quantitative data was collected on android tablets programmed using the Open Data Kit (ODK)-based SurveyCTO platform while qualitative data was collected using a recorder.Once quantitative data were confirmed to be complete, the data was approved for synchronization. Data were electronically transmitted to a secure password protected SurveyCTO server at the APHRC office. Backup versions of the data remained in the encrypted and password-protected tablets until the end of field activities when all the data were considered to have been synchronized. Subsequently, tablet was securely and permanently cleaned. Data on the server were retrieved by the data manager and then downloaded for use. For qualitative data, audio recordings from qualitative interviews were transcribed and saved in MS Word format. The transcripts were stored electronically in password protected computers and were only accessible to the evaluation team working on the project.

Response rate

N/A

Sampling error estimates

N/A

These three datasets support the findings of the paper "Open Data Intermediaries in Developing Countries" published in the Journal of Community Informatics. The paper explores the concept of open data intermediaries using the theoretical framework of Bourdieu’s social model, particularly his species of capital. Secondary data on intermediaries from Emerging Impacts of Open Data in Developing Countries research was analysed according to a working definition of an open data intermediary presented in this paper, and with a focus on how intermediaries are able to link agents in an open data supply chain, including to grassroots communities. The study found that open data supply chains may comprise multiple intermediaries and that multiple forms of capital may be required to connect the supply and use of open data. The effectiveness of intermediaries can be attributed to their proximity to data suppliers or users, and proximity can be expressed as a function of the type of capital that an intermediary possesses. However, because no single intermediary necessarily has all the capital available to link effectively to all sources of power in a field, multiple intermediaries with complementary configurations of capital are more likely to connect between power nexuses. This study concludes that consideration needs to be given to the presence of multiple intermediaries in an open data ecosystem, each of whom may possess different forms of capital to enable the use of open data.

Data:

1. Data for 27 Asian cases extracted from the 17 Emerging Impacts of Open Data in Developing Countries case study reports.
2. Data for 4 African cases extracted from the 17 Emerging Impacts of Open Data in Developing Countries case study reports.
3. Tabulated data of findings for types of capital, organisational type and primary source of revenue for each of the 32 open data intermediaries included in the study.

The study sites, study population, and secondary data sources.

Gross enrolment ratio, primary and secondary, male (%) in Myanmar was reported at 87.07 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Myanmar - Gross enrolment ratio, primary and secondary, male - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

This dataset is a compilation and synthesis of secondary data in Puerto Rico corresponding to the following topics: Human population changes near coral reefs, Economic impact of coral reef fishing to jurisdiction, Economic impact of dive/snorkel tourism to jurisdiction, Community well-being, Physical infrastructure, and Governance. Data are collected from a variety of publicly available sources to supplement primary data collected through resident surveys. These secondary data are collected to address topics outside the scope of NCRMP resident surveys, and are collected on an annual basis throughout the US coral reef jurisdictions. The primary data that were collected as part of this study in Puerto Rico are available in the NCEI archive: https://www.ncei.noaa.gov/archive/accession/NCRMP-Socio-PR

The COVID-19 pandemic left India’s social and economic identity trembling with the majority of its brunt being borne by the downtrodden and migrant populations of India. Experts have reported that the sheer scale of the problem at hand could easily be one the largest migration crises India has ever faced with estimates of up to 400 million workers being unable to afford basic necessities (Nair & Verma, 2020). This research study aims to investigate and outline the contributing factors of this crisis by analyzing the overarching impacts of the COVID-19 pandemic on Indian society since the pandemic’s inception. This will be done by specializing data sources and analysis in three foundational aspects: (a) mental health, (b) gender gap, and (c) caste & socioeconomic barriers. The methodology will consist of primary and secondary data sources which when implemented effectively will paint a holistic picture of the adversities and obstacles faced by the migrant laborers. The study’s ultimate aim is to provide comprehensive analysis of the effects of government policies on migrants in India and to suggest detailed reforms in order to create an improved environment for the internal migrants in the country. Utilizing the results of the data analysis and its inferences, we suggest both short-term, and long-term solutions to this rapidly deteriorating migration crisis including: government distribution of COVID-19 vaccinations, providing wage support for migrant workers, and the building of a stronger framework to allow for safer migration throughout the country. Considering the implications and limitations of this research as universal inapplicability of potential solutions continue to persist in political scenarios due to corruption and politicize agendas, the paper discusses how current foundational elements within India can be strengthened in order to better favor the lives of the migrant workers therefore increasing the overall sustainability of the entire Indian workforce and economy.

