Dataset from Ministry of Culture, Community and Youth. For more information, visit https://data.gov.sg/datasets/d_05fffefe9045d234eb140d7db0acdeb9/view

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 Singapore, registration was with Accounting and Corporate Regulatory Authority (ACRA).

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 Singapore 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).

Response rate

Overall survey response rate was 5.2%.

Singapore Employed Person: Resident: Age 25-29 data was reported at 203.700 Person th in 2024. This records a decrease from the previous number of 215.600 Person th for 2023. Singapore Employed Person: Resident: Age 25-29 data is updated yearly, averaging 208.000 Person th from Jun 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 234.900 Person th in 1990 and a record low of 193.000 Person th in 2022. Singapore Employed Person: Resident: Age 25-29 data remains active status in CEIC and is reported by Ministry of Manpower. The data is categorized under Global Database’s Singapore – Table SG.G075: Labour Force Survey: Ref. General Household Survey (GHS): Employment: By Age and Sex.

Dataset from Health Promotion Board. For more information, visit https://data.gov.sg/datasets/d_586ef8eb3f0f49a6c871665c2aa5784f/view

Survey data collected from members at Ground-Up Initiative (GUI) in Singapore regarding their engagement and perceptions about nature-placemaking activities held at GUI.- The Frequency of Visits (frequency_visits) was measured by one question, “In the past 2 years, on average how many days per year did you visit GUI? (i.e. 80 (days per year)”. - The Duration of Commitment (commitment) was measured by one question, “Which month and year did you attend an activity in GUI for the first time?”. The duration was attained by calculating months between 1) the date the respondent visited the GUI for the first time and 2) the date the respondent submitted the survey response. For example, if one respondent visited GUI for the first time in November 2017 and submitted the survey response in December 2019, the total duration would be 24 months. - Types of Programs Engaged (number_programs) was measured by the number of program types attended by the respondents. For example, if one responded have attended Balik Kampong and Sketching before, it would be 2. - Social Cohesion and Trust (SoCoh) scale consists of five conceptually related items that aim to assess feelings of “trust, shared norms and values, positive and friendly relationships, and feelings of being accepted and belonging” (Forrest & Kearns, 2001; Sampson et al., 2007). Respondents assessed each statement ranging from “1 strongly disagree” to “7 strongly agree”. - Sense of Community (SOC): The Brief Sense of Community Scale aims to assess four dimensions of sense of community defined in the McMillan and Chavis’ (1986) model.- Connectedness to Nature (CNS): Participants responded to the modified version of Connectedness to Nature Scale (CNS) that intends to assess individuals’ emotional connection to nature and a sense of oneness with the natural world (Mayer & Frantz, 2004). - Intrinsic Motivation Inventory (IMI) is a multidimensional measurement scale developed based on Self-Determination Theory (Ryan & Deci, 2000). We used the modified version of IMI, measuring the respondent’s self-reported interest or enjoyment, perceived competence, and perceived choice about activities in GUI. The interest/enjoyment sub-scale (Intrinsic Motivation or IM) represents the self-report measure of intrinsic motivation. The perceived choice and perceived competence concepts are considered as positive predictors of both self-report and behavioral measures of intrinsic motivation. - Perceived Choice (PChoice) measures how individuals feel they engage in one activity because they choose to do it. - Perceived Competence (PComp) assesses how effective individuals feel when they are performing a task (Monteiro et al., 2015). - Self-Esteem (Self_Est): We used the modified version of Rosenberg Self-Esteem Scale (four items) that assesses respondents’ self-worth by measuring both positive and negative feelings about self (Rosenberg, 1965). - Self-Efficacy (Self_Eff): We used the New General Self-Efficacy Scale (NGSE) that aims to measure respondents’ “beliefs in one’s capabilities to mobilize the motivation, cognitive resources, and courses of action needed to meet given situational demands” (Bandura, 2010).

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden.

The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones.

The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

National.

Analysis unit

Household Individual

Universe

National Population, Both sexes,18 and more years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size: 1512

Stratified, random sample of Singapore citizens Lower age cutoff was set at 15. No clusters were used. Substitution was used when the person no longer lives in the address stated or when the case is not contactable after 3 tries. Sample was stratified by ethnicity at the sample design, and stratified by age after completion of fieldwork.

Remarks about sampling:

1. The majority group was undersampled, and minority ethnic groups oversampled,
2. The questionnaire was slightly modified; however, item numbering remains similar to the WVS master questionnaire.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was translated by a specialist translator. It wasn't back translated to english but was pre-tested. The pre-test cases were selected to reflect as far as possible the profile of the population. One pre-test involving 39 cases. The following WVS questions were excluded: V12, V37- 38, V137-140, V147-162, V163, V168-173, V177-181-190, V200-203, V220-222, V243 Reason(s) not included: Not applicable to the Singapore context. Some questions are politically sensitive. Moreover, the questionnaire was rather long, so most non-core questions were omitted, where possible.

