44 datasets found
  1. Online Survey Software in the US - Market Research Report (2015-2030)

    • ibisworld.com
    Updated May 15, 2025
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    IBISWorld (2025). Online Survey Software in the US - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/united-states/market-research-reports/online-survey-software-industry/
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    Dataset updated
    May 15, 2025
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2015 - 2030
    Area covered
    United States
    Description

    Online survey software developers have seen robust revenue growth over the past five years, driven by heightened demand for real-time feedback amid economic turbulence. Companies across retail, healthcare and the public sector turned to online survey platforms to gauge shifting customer sentiment and employee satisfaction, resulting in a 17.9% surge in revenue in 2022. Research and development (R&D) spending soared as businesses sought product differentiation, while public agencies, like the US Department of Veterans Affairs, adopted survey tools for large-scale feedback. Despite controlling a collective four-fifths of the market, major companies Qualtrics and Momentive Global have remained unprofitable, with heavy R&D expenses and stock-based compensation driving persistent losses. These losses attracted private equity interest, culminating in major acquisitions by Silver Lake and STG in 2023. Revenue has surged at a CAGR of 7.6% to an estimated $2.4 billion over the five years through 2025. Innovation has become central to the online survey software industry, reshaping user experience and competition. Artificial intelligence now allows users to automate question generation, reduce bias and analyze respondents' sentiments. Features like Typeform's jumps and interactive formats have boosted completion rates. As clients expect more from their chosen platform, developers have doubled down on expensive AI enhancements and analytics tools to stay competitive. However, these advancements are costly to develop and maintain. While innovation drives revenue and market relevance, it has also stifled profitability by inflating operational costs and intensifying the need for continuous upgrades. The next five years will likely bring slower revenue growth for online survey software developers as corporate profit slumps and businesses scrutinize discretionary spending. Still, economic uncertainty will maintain demand for survey insights, especially in areas like workforce management and product development. Companies will expand their plan options, offering affordable versions for cost-conscious buyers and premium packages featuring personalized, AI-driven analytics for larger enterprises. Stricter data privacy laws will force platforms to bolster security and transparency. Revenue is set to climb at a CAGR of 2.6% to an estimated $2.7 billion through the end of 2030.

  2. d

    Insight Canada Research's Syndicated Study of the Consumer Infant Formula...

    • search.dataone.org
    • borealisdata.ca
    Updated Dec 28, 2023
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    Insight Canada Research (2023). Insight Canada Research's Syndicated Study of the Consumer Infant Formula and Infant Cereal Markets [Dataset]. http://doi.org/10.5683/SP2/NULKDZ
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    Insight Canada Research
    Description

    Canadian Infant Formula and Infant Cereal Consumer Market is based on the results of 800 interviews conducted with a representative sample of Canadian adult women between the ages of 20 and 45 who have one child or more and who currently feed or fed their child infant formula and/or cereal with the past six months. The survey was fielded between the dates of March 25th and May 6th, 1995. The results are accurate to within +- 3.2% nineteen times out of twenty. Questions surround children's health, children's food and infant formula, and infant food brands.

  3. F

    Percent of Value of Loans, Made Under Participation or Syndication, by Time...

    • fred.stlouisfed.org
    json
    Updated Aug 4, 2017
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    (2017). Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, 91 to 365 Days Before Survey Week, Large Domestic Banks (DISCONTINUED) [Dataset]. https://fred.stlouisfed.org/series/EFTP91T365DXSLNQ
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    jsonAvailable download formats
    Dataset updated
    Aug 4, 2017
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, 91 to 365 Days Before Survey Week, Large Domestic Banks (DISCONTINUED) (EFTP91T365DXSLNQ) from Q3 2012 to Q2 2017 about syndication, 91 to 365 days, pricing terms, large, participation, domestic, percent, loans, banks, depository institutions, and USA.

  4. T

    United States - Percent of Value of Loans, Made Under Participation or...

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 31, 2025
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    TRADING ECONOMICS (2025). United States - Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Informal Commitment, Small Domestic Banks (DISCONTINUED) [Dataset]. https://tradingeconomics.com/united-states/percent-of-value-of-loans-made-under-participation-or-syndication-by-time-that-pricing-terms-were-set-and-by-commitment-during-survey-week-informal-commitment-small-domestic-banks-fed-data.html
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    json, csv, xml, excelAvailable download formats
    Dataset updated
    May 31, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    United States
    Description

    United States - Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Informal Commitment, Small Domestic Banks (DISCONTINUED) was 28.00% in April of 2017, according to the United States Federal Reserve. Historically, United States - Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Informal Commitment, Small Domestic Banks (DISCONTINUED) reached a record high of 51.70 in April of 2016 and a record low of 12.90 in October of 2013. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Informal Commitment, Small Domestic Banks (DISCONTINUED) - last updated from the United States Federal Reserve on May of 2025.