This dataset is a compilation and synthesis of secondary data in South Florida (Martin, Palm Beach, Broward, Miami-Dade, and Monroe Counties) corresponding to the following topics: Human population changes near coral reefs, Economic impact of coral reef fishing to jurisdiction, Economic impact of dive/snorkel tourism to jurisdiction, Community well-being, Physical infrastructure, and Governance. Data are collected from a variety of publicly available sources to supplement primary data collected through resident surveys. These secondary data are collected to address topics outside the scope of NCRMP resident surveys, and are collected on an annual basis throughout the US coral reef jurisdictions. The primary data that were collected as part of this study in Florida are available in NCEI Accession 0161541.

The purpose of the collection of outpatient health statistics is to monitor, evaluate and plan curative and preventive health care at the primary and secondary level of health care system.

Data on outpatient statistics are an important source of information for population health monitoring indicators
and accessibility of outpatient health care activities in Slovenia. Health care providers collect data for each individual contact of the patients with the health service. It is reported by public and private healthcare providers.

Outpatient health statistics record contacts and services at general practicioners and specialist outpatient activities at the secondary level.

We create a synthetic administrative dataset to be used in the development of the R package for calculating quality indicators for administrative data (see: https://github.com/sook-tusk/qualadmin) that mimic the properties of a real administrative dataset according to specifications by the ONS. Taking over 1 million records from a synthetic 1991 UK census dataset, we deleted records, moved records to a different geography and duplicated records to a different geography according to pre-specified proportions for each broad ethnic group (White, Non-white) and gender (males, females). The final size of the synthetic administrative data was 1033664 individuals.National Statistical Institutes (NSIs) are directing resources into advancing the use of administrative data in official statistics systems. This is a top priority for the UK Office for National Statistics (ONS) as they are undergoing transformations in their statistical systems to make more use of administrative data for future censuses and population statistics. Administrative data are defined as secondary data sources since they are produced by other agencies as a result of an event or a transaction relating to administrative procedures of organisations, public administrations and government agencies. Nevertheless, they have the potential to become important data sources for the production of official statistics by significantly reducing the cost and burden of response and improving the efficiency of such systems. Embedding administrative data in statistical systems is not without costs and it is vital to understand where potential errors may arise. The Total Administrative Data Error Framework sets out all possible sources of error when using administrative data as statistical data, depending on whether it is a single data source or integrated with other data sources such as survey data. For a single administrative data, one of the main sources of error is coverage and representation to the target population of interest. This is particularly relevant when administrative data is delivered over time, such as tax data for maintaining the Business Register. For sub-project 1 of this research project, we develop quality indicators that allow the statistical agency to assess if the administrative data is representative to the target population and which sub-groups may be missing or over-covered. This is essential for producing unbiased estimates from administrative data. Another priority at statistical agencies is to produce a statistical register for population characteristic estimates, such as employment statistics, from multiple sources of administrative and survey data. Using administrative data to build a spine, survey data can be integrated using record linkage and statistical matching approaches on a set of common matching variables. This will be the topic for sub-project 2, which will be split into several topics of research. The first topic is whether adding statistical predictions and correlation structures improves the linkage and data integration. The second topic is to research a mass imputation framework for imputing missing target variables in the statistical register where the missing data may be due to multiple underlying mechanisms. Therefore, the third topic will aim to improve the mass imputation framework to mitigate against possible measurement errors, for example by adding benchmarks and other constraints into the approaches. On completion of a statistical register, estimates for key target variables at local areas can easily be aggregated. However, it is essential to also measure the precision of these estimates through mean square errors and this will be the fourth topic of the sub-project. Finally, this new way of producing official statistics is compared to the more common method of incorporating administrative data through survey weights and model-based estimation approaches. In other words, we evaluate whether it is better 'to weight' or 'to impute' for population characteristic estimates - a key question under investigation by survey statisticians in the last decade. This is a synthetic administrative dataset with only 6 variables to enable the calculation of quality indicators in the R package: https://github.com/sook-tusk/qualadmin See also the user manual. The dataset was created from a 1991 synthetic UK census dataset containing over 1 million records by deleting, moving and duplicating records across geographies according to pre-specified proportions within broad ethnic group and gender. The geography variable includes 6 local authorities but they are completely anonymized and labelled 1,2..6. Other variables are (number of categories in parentheses): sex (2), age groups (14), ethnic groups (5) and employment (3). The final size of the synthetic administrative data is 1033664 individuals. The description of the variables are in the data dictionary that is uploaded with the data.

