In 2021, the global social media analytics market was valued at roughly seven billion U.S. dollars. It was expected to grow to 8.5 billion in 2022 and surpass 26 billion dollars in 2028. Social media analytics tools are used, among others, to manage customer experience, as well as marketing management, and to gain competitive intelligence.

The Clinical Data Analytics in Healthcare market is rapidly evolving, serving as a crucial component in enhancing the quality of patient care and optimizing operational efficiencies within healthcare organizations. As healthcare systems increasingly rely on data-driven decision-making processes, the demand for sophi

The global social media analytics market size was valued at USD 14.0 Billion in 2024. Looking forward, IMARC Group estimates the market to reach USD 83.11 Billion by 2033, exhibiting a CAGR of 21.9% from 2025-2033. North America currently dominates the market in 2024, holding a market share of over 33.0% in 2024. The social media analytics market share is driven by the rising need for data analytics that enhances decision-making processes, increasing utilization of various social media platforms, and the growing focus on quick and effective responses to customer inquiries.

IMARC Group provides an analysis of the key trends in each segment of the market report, along with forecasts at the global, regional, and country levels from 2025-2033. Our report has categorized the market based on component, deployment mode, organization size, application, and end user.

The global social media analytics and monitoring software industry in 2024 had Linkfluence as the market leader with a share of over 56 percent, followed by Meltwater and Netvibes who had market shares of approximately 18 and 8.1 percent, respectively. When social media analytics and monitoring software are combined, it optimizes the extracted social media insights gained through analyzing and monitoring social media data.

The Cross-National Time-Series Data Archive provides more than 200 years of annual data for nations and empires of the world including those that no longer exist. It covers demographic, social, political, and economic topics. Select data goes back to 1815. Not all indicators are available for all countries or in all years. Fore data definitions, list of variables and countries covered, consult the accompanying codebook and user manuals. More information on topics, list of variables and countries covered is also available on CNTS website. DATA AVAILABLE FOR YEARS: 1815-2023

One of the first steps in a reference interview is determining what is it the user really wants or needs. In many cases, the question comes down to the unit of analysis: what is it that is being investigated or researched? This presentation will take us through the concept of the unit of analysis so that we can improve our reference service — and make our lives easier as a result! Note: This presentation precedes Working with Complex Surveys: Canadian Travel Survey by Chuck Humphrey (14-Mar-2002).

International Data & Economic Analysis (IDEA) is USAID's comprehensive source of economic and social data and analysis. IDEA brings together over 12,000 data series from over 125 sources into one location for easy access by USAID and its partners through the USAID public website. The data are broken down by countries, years and the following sectors: Economy, Country Ratings and Rankings, Trade, Development Assistance, Education, Health, Population, and Natural Resources. IDEA regularly updates the database as new data become available. Examples of IDEA sources include the Demographic and Health Surveys, STATcompiler; UN Food and Agriculture Organization, Food Price Index; IMF, Direction of Trade Statistics; Millennium Challenge Corporation; and World Bank, World Development Indicators. The database can be queried by navigating to the site displayed in the Home Page field below.

The Social Analytics Applications market has swiftly evolved into an indispensable sector within the broader landscape of digital marketing and data analysis. As businesses increasingly recognize the need to gauge consumer sentiment and engagement across social media platforms, the demand for sophisticated social an

The NATCOOP project set out to study how nature shapes the preferences and incentives of economic agents and how this in turn affects common-pool resource management. Imagine a group of fishermen targeting a species that requires a lot of teamwork to harvest. Do these fishers become more social over time compared to fishers that work in a more solitary manner? If so, does this have implications for how the fishery should be managed? To study this, the NATCOOP team travelled to Chile and Tanzania and collected data using surveys and economic experiments. These two very different countries have a large population of small-scale fishermen, and both host several distinct types of fisheries. Over the course of five field trips, the project team surveyed more than 2500 fishermen with each field trip contributing to the main research question by measuring fishermen’s preferences for cooperation and risk. Additionally, each fieldtrip aimed to answer another smaller research question that was either focused on risk taking or cooperation behavior in the fisheries. The data from both surveys and experiments are now publicly available and can be freely studied by other researchers, resource managers, or interested citizens. Overall, the NATCOOP dataset contains participants’ responses to a plethora of survey questions and their actions during incentivized economic experiments. It is available in both the .dta and .csv format, and its use is recommended with statistical software such as R or Stata. For those unaccustomed with statistical analysis, we included a video tutorial on how to use the data set in the open-source program R.

