This is a hands-on workshop on the management of qualitative social science data, with a focus on data sharing and transparency. While the workshop addresses data management throughout the lifecycle – from data management plan to data sharing – its focus is on the particular challenges in sharing qualitative data and in making qualitative research transparent. One set of challenges concerns the ethical and legal concerns in sharing qualitative data. We will consider obtaining permissions for sharing qualitative data from human participants, strategies for (and limits of) de-identifying qualitative data, and options for restricting access to sensitive qualitative data. We will also briefly look at copyright and licensing and how they can inhibit the public sharing of qualitative data.

A second set of challenges concerns the lack of standardized guidelines for making qualitative research processes transparent. Following on some of the themes touched on in the talk, we will jointly explore some cutting edge approaches for making qualitative research transparent and discuss their potentials as well as shortcomings for different forms of research.

Qualitative Data Analysis Software Market Outlook

In 2023, the global qualitative data analysis software market size was valued at approximately USD 1.2 billion. With an impressive compound annual growth rate (CAGR) of 15%, the market is projected to reach USD 3.3 billion by 2032. This growth is driven by an increasing demand for data-driven decision-making processes across various industries, as well as advancements in artificial intelligence and machine learning technologies that are enhancing the capabilities of qualitative data analysis tools. Organizations are increasingly recognizing the value of qualitative insights, which complement quantitative data by providing deeper, context-rich understanding of phenomena, which is a significant growth factor in this market.

The demand for qualitative data analysis software is expanding due to the growing need for holistic research methods that incorporate diverse data types. In academic research, qualitative data analysis plays a critical role in understanding complex social phenomena by analyzing text, audio, video, and images. The rise of interdisciplinary studies that demand robust qualitative analysis solutions is propelling software adoption. Additionally, the business and enterprise sector has increasingly leveraged these tools to extract consumer insights from unstructured data sources like social media, reviews, and customer feedback. These insights are crucial for developing marketing strategies and enhancing customer engagement, thus driving market growth.

Healthcare is another sector significantly contributing to the market's expansion. Qualitative data analysis is crucial for understanding patient narratives and improving patient-centered care models. With the shift towards personalized medicine, healthcare providers are utilizing qualitative insights to better comprehend patient experiences and treatment outcomes. Moreover, the integration of qualitative data analysis tools with other healthcare systems is enhancing clinical research and operational efficiency. The continuous development in healthcare analytics and the increasing volume of healthcare data are expected to further boost demand in this sector.

Government and public sector organizations are also adopting qualitative data analysis software to improve policy formulation and public services. By analyzing feedback from citizens and stakeholders, governments can make informed decisions that address public needs more effectively. The growing emphasis on transparency and accountability in governance is driving the adoption of these tools. Additionally, the ongoing digital transformation across public sectors globally is facilitating the integration of advanced data analysis tools in government operations, thus contributing to the market's growth.

Regionally, North America dominates the market due to its advanced technological infrastructure and high adoption rate of data-driven decision-making processes across various sectors. Europe follows, with a strong presence of academic research institutions and enterprises investing in qualitative data analysis tools. The Asia Pacific region is expected to witness the fastest growth, driven by rapid digitalization and increasing research activities in countries like China, India, and Japan. Latin America and the Middle East & Africa regions are also beginning to explore the potential of qualitative data analysis, although they currently constitute a smaller portion of the market.

Component Analysis

The qualitative data analysis software market is segmented by component into software and services. The software segment is the backbone of the market, offering a variety of tools that allow users to code, categorize, and analyze qualitative data. The demand for sophisticated software solutions is rising as organizations seek tools that offer enhanced features such as data visualization, collaboration capabilities, and integration with other data sources. The push towards comprehensive data analysis platforms that can manage large datasets and provide intuitive interfaces is driving innovation in software development. Furthermore, the integration of artificial intelligence into these software solutions is significantly enhancing their capabilities, making them more efficient and reducing the time required for data analysis.

