This dataset were generated from, on the one hand, music performance analysis on two samples of recording excerpts and, on the other, data from a survey of music perception using the same musical recording excerpts as the analysis set, conducted with several groups of university students. The intent of the data set is to have the date to later be able to compare different music performance parameters with their perception, as seen in the qualitative and quantitative ratings by non-musician listeners.

This manual provides guidance on how to create a pair analysis file and on the appropriate weights and design variables needed to analyze pair data, and it provides example code in multiple software packages.

The data and analysis of the surveys to study the users' opinion about the presence of an avatar during a learning experience in Mixed Reality. Also there are demographic data and the open questions collected. This data was used in the paper Evaluating the Effectiveness of Avatar-Based Collaboration in XR for Pump Station Training Scenarios for the GeCon 2024 Conference.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

These datasets contain cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. These specific iterations of data reflect cleaning and standardization so that data can be analyzed using Python. Ultimately, the three files reflect the removal of survey begin/end times, other data auto-recorded by Qualtrics, blank rows, blank responses after question four (the first section of the survey), and non-United States responses. Note that State names for "What state is your library located in?" (Q36) were also standardized beginning in Impact_of_COVID_on_Tech_Services_Clean_3.csv to aid in data analysis. In this step, state abbreviations were spelled out and spelling errors were corrected.

Analysis of ‘Survey data: Mapping of transnational collaborative partnerships’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/1a76e5d7-3912-455d-8662-cd9870d4c2b9 on 13 November 2021.

--- Dataset description provided by original source is as follows ---

Survey data from 2018: Mapping of transnational collaborative partnerships

--- Original source retains full ownership of the source dataset ---

DuPage County is seeking bids for Survey and Data Analysis System due 2024-05-14T05:00:00.000Z

Replication files for the article "IPD Meta-Analysis of Complex Survey Data: A Simulation Study"

Healthcare Survey Software Market Outlook

The global healthcare survey software market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 5.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 16.2% during the forecast period. The key growth factor propelling the healthcare survey software market includes the increasing importance of patient feedback and data analytics in improving healthcare services and outcomes.

Healthcare organizations are increasingly recognizing the value of patient feedback in enhancing the quality of care. As healthcare becomes more patient-centric, the demand for efficient tools to gather, analyze, and act on patient feedback has surged. Survey software provides a streamlined method for collecting patient opinions on various aspects of their care, including satisfaction with treatment, interaction with healthcare staff, and overall experience. This feedback is invaluable for healthcare providers aiming to improve service delivery, patient satisfaction, and clinical outcomes.

Another significant growth factor is the advancement in data analytics and integration capabilities. Modern healthcare survey software solutions are equipped with powerful analytics tools that allow organizations to derive actionable insights from collected data. These insights can be used to identify trends, pinpoint areas for improvement, and make informed decisions. The ability to integrate survey data with electronic health records (EHRs) and other healthcare systems further enhances the utility of these platforms, providing a comprehensive view of patient health and experiences.

The shift towards digitalization in healthcare is also driving market growth. The adoption of digital tools for various administrative and clinical tasks has increased efficiency and accuracy, and survey software is no exception. The convenience of deploying surveys through digital platforms, including mobile and web applications, ensures higher response rates and real-time data collection. Additionally, the cloud-based deployment of survey software solutions offers scalability, remote accessibility, and reduced IT overheads, which are particularly beneficial for large healthcare organizations.

Survey Software plays a pivotal role in the healthcare industry by providing comprehensive tools for collecting and analyzing patient feedback. These platforms enable healthcare providers to design customized surveys that capture detailed patient experiences, leading to more personalized care. The integration of survey software with existing healthcare systems allows for seamless data collection and analysis, offering insights that drive improvements in patient care and operational efficiency. As the demand for precise and actionable patient feedback grows, survey software becomes an indispensable asset in the healthcare sector, facilitating better decision-making and enhancing patient satisfaction.

Regionally, North America holds the largest market share due to the advanced healthcare infrastructure, high adoption rate of digital technologies, and the presence of major market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Factors contributing to this growth include increasing investments in healthcare infrastructure, rising awareness about the importance of patient feedback, and the burgeoning use of digital healthcare solutions. Europe also shows significant potential, driven by regulatory support for patient-centric care and the adoption of advanced healthcare technologies.