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts. Note that the pre-registration can be found here: https://osf.io/xs5wf.

ESSTED aims to help students develop and practice their quantitative skills and learn to evaluate and use quantitative evidence. The project involves an interdisciplinary team working to integrate more quantitative data and methods within the social science undergraduate curriculum at the University. The project focuses on embedding relevant quantitative data and methods within substantively focused course units in Politics and Sociology. We deposit only the student survey data from among all our mixed methods data; this is because of anonymity and confidentiality issues relating to the other data. The student survey was conducted on University of Manchester campus 2012-2013 with followup in classrooms in 2014/5.

This project widens the embedding of quantitative data and methods in undergraduate social science.The main strategy is to support lecturers in changing their curriculum and pedagogy to encourage students’ active learning, building up skills through explicit scaffolding of learning.

The project sponsors workshops for teachers/lecturers across the UK, disseminates new teaching/learning methods online and by publication; and evaluates the impact of these changes and how to sustain the changes in teaching methods over time. Two Departments (Sociology and Politics) work closely with the co-investigators.The degrees of BA Econ, BSc Econ, and BA in Social Sciences degree (BASS) are affected.The money supports a research assistant, videos, and other costs.

The proposed step-change in QM embedding involves:

Getting students to generate and interpret their own quantitative survey data on substantive topics. New data about the students themselves follows the question wording found in national surveys. Students also use national and international data.

The project also trains faculty to deal with QM teaching.

It publicises useful Online Education Resources (OERs).

The project is delivered partly by staff in the UK Data Service and in MIMAS, the national data delivery and support service.

United States Avg Weekly Earnings: PB: Social Science & Humanities Research data was reported at 1,236.670 USD in May 2018. This records a decrease from the previous number of 1,277.350 USD for Apr 2018. United States Avg Weekly Earnings: PB: Social Science & Humanities Research data is updated monthly, averaging 1,204.960 USD from Mar 2006 (Median) to May 2018, with 147 observations. The data reached an all-time high of 1,366.750 USD in Aug 2009 and a record low of 982.940 USD in May 2012. United States Avg Weekly Earnings: PB: Social Science & Humanities Research data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G032: Current Employment Statistics Survey: Average Weekly and Hourly Earnings.

The global market for App Data Statistics Tools is experiencing robust growth, driven by the increasing adoption of mobile applications across various sectors and the rising need for data-driven decision-making. This market, estimated at $2.5 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors, including the escalating demand for precise user behavior analysis, the necessity for enhanced app performance optimization, and the growing importance of personalized user experiences. The market is segmented by tool type (customized vs. universal) and application (social, information, gaming, e-commerce, tools, and others). The rise of sophisticated analytics platforms offering comprehensive data visualization and insightful reporting contributes significantly to the market's growth. Furthermore, the increasing adoption of cloud-based solutions simplifies data storage and analysis, enabling businesses of all sizes to leverage app data effectively. Competitive forces are shaping the landscape, with established players and emerging startups continuously innovating to offer advanced features and cater to the diverse needs of developers and businesses. The North American market currently holds a significant share, largely due to the concentration of technology companies and early adoption of advanced analytics tools. However, Asia-Pacific is expected to exhibit the fastest growth during the forecast period, driven by the burgeoning mobile app market in countries like India and China. The market faces certain restraints, such as data privacy concerns and the complexity of integrating different analytics tools. Nevertheless, the continued evolution of mobile app technology, alongside the development of more user-friendly and cost-effective analytics platforms, will continue to propel market expansion over the next decade. This growth underscores the strategic value of app data analytics in understanding user behavior, improving app functionality, and ultimately maximizing business success in the competitive mobile landscape.

Collecting network information on political elites using conventional methods such as surveys and text records is challenging in authoritarian and/or conflict-ridden states. I introduce a data collection method for elite networks using scraping algorithms to capture public co-appearances at political and social events. Validity checks using existing data show the method effectively replicates interaction-based networks but not networks based on behavioral similarities; in both cases, measurement error remains a concern. Applying the method to Nigeria illustrates that patronage---measured in terms of public connectivity---does not drive national-oil-company appointments. Given that theories of elite behavior aim to understand individual-level interactions, the applicability of data using this technique is well-suited to situations where intrusive data collection is costly or prohibitive.