In contrast, the services segment encompasses a range of offerings including consulting, implementation, training, and support services. As organizations increasingly adopt sophisticated qualitative data analysis tools, there is a growing need for professional services to ensure

In 2022, ************** were the most used traditional qualitative methodologies in the market research industry worldwide. During the survey, ** percent of respondents stated that they regularly used this method. Second in the list was data visualization/dashboards, where ** percent of respondents gave this as their answer.

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 23-Jun-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute. The dataset forms part of three outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023:

· Fostering cultures of open qualitative research: Dataset 1 – Survey Responses · Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts · Fostering cultures of open qualitative research: Dataset 3 – Coding Book

The project was funded with £13,913.85 of Research England monies held internally by the University of Sheffield - as part of their ‘Enhancing Research Cultures’ scheme 2022-2023.

The dataset aligns with ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee (ref: 051118) on 23-Jan-2021. This includes due concern for participant anonymity and data management.

ORDA has full permission to store this dataset and to make it open access for public re-use on the basis that no commercial gain will be made form reuse. It has been deposited under a CC-BY-NC license. Overall, this dataset comprises:

· 15 x Interview transcripts - in .docx file format which can be opened with Microsoft Word, Google Doc, or an open-source equivalent.

All participants have read and approved their transcripts and have had an opportunity to retract details should they wish to do so.

Participants chose whether to be pseudonymised or named directly. The pseudonym can be used to identify individual participant responses in the qualitative coding held within the ‘Fostering cultures of open qualitative research: Dataset 3 – Coding Book’ files.

For recruitment, 14 x participants we selected based on their responses to the project survey., whilst one participant was recruited based on specific expertise.

· 1 x Participant sheet – in .csv format which may by opened with Microsoft Excel, Google Sheet, or an open-source equivalent.

The provides socio-demographic detail on each participant alongside their main field of research and career stage. It includes a RespondentID field/column which can be used to connect interview participants with their responses to the survey questions in the accompanying ‘Fostering cultures of open qualitative research: Dataset 1 – Survey Responses’ files.

The project was undertaken by two staff:

Co-investigator: Dr. Itzel San Roman Pineda ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk Postdoctoral Research Assistant Labelled as ‘Researcher 1’ throughout the dataset

Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk Research Associate iHuman Institute, Social Research Institutes, Faculty of Social Science Labelled as ‘Researcher 2’ throughout the dataset

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, clinical researchers struggle to find real-world examples of Open Data sharing. The aim of this 1 hr virtual workshop is to provide real-world examples of Open Data sharing for both qualitative and quantitative data. Specifically, participants will learn: 1. Primary challenges and successes when sharing quantitative and qualitative clinical research data. 2. Platforms available for open data sharing. 3. Ways to troubleshoot data sharing and publish from open data. Workshop Agenda: 1. “Data sharing during the COVID-19 pandemic” - Speaker: Srinivas Murthy, Clinical Associate Professor, Department of Pediatrics, Faculty of Medicine, University of British Columbia. Investigator, BC Children's Hospital 2. “Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project.” - Speaker: Maggie Woo Kinshella, Global Health Research Coordinator, Department of Obstetrics and Gynaecology, BC Children’s and Women’s Hospital and University of British Columbia This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Srinivas Murthy: Data sharing during the COVID-19 pandemic presentation and accompanying PowerPoint slides. Maggie Woo Kinshella: Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

Data management is a critical aspect of empirical research. Unfortunately, principles of good data management are rarely taught to social scientists in a systematic way as part of their methods training. As a result, researchers often do things in an ad hoc fashion and have to learn from their mistakes.

The Qualitative Data Repository (QDR, www.qdr.org) presented a webinar on social science data management, with a special focus on keeping qualitative data safe and secure. The webinar will emphasize best practices with the aim of helping participants to save time and minimize frustration in their future research endeavors. We will cover the following topics:

1) The value of planning and Data Management Plans (DMPs)

2) Transparency and data documentation

3) Ethical, legal, and logistical challenges to sharing qualitative data and best practices to address them

4) Keeping data safe and secure.