  5. T

    United States - Percent of Value of Loans, Made Under Participation or...

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Jul 15, 2025
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    TRADING ECONOMICS (2020). United States - Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Informal Commitment, All Commercial Banks (DISCONTINUED) [Dataset]. https://tradingeconomics.com/united-states/percent-of-value-of-loans-made-under-participation-or-syndication-by-time-that-pricing-terms-were-set-and-by-commitment-during-survey-week-informal-commitment-all-commercial-banks-fed-data.html
    Explore at:
    csv, excel, xml, jsonAvailable download formats
    Dataset updated
    Jul 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    United States
    Description

    United States - Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Informal Commitment, All Commercial Banks (DISCONTINUED) was 29.00% in April of 2017, according to the United States Federal Reserve. Historically, United States - Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Informal Commitment, All Commercial Banks (DISCONTINUED) reached a record high of 37.90 in January of 2016 and a record low of 3.10 in October of 2013. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, During Survey Week, Informal Commitment, All Commercial Banks (DISCONTINUED) - last updated from the United States Federal Reserve on July of 2025.

  6. d

    Audience Targeting Data | 330M+ Global Devices | Audience Data & Advertising...

    • datarade.ai
    .json, .csv
    Updated Feb 4, 2025
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    DRAKO (2025). Audience Targeting Data | 330M+ Global Devices | Audience Data & Advertising | API Delivery [Dataset]. https://datarade.ai/data-products/audience-targeting-data-330m-global-devices-audience-dat-drako
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    .json, .csvAvailable download formats
    Dataset updated
    Feb 4, 2025
    Dataset authored and provided by
    DRAKO
    Area covered
    Czech Republic, Armenia, Eritrea, Namibia, Equatorial Guinea, Serbia, San Marino, Russian Federation, Curaçao, Suriname
    Description

    DRAKO is a Mobile Location Audience Targeting provider with a programmatic trading desk specialising in geolocation analytics and programmatic advertising. Through our customised approach, we offer business and consumer insights as well as addressable audiences for advertising.

    Mobile Location Data can be meaningfully transformed into Audience Targeting when used in conjunction with other dataset. Our expansive POI Data allows us to segment users by visitation to major brands and retailers as well as categorizes them into syndicated segments. Beyond POI visits, our proprietary Home Location Model determines residents of geographic areas such as Designated Market Areas, Counties, or States. Relatedly, our Home Location Model also fuels our Geodemographic Census Data segments as we are able to determine residents of the smallest census units. Additionally, we also have audiences of: ticketed event and venue visitors; survey data; and retail data.

    All of our Audience Targeting is 100% deterministic in that it only includes high-quality, real visits to locations as defined by a POIs satellite imagery buildings contour. We never use a radius when building an audience unless requested. We have a horizontal accuracy of 5m.

    Additionally, we can always cross reference your audience targeting with our syndicated segments:

    Overview of our Syndicated Audience Data Segments: - Brand/POI segments (specific named stores and locations) - Categories (behavioural segments - revealed habits) - Census demographic segments (HH income, race, religion, age, family structure, language, etc.,) - Events segments (ticketed live events, conferences, and seminars) - Resident segments (State/province, CMAs, DMAs, city, county, sub-county) - Political segments (Canadian Federal and Provincial, US Congressional Upper and Lower House, US States, City elections, etc.,) - Survey Data (Psychosocial/Demographic survey data) - Retail Data (Receipt/transaction data)

    All of our syndicated segments are customizable. That means you can limit them to people within a certain geography, remove employees, include only the most frequent visitors, define your own custom lookback, or extend our audiences using our Home, Work, and Social Extensions.

