Abstract

The Quality Preschool for Ghana Impact Evaluation 2016, Midline survey (QP4G-ML 2016) was approved by the Strategic Impact Evaluation Fund (SIEF) of the World Bank on August 2015 in the Great Accra Region of Ghana. The official project name is called "Testing and scaling-up supply- and demand-side interventions to improve kindergarten educational quality in Ghana”, known as “Quality Preschool for Ghana (QP4G)”.

The project seeks to increase the quality of preschool education during the two years of universal Kindergarten (KG) in Ghana through intervening in the supply-side (i.e., teacher in-service training) and the demand side (i.e., increasing parental awareness for developmentally appropriate quality early education).

The primary goal of the impact evaluation is to test the efficacy of a potentially scalable (8-day) in-service teacher training to improve the quality of KG teacher practices and interactions with children and to improve children’s development, school readiness and learning in both private and public preschools in the Greater Accra Region of Ghana. Additional goals of this evaluation are: to test the added value of combining a scalable (low-cost) parental awareness intervention with teacher in-service training; to compare implementation challenges in public and private schools; and to examine several important sources of potential heterogeneity of impact, primarily impacts in public vs. private schools.

The current submission is for the Midline Survey, conducted with 3 types of respondents across two phases – School survey and Caregiver [household] surveys. The school survey was conducted from May to July 2016 and consisted of collecting the following data: (a) direct assessments of children’s school readiness, (b) surveys of KG teachers, (c) direct observation of inventory of facilities within KG classrooms [environmental scan]; videotaping of KG classroom processes, teaching, and learning (not being submitted); as well as video coding of KG classroom video recordings using Teacher Instructional Practices and Processes Systems (instrument not being submitted). The caregiver survey was conducted via phone from August to September 2016 on primary caregivers of KG children. The caregiver survey sought information on caregivers’ background, poverty status, involvement or participation in school and home activities, and perception about ECD. Overall, the Midline Survey was conducted from May to September 2016 for all respondents.

Geographic coverage

Urban and Peri-Urban Districts, Greater Accra Region

Analysis unit

Units of analysis include individuals (KG teachers, children, caregivers), KG classrooms and preschools.

Universe

The survey universe is 6 poor districts in the Greater Accra Region. We sampled 240 schools, 108 public (Govt.) schools and 132 private schools. The population of interest is KG teachers and children in KG 1 and KG 2 classrooms in these schools, as well as the caregivers of sampled students.

Kind of data

Sample survey data [ssd]

Sampling procedure

This impact evaluation applies a cluster-randomized design. Eligible schools were randomly selected to participate in the study. The eligible population was schools with KG 1 and KG 2 classrooms (the two years of universal preprimary education) in six districts in the Greater Accra Region. In these six districts, we have sampled 240 schools; 108 public schools and 132 private schools in total.

The unit of randomization for this randomized control trial (RCT) is schools, whereby eligible schools (stratified by public and private sector schools) are randomly assigned to: (1) in-service teacher-training program only; (2) in-service teacher-training program plus parental awareness program; or (3) control (current standard operating) condition.

The sampling frame for this study was based on data in the Education Management Information System (EMIS) from the Ghana Education Service. This data was verified in a 'school listing exercise' conducted in May 2015.

Sample selection was done in four stages: The first stage involved purposive selection of six districts within the region based on two criteria: (a) most disadvantaged (using UNICEF's District League Table scores, out of sixteen total districts); and (b) close proximity to Accra Metropolitan for travel for the training of the KG teachers. The six selected municipals were La Nkwantanang-Madina Municipal, Ga Central Municipal, Ledzokuku-Krowor Municipal, Adentan Municipal, Ga South Municipal and Ga East Municipal.

The second stage involved the selection of public and private schools from each of the selected districts in the Accra region. We found 678 public and private schools (schools with kindergarten) in the EMIS database. Of these 361 schools were sampled randomly (stratified by district and school type) for the school listing exercise, done in May 2015. This was made up of 118 public schools and 243 private schools. The sampling method used for the school listing exercise was based on two approaches depending on the type of school. For the public schools, the full universe of public schools (i.e., 118) were included in the school listing exercise. However, private schools were randomly sampled using probability proportional to the size of the private schools in each district. Specifically, the private schools were sampled in each district proportionate to the total number of district private schools relative to the total number of private schools. In so doing, one school from the Ga South Municipal was removed and added to Ga Central so that all districts have a number of private schools divisible by three. This approach yielded 122 private schools. Additionally, 20 private schools were randomly selected from each of the districts (i.e., based on the remaining list of private schools in each district following from the first selection) to serve as replacement lists. The replacement list was necessary given the potential refusals from the private schools. There were no replacement lists for the public schools since all public schools would automatically qualify for participation.

The third stage involved selecting the final sample for the evaluation using the sampling frame obtained through the listing exercise. A total of 240 schools were randomly selected, distributed by district and sector. Schools were randomized into treatment groups after the first round of baseline data collection was completed.

