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  • What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

  • How does social media shape body image in teenagers?
  • How do children and adults interpret healthy eating in the UK?
  • What factors influence employee retention in a large organization?
  • How is anxiety experienced around the world?
  • How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Qualitative research approaches
Approach What does it involve?
Grounded theory Researchers collect rich data on a topic of interest and develop theories .
Researchers immerse themselves in groups or organizations to understand their cultures.
Action research Researchers and participants collaboratively link theory to practice to drive social change.
Phenomenological research Researchers investigate a phenomenon or event by describing and interpreting participants’ lived experiences.
Narrative research Researchers examine how stories are told to understand how participants perceive and make sense of their experiences.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

  • Observations: recording what you have seen, heard, or encountered in detailed field notes.
  • Interviews:  personally asking people questions in one-on-one conversations.
  • Focus groups: asking questions and generating discussion among a group of people.
  • Surveys : distributing questionnaires with open-ended questions.
  • Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
  • You take field notes with observations and reflect on your own experiences of the company culture.
  • You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
  • You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

  • Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
  • Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
  • Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
  • Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
  • Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative data analysis
Approach When to use Example
To describe and categorize common words, phrases, and ideas in qualitative data. A market researcher could perform content analysis to find out what kind of language is used in descriptions of therapeutic apps.
To identify and interpret patterns and themes in qualitative data. A psychologist could apply thematic analysis to travel blogs to explore how tourism shapes self-identity.
To examine the content, structure, and design of texts. A media researcher could use textual analysis to understand how news coverage of celebrities has changed in the past decade.
To study communication and how language is used to achieve effects in specific contexts. A political scientist could use discourse analysis to study how politicians generate trust in election campaigns.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

  • Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

  • Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

  • Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

  • Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

  • Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

  • Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

  • Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

  • Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Chi square goodness of fit test
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Inclusion and exclusion criteria

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

  • Grounded theory involves collecting data in order to develop new theories.
  • Ethnography involves immersing yourself in a group or organization to understand its culture.
  • Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
  • Phenomenological research involves investigating phenomena through people’s lived experiences.
  • Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

  • Prepare and organize your data.
  • Review and explore your data.
  • Develop a data coding system.
  • Assign codes to the data.
  • Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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  • Published: 11 June 2024