This dataset is a compilation and synthesis of secondary data in Hawaii corresponding to the following topics: Human population changes near coral reefs, Economic impact of coral reef fishing to jurisdiction, Economic impact of dive/snorkel tourism to jurisdiction, Community well-being, Physical infrastructure, and Governance. Data are collected from a variety of publicly available sources to supplement primary data collected through resident surveys. These secondary data are collected to address topics outside the scope of NCRMP resident surveys, and are collected on an annual basis throughout the US coral reef jurisdictions. The primary data that were collected as part of this study in Hawaii are available in NCEI Accession 0161545.

This table contains some of the science results from the Nuclear Spectroscopic Telescope Array (NuSTAR) Serendipitous Survey. The catalog incorporates data taken during the first 40 months of NuSTAR operation, which provide ~20 Ms of effective exposure time over 331 fields, with an areal coverage of 13 deg². The primary catalog (available as the HEASARC NUSTARSSC table) contains 498 sources (the abstract of the reference paper states that there are 497 sources) detected in total over the 3-24 keV energy range. There are 276 sources with spectroscopic redshifts and classifications, largely resulting from the authors' extensive campaign of ground-based spectroscopic follow-up. The authors characterize the overall sample in terms of the X-ray, optical, and infrared source properties. The sample is primarily composed of active galactic nuclei (AGN), detected over a large range in redshift from z = 0.002 to 3.4 (median redshift z of 0.56), but also includes 16 spectroscopically confirmed Galactic sources. There is a large range in X-ray flux, from log (f_3-24_keV) ~ -14 to -11 (in units of erg s^-1 cm^-2), and in rest-frame 10-40 keV luminosity, from log (L_10-40keV) ~ 39 to 46 (in units of erg s^-1), with a median of 44.1. Approximately 79% of the NuSTAR sources have lower-energy (<10 keV) X-ray counterparts from XMM-Newton, Chandra, and Swift XRT observations. The mid-infrared (MIR) analysis, using WISE all-sky survey data, shows that MIR AGN color selections miss a large fraction of the NuSTAR-selected AGN population, from ~15% at the highest luminosities (L_X > 10⁴⁴ erg s^-1) to ~80% at the lowest luminosities (L_X < 10⁴³ erg s^-1). The authors' optical spectroscopic analysis finds that the observed fraction of optically obscured AGN (i.e., the type 2 fraction) is F_Type2 = 53 (+14, -15) per cent, for a well-defined subset of the 8-24 keV selected sample. This is higher, albeit at a low significance level, than the type 2 fraction measured for redshift- and luminosity-matched AGNs selected by < 10 keV X-ray missions. This table contains the Secondary NuSTAR Serendipitous Source Catalog of 64 sources found using wavdetect to search for significant emission peaks in the FPMA and FPMB data separately (see Section 2.1.1 of Alexander et al. 2013, ApJ, 773, 125) and in the combined A+B data. These sources are listed in Table 7 of the reference paper. This method was developed alongside the primary one (Section 2.3 of the reference paper) in order to investigate the optimum source detection methodologies for NuSTAR and to identify sources in regions of the NuSTAR coverage that are automatically excluded in the primary source detection. The authors emphasize that these secondary sources are not used in any of the science analyses presented in their paper. Nevertheless, these secondary sources are robust NuSTAR detections, some of which will be incorporated in future NuSTAR studies, and for many of them (35 out of the 43 sources with spectroscopic identifications) the authors have obtained new spectroscopic redshifts and classifications through their follow-up program. The X-ray photometric parameters for 4 sources are left blank as in these cases the A+B data prohibit reliable photometric constraints. Additional information on these Secondary Catalog sources that the authors obtained using optical spectroscopy is available in Table 8 of the reference paper (q.v.). This table does NOT contain the the 498 sources in the Primary NuSTAR Serendipitous Source Catalog that were found using the source detection procedure described in Section 2.3 of the reference paper, and that are listed in Table 5 (op. cit.). This table was created by the HEASARC in July 2017 based on the machine-readable version of Table 7 from the reference paper, the Secondary NuSTAR Serendipitous Source Catalog, that was obtained from the ApJ web site. This is a service provided by NASA HEASARC .

