Science method
Qualitative Research - Science method
Qualitative Research are research that derives data from observation, interviews, or verbal interactions and focuses on the meanings and interpretations of the participants (From Holloway and Wheeler, "Ethical issues in qualitative nursing research," Nursing Ethics, 1995 Sep; 2(3): 223-232).
Questions related to Qualitative Research
Me and my fellow student researchers are currently writing a paper on how engagement with online freedom walls may correlate with students' perception of their learning environment. Due to this, we have chosen an explanatory sequential research design, to further understand why our quantitative data is the way it is. However, we are quite unsure whether the qualitative phase of our study is phenomenological or narrative in nature. May someone please enlighten us? Thank you.
Qualitative research is a powerful tool in the social sciences, humanities, education and other disciplines. Unlike quantitative research which focuses on numbers and statistical analysis, qualitative research seeks to understand the deeper meanings, experiences and perspectives of individuals and groups. It provides rich, detailed insights that help researchers gain a holistic understanding of complex phenomena. This article explores the best practices for conducting qualitative research. These practices guide you in designing, collecting, analyzing and presenting qualitative data in a way that ensures rigor, reliability and ethical integrity.
Qualitative research is a method of inquiry that focuses on exploring phenomena from a subjective, in-depth perspective. It is often used to investigate complex social, cultural, psychological or behavioral issues that cannot be easily quantified. Unlike quantitative research which collects numerical data and uses statistical methods to test hypotheses, qualitative research deals with non-numerical data such as interviews, focus groups, observations and textual analysis.
Characteristics of Qualitative Research
Qualitative research is often used to explore new areas of study where little prior knowledge exists. It emphasizes understanding the context in which the research occurs, such as the cultural, social or historical backdrop. Qualitative research design is flexible as it allows researchers to adapt their approach as they collect and analyze data. It also focuses on personal experiences, meanings and interpretations. Common qualitative research methods include; interviews (structured, semi-structured, or unstructured), focus groups, observations, case studies, ethnography, content analysis and narrative analysis.
Best Practices in Qualitative Research
When designing and conducting qualitative studies, you need to consider the following key practices to ensure the quality and rigor of your qualitative research.
· Clear research objectives; before beginning any qualitative study, it is crucial to clearly define the research objectives. These objectives should address the research question or problem that you aim to explore. A focused and well-defined research question will guide the design, data collection and analysis stages of the research. Your research question should be open-ended and exploratory to allow for detailed responses.
For example: Instead of asking, "How many people use social media?" a qualitative question would be, "How do individuals experience social media use in their daily lives?"
As AI tools are quite prominent so experienced researchers can tell it . Whether it is ethical or not...
In the rapidly evolving world of academic research, artificial intelligence (AI) is emerging as a transformative force. While AI has made its mark in industries like healthcare, finance and retail, its potential in research methodology is equally profound. Researchers in various fields are increasingly leveraging AI to enhance the efficiency, accuracy and scope of their research processes.
Research methodology refers to the techniques and processes used to collect, analyze and interpret data. Traditionally, this has involved manual processes, including designing experiments, conducting surveys and analyzing results. However, the advent of AI is reshaping these methods, offering new tools to automate tasks, uncover patterns and perform complex analyses that were previously time-consuming or impossible. This article explores how AI is being integrated into research methodology, focusing on its impact on data collection, analysis, hypothesis testing and overall research efficiency. It also discusses the benefits and challenges of incorporating AI into research practices.
AI in Data Collection
Automate data gathering; one of the most time-consuming aspects of research is data collection. Traditionally, researchers rely on manual data-gathering methods such as surveys, interviews and fieldwork. However, AI is revolutionizing this process by automating data collection, thereby saving time and reducing human error.
Web scraping and Text mining; AI-powered tools can now collect vast amounts of data from online sources in real time. Web scraping and text mining are common techniques used to extract useful data from websites, journals and other digital repositories. For instance, AI algorithms can scan academic databases and extract relevant research papers, articles and other scholarly content, significantly reducing the time spent searching for sources. Similarly, text mining can help researchers analyze large datasets of unstructured text. AI systems can automatically identify key themes, trends, and even sentiment from text-based data, making it easier to derive meaningful insights.
Surveys and questionnaires; AI can also be used to streamline the process of survey design and distribution. Natural Language Processing (NLP) algorithms can assist in generating relevant questions for participants, automatically adjusting the complexity of language based on respondent demographics. Additionally, AI can analyze responses in real-time, identifying patterns and trends across diverse groups without manual coding.