    In addition to our syndicated segments, we’re also able to run custom queries return to you all the Mobile Ad IDs (MAIDs) seen at in a specific location (address; latitude and longitude; or WKT84 Polygon) or in your defined geographic area of interest (political districts, DMAs, Zip Codes, etc.,)

    Beyond just returning all the MAIDs seen within a geofence, we are also able to offer additional customizable advantages: - Average precision between 5 and 15 meters - CRM list activation + extension - Extend beyond Mobile Location Data (MAIDs) with our device graph - Filter by frequency of visitations - Home and Work targeting (retrieve only employees or residents of an address) - Home extensions (devices that reside in the same dwelling from your seed geofence) - Rooftop level address geofencing precision (no radius used EVER unless user specified) - Social extensions (devices in the same social circle as users in your seed geofence) - Turn analytics into addressable audiences - Work extensions (coworkers of users in your seed geofence)

    Data Compliance: All of our Audience Targeting Data is fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. This data comes with an associated location which is how we are able to segment using geofences.

    Data Quality: In addition to partnering with trusted SDKs, DRAKO has additional screening methods to ensure that our mobile location data is consistent and reliable. This includes data harmonization and quality scoring from all of our partners in order to disregard MAIDs with a low quality score.

  7. r

    Surveying Equipment Market Market Projections to 2034 | Share, Size & Demand...

    • reportsanddata.com
    Updated Jun 15, 2025
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    The citation is currently not available for this dataset.
    Explore at:
    Dataset updated
    Jun 15, 2025
    Dataset authored and provided by
    Reports and Data
    Time period covered
    2024 - 2030
    Area covered
    Global
    Description

    Get professional Surveying Equipment Market research featuring size and share analysis with growth forecasts. Premium syndicated data for strategic business intelligence.

  8. BIRPS (British Institutes Reflection Profiling Syndicate) Westline Seismic...

    • data.wu.ac.at
    • metadata.bgs.ac.uk
    • +1more
    html
    Updated Aug 18, 2018
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    British Geological Survey (2018). BIRPS (British Institutes Reflection Profiling Syndicate) Westline Seismic Survey Data (1993) [Dataset]. https://data.wu.ac.at/odso/data_gov_uk/ZDBlNDA4M2UtMThhMy00Yzc4LWExNjUtNzg0OGQ2ZTFlNDdk
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    htmlAvailable download formats
    Dataset updated
    Aug 18, 2018
    Dataset provided by
    British Geological Surveyhttps://www.bgs.ac.uk/
    Area covered
    dce4f595c9a98b70ad2ee115c494b56c95aa7461
    Description

    WESTLINE was acquired by BIRPS (the British Institutions Reflection Profiling Syndicate). The seismic data image the faulted conjugate margins of the Rockall Trough and the intrabasinal sediments. The seismic data were shot to 18 s two-way time along a single 450 km-long transect.

  9. f

    Propensity scores matching analysis (PSM).

    • figshare.com
    xls
    Updated Jun 21, 2023
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    Ping Chen; Li Deng (2023). Propensity scores matching analysis (PSM). [Dataset]. http://doi.org/10.1371/journal.pone.0281255.t007
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    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Ping Chen; Li Deng
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Chinese traditional culture is characterized by "Quan Zi" culture with a "differential order pattern". As a special informal institutional arrangement, "Quan Zi" plays an important role in the capital market. This paper investigates how Venture Capital Quan Zi affects the stock mispricing of invested companies. Using the syndicate investment data of China’s venture capital institutions from 2009 to 2019, this study documents that Venture Capital Quan Zi significantly increases the positive deviations of stock prices of Quan Zi-backed firms but has no obvious influence on the negative deviations, showing an asymmetric effect on stock mispricing. In addition, this effect is dynamic. Stock mispricing significantly increased in the lock-up period and the following year, but then gradually weakened. Mechanism tests suggest that, on the one hand, Venture Capital Quan Zi increases a company’s earnings manipulation, thus raising investors’ expectations to push up stock prices. On the other hand, Venture Capital Quan Zi boosts the stock price through market reaction channels, increasing institutional investors’ shareholdings, positive media coverage and stock liquidity. This paper has high theoretical and applied value to guide the orderly competition of capital and the supervision of institutional investors.

  10. F

    Percent of Value of Loans, Made Under Participation or Syndication, by Time...