The displayed data shows the results of an expert survey conducted in the United Kingdom in 2019 on the implementation status of the analysis of data from social media platforms at logistics companies. Participants of the survey were employees who have been working in logistics for at least 2 years. Some 19 percent of respondents stated that the implementation of social media data analysis is out of the question for their company.

International Journal of Data Science and Analytics Acceptance Rate - ResearchHelpDesk - International Journal of Data Science and Analytics - Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations.

United States AHE: sa: PW: PB: Social Science & Humanities Research data was reported at 44.560 USD in Dec 2024. This records a decrease from the previous number of 44.820 USD for Nov 2024. United States AHE: sa: PW: PB: Social Science & Humanities Research data is updated monthly, averaging 24.270 USD from Jan 1990 (Median) to Dec 2024, with 420 observations. The data reached an all-time high of 45.130 USD in Oct 2024 and a record low of 13.620 USD in Feb 1990. United States AHE: sa: PW: PB: Social Science & Humanities Research data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G071: Current Employment Statistics Survey: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

This release presents experimental statistics from the Mental Health Services Data Set (MHSDS), using final submissions for April 2016 and provisional submissions for May 2016. This is the fifth monthly release from the dataset, which replaces the Mental Health and Learning Disabilities Dataset (MHLDDS). As well as analysis of waiting times, first published in March 2016, this release includes elements of the reports that were previously included in monthly reports produced from final MHLDDS submissions. In this publication a new data file has been produced to present the data for people identified as having learning disabilities and/or autistic spectrum disorder (LDA) characteristics. Because of the scope of the changes to the dataset (resulting in the name change to MHSDS and the new name for these monthly reports) it will take time to re-introduce all possible measures that were previously part of the MHLDS Monthly Reports. Additional measures will be added to this report in the coming months. Further details about these changes and the consultation that informed were announced in November. From January 2016 the release includes information on people in children and young people's mental health services, including CAMHS, for the first time. Learning disabilities and autism services have been included since September 2014. This release of final data for April 2016 comprises: - An Executive Summary, which presents national-level analysis across the whole dataset and also for some specific service areas and age groups - Data tables about access and waiting times in mental health services for the based on provisional data for the period 1 March 2016 to 31 May 2016. - A monthly data file which presents 92 measures for mental health, learning disability and autism services at National, Provider and Clinical Commissioning Group (CCG) level. - A Currency and Payments (CAP) data file, containing three measures relating to people assigned to Adult Mental Health Care Clusters. Further measures will be added in future releases. - A data file containing the measures relating to people with learning disabilities and/or autism. - Exploratory analysis of the coverage and completeness of access and waiting times statistics for people entering the Early Intervention in Psychosis pathway. - A set of provider level data quality measures for both months. The report comprises of validity measures for various data items at National and Provider level. From the publication of April data, a coverage report is included showing the number of providers submitting each month and number of records submitted. - A metadata file, which provide contextual information for each measure, including a full description, current uses, method used for analysis and some notes on usage. We will release the reports as experimental statistics until the characteristics of data flowed using the new data standard are understood. A correction has been made to this publication on 10 September 2018. This amendment relates to statistics in the monthly CSV data file; the specific measures effected are listed in the “Corrected Measures” CSV. All listed measures have now been corrected. NHS Digital apologises for any inconvenience caused.

The last decade has seen substantial advances in statistical techniques for the analysis of network data, and a major increase in the frequency with which these tools are used. These techniques are designed to accomplish the same broad goal, statistically valid inference in the presence of highly interdependent relationships, but important differences remain between them. We review three approaches commonly used for inferential network analysis---the Quadratic Assignment Procedure, Exponential Random Graph Model, and Latent Space Network Model---highlighting the strengths and weaknesses of the techniques relative to one another. An illustrative example using climate change policy network data shows that all three network models outperform standard logit estimates on multiple criteria. This paper introduces political scientists to a class of network techniques beyond simple descriptive measures of network structure, and helps researchers choose which model to use in their own research.

The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.