Remote data collection; for fields that require real-time data, such as environmental studies or public health, AI-enabled IoT (Internet of Things) devices can continuously gather data from sensors placed in the field. This method ensures that data collection is consistent and accurate, reducing human error and increasing the scope of research.
AI in Data Analysis
Enhance quantitative and qualitative data analysis: Once data is collected, the next step is data analysis. AI has significantly improved both quantitative and qualitative data analysis, allowing researchers to work with much larger datasets and uncover patterns that were previously hidden.
Statistical analysis and predictive modeling: AI's ability to perform complex statistical analysis and predictive modeling has made it indispensable in quantitative research. Machine learning (ML) algorithms can be trained to identify correlations, predict outcomes, and provide insights that would be challenging to discern manually. For example, regression analysis, a common statistical method used to understand relationships between variables, can now be automated using AI tools, allowing researchers to model and predict trends faster and with greater precision. Moreover, predictive analytics can help researchers anticipate future outcomes based on historical data, making it a valuable tool in fields like economics, epidemiology, and climate science.
Data visualization: AI can also aid in data visualization, converting complex datasets into easily interpretable charts, graphs, and diagrams. By using AI-driven visualization tools, researchers can instantly generate visual representations of their data, helping to communicate findings more effectively to both academic and non-academic audiences.
Qualitative data analysis with NLP: For qualitative research, AI-powered Natural Language Processing (NLP) algorithms are increasingly being used to analyze text-based data, such as interviews, focus group discussions, and open-ended survey responses. NLP algorithms can detect themes, emotions, and sentiments in textual data, enabling researchers to process large volumes of qualitative data efficiently. NLP also allows for the categorization of responses and the identification of key trends across multiple sources, which is especially useful in fields like social sciences, psychology, and humanities.
Automated coding and Thematic analysis: AI tools can assist researchers in coding qualitative data, a process that typically requires manual intervention. Automated coding software can identify and categorize patterns in text data, allowing researchers to organize and analyze large datasets with greater speed and consistency.
Hello, I am a novice researcher and I would like to do a qualitative study exploring barriers and facilitators. I had planned to use the theoretical. Domain framework consisting of 14 domains to guide to my questions. But due to the exploratory nature of qualitative research, I was going to use a funnel system going from broad to more specific questions related to my framework. However, I am now concerned that there would be too many questions in my focus group. Does anybody know what is the upper limit of questions within a focus group or how could I get around this issue. Thanks. Jim.
Hello everyone,
I am exploring the use of Q methodology in education and have some questions regarding its application:
- As Q methodology leans towards a qualitative research approach, is it necessary to validate the reliability and validity of the questionnaires used, or can we directly adapt established questionnaires for our specific context?
- In Q methodology, the balance between positive and negative statements is typically aimed for. However, many questionnaires predominantly feature positive statements. How do you typically adjust the items to achieve a balanced distribution?
I am a PhD student studying inclusion and teaching foreign languages to learners with special educational needs. A few months ago I started my research of inclusion in rural schools in Russia. I see it as a qualitative research with text analysis of several interviews of rural teachers. I thought that problems of rural schools are similar to a large extent in different countries, and it would be interesting to compare the results and write up an article together.
So if there is anyone who would like to participate in this research in other countries and work together at the article, please let me know.
Data analysis is a fundamental aspect of academic research, enabling researchers to make sense of collected data, draw meaningful conclusions, and contribute to the body of knowledge in their field. This article examines the critical role of data analysis in academic research, discusses various data analysis techniques and their applications, and provides tips for interpreting and presenting data effectively.
Overview of Data Analysis in Research
Data analysis involves systematically applying statistical and logical techniques to describe, summarize, and evaluate data. It helps researchers identify patterns, relationships, and trends within the data, which are essential for testing hypotheses and making informed decisions. Effective data analysis ensures the reliability and validity of research findings, making it a cornerstone of academic research.
Descriptive vs. Inferential Statistics
1. Descriptive Statistics:
• Purpose: Descriptive statistics summarize and describe the main features of a dataset. They provide simple summaries about the sample and the measures.
• Techniques: Common techniques include measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and graphical representations (histograms, bar charts, scatter plots).
• Applications: Descriptive statistics are used to present basic information about the dataset and to highlight potential patterns or anomalies.