The Space Omics and Medical Atlas (SOMA) and international astronaut biobank

  • Eliah G. Overbey   ORCID: orcid.org/0000-0002-2866-8294 1 , 2 , 3 , 4 ,
  • JangKeun Kim   ORCID: orcid.org/0000-0002-8733-9925 1 , 2 ,
  • Braden T. Tierney   ORCID: orcid.org/0000-0002-7533-8802 1 , 2 ,
  • Jiwoon Park   ORCID: orcid.org/0000-0003-0045-1429 1 , 2 ,
  • Nadia Houerbi 1 , 2 ,
  • Alexander G. Lucaci 1 , 2 ,
  • Sebastian Garcia Medina 1 , 2 ,
  • Namita Damle 1 ,
  • Deena Najjar   ORCID: orcid.org/0009-0009-7950-2866 5 ,
  • Kirill Grigorev 1 , 2 ,
  • Evan E. Afshin 1 , 2 ,
  • Krista A. Ryon 1 ,
  • Karolina Sienkiewicz 2 , 6 ,
  • Laura Patras 7 , 8 ,
  • Remi Klotz   ORCID: orcid.org/0000-0003-2100-0635 9 ,
  • Veronica Ortiz 9 ,
  • Matthew MacKay 6 ,
  • Annalise Schweickart   ORCID: orcid.org/0000-0001-9691-3741 2 , 6 ,
  • Christopher R. Chin   ORCID: orcid.org/0000-0002-2140-3197 1 ,
  • Maria A. Sierra 6 ,
  • Matias F. Valenzuela 10 ,
  • Ezequiel Dantas   ORCID: orcid.org/0000-0003-4934-4632 11 , 12 ,
  • Theodore M. Nelson   ORCID: orcid.org/0000-0002-8600-0444 13 ,
  • Egle Cekanaviciute   ORCID: orcid.org/0000-0003-3306-1806 14 ,
  • Gabriel Deards 6 ,
  • Jonathan Foox 1 , 2 ,
  • S. Anand Narayanan 15 ,
  • Caleb M. Schmidt 16 , 17 , 18 ,
  • Michael A. Schmidt 16 , 17 ,
  • Julian C. Schmidt 16 , 17 ,
  • Sean Mullane 19 ,
  • Seth Stravers Tigchelaar 19 ,
  • Steven Levitte 19 , 20 ,
  • Craig Westover 1 ,
  • Chandrima Bhattacharya 6 ,
  • Serena Lucotti 7 ,
  • Jeremy Wain Hirschberg 1 ,
  • Jacqueline Proszynski 1 ,
  • Marissa Burke   ORCID: orcid.org/0000-0001-5647-3358 1 ,
  • Ashley Kleinman 1 ,
  • Daniel J. Butler 1 ,
  • Conor Loy 21 ,
  • Omary Mzava 21 ,
  • Joan Lenz 21 ,
  • Doru Paul 22 ,
  • Christopher Mozsary 1 ,
  • Lauren M. Sanders 14 ,
  • Lynn E. Taylor 23 ,
  • Chintan O. Patel 24 ,
  • Sharib A. Khan 24 ,
  • Mir Suhail 24 ,
  • Syed G. Byhaqui 24 ,
  • Burhan Aslam 24 ,
  • Aaron S. Gajadhar 25 ,
  • Lucy Williamson 25 ,
  • Purvi Tandel 25 ,
  • Qiu Yang 25 ,
  • Jessica Chu 25 ,
  • Ryan W. Benz 25 ,
  • Asim Siddiqui 25 ,
  • Daniel Hornburg   ORCID: orcid.org/0000-0002-6618-7774 25 ,
  • Kelly Blease 26 ,
  • Juan Moreno 26 ,
  • Andrew Boddicker   ORCID: orcid.org/0000-0001-7957-8283 26 ,
  • Junhua Zhao   ORCID: orcid.org/0009-0006-7672-1084 26 ,
  • Bryan Lajoie 26 ,
  • Ryan T. Scott   ORCID: orcid.org/0000-0003-0654-5661 27 ,
  • Rachel R. Gilbert 27 ,
  • San-huei Lai Polo 27 ,
  • Andrew Altomare 26 ,
  • Semyon Kruglyak 26 ,
  • Shawn Levy 26 ,
  • Ishara Ariyapala 28 ,
  • Joanne Beer   ORCID: orcid.org/0000-0001-8583-8467 28 ,
  • Bingqing Zhang 28 ,
  • Briana M. Hudson 29 ,
  • Aric Rininger 29 ,
  • Sarah E. Church   ORCID: orcid.org/0000-0002-7194-4282 29 ,
  • Afshin Beheshti   ORCID: orcid.org/0000-0003-4643-531X 30 , 31 ,
  • George M. Church   ORCID: orcid.org/0000-0001-6232-9969 32 ,
  • Scott M. Smith   ORCID: orcid.org/0000-0001-9313-7900 33 ,
  • Brian E. Crucian 33 ,
  • Sara R. Zwart   ORCID: orcid.org/0000-0001-8694-0180 34 ,
  • Irina Matei   ORCID: orcid.org/0000-0002-5712-8430 7 , 12 ,
  • David C. Lyden   ORCID: orcid.org/0000-0003-0193-4131 7 , 12 ,
  • Francine Garrett-Bakelman 35 , 36 ,
  • Jan Krumsiek   ORCID: orcid.org/0000-0003-4734-3791 1 , 2 , 6 ,
  • Qiuying Chen 37 ,
  • Dawson Miller 37 ,
  • Joe Shuga 38 ,
  • Stephen Williams 38 ,
  • Corey Nemec   ORCID: orcid.org/0000-0002-6566-1753 38 ,
  • Guy Trudel   ORCID: orcid.org/0000-0001-5254-4294 39 , 40 , 41 ,
  • Martin Pelchat 42 ,
  • Odette Laneuville   ORCID: orcid.org/0000-0003-3124-3892 43 ,
  • Iwijn De Vlaminck   ORCID: orcid.org/0000-0001-6085-7311 21 ,
  • Steven Gross 37 ,
  • Kelly L. Bolton 44 ,
  • Susan M. Bailey   ORCID: orcid.org/0000-0001-5595-9364 23 , 45 ,
  • Richard Granstein 46 ,
  • David Furman   ORCID: orcid.org/0000-0002-3654-9519 10 , 47 , 48 , 49 ,
  • Ari M. Melnick   ORCID: orcid.org/0000-0002-8074-2287 12 , 22 ,
  • Sylvain V. Costes   ORCID: orcid.org/0000-0002-8542-2389 14 ,
  • Bader Shirah   ORCID: orcid.org/0000-0001-6493-2155 50 ,
  • Anil S. Menon   ORCID: orcid.org/0000-0002-6886-3553 34 ,
  • Jaime Mateus 19 ,
  • Cem Meydan   ORCID: orcid.org/0000-0002-0663-6216 1 , 2 , 22 &
  • Christopher E. Mason   ORCID: orcid.org/0000-0002-1850-1642 1 , 2 , 3 , 51 , 52  