    • fred.stlouisfed.org
    json
    Updated Aug 4, 2017
    + more versions
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    (2017). Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, 91 to 365 Days Before Survey Week, U.S. Branches and Agencies of Foreign Banks (DISCONTINUED) [Dataset]. https://fred.stlouisfed.org/series/EFTP91T365DXFBNQ
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    jsonAvailable download formats
    Dataset updated
    Aug 4, 2017
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    Graph and download economic data for Percent of Value of Loans, Made Under Participation or Syndication, by Time that Pricing Terms Were Set and by Commitment, 91 to 365 Days Before Survey Week, U.S. Branches and Agencies of Foreign Banks (DISCONTINUED) (EFTP91T365DXFBNQ) from Q3 2012 to Q2 2017 about syndication, 91 to 365 days, pricing terms, foreign, participation, percent, loans, banks, depository institutions, and USA.

  11. BIRPS (British Institutes Reflection Profiling Syndicate) MONA LISA seismic...

    • metadata.bgs.ac.uk
    • data-search.nerc.ac.uk
    • +1more
    jsp
    Updated 1993
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    British Geological Survey (1993). BIRPS (British Institutes Reflection Profiling Syndicate) MONA LISA seismic survey (1993-1995) [Dataset]. https://metadata.bgs.ac.uk/geonetwork/srv/api/records/c425c9fc-cb6e-352c-e044-0003ba9b0d98
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    jspAvailable download formats
    Dataset updated
    1993
    Dataset provided by
    British Geological Surveyhttps://www.bgs.ac.uk/
    Natural Enironmental Research Council
    Authors
    British Geological Survey
    License

    http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/INSPIRE_Directive_Article13_1dhttp://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/INSPIRE_Directive_Article13_1d

    Time period covered
    Apr 1993 - Jul 1995
    Area covered
    Description

    MONA LISA (Marine and Onshore North Sea Acquisition for Lithospheric Seismic Analysis) seismic data was acquired by BIRPS (the British Institutions Reflection Profiling Syndicate) across the Ringkobing-Fyn High of the central North Sea were designed to study the crust near a hypothesized Caledonian age triple junction associated with the colliding continental crust of Laurentia, Avalonia (Gondwanaland) and Baltica. The specific target was the eastward continuation of the Caledonian Front (Iapetus Suture), as previously recognised on NEC (North East Coast line), MOBIL (Measurements over Basins to Image Lithosphere), NSDP (North Sea Deep Profile) and BABEL (Baltic and Bothnian Echoes from the Lithosphere) profiles, in northern Europe. 1112 km of data were acquired, recorded to 26 s two-way time.

  12. BIRPS (British Institutes Reflection Profiling Syndicate) 3DEPR-ARAD seismic...

    • data.europa.eu
    • metadata.bgs.ac.uk
    • +3more
    unknown
    Updated Oct 11, 2021
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    British Geological Survey (BGS) (2021). BIRPS (British Institutes Reflection Profiling Syndicate) 3DEPR-ARAD seismic data (1997) [Dataset]. https://data.europa.eu/data/datasets/birps-british-institutes-reflection-profiling-syndicate-3depr-arad-seismic-data-1997
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    unknownAvailable download formats
    Dataset updated
    Oct 11, 2021
    Dataset provided by
    British Geological Surveyhttps://www.bgs.ac.uk/
    Authors
    British Geological Survey (BGS)
    Description

    The 3D multi channel seismic data were acquired as part of a collaborative investigation into models of magmatic segmentation between the Scripps Institute of Oceanography and BIRPS (the British Institutions Reflection Profiling Syndicate). The 3D EPR (East Pacific Rise) ARAD (Anatomy of a Ridge Axis Discontinuity) EW9707 cruise was undertaken in September and October 1997 to provide both refelction and wide-angle seismic data for the study of the overlapping spreading centre (OSC) at 9 degrees 3 minutes N on the East Pacific Rise. The data were acquired with a single source and a single streamer with a nominal line spacing of 100 m. The 3D-EPR ARAD survey was a joint NERC/NSF (US National Science Foundation) funded project and the copyright for this survey is held jointly. Reference: Bazin, S. Harding, AJ. et al. (2001) Three-dimensional shallow crustal emplacement at the 9 degree 03 minute N overlapping spreading center on the East Pacific Rise, Journal of Geophysical Research.