2. Inferential Statistics:
• Purpose: Inferential statistics allow researchers to make inferences and predictions about a population based on a sample of data. They help determine the probability that an observed difference or relationship is due to chance.
• Techniques: Common techniques include hypothesis testing (t-tests, chi-square tests), confidence intervals, regression analysis, and ANOVA (analysis of variance).
• Applications: Inferential statistics are used to test hypotheses, estimate population parameters, and make predictions about future trends.
Qualitative Data Analysis Methods
1. Content Analysis:
• Purpose: Content analysis involves systematically coding and categorizing textual or visual data to identify patterns, themes, and meanings.
• Applications: Used in fields such as sociology, psychology, and media studies to analyze interview transcripts, open-ended survey responses, and media content.
2. Thematic Analysis:
• Purpose: Thematic analysis focuses on identifying and analyzing themes or patterns within qualitative data.
• Applications: Commonly used in social sciences to analyze interview data, focus group discussions, and qualitative survey responses.
3. Grounded Theory:
• Purpose: Grounded theory involves generating theories based on data collected during the research process. It is an iterative process of data collection and analysis.
• Applications: Used in fields such as sociology, education, and health sciences to develop new theories grounded in empirical data.
4. Narrative Analysis:
• Purpose: Narrative analysis examines the stories or accounts provided by participants to understand how they make sense of their experiences.
• Applications: Used in psychology, anthropology, and literary studies to analyze personal narratives, life histories, and case studies.
Tools and Software for Data Analysis
1. Statistical Software:
• SPSS: Widely used for statistical analysis in social sciences. It offers a range of statistical tests and data management tools.
• R: A powerful open-source software for statistical computing and graphics. It is highly extensible and widely used in academia.
• SAS: A comprehensive software suite for advanced analytics, multivariate analysis, and data management.
2. Qualitative Data Analysis Software:
• NVivo: A popular software for qualitative data analysis, offering tools for coding, categorizing, and visualizing qualitative data.
• ATLAS.ti: Another widely used software for qualitative research, providing tools for coding, memoing, and network visualization.
3. Data Visualization Tools:
• Tableau: A powerful data visualization tool that helps create interactive and shareable dashboards.
• Microsoft Power BI: A business analytics tool that provides interactive visualizations and business intelligence capabilities.
Tips for Interpreting and Presenting Data
1. Understand Your Data: Before analyzing data, ensure you have a thorough understanding of its source, structure, and limitations. This helps in selecting appropriate analysis techniques and interpreting results accurately.
2. Use Clear Visualizations: Visual representations such as charts, graphs, and tables can make complex data more accessible and understandable. Choose the right type of visualization for your data and ensure it is clear and well-labelled.
3. Contextualize Findings: Interpret your data in the context of existing literature and theoretical frameworks. Discuss how your findings align with or differ from previous research.
4. Report Limitations: Be transparent about the limitations of your data and analysis. Discuss potential sources of bias, measurement errors, and the generalizability of your findings.
5. Communicate Clearly: Present your data and findings in a clear and concise manner. Avoid jargon and technical language that may confuse readers. Use straightforward language and provide explanations for complex concepts.
In conclusion, data analysis plays a crucial role in academic research, enabling researchers to draw meaningful conclusions and contribute to their field. By understanding different data analysis techniques, utilizing appropriate tools, and following best practices for interpreting and presenting data, researchers can enhance the quality and impact of their work.
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For both FGDs questions and in-depth interview questions.
To keep it very precise, I would say:
Qualitative Research explores the why and how, diving deep into emotions, opinions, and experiences.
Quantitative Research focuses on the what and how much, giving us numbers, patterns, and trends.
You can also get some information here.
what are your insights on the topic?
Presenting themes effectively includes:
- Writing a brief description of each theme.
- Using direct quotes from stakeholders to support the themes (e.g., a principal might say, "We lack proper training in integrating health into our curriculum.").
- Creating tables or diagrams to visually represent the relationships between themes.
- Relating the themes back to your research questions and objectives. For example, if "Teacher Training Needs" is a theme, describe its significance and provide quotes or examples.
Citation: Braun, V., & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners. SAGE Publications.
If I have done complete quantitative research, then complete qualitative research, then complete experimental research, and interpret all separately, then what will be my research design?