Component Analysis

The healthcare survey software market is segmented by component into software and services. The software segment includes various types of survey software solutions that facilitate the creation, distribution, and analysis of surveys. These solutions come with features like customizable templates, automated survey distribution, and advanced analytics. The growing need for real-time feedback and data-driven decision-making is driving the adoption of sophisticated software solutions in the healthcare sector.

Within the software segment, the demand for advanced analytics tools is particularly high. These tools enable healthcare providers to analyze survey data comprehensively and derive actionable insights. The integration capabilities of software solutions with other healthcare systems, such as EHRs and patient management systems, further enh

Market Size and Growth: The global online survey tool market is projected to reach a value of $1608.6 million by 2033, expanding at a CAGR of 2.3% from 2025 to 2033. The growing demand for gathering customer feedback, improving employee engagement, and conducting market research is driving the market's growth. Key technological advancements such as mobile-friendly surveys and advanced data analytics capabilities are further fueling adoption. Key Trends and Competitive Landscape: The increasing popularity of online survey tools in industries such as healthcare, education, and retail is a major trend shaping the market. Moreover, the rise of artificial intelligence (AI) and machine learning (ML) is enabling survey tools to improve accuracy, reduce bias, and generate insights in real-time. The market is highly competitive, with established players such as SurveyMonkey, Google, and Typeform dominating the market. However, numerous emerging startups are entering the market, offering specialized solutions and innovative features.

education and skills - hands-up survey analysis by travel mode (2021)

This data set is provided by EOS-EARTHDATA (formerly EOS-Webster). It provides acreage, production and yield statistics for U.S. field crops from the National Agricultural Statistical Service (NASS) for the years 1970 through 2003. Data can be subset by irrigated and non-irrigated areas. Sucrose content, where applicable, is also included. Data are at the county scale and include all counties in the conterminous USA. No spatial subsets are available. For more information, see the Data Guide. Data after 2003 may be obtained from NASS.

Analysis of ‘Great Streets Business Survey 2015’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e82a863e-79fb-4a6d-839e-bb680014befb on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Learn more about Mayor Garcetti's Great Streets Initiative here http://lagreatstreets.org/ Survey was taken May-June 2015.

--- Original source retains full ownership of the source dataset ---

SDA@Chass provides access to Canadian microdata (including DLI surveys), to aggregated census data, and to various other collections of data (opinion polls, international data, etc.). Users can analyze datasets online, download data, or consult relevant documentation.

This dataset contains the script used to interview the archivists at the DGLAB (General Directorate of Archives, Libraries and Books) and the questions analysed in the DGLAB satisfaction survey. Both documents have been translated into English. It also contains the form with the interview questions in Portuguese, filled by the archivists who were unavailable for the interviews.

The online survey software and tools market is experiencing robust growth, projected to reach a substantial market size. The market's Compound Annual Growth Rate (CAGR) of 9.2% from 2019-2033 indicates a consistent upward trajectory, driven by several key factors. The increasing need for businesses to gather customer feedback efficiently and cost-effectively is a major catalyst. Furthermore, the rising adoption of digital technologies across various industries, coupled with the growing sophistication of survey tools (including advanced analytics and integrations with other business software), fuels market expansion. The ability to automate data collection, analysis, and reporting significantly reduces operational overhead and improves decision-making speed for companies of all sizes. This demand is further amplified by the need for real-time insights into customer sentiment, brand perception, and market trends, all readily achievable through sophisticated online survey platforms. Several factors contribute to this growth, including the ease of use and accessibility of online survey platforms, the affordability of many solutions, and the wide range of features offered. The market comprises diverse players ranging from established giants like SurveyMonkey and Qualtrics to specialized niche providers. This competition fosters innovation and drives down prices, making these tools accessible to a broader range of users. While data security and privacy remain crucial concerns, robust solutions are being continuously developed to mitigate these risks. The market’s segmentation is likely to evolve with the emergence of new features and tools catering to specialized industries and customer needs, maintaining strong growth over the forecast period. The projected market value for 2025, considering the 9.2% CAGR and the 2019-2024 historical period, is estimated to be significantly larger than 1742.7 million, showcasing strong market potential.