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Spaceflight induces molecular, cellular, and physiological shifts in astronauts and poses myriad biomedical challenges to the human body, which are becoming increasingly relevant as more humans venture into space 1-6 . Yet, current frameworks for aerospace medicine are nascent and lag far behind advancements in precision medicine on Earth, underscoring the need for rapid development of space medicine databases, tools, and protocols. Here, we present the Space Omics and Medical Atlas (SOMA), an integrated data and sample repository for clinical, cellular, and multi-omic research profiles from a diverse range of missions, including the NASA Twins Study 7 , JAXA CFE study 8,9 , SpaceX Inspiration4 crew 10-12 , plus Axiom and Polaris. The SOMA resource represents a >10-fold increase in publicly available human space omics data, with matched samples available from the Cornell Aerospace Medicine Biobank. The Atlas includes extensive molecular and physiological profiles encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiome data sets, which reveal some consistent features across missions, including cytokine shifts, telomere elongation, and gene expression changes, as well as mission-specific molecular responses and links to orthologous, tissue-specific murine data sets. Leveraging the datasets, tools, and resources in SOMA can help accelerate precision aerospace medicine, bringing needed health monitoring, risk mitigation, and countermeasures data for upcoming lunar, Mars, and exploration-class missions.

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Author information, authors and affiliations.

Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA

Eliah G. Overbey, JangKeun Kim, Braden T. Tierney, Jiwoon Park, Nadia Houerbi, Alexander G. Lucaci, Sebastian Garcia Medina, Namita Damle, Kirill Grigorev, Evan E. Afshin, Krista A. Ryon, Christopher R. Chin, Jonathan Foox, Craig Westover, Jeremy Wain Hirschberg, Jacqueline Proszynski, Marissa Burke, Ashley Kleinman, Daniel J. Butler, Christopher Mozsary, Jan Krumsiek, Cem Meydan & Christopher E. Mason

The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA

Eliah G. Overbey, JangKeun Kim, Braden T. Tierney, Jiwoon Park, Nadia Houerbi, Alexander G. Lucaci, Sebastian Garcia Medina, Kirill Grigorev, Evan E. Afshin, Karolina Sienkiewicz, Annalise Schweickart, Jonathan Foox, Jan Krumsiek, Cem Meydan & Christopher E. Mason

BioAstra, Inc, New York, NY, USA

Eliah G. Overbey & Christopher E. Mason

Center for STEM, University of Austin, Austin, TX, USA

Eliah G. Overbey

Albert Einstein College of Medicine, Bronx, NY, USA

Deena Najjar

Tri-Institutional Biology and Medicine program, Weill Cornell Medicine, New York, NY, USA

Karolina Sienkiewicz, Matthew MacKay, Annalise Schweickart, Maria A. Sierra, Gabriel Deards, Chandrima Bhattacharya & Jan Krumsiek

Children’s Cancer and Blood Foundation Laboratories, Departments of Pediatrics and Cell and Developmental Biology, Drukier Institute for Children’s Health, Weill Cornell Medicine, New York, NY, USA

Laura Patras, Serena Lucotti, Irina Matei & David C. Lyden

Department of Molecular Biology and Biotechnology, Center of Systems Biology, Biodiversity and Bioresources, Faculty of Biology and Geology, Babes-Bolyai University, Cluj-Napoca, Romania