The survey respondents were sampled using different sampling techniques: a. KG teachers: The research team sampled two KG teachers from each school; one from KG1 and KG2. KG teachers were sampled using purposive sampling method. In schools where there were more than two KG classes, the KG teachers from the "A" stream were selected. For the treatment schools, all KG teachers were invited to participate in the teacher training program. b. KG child-caregiver pair: The research team sampled KG children and their respective caregivers using simple random sampling method. Fifteen KG children-caregivers pair were sampled from each school. For schools with less than 15 KG children (8 from KG1, 7 from KG2 where possible), all KG children were included in the survey. KG children were selected from the same class as the selected KG teacher. The survey team used the class register to randomly select KG children who were present on the day of the school visit. Sampling was not stratified by gender or age. The caregivers of these selected child respondents were invited to participate in the survey. The research team sought informed consent from the school head teacher, caregivers, as well as child respondents.

Mode of data collection

Other [oth]

Research instrument

Data were collected at Midline Survey using structured questionnaires or forms.

Child Direct Assessment: The KG Child Assessment was conducted using the International Development and Early Learning Assessment (IDELA) tool designed by Save the Children. IDELA was adapted based on extensive pre-testing and piloting by different members of the evaluation team. The adapted version measured five indicators of ECD. The indicators were early numeracy skills, language/literacy skills and development, physical well-being and motor development, socio-emotional development, and approaches to learning. IDELA contained 28 items. In addition, one task was added – the Pencil Tap – to assess executive function skills. Apart from the English language, IDELA was translated and administered into three local languages, namely, Twi, Ga, and Ewe. These local language versions had gone through rigorous processes of translation and back translation. The IDELA tool has not been shared as Save the Children have proprietary rights over this.

KG Class Environmental Scan: The KG classroom observation involved taking inventories of the KG classrooms [environmental scan] and conducting video recordings of the classroom processes. The KG Class Environmental Scan tool was designed to take inventories of the facilities in the KG classrooms. The classroom video recordings have not been shared as they contain PIIs.

TIPPS: The video recordings taken during the classroom observations were coded using an early childhood education adapted version of Teacher Instructional Practices and Processes Systems (TIPPS). Seidman, Raza, Kim, and McCoy (2014) of New York University developed the TIPPS instrument. TIPPS observes nineteen key concepts of teacher practices and classroom processes that influence children’s cognitive and social-emotional development. The concept sheet was used to code the kindergarten classroom videos. The TIPPS tool has not been shared as

This repository contains the replication-ready survey dataset used in Zhao & Liu (2025): *Explaining the Trust Paradox: How Foreign Media Strengthens Government Confidence via Political–Economic Awareness in China*.

* `data.csv` – Cleaned respondent-level dataset (N = 3,788; 110 variables) used for all analyses reported in the article. Personally identifiable information has been removed in compliance with the CNSDA license.
* `codebook.pdf` – Variable names, wording, scales, and basic descriptive statistics. *(to be added by uploader if needed)*

The dataset originates from the publicly available "2021 Internet Users' Social Awareness Survey" released by the Chinese National Survey Data Archive (CNSDA). We performed basic cleaning (variable renaming, numeric recoding, and removal of direct identifiers). Cleaning scripts are available upon request.

Abstract

Innovative firms with good ideas may still struggle to fine-tune them to the stage where they can attract outside funding. We conduct a five-country randomized experiment that tests the impact of an investment readiness program. Firms then pitched their ideas to independent judges. The program resulted in a 0.3 standard deviation increase in the investment readiness score. Two years later, the average impacts on firm investment outcomes are positive, but small in magnitude, and not statistically significant. Larger and statistically significant impacts on receiving outside funding occur for smaller firms, and for firms with lower likelihoods of otherwise being funded.

Geographic coverage

Firms which entered the investment readiness program were drawn from Croatia, Kosovo, Macedonia, Montenegro and Serbia

Universe

Firms which applied to the investment readiness program

Kind of data

Sample survey data [ssd]

Sampling procedure

To participate in the program, a firm had to be legally registered in at least one of the five countries: Croatia, Kosovo, Macedonia, Montenegro or Serbia. The firm had to be a micro, small, or medium-enterprise, defined as having fewer than 250 employees, and an annual turnover below 50 million euros. It had to be innovative, meaning that “it will in the foreseeable future develop products, services, or processes which are new or substantially improved compared to the state of the art in its industry, and which carry a risk of technological or industrial failure”, and could not be on a sanctions list or operating in a set of negative activities (e.g. gambling or alcohol production). Applicants had to apply online, with the data from this application form providing the baseline data for this study. More than 1,200 applications were started online, and a total of 584 full applications were received. These were screened for eligibility, resulting in 346 firms being selected as eligible for the program.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires for the Western Balkans Investment Readiness Surveys were organized as follows: - Application Form : Pioneers of the Balkans - Pioneers of the Balkans - Follow-up survey (spring 2016) - World Bank "Pioneers of the Balkans" - Follow-up Survey 2017

Response rate

Our main outcomes come from two rounds of follow-up surveys, in which we attempted to interview all firms, not only those who had participated in the pitch competition. The first round, intended to measure short-term effects, was taken between April and August 2016, corresponding to a period of approximately six months after the end of the investment readiness program and judging. The overall survey response rate was 79.2 percent, and does not differ significantly between treatment (79.9%) and control (78.5%). In addition, we collected information on operating status, number of employees, and whether negotiations for an outside investment had occurred for a further 12 percent of firms , resulting in basic data being available for 92.2 percent of firms. The second follow-up survey took place between August 2017 and March 2018, corresponding to an average of two years since the intervention. The overall survey response rate for this second follow-up was 85.0 percent, and again does not differ significantly between treatment (86.2%) and control (83.7%), with data on firm operating status and receipt of equity available for 94.5% of firms.