Thematic analysis is a method for identifying, analyzing, and reporting patterns (themes) in qualitative data. It’s widely used because it helps researchers make sense of large amounts of data by grouping similar ideas or topics. For example, if you interview school stakeholders about health promotion in schools, thematic analysis can help uncover common concerns like lack of awareness, infrastructure challenges, or the need for teacher training.
Citation: Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
Me ha ingresado la duda respecto a un proyecto de investigación cualitativa se le tiene que formular una hipótesis tal cual se hace en las investigaciones cuantitativas. Sé que son supuestos teóricos objetivos en las experimentales, sin embargo en las que corresponden en la investigación-acción son subjetivas, digamos creencias a partir de las experiencias y desde esos hechos se analizan y se establecen lógicas...
What are methods to create meaningful models and conclusion by perusing of essay. What these techniques are known as in research methodology subject
I'm working with biographical qualitative interviews and have a very intersting single case that would allow an in-depth investegation of the worldview and religiosity of an extreme-right individual. I was now wondering if there are journals in social science that publish qualitative research findings derived from a single case?
I read with interest the answers provided in Q&A section. It has been observed that these answers run in higher number which is usually sufficient to be utilized as qualitative research participants. Could these answers be analyzed through discourse analysis, with NVivo or ATLAS.ti to highlight a phenomenon from epistemological and ontological perspective? I would be grateful for the kindness of the research community at ResearchGate towards their response.
How do conceptual frameworks support qualitative research differently than theoretical frameworks?
What is the potential of artificial intelligence in qualitative research? Has anyone had successful experiences to share?
School health researchers can reduce bias by having multiple team members review and code the interviews independently and cross check for the mistakes, we would have zoom calls for the same , using software tools like Atlas ti (The web version gave independence of sharing the files with all the research team or Invivo also helps for systematic analysis, and by verifying emerging themes with stakeholders where possible (Creswell & Poth, 2018).
All the above explained are good practices for team based research work, in my case since it was scholar work, had to slog a little for feedback from outsiders or friend circles who are working on qualitative research work.
Researchers enhance rigor by applying strategies like triangulation, member checking, and maintaining an audit trail. Coding consistency and reflexivity also support credible, transferable, and confirmable findings.
CohenMiller, A., Saban, G.A. and Bayeta, R., 2022. Rigor in Qualitative Research. The SAGE Handbook of Qualitative Research in the Asian Context. Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne: SAGE Publications, pp.327-343.
Thematic analysis identifies and reports patterns within data without necessarily generating a new theory, while grounded theory aims to develop a theory grounded in the data through an iterative, inductive process.
O’Callaghan, C., Dwyer, J. and Schofield, P., 2024. Thematic analysis informed by grounded theory (TAG) in healthcare research: foundations and applications. Qualitative Research in Psychology, pp.1-28.
Do you have a questionnaire for qualitative research?
Muñoz, Lucio, 2002. Non-Traditional Research Methods and Regional Planning Needs in Developing Countries: Is There An Ideal Methodology?, In: THEOMAI, Issue 6, Second Semester, Argentina
Hi everyone,
For a qualitative research study, we are scoping for potential transcription software to transcribe our interviews. We have the following criteria in mind:
- We are looking for affordable software (maximum cost of $200 for 16 hours/960 minutes of transcripts).
- Software should be good at transcribing East-African accents in English (specifically, Ugandan accents).
- Software should have high data protection mechanisms in place. At minimum, it should be compliant with GDPR legislation.
I already came across Otter.ai, Trint, Sonix.ai, and Rev.com. I am wondering if you have used any of this software before and can provide feedback? Other suggestions for software that meets the aforementioned criteria are also welcome.
Thank you in advance for your responses!
I am undergoing a project work that is in marketing domain , understanding consumer behaviour . I have to do sentimental analysis for that I need tool so that i can use it on secondary data . I am using atlas.ti by now . I am in search of tool highly used by researchers for consumer behaviour basically for sentiment analysis , in order to extract meaningful information like factors that are influencing customers for adoption
I am concucting a qualitative research for my PhD. I am nearing data completion and analysis. Can I write a paper based on my current research?
Can a phd student write and publish a paper on the thesis topic before submiting the thesis?
Because quantitative research is rooted in the scientific method, specific terminology for determining the relationships between independent variables and dependent variables must be used. However, in qualitative research, the use and terminology can sometimes raise so many a question. In my case I am using cultural diplomacy of Morocco (cause) as an independent variable, promotion of national interests (outcome) as the dependent variable.