Laura Patras

Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

Remi Klotz, Veronica Ortiz & Min Yu

Buck Institute for Research on Aging, Novato, CA, USA

Matias F. Valenzuela & David Furman

Division of Endocrinology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA

Ezequiel Dantas

Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA

Ezequiel Dantas, Irina Matei, David C. Lyden & Ari M. Melnick

Department of Microbiology & Immunology, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, NY, USA

Theodore M. Nelson

Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA

Egle Cekanaviciute, Lauren M. Sanders & Sylvain V. Costes

Department of Health, Nutrition, and Food Sciences, Florida State University, Tallahassee, FL, USA

S. Anand Narayanan

Sovaris Aerospace, Boulder, Colorado, USA

Caleb M. Schmidt, Michael A. Schmidt & Julian C. Schmidt

Advanced Pattern Analysis and Human Performance Group, Boulder, Colorado, USA

Department of Systems Engineering, Colorado State University, Fort Collins, Colorado, USA

Caleb M. Schmidt

Space Exploration Technologies Corporation (SpaceX), Hawthorne, CA, USA

Sean Mullane, Seth Stravers Tigchelaar, Steven Levitte & Jaime Mateus

Division of Pediatric Gastroenterology, Stanford University, Palo Alto, CA, USA

Steven Levitte

Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA

Conor Loy, Omary Mzava, Joan Lenz & Iwijn De Vlaminck

Division of Hematology/Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA

Doru Paul, Ari M. Melnick & Cem Meydan

Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, USA

Lynn E. Taylor & Susan M. Bailey

TrialX Inc., New York, NY, USA

Chintan O. Patel, Sharib A. Khan, Mir Suhail, Syed G. Byhaqui & Burhan Aslam

Seer, Inc., Redwood City, CA, USA

Aaron S. Gajadhar, Lucy Williamson, Purvi Tandel, Qiu Yang, Jessica Chu, Ryan W. Benz, Asim Siddiqui & Daniel Hornburg

Element Biosciences, San Diego, CA, USA

Kelly Blease, Juan Moreno, Andrew Boddicker, Junhua Zhao, Bryan Lajoie, Andrew Altomare, Semyon Kruglyak & Shawn Levy

KBR; Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA

Ryan T. Scott, Rachel R. Gilbert & San-huei Lai Polo

Alamar Biosciences, Inc, 47071 Bayside Parkway, Fremont, CA, USA

Ishara Ariyapala, Joanne Beer & Bingqing Zhang

NanoString Technologies, Seattle, WA, USA

Briana M. Hudson, Aric Rininger & Sarah E. Church

Blue Marble Space Institute of Science; Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA

  • Afshin Beheshti

Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Harvard Medical School and the Wyss Institute, Boston, MA, USA

George M. Church

National Aeronautics and Space Administration, Johnson Space Center, Human Health and Performance Directorate, Biomedical Research and Environmental Sciences Division, Houston, TX, USA

Scott M. Smith & Brian E. Crucian

University of Texas Medical Branch, Galveston, TX, USA

Sara R. Zwart & Anil S. Menon

Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA

Francine Garrett-Bakelman

Department of Medicine, Division of Hematology & Oncology, University of Virginia, Charlottesville, VA, USA

Department of Pharmacology, Weill Cornell Medicine, New York, NY, USA

Qiuying Chen, Dawson Miller & Steven Gross

10x Genomics, Pleasanton, CA, USA

Joe Shuga, Stephen Williams & Corey Nemec

Bone and Joint Research Laboratory, Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa, Ontario, Canada

Department of Medicine, Division of Physiatry, The Ottawa Hospital, Room 2505G, 505 Smyth Road, Ottawa, Ontario, Canada

Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Room 1321, Ottawa, Ontario, Canada

Department of Biochemistry, Microbiology, and Immunology, Roger Guindon Hall, Room 4111A, University of Ottawa, Ottawa, Canada

Martin Pelchat

Department of Biology, Gendron Hall Room 3-372, University of Ottawa, Ottawa, Canada

Odette Laneuville

Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA

Kelly L. Bolton

Cell and Molecular Biology Program, Colorado State University, Fort Collins, CO, USA