Between 2018 and 2021 PIs for National Science Foundation Awards # 1758781 and 1758814 EAGER: Collaborative Research: Developing and Testing an Incubator for Digital Entrepreneurship in Remote Communities, in partnership with the Tanana Chiefs Conference, the traditional tribal consortium of the 42 villages of Interior Alaska, jointly developed and conducted large-scale digital and in-person surveys of multiple Alaskan interior communities. The survey was distributed via a combination of in-person paper surveys, digital surveys, social media links, verbal in-person interviews and telephone-based responses. Analysis of this measure using SAS demonstrated the statistically significant need for enhanced digital infrastructure and reworked digital entrepreneurial and technological education in the Tanana Chiefs Conference region. 1. Two statistical measures were created during this research: Entrepreneurial Readiness (ER) and Digital Technology needs and skills (DT), both of which showed high measures of internal consistency (.89, .81). 2. The measures revealed entrepreneurial readiness challenges and evidence of specific addressable barriers that are currently preventing (serving as hindrances) to regional digital economic activity. The survey data showed statistically significant correlation with the mixed-methodological in-person focus groups and interview research conducted by the PIs and TCC collaborators in Hughes and Huslia, AK, which further corroborated stated barriers to entrepreneurship development in the region. 3. Data generated by the survey and fieldwork is maintained by the Tanana Chiefs Conference under data sovereignty agreements. The survey and focus group data contains aggregated statistical/empirical data as well as qualitative/subjective detail that runs the risk of becoming personally identifiable especially due to (but not limited to) to concerns with exceedingly small Arctic community population sizes. 4. This metadata is being provided in order to serve as a record of the data collection and analysis conducted, and also to share some high-level findings that, while revealing no personal information, may be helpful for policymaking, regional planning and efforts towards educational curricular development and infrastructural investment. The sample demographics consist of 272 women, 79 men, and 4 with gender not indicated as a response. Barriers to Entrepreneurial Readiness were a component of the measure. Lack of education is the #1 barrier, followed closely by lack of access to childcare. Among women who participated in the survey measure, 30% with 2 or more children report lack of childcare to be a significant barrier to entrepreneurial and small business activity. For entrepreneurial readiness and digital economy, the scales perform well from a psychometric standpoint. The summary scores are roughly normally distributed. Cronbach’s alphas are greater than 0.80 for both. They are moderately correlated with each other (r = 0.48, p < .0001). Men and women do not differ significantly on either measure. Education is significantly related to the digital economy measure. The detail provided in the survey related to educational needs enabled optimized development of the Incubator for Digital Entrepreneurship in Remote Communities. Enhanced digital entrepreneurship training with clear cultural linkages to traditions and community needs, along with additional childcare opportunities are two among several specific recommendations provided to the TCC. The project PIs are working closely with the TCC administration and community members related to elements of culturally-aligned curricular development that respects data tribal sovereignty, local data management protocols, data anonymity and adherence to human subjects (IRB) protocols. While the survey data is currently embargoed and unable to be submitted publicly for reasons of anonymity, the project PIs are working with the NSF Arctic Data Center towards determining pathways for sharing personally-protected data with the larger scientific community. These approaches may consist of aggregating and digitally anonymizing sensitive data in ways that cannot be de-aggregated and that meet agency and scientific community needs (while also fully respecting and protecting participants’ rights and personal privacy). At present the data sensitivity protocols are not yet adapted to TCC requirements and the datasets will remain in their care.

This dataset accompanies the manuscript “Explaining the Trust Paradox: How Foreign Media Strengthens Government Confidence via Political–Economic Awareness—and for Whom—in China” (Zhao & Liu, 2025, submitted to Political Communication). It contains the fully de-identified, replication-ready microdata from the 2021 Internet Users’ Social Awareness Survey (N = 3,788), as well as metadata and documentation files.

Contents:

• data.csv: Cleaned, de-identified respondent-level dataset (all direct identifiers, IP addresses, and timestamps removed).

• README.md: Detailed description of variables, data provenance, and usage instructions.

• LICENSE.txt: Licensing information (CC-BY-4.0 for data, MIT for code).

• CITATION.cff: Citation metadata for automated referencing.

Provenance:

The original survey was conducted by the Chinese National Survey Data Archive (CNSDA). This version has been processed to ensure full compliance with data protection and journal transparency requirements.

The current study aims to examine lecturer readiness for English Medium Instruction (EMI) in higher educational institutions and the contextual influences of gender, age, academic qualification, teaching experience, EMI course teaching involvement, and EMI training. A quantitative research design was employed, and a survey questionnaire was completed by 227 lecturers (out of 250 invited participants) from private universities in Klang Valley, Malaysia to gauge self-ratings of personal knowledge, skills, abilities, and attitudes in educating EMI courses. The collected data were subsequently analysed via the Statistical Package for Social Sciences (SPSS) version 27.0 software before revealing the findings from the inferential statistics of the t-test and one-way analysis of variance (ANOVA) on lecturers’ gender, age, academic qualification, teaching experience, EMI course teaching involvement, and EMI training. Resultantly, the important role of lecturers’ knowledge, understanding, skills, abilities, and attitudes was highlighted to further enhance intercultural communicative competence in managing the increasingly diversified student body in EMI classrooms.