  13. Namibia Financial Inclusion Survey - Namibia

    • microdata.nsanamibia.com
    Updated Apr 25, 2025
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    Namibia Statistics Agency (2025). Namibia Financial Inclusion Survey - Namibia [Dataset]. https://microdata.nsanamibia.com/index.php/catalog/4
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    Dataset updated
    Apr 25, 2025
    Dataset authored and provided by
    Namibia Statistics Agencyhttps://nsa.org.na/
    Time period covered
    2017
    Area covered
    Namibia
    Description

    Abstract

    This report presents the main results of the 2017 Namibia Financial Inclusion Survey. The survey was conducted by the Namibia Statistics Agency, in all 14 regions of Namibia, with funding from the Bank of Namibia and the World Bank. By design, the NFIS surveys was intended to involve a range of stakeholders through syndicate membership to enrich the entire survey process through cross-cutting learning, sharing of information, and to facilitate the extended utilization of the final data. A nationally representative sample of Namibians 16 years and older was employed. During October and November 2017 1863 face-to-face interviews were conducted, one interview per selected household. The data was captured into a tablet-based questionnaire using the Survey-To-Go application. The data collected was weighted to reflect the adult/eligible population (i.e. aged 16 years or older) in Namibia, as this is the minimum age legally allowed for any individual to make use of formal financial products in their own capacity. It is also important to note that the results of 2017 are representative only at national and urban/rural areas levels, but not regional.

    · To measure the levels of financial inclusion (inclusive of formal and informal usage) · To describe the landscape of access (type of products and services used by financially included individuals) · To identify the drivers of, and barriers to the usage of financial products and services · To track and compare results and provide an assessment of changes and reasons thereof (including possible impacts of interventions to enhance access) · To stimulate evidence-based dialogue that will ultimately lead to effective public/private sector interventions that will increase and deepen financial inclusion strategies · Provide information on new opportunities for increased financial inclusion and usage.

    Geographic coverage

    National sampling frame is a list of small geographical areas called Primary Sampling Units (PSUs). There are a total of 6453 PSUs in Namibia that were created using the enumeration areas (EA) of the 2011 Population and Housing Census. The measure of size in the frame is the number of households within the PSU as reflected in the 2011 Census. The frame units were stratified first by region, and then by urban/rural areas within each region.

    The results are only representative at national level, but not at regional level.

    Analysis unit

    Individuals, households

    Universe

    The target population for the NFIS 2017 was all people aged 16 and above who live in private households in Namibia. The eligible population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in this survey. However, private households within institutional settings such as teachers' houses in school premises were covered.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The target population for the NFIS 2017 was eligible members of private households in Namibia. The eligible population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in this survey. However, private households within institutional settings such as teachers' houses in school premises were covered. The sample design was a stratified three-stage cluster sample, where the first stage units were the PSUs, the second stage units were the households and the third stage were the eligible members, that is individuals who, by the time of the survey were 16 years or older, available during the duration of survey, mentally/physically capable to be interviewed and have resided in the selected household for at least six month preceding the survey. The age limit for the eligibility criteria was based on the fact that only individuals aged 16 years or above are officially authorized to get personal formal financial products (such as open a personal bank account) from formal financial institutions in Namibia, which makes them the target population of the financial sector. Only one individual was interviewed per selected household

    The national sampling frame was used to select the first stage units (PSUs). The national sampling frame is a list of small geographical areas called Primary Sampling Units (PSUs) created using the enumeration areas (EAs) of 2011 Population and Housing Census. There are a total of 6 453 PSUs in Namibia. A total of 151 PSUs were selected from all the 14 regions, and 2 114 households were drawn from them, constituting the sample size. Power allocation procedures were adopted to distribute the samples across the regions so that the smaller regions will get adequate samples.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The 2017 NFIS questionnaire was made up of 13 sections in total. The questionnaire was transmitted onto CAPI (Computer aided Personal Interview) using the Survey-To-Go application.

    Cleaning operations

    The data processing methodology that was adopted for this study was the Computer Assisted Personal Interview. Data management series of operations to collect, transmit, clean and store the survey data were designed using SurveyToGo computer system onto the Dubloo platform.

    Data entry is very crucial, since the quality of data collected impact heavily on the output. The collection process was designed to ensure that the data gathered are both defined and accurate, so that subsequent decisions based on the findings are valid.

    Response rate

    After data processing, 1863 out of 2114 sampled households were successfully interviewed, resulting in 88.1 percent response rate which is highly satisfactory given that the NSA subscribes to a response rate of 80 percent for all data collection in the social statistics domain. Overall, the rural response is higher than the urban response.

    It was not possible to interview all the selected households when the household sample was implemented, due to refusals or non-contacts.