Attribution: Parts of this presentation are based on slides used in a course co-taught by personnel from QDR and the UK Data Service. All materials provided under a CC-BY license.

Qualitative Data Analysis Software Market size was valued at USD 1.2 Billion in 2024 and is projected to reach USD 1.9 Billion by 2032, growing at a CAGR of 6% from 2026 to 2032.

Global Qualitative Data Analysis Software Market Overview

In the report, the market outlook section mainly encompasses the fundamental dynamics of the market which include drivers, restraints, opportunities, and challenges faced by the industry. Drivers and restraints are intrinsic factors whereas opportunities and challenges are extrinsic factors of the market.

The proliferation of open-source frameworks for big data analytics and the ability of powerful HPC systems to process data at higher resolutions drive the Qualitative Data Analysis Software Market. High investment costs involved in the deployment of HPC systems, Government rules, and regulations act as a restrain to the market.

The data comprises of two datasets. The first consists of text files of anonymised transcripts from focus group discussions (FGDs) on livelihood activities, ecosystem services and the prevalent human and animal health problems in irrigated and non-irrigated areas in northeastern Kenya. The second comprises of scores from proportional piling exercises which showed the distribution of wealth categories and livestock species kept. The study was conducted between August and October, 2013 and the data were collected as open-ended meeting notes and audio clips captured using digital recorders. Written/thumb print consent was always obtained from each individual in the group. All the discussions were also recorded, with the participant's permission. Thirteen FGDs were held in the irrigated areas in Bura and Hola, Tana River County involving farmers who grew a variety of crops for subsistence and commercial purposes. The others were held in Ijara and Sangailu, Garissa County inhabited by transhumance pastoralists. Each group comprised of 10 to 12 people and the discussions were guided by a check list. The transcribed documents were formatted in Microsoft Word (2013) and saved as text files in preparation for analysis. The aim of the study was to collate perceptions of land use change and their effects on ecosystem services. The data were collected by enumerators trained by experienced researchers from the University of Nairobi and the International Livestock Research Institute (Kenya). This dataset is part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC). The research was funded by NERC project NE-J001570-1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Additional funding was provided by the CGIAR Research Program Agriculture for Nutrition and Health.

This dataset has an access level Restricted, which means it is not available via direct download but must be requested. Our accessing research data guidance outlines the reasons access may be limited and the request process. In order to request access to this data please complete the data request form.*

Spreadsheets about Qualitative Research prevalence over time.These spreadsheets contain the data used in the figures of the following paper, plus over 100 additional figures.Is research with qualitative data more prevalent and impactful now? Interviews, case studies, focus groups and ethnographies.Library & Information Science Research.

We propose a framework for meta-analysis of qualitative causal inferences. We integrate qualitative counterfactual inquiry with an approach from the quantitative causal inference literature called extreme value bounds. Qualitative counterfactual analysis uses the observed outcome and auxiliary information to infer what would have happened had the treatment been set to a different level. Imputing missing potential outcomes is hard and when it fails, we can fill them in under best- and worst-case scenarios. We apply our approach to 63 cases that could have experienced transitional truth commissions upon democratization, 8 of which did. Prior to any analysis, the extreme value bounds around the average treatment effect on authoritarian resumption are 100 percentage points wide; imputation shrinks the width of these bounds to 51 points. We further demonstrate our method by aggregating specialists' beliefs about causal effects gathered through an expert survey, shrinking the width of the bounds to 44 points.

Qualitative health data are rarely shared in the United States. The U.S. qualitative researchers (N = 425) were surveyed on the barriers and facilitators of sharing qualitative health or sensitive research data. Most researchers (96%) have never shared qualitative data in a repository. Primary concerns were lack of participant permission to share data, data sensitivity, and breaching trust. Willingness to share would increase if participants agreed and if sharing increased the societal impact of their research. Key resources to increase willingness to share were funding, guidance, and de-identification assistance. Public health and biomedical researchers were most willing to share.