Susan M. Bailey

Department of Dermatology, Weill Cornell Medicine, New York, NY, USA

Richard Granstein

Cosmica Biosciences Inc., San Francisco, CA, USA

David Furman

Stanford 1000 Immunomes Project, Stanford School of Medicine, Stanford University, Stanford, CA, USA

Institute for Research in Translational Medicine, Universidad Austral, CONICET, Pilar, Buenos Aires, Argentina

Department of Neuroscience, King Faisal Specialist Hospital & Research Centre, Jeddah, Saudi Arabia

Bader Shirah

The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, NY, USA

Christopher E. Mason

WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA

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Corresponding authors

Correspondence to Eliah G. Overbey , Cem Meydan or Christopher E. Mason .

Supplementary information

Supplementary information.

This file contains Supplementary Figures 1-3, Supplementary Tables 4 and 9, Supplementary Note 1 and additional references.

Reporting Summary

Supplementary table 1.

Sample Information. Comprehensive list of samples collected from each crew member, at each timepoint, for each assay. Tab 1 is an overview of which samples are present at each timepoint. Tab 2 is an itemized list of each sample, including the number of sequenced DNA/RNA molecules for sequencing assays.

Supplementary Table 2

OSDR Studies. Comprehensive list of prior studies in OSDR for previous assays on human, metagenomic, and metatranscriptomic samples.

Supplementary Table 3

Sequencing and Mass Spectrometry Stats Tables. Sequencing and mass spectrometry statistics for multiome, TCR, BCR, cfRNA, dRNA, and proteomics assays.

Supplementary Table 5

cfRNA Calculations. Tissue of origin analysis from cfRNA sequencing. Tab 1 contains fractions of cell type specific RNA enrichment. Tab 2 contains comparisons between timepoints.

Supplementary Table 6

Recovery Profile Pathways. Overrepresented KEGG pathways during recovery from spaceflight in PBMCs. Tabs are split for CD4+ T cells, CD8+ T cells, CD14+ monocyte and CD16+ monocytes.

Supplementary Table 7

Metagenome and Metatranscriptome CVs. Species-level CV calculations across crew members for metagenomic and metatranscriptomic samples from oral, nasal, and skin swab samples.

Supplementary Table 8

Human Omics CVs. Gene/analyte-level CV calculations across crew members for NULISAseq, EVP proteomic, plasma proteomic, metabolomic, dRNA-seq and short read RNA-seq assays. GSEA pathway enrichment is calculated for pre-flight, post-flight (R+1), and recovery time intervals.

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Overbey, E.G., Kim, J., Tierney, B.T. et al. The Space Omics and Medical Atlas (SOMA) and international astronaut biobank. Nature (2024). https://doi.org/10.1038/s41586-024-07639-y

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Build a Corporate Culture That Works

qualitative research paper pdf example

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their culture in such a way that the words become an organizational reality that molds employee behavior as intended.

All too often a culture is described as a set of anodyne norms, principles, or values, which do not offer decision-makers guidance on how to make difficult choices when faced with conflicting but equally defensible courses of action.

The trick to making a desired culture come alive is to debate and articulate it using dilemmas. If you identify the tough dilemmas your employees routinely face and clearly state how they should be resolved—“In this company, when we come across this dilemma, we turn left”—then your desired culture will take root and influence the behavior of the team.

To develop a culture that works, follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value statement.

Start by thinking about the dilemmas your people will face.

Idea in Brief

The problem.

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their corporate culture in such a way that the words become an organizational reality that molds employee behavior as intended.

What Usually Happens

How to fix it.

Follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value.

At the beginning of my career, I worked for the health-care-software specialist HBOC. One day, a woman from human resources came into the cafeteria with a roll of tape and began sticking posters on the walls. They proclaimed in royal blue the company’s values: “Transparency, Respect, Integrity, Honesty.” The next day we received wallet-sized plastic cards with the same words and were asked to memorize them so that we could incorporate them into our actions. The following year, when management was indicted on 17 counts of conspiracy and fraud, we learned what the company’s values really were.