Abstract

The Palestinian society's access to information and communication technology tools is one of the main inputs to achieve social development and economic change to the status of Palestinian society; on the basis of its impact on the revolution of information and communications technology that has become a feature of this era. Therefore, and within the scope of the efforts exerted by the Palestinian Central Bureau of Statistics in providing official Palestinian statistics on various areas of life for the Palestinian community, PCBS implemented the household survey for information and communications technology for the year 2023. The main objective of this report is to present the trends of accessing and using information and communication technology by households and individuals in Palestine, and enriching the information and communications technology database with indicators that meet national needs and are in line with international recommendations.

Geographic coverage

Palestine, West Bank, Gaza strip

Analysis unit

Household, Individual

Universe

All Palestinian households and individuals (10 years and above) whose usual place of residence in 2023 was in the state of Palestine.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of master sample which were enumerated in the 2017 census. Each enumeration area consists of buildings and housing units with an average of about 150 households. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample Size The sample size is 8,040 households.

Sampling Design The sample is three stages stratified cluster (pps) sample. The design comprised three stages: Stage (1): Selection a stratified sample of 536 enumeration areas with (pps) method. Stage (2): Selection a stratified random sample of 15 households from each enumeration area selected in the first stage. Stage (3): Selection one person of the (10 years and above) age group in a random method by using KISH TABLES.

Sample Strata The population was divided by: 1- Governorate (16 governorates, where Jerusalem was considered as two statistical areas) 2- Type of Locality (urban, rural, camps).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaire The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on Individuals (10 years and above) about computer use, access to the Internet, possession of a mobile phone, information threats, and E-commerce.

Cleaning operations

Field Editing and Supervising

• Data collection and coordination were carried out in the field according to the pre-prepared plan, where instructions, models and tools were available for fieldwork. • Audit process on the PC-Tablet is through the establishment of all automated rules and the office on the program to cover all the required controls according to the criteria specified. • For the privacy of Jerusalem (J1) data were collected in a paper questionnaire. Then the supervisor verifies the questionnaire in a formal and technical manner according to the pre-prepared audit rules. • Fieldwork visits was carried out by the project coordinator, supervisors and project management to check edited questionnaire and the performance of fieldworkers.

Data Processing

Programming Consistency Check The data collection program was designed in accordance with the questionnaire's design and its skips. The program was examined more than once before the conducting of the training course by the project management where the notes and modifications were reflected on the program by the Data Processing Department after ensuring that it was free of errors before going to the field.

Using PC-tablet devices reduced data processing stages, and fieldworkers collected data and sent it directly to server, and project management withdraw the data at any time.

In order to work in parallel with Jerusalem (J1), a data entry program was developed using the same technology and using the same database used for PC-tablet devices.

Data Cleaning After the completion of data entry and audit phase, data is cleaned by conducting internal tests for the outlier answers and comprehensive audit rules through using SPSS program to extract and modify errors and discrepancies to prepare clean and accurate data ready for tabulation and publishing.

Response rate

The response rate reached 83.7%.

Sampling error estimates

Sampling Errors Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, there is no problem to disseminate results at the national level and at the level of the West Bank and Gaza Strip.

Non-Sampling Errors Non-Sampling errors are possible at all stages of the project, during data collection or processing. These are referred to non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, as well as practical and theoretical training during the training course.

The implementation of the survey encountered non-response where the case (household was not present at home) during the fieldwork visit become the high percentage of the non-response cases. The total non-response rate reached 16.3%.

Abstract (en): This round of Eurobarometer surveys queried respondents on standard Eurobarometer measures, such as how satisfied they were with their present life, whether they attempted to persuade others close to them to share their views on subjects they held strong opinions about, whether they discussed political matters, what their expectations were for the next 12 months, and how they viewed economic and social issues in their country compared to the European Union (EU). Additional questions focused on the respondents' knowledge of and opinions on the EU, including how well-informed they felt about the it, what sources of information about the EU they used, whether their country had benefited from being an EU member (or would benefit from being a future member), and the extent of their personal interest in EU matters. Another major focus of the surveys was personal data privacy. The survey asked respondents about their knowledge of the rules and requirements in protecting personal data, the ability of the law to protect citizens from entities accessing their information, and whether law enforcement should be able to access personal information for the purpose of fighting crime and terrorism. For the second major focus of the survey, the national economy, respondents were asked to evaluate their personal financial situation and their nation's economy, as well as to estimate the official growth rate (Gross Domestic Product), inflation rate, and unemployment rate, and then to compare these rates to those from previous or future years. Respondents also provided their opinion about the use of statistical information, especially for political decision-making. As a final major focus, respondents were asked about their interest in scientific research including how the media presents information about scientific research and what types of media they access to get information about this topic. Additional questions were asked of respondents in regard to globalization and involvement of the EU in this process, the 50th anniversary of EU achievements, the development of environmental, foreign, and immigration policies, and the European Council presidency. Demographic and other background information includes respondent's age, gender, nationality, origin of birth (personal and parental), marital status, left-to-right political self-placement, occupation, age when stopped full-time education, household composition, ownership of a fixed or a mobile telephone and other durable goods, type and size of locality, region of residence, and language of interview (select countries). Please review the "Weighting Information" section of the ICPSR codebook for this Eurobarometer study. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Citizens of the EU aged 15 and over residing in the 27 EU member countries: Austria, Belgium, Bulgaria, Republic of Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and the United Kingdom, plus the citizens in the two EU candidate countries: Croatia and Turkey, and the citizens in the Turkish Cypriot Community and the Former Yugoslav Republic of Macedonia Smallest Geographic Unit: country Multistage national probability samples. 2010-06-29 The data have been further processed by GESIS. The SPSS, SAS, and Stata setup files, SPSS and Stata system files, SAS transport (CPORT) file, tab-delimited ASCII data file, and codebook have been updated.2008-02-20 Data for all previously-embargoed variables are now available. This collection now contains data for the Former Yugoslav Republic of Macedonia (FYROM), and the addition of seven variables. The data have been further processed by the ZA. The codebook, SPSS, SAS and Stata setup files, SPSS and Stata system files, a SAS transport (CPORT) file, and a tab-delimited ASCII data file have been updated. face-to-face interviewThe original data collection was carried out by TNS Opinion and Social on request of the European Commission.T...