Most preferred sample type for diagnostic testing, ranked globally.

Copies of Anaconda 3 Jupyter Notebooks and Python script for holistic and clustered analysis of "The Impact of COVID-19 on Technical Services Units" survey results. Data was analyzed holistically using cleaned and standardized survey results and by library type clusters. To streamline data analysis in certain locations, an off-shoot CSV file was created so data could be standardized without compromising the integrity of the parent clean file. Three Jupyter Notebooks/Python scripts are available in relation to this project: COVID_Impact_TechnicalServices_HolisticAnalysis (a holistic analysis of all survey data) and COVID_Impact_TechnicalServices_LibraryTypeAnalysis (a clustered analysis of impact by library type, clustered files available as part of the Dataverse for this project).

dataset used in descriptive analysis

Abstract

Protracted and new displacements of large numbers of people as well as complex conflict dynamics continue to be a major issue in Darfur. In 2020, an estimated 2.5 million people were internally displaced and close to 400,000 Darfuris refugees resided in neighbouring countries. The political transition following years of conflict paved the way for the signing of the Juba Peace Agreement (JPA) in 2020. The peace agreement aims to address the root causes of conflict but also establishes durable solutions for displaced populations as a necessity for lasting peace in Darfur. In 2021, the Government furthermore initiated work on a National Strategy on Solutions, which will offer a critical strategic framework and operational roadmap towards solutions for displaced communities in Sudan.

In 2017, the Government of Sudan (GoS) and the international community agreed on the need to collectively support Durable Solutions for IDPs, returnees, and their host communities to end the situation of protracted displacement. The collaboration on Durable Solutions between the GoS and international community resulted in two Durable Solution pilots in respectively El Fasher (North Darfur) and Um Dukhun (Central Darfur). JIPS provided technical support for the scale-up of the durable solutions analysis across Darfur under the Central Emergency Relief Fund (CERF).

Focusing on nine localities, including urban areas, the data collection exercises build directly on the durable solutions analysis approach piloted in El Fasher in 2019. The Durable Solutions Working Group (DSWG) identified a joint evidence base and a collaborative approach as priorities and therefore undertook a joint area-based profiling exercise, focusing on the Abu Shouk and El Salaam IDP camps on the outskirts of El Fasher.

The focus was set on profiling of IDPs (in camp settlements and out of camps), IDP returnees, refugee returnees, and non-displaced. The profiling exercises are aimed at: i.Informing CERF programming and Action Plan development in each state/locality; ii.Provide the baseline of the agreed upon CERF outcome/output indicators (for later measurement of impact); and iii.Inform broader UNHCR programming beyond the Fund.

Geographic coverage

Kaas locality within South Darfur State. Considering the difference in the geographic context (urban vs. rural) within Kass, it was agreed to divide Kass into two separate clusters (urban and rural) and treat them as separate entities. Hence, each cluster is considered as a locality within Kass

Analysis unit

Households

Universe

All IDP returnees, refugee returnees, IDPs in camps and out of camps, and non-displaced populations across Kaas.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling followed a systematic simple random approach, through which the households were treated as the primary sampling unit. The sample size for each target group was identified proportionately based on the group's population size. The sampling is designed to produce results representative for each target group in the targeted area of the locality. Analysis at the settlement level is not possible. The selection of settlements included in each locality is based on a prioritization by partner agencies and local partners based on the programmatic scope of the CERF. The data is thus not representative of whole locality, but the specific geographic scope targeted within the locality.

The total sample included: 792 households (HHs), including IDPs in camps and the town (394 HHs), and non-displaced (398 HHs). In Kass rural cluster, the total achieved sample sizes included: 343 IDPs households residing outside of camps and 543 IDP-returnee households. Additionally, 66 non-displaced households and 50 return refugee households were captured but excluded from the analysis due to the small sample sizes.

The sample frame of the household survey was based on the population estimates of each target group, that were provided by key informants and validated through fieldwork missions.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Some households with over 14 members have had individuals removed from their household roster due to anonymization techniques.