The present study investigates primary and secondary sources of organic carbon for Bakersfield, CA, USA as part of the 2010 CalNex study. The method used here involves integrated sampling that is designed to allow for detailed and specific chemical analysis of particulate matter (PM) in the Bakersfield airshed. To achieve this objective, filter samples were taken during thirty-four 23-hr periods between 19 May and 26 June 2010 and analyzed for organic tracers by gas chromatography – mass spectrometry (GC-MS). Contributions to organic carbon (OC) were determined by two organic tracer-based techniques: primary OC by chemical mass balance and secondary OC by a mass fraction method. Radiocarbon (14C) measurements of the total organic carbon were also made to determine the split between the modern and fossil carbon and thereby constrain unknown sources of OC not accounted for by either tracer-based attribution technique. From the analysis, OC contributions from four primary sources and four secondary sources were determined, which comprised three sources of modern carbon and five sources of fossil carbon. The major primary sources of OC were from vegetative detritus (9.8%), diesel (2.3%), gasoline (<1.0%), and lubricating oil impacted motor vehicle exhaust (30%); measured secondary sources resulted from isoprene (1.5%), α-pinene (<1.0%), toluene (<1.0%), and naphthalene (<1.0%, as an upper limit) contributions. The average observed organic carbon (OC) was 6.42 ± 2.33 μgC m-3. The 14C derived apportionment indicated that modern and fossil components were nearly equivalent on average; however, the fossil contribution ranged from 32-66% over the five week campaign. With the fossil primary and secondary sources aggregated, only 25% of the fossil organic carbon could not be attributed. Whereas, nearly 80% of the modern carbon could not be attributed to primary and secondary sources accessible to this analysis, which included tracers of biomass burning, vegetative detritus and secondary biogenic carbon. The results of the current study contributes source-based evaluation of the carbonaceous aerosol at CalNex Bakersfield. This dataset is associated with the following publication: Sheesley, R., P. Dev Nallathamby, J. Surratt, A. Lee, M. Lewandowski, J. Offenberg, M. Jaoui, and T. Kleindienst. Constraints on primary and secondary particulate carbon sources using chemical tracer and 14C methods during CalNex-Bakersfield. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 166: 204-214, (2017).

This dataset is a compilation and synthesis of secondary data in the US Virgin Islands (USVI) corresponding to the following topics: Human population changes near coral reefs, Economic impact of coral reef fishing to jurisdiction, Economic impact of dive/snorkel tourism to jurisdiction, Community well-being, Physical infrastructure, and Governance. Data are collected from a variety of publicly available sources to supplement primary data collected through resident surveys. These secondary data are collected to address topics outside the scope of NCRMP resident surveys, and are collected on an annual basis throughout the US coral reef jurisdictions. The primary data that were collected as part of this study in USVI are available in the NCEI archive: https://www.ncei.noaa.gov/archive/accession/NCRMP-Socio-USVI

This Study Covers and Explores the Cost Leadership, Differentiation, and Focused Strategies on Firms’ Competitiveness within Kandahar Province. the purpose of this study is to determine the influence of Cost Leadership, Differentiation and Focused Strategies on Firm’s Competitiveness in Kandahar Province. The study will concentrate on the influence of Cost Leadership, Differentiation and Focused Strategies on Firms’ Competitiveness. This is a quantitative research and the data for the research have been collected from Primary and Secondary Sources respectively. Primary data have been collected by well-established and circulated questionnaire (x156) and secondary data have been collected from journals, books, chapter notes, and well known internet websites. The data is analyzed by IBM SPSS Statistics Program after collection for deriving the required results and drawing viable conclusions accordingly. The research involved detailed review of published and unpublished literature relevant to the study. The data used within the research is collected from both Primary and Secondary Sources of data. Primary data is collected from well design and structured 5 Point Likert Scale Questionnaire, which is distributed to x156 employees of the AKBF, and Secondary Data is collected from well-known and controlled internet websites, books, journals and chapter notes. The data is Categorized and analyzed in IBM SPSS Statistics after collection in order to draw viable conclusions for the study. The findings of the research conducted that Cost Leadership, Differentiation and Focused Strategies have significance influence and impact on firm’s competitiveness.