Response rate

Response rate is 79%, and non-response rate is 21%. Total number of starting names/addresses: 2610 - Addresses which could not be traced at all: 183 - Addresses established as empty, demolished or containing no private dwellings: 26 - No contact with selected person: 341 - Refusal at selected address: 379 - Personal refusal by selected respondent: 379 - Full productive interview: 1512 - Partial productive interview: 21

Sampling error estimates

Estimated error: 2.6

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_023c0f38584a4b42587ebd74bb773db8/view

This dataset contains the data used for all statistical analysis in our publication "Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering", summarised in a single .csv file. For more details on the study methodology, please refer to our manuscript: Ooi, K.; Lam, B.; Hong, J.; Watcharasupat, K. N.; Ong, Z.-T.; Gan, W.-S. Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering. Sustainability, 2022. For our replication code utilising this data, please refer to our Github repository: https://github.com/ntudsp/singapore-soundscape-site-selection-survey A short explanation of the columns in the .csv file is as follows: Full of life & exciting [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Full of life & exciting". Full of life & exciting [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Full of life & exciting". Full of life & exciting [# times visited]: The number of times that the participant had visited the chosen location they considered "Full of life & exciting" before, as reported by the participant. Full of life & exciting [Duration]: The average duration per visit to the chosen location the participant considered "Full of life & exciting", as reported by the participant. Chaotic & restless [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Chaotic & restless". Chaotic & restless [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Chaotic & restless". Chaotic & restless [# times visited]: The number of times that the participant had visited the chosen location they considered "Chaotic & restless" before, as reported by the participant. Chaotic & restless [Duration]: The average duration per visit to the chosen location the participant considered "Chaotic & restless", as reported by the participant. Calm & tranquil [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Calm & tranquil". Calm & tranquil [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Calm & tranquil". Calm & tranquil [# times visited]: The number of times that the participant had visited the chosen location they considered "Calm & tranquil" before, as reported by the participant. Calm & tranquil [Duration]: The average duration per visit to the chosen location the participant considered "Calm & tranquil", as reported by the participant. Boring & lifeless [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Boring & lifeless". Boring & lifeless [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Boring & lifeless". Boring & lifeless [# times visited]: The number of times that the participant had visited the chosen location they considered "Boring & lifeless" before, as reported by the participant. Boring & lifeless [Duration]: The average duration per visit to the chosen location the participant considered "Boring & lifeless", as reported by the participant.

Singapore DBUs: Monetary Survey: Net Foreign Position: Banks data was reported at -67,222.600 SGD mn in Sep 2018. This records a decrease from the previous number of -64,959.200 SGD mn for Aug 2018. Singapore DBUs: Monetary Survey: Net Foreign Position: Banks data is updated monthly, averaging -5,829.000 SGD mn from Jan 1991 (Median) to Sep 2018, with 333 observations. The data reached an all-time high of 50,674.800 SGD mn in Sep 2009 and a record low of -67,250.600 SGD mn in Jun 2018. Singapore DBUs: Monetary Survey: Net Foreign Position: Banks data remains active status in CEIC and is reported by Monetary Authority of Singapore. The data is categorized under Global Database’s Singapore – Table SG.KA004: Monetary Survey: Domestic Banking Units (DBUs).

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/ .

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

NA Exclusions: NA Design effect: 1.68

Mode of data collection

Computer-Assisted Telephone Interviewing [CATI]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 4. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

According to a survey conducted in Singapore from the end of January to February in 2024, ** percent of respondents in Singapore indicated that they get their news from Mothership.sg on a weekly basis. By comparison, *** percent of respondents stated that they access Zaobao online.

Business confidence survey in Singapore, March, 2025 The most recent value is 16 points as of Q1 2025, an increase compared to the previous value of 10 points. Historically, the average for Singapore from Q2 1970 to Q1 2025 is 12.6 points. The minimum of -57 points was recorded in Q1 2009, while the maximum of 54 points was reached in Q2 1976. | TheGlobalEconomy.com

This statistic shows the share of respondents who indicated that they have read at least one physical book and/or an e-book in Singapore in the last twelve months, in 2016. During the period surveyed, ** percent of participants who have read at least one book in the past twelve months indicated that they have read physical books. In comparison, ** percent indicated that they have read e-books.

This survey among adult Singaporeans tracks the performance of key HPB indicators. The health knowledge, attitude and practice of the general population were assessed. This survey has been discontinued since 2011.

MAS Forecast: MAS Core Inflation: YoY: Median data was reported at 2.000 % in Dec 2018. This records an increase from the previous number of 1.900 % for Sep 2018. MAS Forecast: MAS Core Inflation: YoY: Median data is updated quarterly, averaging 1.600 % from Jun 2012 (Median) to Dec 2018, with 27 observations. The data reached an all-time high of 2.800 % in Jun 2012 and a record low of 0.400 % in Mar 2016. MAS Forecast: MAS Core Inflation: YoY: Median data remains active status in CEIC and is reported by Monetary Authority of Singapore. The data is categorized under Global Database’s Singapore – Table SG.I019: MAS Core Inflation: Forecast: Monetary Authority of Singapore: Survey of Professional Forecasters.

Data is derived from the Sample Household Survey (SHS) which is conducted once every 5 years.

HDB resident population refers to Singapore citizens and Singapore permanent residents (SPRs) residing in HDB flats, excluding subtenants.

This statistic shows the share of teenaged respondents who indicated that they have read at least one physical book and/or an e-book in Singapore in the last twelve months, in 2016. During the period surveyed, ** percent of teenaged participants who have read at least one book in the past twelve months indicated that they have read physical books. In comparison, ** percent indicated that they have read e-books.

This statistic shows the result of a survey on types of products that are worth buying in premium quality according to Singaporean consumers as of 2018. During the survey period, 36 percent of the survey participants in Singapore stated that they bought premium personal electronic products. The next preferred products to buy premium were clothing and shoes, with 35 percent of survey participants stating that they bought these premium products.

Data is derived from the Sample Household Survey (SHS) which is conducted once every 5 years. Data is based on HDB resident population aged 15 years and above rounded off to the nearest 1,000.

HDB resident population refers to Singapore citizens and Singapore permanent residents (SPRs) residing in HDB flats, excluding subtenants.

Unemployed persons refer to persons aged 15 years and over who are currently not working but were actively looking for work at the point of survey. They include persons who are not working but are taking steps to start their own business or taking up a new job after the survey period.