    Sampling error estimates

    The most common measure of quality of the survey estimates reported from the sample surveys was the level of precision of the estimates. The quality indicators are meant to ascertain the analysis about the level of precision of the estimates at different domains. The statistical precision of the survey estimates were expressed using different types of statistics such as Standard errors (SE), the coefficient of variation (CV) and the Confidence Interval (CI). These statistics were used to indicate the level of precision of the survey estimates in estimating the population parameters of interest. There are a number of factors that can affect the precision of the survey estimates namely the size of the sample relative to the population size, the sample design and the variability of the characteristics of interest in the population. The data quality indicators were discussed in details in the following sub-section.

  14. BIRPS (British Institutes Reflection Profiling Syndicate) Faeroe-Iceland...

    • data.wu.ac.at
    • portal.medin.org.uk
    • +3more
    html
    Updated Aug 18, 2018
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    British Geological Survey (2018). BIRPS (British Institutes Reflection Profiling Syndicate) Faeroe-Iceland Ridge Experiment (FIRE) seismic data (1994) [Dataset]. https://data.wu.ac.at/odso/data_gov_uk/ZTg0MzkxNTUtYzAzMS00OTllLTgxODQtZTgwYWJmYzNiNDQz
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    htmlAvailable download formats
    Dataset updated
    Aug 18, 2018
    Dataset provided by
    British Geological Surveyhttps://www.bgs.ac.uk/
    Area covered
    022e99a7a2d222db256e043e987f8778e8664756
    Description

    Faeroe-Iceland Ridge Experiment (FIRE) was acquired by BIRPS (the British Institutions Reflection Profiling Syndicate). The primary target was anomalously thick oceanic crust along the Faeroe-Iceland Ridge that was possibly formed by underplating due to the proximity of the Iceland hotspot. FIRE made use of 110 land seismometers to record the airgun shots. The resulting velocity models and reflector geometries have proved critical in interpretation of variations in crustal volumes along the ridge. The data were recorded to 23 s two-way time.

  15. d

    Idaho Geological Survey mine property scan ID: P09_011

    • datadiscoverystudio.org
    Updated Aug 2, 2018
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    (2018). Idaho Geological Survey mine property scan ID: P09_011 [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/d410c7567d6b472395ddd898a1c7798c/html
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    Dataset updated
    Aug 2, 2018
    Description

    Property ID, name(s), document type, and description: SP0219; Silver Syndicate, Rambo Area; map: Claim map.

  16. B

    Environics International Environmental Monitor, 2000

    • borealisdata.ca
    Updated Apr 14, 2025
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    Environics (2025). Environics International Environmental Monitor, 2000 [Dataset]. http://doi.org/10.5683/SP3/IAVDO4
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 14, 2025
    Dataset provided by
    Borealis
    Authors
    Environics
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Area covered
    Nigeria, United Kingdom, Germany, Netherlands, Spain, Peru, Poland, Chile, Russian Federation, Italy
    Description

    Environics International Environmental Monitors (EIEM) is a syndicated annual survey of global public opinion on a variety of environmental and natural resource issues. The findings of the 2000 Survey are based on the results of face-to face or telephone interviews with representative samples of about 1,000 citizens in each of 34 countries on all continents, and at varying stages of economic development. This represents about 70% of the world's population. Research was conducted by respected social research institutes in each country between April 4, 2000 and July 10, 2000. The survey consisted of 20 minutes of questions dealing with topics such as national problems, extreme weather patterns, personal concern about the environment, the scope of concern, effects of pollution, effects on human health, economic impacts of environmental clean-up, feelings about empowerment and personal action, industry performance, governance, climate change, biotechnology, chemicals, and automobiles. A number of additional questions were asked in fewer countries.

  17. d

    Idaho Geological Survey mine property scan ID: P06_057

    • datadiscoverystudio.org
    Updated Jul 15, 2018
    + more versions
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    (2018). Idaho Geological Survey mine property scan ID: P06_057 [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/a4e15e4a19594cdd90ddc9c0ca4309a1/html
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    Dataset updated
    Jul 15, 2018
    Description

    Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

  18. d

    Dry and abandoned well: Record Number 102922

    • datadiscoverystudio.org
    Updated Dec 15, 2017
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    (2017). Dry and abandoned well: Record Number 102922 [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/be50294b3bf743769c836a5aae2a67fc/html
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    Dataset updated
    Dec 15, 2017
    Description

    Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

  19. Bonaparte Gulf Gravity Survey Western Australia, 1959 By Gulf Oil Syndicate

    • data.gov.au
    pdf
    Updated Jan 1, 1964
    + more versions
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    Commonwealth of Australia (Geoscience Australia) (1964). Bonaparte Gulf Gravity Survey Western Australia, 1959 By Gulf Oil Syndicate [Dataset]. https://data.gov.au/dataset/ds-ga-a05f7893-0018-7506-e044-00144fdd4fa6
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    pdfAvailable download formats
    Dataset updated
    Jan 1, 1964
    Dataset provided by
    Geoscience Australiahttp://ga.gov.au/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Western Australia, Joseph Bonaparte Gulf, Australia
    Description

    This report describes a gravity survey conducted in the Bonaparte Gulf Basin, Western Australia, by Mines Administration Pty Limited, for Gulf Oil Syndicate, during September, 1959. The survey took …Show full descriptionThis report describes a gravity survey conducted in the Bonaparte Gulf Basin, Western Australia, by Mines Administration Pty Limited, for Gulf Oil Syndicate, during September, 1959. The survey took place over part of Permit to Explore 127H. Simultaneously, a field geological reconnaissance along the western margin of the Basin was made by E.P. Utting, consulting geologist, to help evaluate the area. The gravity survey was intended to extend a regional gravity survey of the southern and north-eastern portions of the Basin previously undertaken by the Bureau of Mineral Resources, Associated Australian Oilfields N.L., and Westralian Oil Limited. Gravity values were observed and plotted over an area of approximately 1400 square miles, bordered on the north by Joseph Bonaparte Gulf and on the east by the Western Australia/Northern Territory border. The gravity results have, to some extent, indicated the shape of the basement surface of that part of the Bonaparte Gulf Basin covered by Gulf Oil Syndicate's Permit to Explore 127H. Gravity stations are too widely separated to support the computation of a residual maximum. Bouguer "highs" are interpreted as features on the basin floor which may have encouraged Devonian reef development.

  20. O

    ARGYLE SYNDICATE 3

    • data.qld.gov.au
    Updated May 9, 2023
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    Geological Survey of Queensland (2023). ARGYLE SYNDICATE 3 [Dataset]. https://www.data.qld.gov.au/dataset/bh067532
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    Dataset updated
    May 9, 2023
    Dataset authored and provided by
    Geological Survey of Queensland
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description
Share
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IBISWorld (2025). Online Survey Software in the US - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/united-states/market-research-reports/online-survey-software-industry/
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Online Survey Software in the US - Market Research Report (2015-2030)

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Dataset updated
May 15, 2025
Dataset authored and provided by
IBISWorld
License

https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

Time period covered
2015 - 2030
Area covered
United States
Description

Online survey software developers have seen robust revenue growth over the past five years, driven by heightened demand for real-time feedback amid economic turbulence. Companies across retail, healthcare and the public sector turned to online survey platforms to gauge shifting customer sentiment and employee satisfaction, resulting in a 17.9% surge in revenue in 2022. Research and development (R&D) spending soared as businesses sought product differentiation, while public agencies, like the US Department of Veterans Affairs, adopted survey tools for large-scale feedback. Despite controlling a collective four-fifths of the market, major companies Qualtrics and Momentive Global have remained unprofitable, with heavy R&D expenses and stock-based compensation driving persistent losses. These losses attracted private equity interest, culminating in major acquisitions by Silver Lake and STG in 2023. Revenue has surged at a CAGR of 7.6% to an estimated $2.4 billion over the five years through 2025. Innovation has become central to the online survey software industry, reshaping user experience and competition. Artificial intelligence now allows users to automate question generation, reduce bias and analyze respondents' sentiments. Features like Typeform's jumps and interactive formats have boosted completion rates. As clients expect more from their chosen platform, developers have doubled down on expensive AI enhancements and analytics tools to stay competitive. However, these advancements are costly to develop and maintain. While innovation drives revenue and market relevance, it has also stifled profitability by inflating operational costs and intensifying the need for continuous upgrades. The next five years will likely bring slower revenue growth for online survey software developers as corporate profit slumps and businesses scrutinize discretionary spending. Still, economic uncertainty will maintain demand for survey insights, especially in areas like workforce management and product development. Companies will expand their plan options, offering affordable versions for cost-conscious buyers and premium packages featuring personalized, AI-driven analytics for larger enterprises. Stricter data privacy laws will force platforms to bolster security and transparency. Revenue is set to climb at a CAGR of 2.6% to an estimated $2.7 billion through the end of 2030.

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