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

Stay updated with Market Research Intellect's Qualitative Data Analysis Software Market Report, valued at USD 450 million in 2024, projected to reach USD 1.1 billion by 2033 with a CAGR of 10.5% (2026-2033).

This study uses data from demographic surveys and semi-structured, in-depth qualitative interviews with 30 individuals who had experience participating in sensitive qualitative research studies to explore their understanding and concerns about qualitative data sharing. Participants were recruited from a research volunteer registry based in the Midwestern United States for an online pre-interview demographics survey and a phone interview.

The qualitative data that is coded by the researcher

Please be advised that this project is intended solely for instructional purposes and should not be used for actual research. This dataset is intended to complement the instructional material and provide a hands-on learning experience for the workshop: Handling and Sharing Qualitative Data Responsibly and Effectively.

This hypothetical research project is designed to demonstrate key concepts related to human subject qualitative data management and thematic analysis coding. It includes interview transcripts generated with ChatGPT 4.0 Mini for a fictional graduate student in Communication named Sarah, whose main research question is: How do content creators/digital influencers view their role in shaping their followers' consumer behavior, and what ethical dilemmas do they face when promoting products?

Given the novelty of this research topic and the limited academic literature available, Sarah hopes that the insights gained from this small-scale qualitative exploratory study will help identify key variables for a larger survey study with a representative sample of content creators/digital influencers across the U.S.

Sarah has previous experience with quantitative methods but is very new to qualitative research and could use our help for better handling the data. Having already conducted six short structured interviews with subjects from top revenue niches (i.e., Home Decor and DYI, Travel & Adventure, Fashion & Style, Health & Wellness, Finance & Investment, Beauty & Skincare) and planning to conduct a dozen more, Sarah is eager to begin engaging with the data she has collected so far and deciding how to best organize and interpret it. We’ll be walking her through this process, providing the necessary guidance and support for effective and responsible data management.

Interviews were conducted over Zoom and audio recorded with participants' consent. The interview included four main questions, which were consistent across all interviews:

Q1. Please tell me a little about your work as a content creator/digital influencer how it started, and how you have established yourself in your current niche.

Q2. In what ways do you believe content creators/digital influencers shape consumer behavior? Could you share any examples?

Q3. What strategies would you say content creators/digital influencers typically use to increase sales of sponsored products and services? Which ones have you used? What worked and what did not work for you? Why?

Q4. In your view, what are the essential ethical responsibilities that content creators and digital influencers should uphold? Can you share any personal experiences that illustrate these responsibilities in action?

Each interview generated approximately 15 minutes of audio recording, which Sarah manually transcribed. Sarah decided to keep the transcription true to the recordings and seek assistance to mitigate any risk of identification.

Financial overview and grant giving statistics of Consortium for Qualitative Research Methods

Qualitative Data Analysis Software Market Overview: The global qualitative data analysis (QDA) software market is poised for significant growth, with a market size projected to reach XXX million by 2033, registering a CAGR of XX% from 2025 to 2033. The rising need for efficient data analysis and interpretation in various industries, such as market research, customer experience management, and social media analysis, drives market expansion. Additionally, the increasing adoption of cloud-based QDA solutions and advances in artificial intelligence and machine learning contribute to market growth. Market Drivers, Trends, and Restraints: The demand for QDA software is driven by the increasing complexity and volume of qualitative data, the need for in-depth insights, and the growing awareness of the importance of qualitative research. Key market trends include the adoption of mobile and cloud-based QDA solutions, the integration of AI and machine learning for data interpretation, and the emergence of specialized software for specific industries. However, challenges such as data security and privacy concerns, the high cost of advanced QDA tools, and the need for specialized skills and training could restrain market growth to some extent.

Data represent information extracted from published literature meeting filtering criteria regarding quantification of among-individual variation in spatial behaviors. Information includes manuscript identifiers, descriptions of study design, as well as information directly input into a statistical meta-analysis regression framework.