Your opinions about the use of this terminology and its application are highly appreciated.
I am undertaking a structured literature review of qualitative research as part of my masters dissertation. I am using JBI checklist for qualitative research for assessing the quality of my included articles. Most of the nine articles I have identified through my search do not specify the philosophical and methodological perspective although I expect each has alluded to this. I have read many article to familiarise myself with the philosophical and methodological approaches used in qualitative research however I am still struggling to identify both in the articles I have in my review. Any help would be greatly appreciated!
Generative AI (GenAI) in qualitative research raises several ethical concerns, including the potential for bias amplification, challenges with informed consent, and risks to privacy and confidentiality. It also questions the authenticity and trustworthiness of AI-generated data, as well as the transparency and accountability of AI-driven analysis. Additionally, the use of GenAI may alter the researcher-participant relationship and reduce critical engagement, while also complicating issues of intellectual property and authorship. To address these concerns, researchers must ensure ethical practices by maintaining transparency, integrity, and respect for participants.
Instead of data saturation I will prefer the sample quantity selection as we do selection in quantitative research. Thank you
Employing a pragmatic inquiry research design, looking for published research using this method, employing qualitative research data collection methods of semi-structured interview and focus groups for example rather than mixed methods.
I'm looking into doing qualitative research on the catalytic effect of life crisis on how we humans learn and live. I'm curious if there has been art- based / autoethnographic research done on life crises as rites of passage using indigenous research framework .
Dear colleagues,
I wonder if combining different research methods, namely literature review, semi-structured interviews and structured questionnaire survey will help to ensure validity and reliability of our qualitative study. This study is about studying the barriers and prospects for transition to renewables.
At the moment, I am not planning to statistically process the data gathered through questionnaire survey as I plan to use them in another article. I am planning to compare results received through literature review, interviews and questionnaire survey.
I am new in this area (qualitative research) and would appreciate your guidance very much. Am I on the right way or may be I need to choose a different direction. Thank you!
Greetings!
I am looking for materials on quantitative and qualitative research. What are the methods of collecting information and the principles of their implementation?
I would like to make an assessment of the city's restaurant market.
My concerns about wasting data and the difficulty in finding interviewers for a pilot study.
Employing a pragmatic inquiry research design, looking for published research using this method, employing qualitative research data collection methods of semi-structured interview and focus groups for example rather than mixed methods.
Autonomous Language Learning can be implemented from primary to tertiary education. Practioners and students report on its effectiveness, however, there appears to be little quantitative or qualitative research on the results and benefits of this approach.
I am writing about the Ph.D. pursuing research scholars in the HR domain regarding the problems faced in finding and publishing their research articles in Scopus Indexed Journals only.
I have decided that I am Pursuing Qualitative Research.
Since the "Publication Rate" is the norm the Researchers are bound by the Institutions.
Could anyone please provide me with your guidance, on whether it would be possible or not, and if yes what should I follow?
One of the most intriguing frontiers in contemporary research is the application of Generative Artificial Intelligence (GenAI) in qualitative research. This technological advance offers tools that can complement the work of researchers in generating data, developing simulations, and analyzing complex information. Imagine the ability to model interviews with avatars that learn and adapt in real time or to automatically generate codes and themes from vast qualitative narratives. Indicate what you think about these issues.
1. While GenAI can increase efficiency and reveal unexpected patterns, it also raises fundamental questions related to data authenticity and reliability. How can we ensure that the ‘voices’ created by GenAI accurately represent the perspectives of research participants?
2. What is the impact of GenAI on the interpretation and representation of human experiences?
3. What ethical implications does GenAI represent in qualitative research?
4. How do you and your research team use GenAI for qualitative research and what practical implications does it have?
5. How can we develop protocols and guidelines that ensure GenAI is applied ethically and responsibly?
6. It is essential to recognize and mitigate any bias built into GenAI algorithms that may distort or oversimplify the richness of human experiences. How can these biases be mitigated?
7. What will be the role of GenAI in qualitative research in the future?
Thanks in advance for your comments.
Can the case writer be the protagonist or a character in the case while writing it as a practitioner and researcher? What qualitative research parameters must be followed for passing the reviewer/s decision or meeting the Journal guidelines?