This dataset is a compilation and synthesis of secondary data in Guam corresponding to the following topics: Human population changes near coral reefs, Economic impact of coral reef fishing to jurisdiction, Economic impact of dive/snorkel tourism to jurisdiction, Community well-being, Physical infrastructure, and Governance. Data are collected from a variety of publicly available sources to supplement primary data collected through resident surveys. These secondary data are collected to address topics outside the scope of NCRMP resident surveys, and are collected on an annual basis throughout the US coral reef jurisdictions. The primary data that were collected as part of this study in Guam are available in the NCEI archive: https://www.ncei.noaa.gov/archive/accession/NCRMP-Socio-Guam

Purpose: There is a lack of available evidence regarding the treatment pattern of switches and add-ons for individuals aged 65 years or older with epilepsy during the first years from the time they received their first anti-seizure medication because of the lack of valid methods. Therefore, this study aimed to develop an algorithm for identifying switches and add-ons using secondary data sources for anti-seizure medication users.Methods: Danish nationwide databases were used as data sources. Residents in Denmark between 1996 and 2018 who were diagnosed with epilepsy and redeemed their first prescription for anti-seizure medication after epilepsy diagnosis were followed up for 730 days until the end of the follow-up period, death, or emigration to assess switches and add-ons occurred during the follow-up period. The study outcomes were the overall accuracy of the classification of switch or add-on of the newly developed algorithm.Results: In total, 15870 individuals were included in the study population with a median age of 72.9 years, of whom 52.0% were male and 48.0% were female. A total of 988 of the 15879 patients from the study population were present during the 730-day follow-up period, and 988 individuals (6.2%) underwent a total of 1485 medication events with co-exposure to two or more anti-seizure medications. The newly developed algorithmic method correctly identified 9 out of 10 add-ons (overall accuracy 92%) and 9 out of 10 switches (overall accuracy 88%).Conclusion: The majority of switches and add-ons occurred early during the first 2 years of disease and according to clinical recommendations. The newly developed algorithm correctly identified 9 out of 10 switches/add-ons.

The data used for this report was collected by Kevin Woods and based on literature reviews as well as semistructured interviews with RFD officials, academics in Thai universities, and the private sector. In all cases, interviewees were told that that the study was funded by the European Forestry Institute and would likely result in a public report. Unless otherwise noted, data on wood import and export volumes was compiled by James Hewitt for the European Forestry Institute. The primary source of these trade statistics originates from the Customs Department of the Kingdom of Thailand. Additional sources were World Trade Atlas and UN Comtrade. Also, Thai newspapers were an important source for secondary data. Donor reports and web-based newspapers were instrumental in obtaining secondary data. Every effort has been made to provide pertinent analyses and accurate quantitative figures on the Thai forest product trade. Also, it should be clear that while this report strives to be as comprehensive as possible regarding the forest law enforcement, governance and forest products trade in Thailand, some aspects of this initiative may not have been captured.

This paper examines whether green financing creates inclusive growth in Ghana. Quantitative time series data spanning 1990 to 2020 were gathered from secondary sources. Secondary data gathered from World development index, UNDP, UNEP, and IEA were used to establish the link between green financing and inclusive growth in Ghana. CO2 emissions per capita and renewable energy as percentage of total primary energy were used as proxies for green financing whilst human development index, education and life expectancy were used as proxies for inclusive growth. The ARDL techniques were adopted to analyse the data. The study finds that clean energy, CO2 emission reductions and education do not create inclusive growth in Ghana in the short-run. Improvement in the human development index and life expectancy creates inclusive growth both in the short and long run. The study demonstrates that education without appropriate skills and employment avenues would not reduce poverty and spurs on inclusive growth. Purposive sampling approach and desk survey method were adopted to gather the data from the World Bank, United Nations, UNDP, IEA, OECD, IMF, GSS, GLSS, and Ghana Multidimensional Poverty documents for the analysis. Explanatory and descriptive techniques were applied to arrive at the conclusions based on the data collected. To situate this study in context, the human development model was used to link the concepts and variables to arrive at a clear conclusion. This econometric model adopted is convenient for any data size (Odhiambo, 2009). Vector auto regression technique is used and the Granger causality model is applied based on the error-correction mechanisms. The paper used ARDL regression and bound test analysis to assess the interconnection between green financing indicators and inclusive growth. The ARDL regression model works by using one or more independent variables to predict the impacts on dependent variable (Kumari & Yadav, 2018). The connection between dependent variable and one or more independent variables were assessed, and since this paper tested for the impact of green finance indicators on inclusive growth, the use of ARDL regression is fit and proper. The econometric model adopted was: INCGROWTH = β0 +β1CO2EM + β2CLENRG + β3EDU + β4LEXP + β5HDI + εi Methodologically, the estimation focus is on how changes in the green financing might affect inclusive growth in Ghana. It is necessary to expatiate the property of time series variables in the ARDL model to determine how well they work with the preferred estimating method before looking at the results. This is usually done using the Unit Root Test.