  • EM Erin Meyer is a professor at INSEAD, where she directs the executive education program Leading Across Borders and Cultures. She is the author of The Culture Map: Breaking Through the Invisible Boundaries of Global Business (PublicAffairs, 2014) and coauthor (with Reed Hastings) of No Rules Rules: Netflix and the Culture of Reinvention (Penguin, 2020). ErinMeyerINSEAD

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1. Introduction

2. formulation of the proposed framework, 3. formulation of a multicomponent monodisperse spheres model, 4. numerical experiments, 5. discussion, 6. conclusions.

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research papers \(\def\hfill{\hskip 5em}\def\hfil{\hskip 3em}\def\eqno#1{\hfil {#1}}\)

JOURNAL OF
APPLIED
CRYSTALLOGRAPHY

Open Access

Qu­antitative selection of sample structures in small-angle scattering using Bayesian methods

a Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba 277-8561, Japan, b Japan Synchrotron Radiation Research Institute, Sayo, Hyogo 679-5198, Japan, c National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan, and d Facalty of Advanced Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan * Correspondence e-mail: [email protected]

Small-angle scattering (SAS) is a key experimental technique for analyzing nanoscale structures in various materials. In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypothesis of the structure of the experimental sample. Traditional model selection methods either rely on qualitative approaches or are prone to overfitting. This paper introduces an analytical method that applies Bayesian model selection to SAS measurement data, enabling a quantitative evaluation of the validity of mathematical models. The performance of the method is assessed through numerical experiments using artificial data for multicomponent spherical materials, demonstrating that this proposed analysis approach yields highly accurate and interpretable results. The ability of the method to analyze a range of mixing ratios and particle size ratios for mixed components is also discussed, along with its precision in model evaluation by the degree of fitting. The proposed method effectively facilitates quantitative analysis of nanoscale sample structures in SAS, which has traditionally been challenging, and is expected to contribute significantly to advancements in a wide range of fields.

Keywords: small-angle X-ray scattering ; small-angle neutron scattering ; nanostructure analysis ; model selection ; Bayesian inference .

SAS measurement data are expressed in terms of scattering intensity that corresponds to a scattering vector, a physical quantity representing the scattering angle. Data analysis requires selection and parameter estimation of a mathematical model of the scattering intensity that contains information about the structure of the specimen. This selection process is critical as it involves assumptions about the structure of the specimen.

We conducted numerical experiments to assess the effectiveness of our proposed method. These experiments are based on synthetic data used to estimate the number of distinct components in a specimen, which was modeled as a mixture of monodisperse spheres of varying radii, scattering length densities and volume fractions. The results demonstrate the high accuracy, interpretability and stability of our method, even in the presence of measurement noise. To discuss the utility of the proposed method, we compare our approach with traditional model selection methods based on the reduced χ -squared error.

In this section, we present a detailed formulation of our algorithm for selecting mathematical models for SAS specimens using Bayesian model selection. The pseudocode for this algorithm is provided in Algorithm 1.

2.1. Bayesian model selection

The likelihood is thus expressed as

Let φ ( K ) be the prior distribution of the parameter K that characterizes the model, and φ ( Ξ | K ) be the prior distribution of the model parameters Ξ . Then, from Bayes' theorem, the posterior distribution of the parameters given the measurement data can be written as

2.2. Calculation of marginal likelihood

Sampling from the joint probability distribution at each inverse temperature gives

2.3. Estimation of model parameters

In this paper, we consider isotropic scattering and focus on the scattering vector's magnitude q , defined as

Monodisperse spheres are spherical particles of uniform radius. The scattering intensity I ( q ,  ξ ) of a specimen composed of sufficiently dilute monodisperse spheres of a single type for the scattering vector magnitude q is given by

To formulate the scattering intensity of a specimen composed of K types of monodisperse sphere, we assume a dilute system and denote the particle size of the k th component in the sample as R k and the scale as S k . The scattering intensity of a sample composed of K types of monodisperse sphere is then given by


An illustration of a mixture of two types of spherical specimen. This shows scenarios with two components ( = 2), including mixtures of spherical particles of different sizes or volume fractions, and aggregates from a single particle type approximated as a large sphere.

The numerical experiments reported in this section were conducted with a burn-in period of 10 5 and a sample size of 10 5 for the REMC. We set the number of replicas for REMC, the values of inverse temperature and the step size of the Metropolis method taking into consideration the state exchange rate and the acceptance rate.