Hello esteemed colleagues,
I am reaching out to the research community to gather insights on the latest and emerging qualitative research topics in the field of supply chain management. As we all know, the supply chain domain is constantly evolving, driven by technological advancements, changing market dynamics, and global challenges. While quantitative research has traditionally dominated the field, qualitative research offers profound insights into the complex, contextual, and human aspects of supply chains.
I am particularly interested in understanding:
- Recent Trends and Innovations: What are the newest trends and innovations in supply chain management that are being explored through qualitative methodologies? This could include case studies, ethnographic research, grounded theory, or narrative analyses.
- Sustainability and Ethical Practices: How are qualitative researchers addressing sustainability, ethical practices, and corporate social responsibility within supply chains? Are there any groundbreaking studies or theories emerging in this area?
- Supply Chain Resilience and Risk Management: With the increasing frequency of disruptions (e.g., pandemics, geopolitical tensions, natural disasters), what qualitative research is being conducted to understand and improve supply chain resilience and risk management?
- Technological Impact: How are emerging technologies (e.g., blockchain, AI, IoT) being studied qualitatively in their application to supply chains? What human, organizational, and strategic dimensions are being uncovered?
- Integration and Collaboration: What insights are being gained about supply chain integration, collaboration, and relationship management from a qualitative perspective?
- Cultural and Behavioral Aspects: How are cultural, behavioral, and organizational factors influencing supply chain practices, and what qualitative research is shedding light on these dimensions?
- Policy and Regulation: Are there any notable qualitative studies exploring the impact of policies, regulations, and trade agreements on supply chains?
I would appreciate it if you could share any recent research papers, ongoing projects, or key conferences and journals that are highlighting these topics. Additionally, personal experiences or insights on promising areas for future qualitative research in supply chain management are highly welcome.
Thank you for your valuable contributions. Looking forward to a rich and engaging discussion.
Best regards,
Usman
Hello,
Currently, I am writing my master thesis proposal. I have been to provide a method paper for (question creation and interview evaluation). It's my first time planning to conduct interviews. I feel confused here.
Any tips how to write the method paper for (question creation and interview evaluation). I would appreciate if someone has a template or an example.
Thanks!
Hi Guys
any Repository of Qualitative interview transcription??
Researchers such as Jim Cummins (2001) or Bilge and Hill Collins (2020), among others, affirm the importance of linking practice with theory. As an educator, the self is an important part of practice and so it is normal for me to draw on personal experience to enrich my research. Does this make my writing subjective? Yes, but then again, is there really objectivity in qualitative research? Is this really a bad thing?
Hi there,
at the moment I am doing a study on migrant worker in Singapore. During my research I met a return migrant who documented his journey with his camera and he gave me all his more than 900 photos for digitalization. It’s a real treasure for me because it gives me insight into the daily life of migrant workers 20 years ago. I would be interested in getting hints for interesting literature with a particular methodological focus how to analyze photos. What are the different steps? Is there may be a helpful software that can support to organize, tag and analyze photos?
Thank you very much for any hint or recommendation!
I am a Tourism student i need a research design about south Cotabato Punta Isla Lake as a Qualitative research
I am working on Venezuelan migrants, a qualitative study for my doctoral dissertation. I am seeking raw data to analyze and create a new scientific investigation.
Reference Article: Learning the craft of organizational research by Richard L Daft (The Academy of Management Review, 1983)
As someone engaging in ethnographic research, are we expected to disclose transcribed data to the journal where we would like publish an article?
I am conducting a study on qualitative research educator and professional identity in the age of generative AI. If you consider qualitative research educator to be a part of your professional identity, please consider participating in our study. Feel free to share this opportunity with your network or anyone you know that might be interested!
Please contact me directly beixi.li@wmich.edu if you have any questions!
🤖 Do you consider yourself a qualitative research educator? We're embarking on a fascinating study on how generative AI, like ChatGPT, is transforming the professional identity of educators.
🎙️ Share your journey, challenges, and triumphs in integrating AI tools into your teaching methods. As part of this innovative research, You will be asked to react to some future scenarios (generated by ChatGPT) where Generative AI is seamlessly integrated in teaching qualitative research.
🔍 Interested in participating? Click the link below to join the study: https://wmich.co1.qualtrics.com/jfe/form/SV_2aCQLEIoqerAdEy
I am using document analysis to explore how Christian philosophies of education are presented.
To start the research, I'm looking for some good keywords to find a research idea. I prefer a qualitative research method to conduct this research.