Abstract

The central statistical offices in most countries place heavy emphasis on constructing sound databases for all activities within the services sector. PCBS’ Services Statistics Program is part of the Economic Statistics Program, which is part of the larger program for establishing the System of Official Statistics for Palestine. PCBS initiated, in the reference year 1994, the economic surveys series. The series includes, in addition to the services survey, surveys on industry, internal trade construction-contractors, and transport and storage sectors for the purpose of establishing a time series database of economic activities in line with international recommendations specified in System of National Account (SNA) 93 and in the UN Manual for Services Statistics. The sampling frame for the different economic surveys was based on the findings of the 2007 Establishment Census conducted by PCBS.

Geographic coverage

West Bank and Gaza Strip.

Analysis unit

Enterprise constitutes the primary sampling unit (PSU)

Universe

Sample from Services Enterprises (private sector)

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the Services Survey is a single-stage stratified random - systematic sample in which the enterprise constitutes the primary sampling unit (PSU). Four levels of strata were used to arrive at an efficient representative sample (i.e. economic activity, size of employment and geographical levels, the kind of profit or non profit).

This methodology was used to draw a sample middle enterprises and whole frame for large enterprises based on the framework of large and middle enterprises alone; for small enterprises data were estimated based on a time series of the results of economic surveys.

Sample size was selected in the Palestinian Territories (1,406) enterprises from (2,894), represents the general framework (large and medium-sized enterprises)

Mode of data collection

Face-to-face [f2f]

Research instrument

There is one form of the services survey questionnaire 2011 of the Palestinian Territory, it's related to the non-financial companies sector. The questionnaire contains the following main variables: 1. The persons engaged in enterprise and compensation of these employees. 2. Value of output from the main activity and secondary activity. 3. Production inputs of goods and services. 4. Payments and transfers. 5. Taxes on production. 6. Assets and capital formation.

Cleaning operations

To ensure the quality and consistency of data, a set of measures was introduced as follows: - Creation of a data entry program prior to the collection of data to ensure this would be ready. - A set of validation rules were applied to the program to check the consistency of data. - The efficiency of the program was pre-tested by entering a few questionnaires including incorrect information and checking its efficiency in capturing the incorrect information. - Well-trained data entry personnel were selected and trained for main data entry. - Weekly data files were received by project management to be checked for accuracy and consistency: correction notes were provided to data entry management for implementation.

Response rate

Response rate: 80.7%

Sampling error estimates

Statistical Errors: The findings of the survey are affected by statistical errors due to using sampling in conducting the survey for the units of the target population, which increases the chances of having variances from the actual values we expect to obtain from the data had we conducted the survey using comprehensive enumeration. The variance of the key goods in the survey was computed and dissemination was carried out on the level of the Palestinian Territory for reasons related to sample design and computation of the variance of the different indicators.

Non-Statistical Errors These types of errors could appear on one or all the survey stages that include data collection and data entry. Response errors: these types of errors are related to responders, fieldworkers, and data entry personnel's, and to avoid mistakes and reduce the impact has been a series of actions that would enhance the accuracy of the data through a process of data collection from the field and the data processing.

Abstract

Between 27 March and 17 August 2020, the South African Government imposed and extended a ban on the sale of all tobacco and vaping products, as part of the lockdown to fight the COVID-19 virus. The South African Lockdown Smoking Survey (SALSS) is an online survey that collected data on cigarette smoking behaviour before, during and after this sales ban. The Research Unit on the Economics of Excisable Products (REEP), based at the University of Cape Town, conducted 3 online surveys between 29 April and 16 November to determine how individuals responded to the ban on cigarette sales during the lockdown, and to evaluate how it impacted the cigarette market in South Africa. The first and second surveys (SALSS Round 1 and Round 2) were designed to assess how cigarette smokers responded to the sales ban in terms of smoking prevalence and purchasing behaviour (prices paid, outlet type and brand choice). The third survey (SALSS Round 3) was conducted after the sales ban had been lifted and focused on how the market adjusted after the ban.