4.1. Generation of synthetic data

(i) Set the number of data points to N = 400 and define the scattering vector magnitudes at N equally spaced points within the interval [0.1, 3] to obtain { q i } i =1 N =400 (nm −1 ).

In this section, we consider cases with pseudo-measurement times of T = 1 and T = 0.1. Generally, smaller values of T indicate greater effects from measurement noise.

4.2. Setting the prior distributions

In the Bayesian model selection framework, prior knowledge concerning the parameters Ξ and the model-characterizing parameter K is set as their prior distributions.

In this numerical experiment, the prior distributions for the parameters Ξ were set as Gamma distributions based on the pseudo-measurement time T used during data generation, while the prior for K was a discrete uniform distribution over the interval [1, 4].


Plots of the prior distributions for various parameters. ( ) Prior distribution of , φ( ). ( ) Prior distribution of ) Prior distribution of , φ( ). ( ) Prior distribution of , φ( ).

4.3. Results for two-component monodisperse spheres based on scale ratio

The ratio of the scale parameters S 1 and S 2 for spheres 1 and 2 during data generation, denoted r S , is defined as


Parameter values used for data generation with varying

  Sphere 1 Sphere 2
Radius (nm) 2 10
Scale 250 {250, 100, 20, 0.5, 0.1, 0.05}
Background (cm ) 0.01
Pseudo-measurement time {1, 0.1}

Fitting to synthetic data generated at various values and residual plots. Panels and show cases for pseudo-measurement times of = 1 and = 0.1, respectively. In plots ( )–( ) and ( )–( ), the scale ratio is displayed in descending order for = 1 and = 0.1, respectively. Black circles represent the generated data and the black dotted lines indicate the true scattering intensity curves. For models = 1, = 2, = 3 and = 4, the fitting curves and residual plots are represented by blue dashed–dotted lines, red dashed lines, orange solid lines and green dotted lines, respectively. Fitting curves were plotted using 1000 parameter samples that were randomly selected from the posterior probability distributions for each model. The width of the distribution of these fitting curves reflects the confidence level at each point.

Results of Bayesian model selection among models = 1–4 for varying values. Panel shows the posterior probability for each model using data generated with a pseudo-measurement time of = 1, and panel shows results for = 0.1. In cases ( )–( ) and ( )–( ), the scale ratio is displayed in descending order for = 1 and = 0.1, respectively. The height of each bar corresponds to the average values calculated for ten data sets generated with different random seeds, with maximum and minimum values shown as error bars. Areas highlighted in red indicate cases where, on average, the highest probability was found for the true model with = 2, while blue backgrounds indicate that models other than = 2 were associated with the highest probability on average.


The number of times each model was associated with the highest probability in numerical experiments for ten data sets generated with different random seeds at each value

) = 1

 
1 2 3 4
( ) 1.0 0 0 0
( ) 0.4 0 0 0
( ) 0.08 0 0 0
( ) 0.002 0 0 0
( ) 0.0004 0 0 0
( ) 0.0002 2 0 0
) = 0.1

 
1 2 3 4
( ) 1.0 0 0 0
( ) 0.4 0 0 0
( ) 0.08 0 0 0
( ) 0.002 0 0 0
( ) 0.0004 1 0 0
( ) 0.0002 0 0 0

4.4. Results for two-component monodisperse spheres based on radius ratio

During synthetic data generation, the ratio of the radii R 1 and R 2 of spheres 1 and 2, denoted r R , was defined as

In this setup, we generated seven types of data by varying the value of r R for pseudo-measurement times of T = 1 and T  = 0.1.


Parameter values used for data generation when varying

  Sphere 1 Sphere 2
Radius (nm) {9.9, 9.7, 9.5, 0.5, 0.5, 0.4, 0.3} 10
Scale 250 100
Background (cm ) 0.01  
Pseudo-measurement time {1, 0.1}  

Fitting to synthetic data generated at various values and residual plots. Panels and show cases for pseudo-measurement times of = 1 and = 0.1, respectively. In plots ( )–( ) and ( )–( ), the radius ratio is displayed in descending order for = 1 and = 0.1, respectively. Black circles represent the generated data and the black dotted lines indicate the true scattering intensity curves. For models = 1, = 2, = 3 and = 4, the fitting curves and residual plots are represented by blue dashed–dotted lines, red dashed lines, orange solid lines and green dotted lines, respectively. Fitting curves were plotted using 1000 parameter samples that were randomly selected from the posterior probability distributions for each model. The width of the distribution of these fitting curves reflects the confidence level at each point.