Successful Qualitative Research: A Practical Guide for Beginners
I would like to access this book, but only get Ch. 1.
Is every qualitative study a case study? After all, it is almost always a matter of researching a small number of people around a specific topic? For example, an evaluation study of a leadership program in which people who took part in it are interviewed, or a study of the reception of queer children's literature among kindergarteners, would it be correct to claim that the type is a case study?
If so, then when is qualitative research not a case study?
How do you check the reliability and validity of qualitative research tools including semi-structure interview questions and self-report?
Thanks for sharing your useful information in advance.
Hello, RG family. My PhD dissertation is a phenomenological study on “Foreign market network and internationalization of Western businesses”. I intend to explore the common lived experiences of Western businesses as they build foreign market network for international expansion. But I’m confused about the research objectives:
Should I adapt my research objectives with similar research on this subject? What if there is no similar study relevant to my chosen research design?
How exactly can I craft research aims and objectives in a phenomenological qualitative study like this?
Your valuable inputs are very well appreciated as usual ✅✅💯💯 Thank you for your contribution.

I have to do a qualitative research in project management. However, I haven’t found/defined a research question? Do you have any interesting topics (trends in project management) that you would recommend to me?
Kindly guide me in this.
Thanks/Regards,
Any advantages of open-ended questions over interview in qualitative research as a tool?
Greetings scientists and the academic community!
I am looking for a couple of papers that explain the process of theory building during the development of qualitative research.
Especially, creation of theoretical contributions from our research (case studies, abductive, inductive and grounded theory research).
Thanks in advance!
Sabrina
I am struggling, to find researchers, to answer to four questions (simple) for my Thesis. I have a feedback, only by one.
Must have a background to Artificial Intelligence.
Hopping for great interest.
Kopitsa K.P.
I need to finalize my research methodology. So, I need to find it. I thank you so much for your help.
I am engaged in an explorative qualitative study on the psychological effects of lockdown and other restrictions in CHina on the mental state of people in China. This includes illness reaction like depression, anxiety disorder and so on and ALSO more specific non-pathological effects in ther sense of emotional disorders. This also includes the question why there is verly little qualitative research in CHina. Why is this so?
Thanks for your response
Hello RG Family! In my transition to qualitative research, I’m confronted with the challenge of validating qualitative interviews.
From my knowledge of quantitative research, I’m well aware that Principal Component Analysis and Cronbach’s Alpha methods are popular for validity and reliability of Likert-scaled questionnaires. But in the case of qualitative interviews, the arena is different. That’s why I need your help.
From your wealth of experience with qualitative research, please describe the most effective methods for carrying out validity and reliability of qualitative interviews. And which software is suitable for this procedure?
Your contributions will be immensely appreciated. Thank you.

Our research adviser told us to use specified terms for validity and reliability in qualitative research, since he referred to these terms as specific for quantitative research. To my understanding, validity and reliability are also used for qualitative data, but are defined differently.
If I want to assess the potential threat of cyber terrorism to the aviation industry of a state (consider 5 aspects: knowledge, awareness, vulnerabilities, response and impact) and have the officials (from the aviation industry) and experts (security experts) as my participants in hopes of providing a literature to contribute to helpjng decision-makers, policy makers to make policies or countermeasures to the threat of cyberterrorism in the future
What methodology can I use under a qualitative study?
I know this is something I should know already but I really need the opinion of other scholars.
Many clinical trialists integrate qualitative and/or mixed methods research as part of their clinical trial projects. Could you please share your experiences and thoughts on the challenges in integrating these methodologies in clinical trials, and how to address them.
Hi,
I was wondering if some qualitative researcher can give me some advice on how to learn how to learn and use quantitative methods in a practical way?
Can you share your experiences? Recommend some course, boook, etc.?
Thanks a lot,
Ester
We are currently conducting a qualitative research on the effects of influencer marketing on purchase behavior. However, before that, our panel has suggested to conduct a pre-survey to (1) identify the products endorsed by influencer marketing that students mostly purchase, and (2)the social media platforms that students purchase influencer-endorsed products. The purpose of the pre-survey will help us narrow down our scope based on the results of the pre-survey by focusing on a specific social media platform and product.
Our question is how can we determine the sample size?
Thank you in advance!
Hi people,
Are there any potential challenges I should be aware of when transitioning from qualitative research in my thesis to a career as a professor in a university setting?
Regards,
Joane R.