Geographic coverage

The survey was advertised to all South Africans using social media, specifically: Twitter (the REEP twitter account), Moya (a data-free (cost-free) instant messenger application) and Change.org (a petition website). As such there was no ex-ante sampling frame or intention to produce statistically representative estimates.

Analysis unit

Individuals

Universe

The SALSS covered all South African adults (aged 18 and older) who were regular cigarette smokers (at least one cigarette per day) at the time the tobacco sales ban was announced.

Kind of data

Qualitative and quantitative data

Sampling procedure

The survey was developed and hosted on the SurveyMonkey website. Social media (Twitter and the petition site www.change.org) was used to advertise the survey and print media outlets were also used in Round 1. Additionally the survey advert was posted on Moya Messenger, a data-free/airtime-free instant messaging service, where user costs were covered by the survey team. Participation was encouraged by offering respondents the opportunity to win a prize.

Mode of data collection

Internet [int]

Research instrument

In each round a single self-administered online survey questionnaire was administered through SurveyMonkey. The Round 1 questionnaire was published in English and Afrikaans. Questionnaires for Rounds 2 and 3 were only published in English.

Cleaning operations

Data collected by REEP was in excel format and in various files depending on the method of contact and interview. These files were collated by DataFirst into a research-ready format.

Notes on data cleaning: · Incomplete surveys are included with a flag to indicate such. · Under 18s have been excluded as they were not supposed to answer the survey. · Prices have been cleaned but those that did not match DataFirst's cleaning algorithms were not dropped but rather stored in a "price - other" variable so that the analyst can decide how to process these. · Similarly, where the respondent indicated being unsure about the price, using words like "around, plus minus" and so on, this is indicated with a flag in the relevant rounds.

Data appraisal

There were some changes to the survey between rounds 2 and 3: in Rounds 1 and 2 answers to questions on prices were open ended, resulting in some messy responses. In Round 3 this question had coded responses. This change may affect the response rate, the accuracy of the responses, and overall continuity. DataFirst coded responses Round 1 and 2 price information questions to improve comparability.

Abstract (en): Since 1972, the General Social Survey (GSS) has been monitoring societal change and studying the growing complexity of American society. The GSS aims to gather data on contemporary American society in order to monitor and explain trends and constants in attitudes, behaviors, and attributes; to examine the structure and functioning of society in general as well as the role played by relevant subgroups; to compare the United States to other societies in order to place American society in comparative perspective and develop cross-national models of human society; and to make high-quality data easily accessible to scholars, students, policy makers, and others, with minimal cost and waiting. GSS questions include such items as national spending priorities, marijuana use, crime and punishment, race relations, quality of life, and confidence in institutions. Since 1988, the GSS has also collected data on sexual behavior including number of sex partners, frequency of intercourse, extramarital relationships, and sex with prostitutes. In 1985 the GSS co-founded the International Social Survey Program (ISSP). The ISSP has conducted an annual cross-national survey each year since then and has involved 58 countries and interviewed over one million respondents. The ISSP asks an identical battery of questions in all countries; the U.S. version of these questions is incorporated into the GSS. The 2016 GSS added in new variables covering information regarding social media use, suicide, hope and optimism, arts and culture, racial/ethnic identity, flexibility of work, spouses work and occupation, home cohabitation, and health. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. All noninstitutionalized, English and Spanish speaking persons 18 years of age or older, living in the United States. Smallest Geographic Unit: census region For sampling information, please see Appendix A of the ICPSR Codebook. computer-assisted personal interview (CAPI), face-to-face interview, telephone interview Please note that NORC may have updated the General Social Survey data files. Additional information regarding the General Social Surveys can be found at the General Social Survey (GSS) Web site.

Abstract

The Global Adult Tobacco Survey (GATS) 2014, a component of Global Tobacco Surveillance System (GTSS), is a global standard for systematically monitoring adult tobacco use and tracking key tobacco control indicators. GATS is designed to produce national and sub-national estimates among adults across countries. The target population includes all non-institutionalized men and women 15 years of age or older who consider the country to be their usual place of residence. All members of the target population will be sampled from the household that is their usual place of residence. GATS is intended to enhance the capacity of countries to design, implement and evaluate tobacco control interventions.

Geographic coverage

National coverage

Analysis unit

Individual

Universe

Non-institutionalized men and women 15 years of age or older

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

The GATS contains Household Questionnaire and Individual Questionnaire. These questionnaires are administered using an electronic data collection device.

Using the IPAQ to Manage Household Assignments and Input Data. Screening and Respondent Selection: The Household Questionnaire Administering the Individual Questionnaire Transmitting Data Using SD CARDs

Section A. Background Characteristics Section B. Tobacco Smoking Section C. Smokeless Tobacco Section D1. Cessation - Tobacco Smoking Section D2. Cessation - Smokeless Tobacco Section E. Secondhand Smoke Section F. Economics - Manufactured Cigarettes Section G. Media Section H. Knowledge, Attitudes & Perceptions

Cleaning operations

Various checks were done, After exiting the Individual Questionnaire, the iPAQ will automatically code the questionnaire as complete (code 400) and display the Select Case screen. If you were not able to complete the Individual Questionnaire, the iPAQ will prompt you to update the Record of Calls with the appropriate result code. (Note that the program will not allow you to update the Record of Calls for an Individual Questionnaire unless the Household Questionnaire is completed and set as 200.)