Results of Bayesian model selection among models = 1–4 for varying values. Panel shows the posterior probability of each model using data generated with a pseudo-measurement time of = 1, and panel shows results for = 0.1. In cases ( )–( ) and ( )–( ), the radius ratio is displayed in descending order for = 1 and = 0.1, respectively. The height of each bar corresponds to the average values calculated for ten data sets generated with different random seeds, with the maximum and minimum values shown as error bars. Areas highlighted in red indicate cases where the true model = 2 was most highly supported, while the blue backgrounds indicate that the likelihood of a model other than = 2 was the highest.


The number of times each model was most highly supported in numerical experiments for ten data sets generated by varying values

) = 1

 
1 2 3 4
( ) 0.99 1 0 0
( ) 0.97 0 0 0
( ) 0.95 0 0 0
( ) 0.5 0 0 0
( ) 0.05 0 0 0
( ) 0.04 1 0 0
( ) 0.03 0 0 0
) = 0.1

 
1 2 3 4
( ) 0.99 0 0 0
( ) 0.97 2 0 0
( ) 0.95 0 0 0
( ) 0.5 0 0 0
( ) 0.05 1 0 0
( ) 0.04 3 0 0
( ) 0.03 0 0 0

5.1. Limitations of the proposed method

5.2. model selection based on χ -squared error.

In SAS data analysis, selecting an appropriate mathematical model for the analysis is a crucial but challenging process. In this subsection, we compare the conventional model selection method based on the χ -squared error with the results of model selection using our proposed method.


The fitting results and residual plots for the data shown in Fig. 3 ( ) were derived using parameters that minimize the χ-squared error from the posterior probability distributions for models ranging from = 1 to = 4. For each of these models, the fitting curves and their corresponding residual plots are represented by blue dashed–dotted lines, red dashed lines, orange solid lines and green dotted lines, respectively. The legend indicates the reduced χ-squared values for each model ( = 1 to = 4).


Model selection results based on reduced χ-squared values

-squared value to 1 for ten data sets generated with different random seeds for each setting = 1. Labels ( ) to ( ) refer to the settings in Figs. 3–4 and Table 2. The cases with the highest level of support for each data set are shown in bold.

 
1 2 3 4
( ) 1.0 0 2 0\sim
( ) 0.4 0 0 1
( ) 0.08 0 0 1
( ) 0.002 0 0 0
( ) 0.0004 0 4 1
( ) 0.0002 0 2 0

In this paper, we have introduced a Bayesian model selection framework for SAS data analysis that quantitatively evaluates model validity through posterior probabilities. We have conducted numerical experiments using synthetic data for a two-component system of monodisperse spheres to assess the performance of the proposed method.

We have identified the analytical limits of the proposed method, under the settings of this study, with respect to the scale and radius ratios of two-component spherical particles, and compared the performance of traditional model selection methods based on the reduced χ -squared.

The numerical experiments and subsequent discussion reveal the range of parameters that can be analyzed using the proposed method. Within that range, our method provides stable and highly accurate model selection, even for data with significant noise or in situations in which qualitative model determination is challenging. In comparison with the traditional method of selecting models based on fitting curves and data residuals, it was found that the proposed method offers greater accuracy and stability.

SAS is used to study specimens with a variety of structures other than spheres, including cylinders, core–shell structures, lamellae and more. The proposed method should be applied to other sample models to determine the feasibility of expanding the analysis beyond the case examined here to broader experimental settings. Future work could benefit from using the proposed method to conduct real data analysis, which is expected to yield new insights through our more efficient analysis approach.

Funding information

This work was supported by JST CREST (grant Nos. PMJCR1761 and JPMJCR1861) from the Japan Science and Technology Agency (JST) and by a JSPS KAKENHI Grant-in-Aid for Scientific Research (A) (grant No. 23H00486).

This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence , which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.

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