Sampling error estimates

Since the report is not ready, no sampling error. The Bureau creates and maintains a National Sampling Survey and Evaluation Programme (NASSEP) household master sampling frame, which provides the framework for designing household surveys to generate different forms of household based data. It has an elaborate infrastructure for data collection, which includes Statistics Offices in all the 47 Counties with trained personne

Data appraisal

Export Data to the SD Card To export data to the SD card, you will need to perform the following steps: 1. Check to be sure that the SD card is set to the unlock position. When the plastic tab is in the up position, the SD card is unlocked, and data can be exported onto the card. 2. Insert the SD card into the card expansion slot at the top of the iPAQ. Make sure the card is inserted fully so that the top of the card is flush with the iPAQ. 3. At the Today screen, tap Start, then tap CMS from the dropdown menu. 4. Confirm that the system clock settings are correct, and enter your password to launch the GATS Case Management System program.

Interview and result code data must be sent back to the Central Office so that project management staff can monitor data collection and protect against data loss.

Age, Sex, Race, and Ethnicity variables from the 1-Year ACS

Contact: District of Columbia, Office of Planning. Email: planning@dc.gov

Geography: District of Columbia

Current Vintage: 2022

ACS Table(s): DP05

Data downloaded from: Census Bureau's API for American Community Survey

Date of API call: January 2, 2024

National Figures: data.census.gov

The United States Census Bureau's American Community Survey (ACS):

• About the Survey
• Geography & ACS
• Technical Documentation
• News & Updates

This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.

Data Note from the Census:

Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

Data Processing Notes:

• This layer is updated automatically when the most current vintage of ACS data is released each year, usually in September. The layer always contains the latest available ACS 1-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.
• Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).
• Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.
• Data processed using R statistical package and ArcGIS Desktop.
• Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. In this post-election survey, major emphasis was placed on the respondent's evaluation of their congressional district's candidates, both the incumbent and opponent, along several dimensions. As in previous American National Election studies, this survey included a series of questions on the media coverage of the campaigns and scales that measured the respondent's positions on major social issues, including urban unrest, protection of the rights of the accused, aid to minority groups, government insurance plan, and women's role in society. The perceived position of the political parties, as well as certain political leaders, on these issues was also ascertained. In addition to the survey data, this file also contains several contextual components consisting of: (1) historical election returns at the state, congressional district, and county levels for elections to the offices of president, governor, and United States senator and representative, 1972-1976, (2) 1978 election returns for primary and general elections to the same offices, including precinct level returns, (3) voter validation variables, (4) information about media structure in the respondent's locale, (5) incumbent characteristics, including information pertaining to the incumbent U.S. representatives of the 95th Congress from the 108 congressional districts sampled in the survey (a major feature of this component is a series of performance ratings that each member of Congress received from certain interest groups and from the Congressional Quarterly), (6) candidate characteristics that apply to the Democratic and Republican candidates for the office of U.S. representative in the 1978 general elections (the latter data were obtained from a 1978 candidate questionnaire that was administered by Congressional Quarterly, Inc.), (7) information prepared by the Federal Election Commission on campaign expenditures and contributions for the offices of U.S. senator and U.S. representative, and (8) U.S. Census Bureau data containing social, economic, and demographic information recorded for the respondent's place of residence. Some of the Census data present information at the congressional district level drawn from the Congressional District Data Book (93rd Congress), as well as county-level Census tabulations prepared from the 1972 County and City Data Book. Additional information includes campaign materials collected from the headquarters of the Democratic and Republican congressional candidates, such as what types of campaign material existed and in how many varieties. Additionally, thematic dimensions of the campaign were coded from the campaign materials. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.. All United States citizens of voting age residing in households. Probability sample of both United States citizens and congressional districts. The sample did not permit estimates of each district's constituency. 2015-11-10 The study metadata was updated2000-03-21 The data for this study are now available in SAS transport and SPSS export formats in addition to the ASCII data file, and a PDF version of the data collection instrument is now available. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables also have been added. For several districts, it was discovered that race (V4) was incorrectly assigned in the 1978 data. In MS03, 24 cases have been recoded to 12. In NY19, 9 cases were recoded to 14, and in NY38, 14 cases were recoded to 24. Also, in case 1352, the congressional district was recoded to district 4. In addition, the six supplementary files containing United States Census Bure...

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of Indonesia, registration are those establishments in possession of TDP (Company registration Certificate)/NIB (Business Identification Number). Both TDP and NIB are included as the implementation of the Omnibus Law on Job Creation from 2020 was being implemented and businesses were transitioning to the new definitions.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Indonesia 2023 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

In addition to the standard set of questions administered to all respondents, the sample was randomly split with two different modules that cover different set of questions: Version A – B-Ready contains additional questions tailored for the Business Ready Report covering infrastructure, trade, government regulations, finance, labor, and other topics. Version B – Green Economy and Taxation covers questions with regards to taxes, green economy, and maternity policies.

The different modules in the dataset are reflected in variable q_version.

Response rate

Overall survey response rate was 41.2%.

This service shows Work Status. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.

This service is symbolized to show the percentage of workers aged 16-64 who work full-time year-round. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right.

Current Vintage: 2017-2021
ACS Table(s): S2302
Data downloaded from: CensusBureau's API for American Community Survey
Date of API call: February 16, 2023
Figures: data.census.gov

The United States Census Bureau's American Community Survey (ACS):

• About the Survey
• Geography & ACS
• Technical Documentation
• News & Updates

This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.

Data Note from the Census:

Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

Data Processing Notes:

• Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).
• The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published.
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This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Title:"Complete Dataset on Social Media Marketing, Green Marketing, and Willingness to Pay Green Premiums Among Chinese Consumers"Description:This dataset supports the research presented in the paper "The Impact of Social Media Marketing on Chinese Consumers' Willingness to Pay Green Premiums: The Mediating Effect of Green Marketing." It includes complete survey data and analytical files investigating how social media marketing influences Chinese consumers' willingness to pay premiums for green products, with green marketing as a mediator.Dataset Contents:1. Primary Data Files: Longyuening_数据_Green Premiums.xlsx (74.56 KB)(Chinese version); LongYuening_Date_Green Premiums.xlsx (77.14 KB)(English version): Raw survey data (Chinese version) containing:Consumer responses on social media marketing effectivenessPerceptions of green marketing initiativesWillingness to pay green premiums (7-point Likert scale)Demographic information (age, gender, income, education level)2. Survey Questionnaire: Longyuening_Questionnaire问卷星_ Green Premiums.docx(Chinese version);Longyuening_QuestionnaireWenjuanxing_Green Premiums.docx (18.56 KB)(English version): Original bilingual questionnaire used for data collection, including:Item wordingsScale anchorsResponse options3. Analysis Files:Structural Equation Modeling (SEM):Longyuening_Path Analysis and SEM_Green Premiums.amw (76.94 KB): AMOS project fileLongyuening_Path Analysis_Green Premiums.amosoutput (1.32 MB): Complete SEM outputLongyuening_Path Analysis_Green Premiums.amp (23.50 KB): AMOS path diagram4. Statistical Analyses:Longyuening_Mediation Effect Analysis_Green Premiums.txt (154 Bytes): Mediation test resultsLongyuening_Reliability Analysis_Green Premiums.htm (888.17 KB): Reliability statistics (Cronbach’sα, composite reliability)Longyuening_Validity Analysis and Exporatory Factor Analysis_Green Premiums.htm (1.10 MB): Exploratory factor analysis (EFA) and validity test resultsKey Features:Complete bilingual dataset (Chinese/English)Comprehensive analytical files for SEM reproductionDetailed survey instrument documentationCleaned and ready-to-use data formatsThis dataset provides researchers with all necessary materials to examine the relationships between social media marketing, green marketing perceptions, and green premium willingness in the Chinese consumer context.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of Viet Nam, the listing from the General Statistics Office of Vietnam, the 2021 Economic Census, was used. The registration agency is the Department of Planning and investment.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Viet Nam 2023 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

The questionnaire implemented in the Viet Nam 2023 WBES included additional questions tailored for the Business Ready Report covering infrastructure, trade, government regulations, finance, labor, and other topics.

Response rate

Overall survey response rate was 31.7%.

Abstract (en): This poll, fielded February 26-28, 2010, solicited respondents' opinion about who produces the highest quality automobiles, their knowledge of the Toyota problems, how well Toyota is handling the problems, the truthfulness of Toyota management, which automobile would they purchase now if looking, whether Toyota will be able to fix current problems with their vehicles, whether these problems caused concern about their safety on the highways, whether the respondent had an automobile and the vehicle manufacturer, whether they approve or disapprove of labor unions, the impact of labor unions on the national economy and working people, and whether labor unions had too much, too little, or the right amount of influence on American life and politics. Other inquiries were made about the academy awards, Dr. Jack Kevorkian, April Fool's Day, the Military's "Don't Ask, Don't Tell" policy, whether they would support a gay person for different prominent positions, sex addiction, daylight savings time, baseball, the Tea Party movement, worrisome man-made hazards, and the underwear bomber. Demographic information includes sex, age, race, education level, household income, marital status, religious preference, type of residential area (e.g., urban or rural), political party affiliation, political philosophy, and voter registration status and participation history. The data contain weight variables that should be used in analyzing the data. According to the CBS News Web site, the data were weighted to match United States Census Bureau breakdowns on age, sex, race, education, and region of the country. The data were also adjusted for the fact that people who share a telephone with others have less chance to be contacted than people who live alone and have their own telephones, and that households with more than one telephone number have more chances to be called than households with only one telephone number. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Created variable labels and/or value labels.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. A variation of random-digit dialing (RDD) using primary sampling units (PSUs) was employed, consisting of blocks of 100 telephone numbers identical through the eighth digit and stratified by geographic region, area code, and size of place. Phone numbers were dialed from RDD samples of both standard land-lines and cell phones. Within households, respondents were selected using a method developed by Leslie Kish and modified by Charles Backstrom and Gerald Hursh (see Backstrom and Hursh, SURVEY RESEARCH. Evanston, IL: Northwestern University Press, 1963). telephone interviewThe data available for download are not weighted and users will need to weight the data prior to analysis.The CASEID variable was reformatted in order to make it a unique identifier.Truncated value labels in variables EDUC, Q37,Q40,Q42, and Q43 were corrected.This data collection was produced by CBS News/60 Minutes, New York, NY.