Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Systematic Sampling Method01:17

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
Convenience Sampling Method00:55

Convenience Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

How Parent Caregivers of Children with Medical Complexity Respond to Patient Safety Events at Home.

Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting·2026
Same author

Developing and Validating Measures of Structural Ableism to Improve Health Outcomes for the Disability Community: Protocol for a Mixed Methods Study.

JMIR research protocols·2026
Same author

Factors Influencing Patient Work to Recover After Spine Surgery: A Patient-centered Systems Approach.

The Journal of neuroscience nursing : journal of the American Association of Neuroscience Nurses·2025
Same author

Flagged for Fraud: Lessons From 3 Case Studies on Detecting Inauthentic Participants in Online Research.

Journal of medical Internet research·2025
Same author

The Use of Technology by the Rural-Dwelling Caregivers of People Living With Dementia to Support Caregiving: Qualitative Interview Study.

JMIR formative research·2025
Same author

Using American Sign Language-Fluent Community Health Navigators to Advance Cancer Screening Adherence through Videoconferencing With Deaf, Deafblind, and Hard of Hearing Adults: Protocol for a Randomized Controlled Trial.

JMIR research protocols·2025
Same journal

Enhancing Patient Participation in Co-Productive Decision-Making With Personal Value Sets: Clinical Trial Prototype.

Journal of participatory medicine·2026
Same journal

Mapping Peer Support in Antidepressant Discussions on Reddit: Pilot Network Analysis.

Journal of participatory medicine·2026
Same journal

Evaluation of a Digital Intervention to Improve the Health Outcomes of Older Adults: Secondary Data Analysis.

Journal of participatory medicine·2026
Same journal

Participatory Digital Twins for Chronic Care: From Predictive Models to Shared Sensemaking.

Journal of participatory medicine·2026
Same journal

Real-World Data Needs Real-World Doctors: When Automation Advances Faster Than Clinical Workflow.

Journal of participatory medicine·2026
Same journal

Experiences and Perceptions Within a Co-Created Drone Transport Initiative With Rural First Nation and Non-First Nation Communities: Semistructured Interview Study.

Journal of participatory medicine·2026
See all related articles
  1. Home
  3. Using Partner-driven Maximum Variance Sampling To Form A Lived Experience Panel: Step-by-step Tutorial.
  1. Home
  3. Using Partner-driven Maximum Variance Sampling To Form A Lived Experience Panel: Step-by-step Tutorial.

Related Experiment Video

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Using Partner-Driven Maximum Variance Sampling to Form a Lived Experience Panel: Step-by-Step Tutorial.

Anna Jolliff1, Teresa Thuemling1, Jessica Arora2

  • 1Center for Research and Innovation in Systems Safety, Department of Anesthesiology, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 800, Nashville, TN, 37203, United States, 1 615-421-4210.

Journal of Participatory Medicine
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Forming diverse lived experience panels (LEPs) enhances research relevance and impact. This guide details a process using community partnerships and maximum variance sampling to recruit varied patient and caregiver advisors for research projects.

Keywords:
advisory boardfamily caregiverslived experience panelmaximum variance samplingpatient engagementtutorial

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Related Experiment Videos

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Area of Science:

  • Patient and Community Engagement in Research
  • Health Services Research
  • Qualitative Research Methods

Background:

  • Collaborative research with patients and caregivers increases rigor, relevance, and impact.
  • Lived Experience Panels (LEPs) are groups of individuals with personal experience relevant to a research topic, facilitating patient and caregiver engagement.
  • Effective LEPs require intentional composition to reflect the diversity of the community of focus.

Purpose of the Study:

  • To outline a process for forming maximally variable lived experience panels (LEPs).
  • To guide researchers in recruiting diverse patient and caregiver advisors for research projects.
  • To share lessons learned and keys to success in LEP formation.

Main Methods:

  • Establishing foundational partnerships with academic and community stakeholders.
  • Collaboratively developing promotional materials and an interest survey for recruitment.
  • Utilizing community-embedded channels for promotion and maximum variance sampling for selection.

    • Main Results:

    • Detailed outcomes include promotional channel productivity, LEP formation timeline, applicant sociodemographic variability, and member feedback.
    • A thoughtfully designed interest survey and collaboration with community partners were key to recruiting a diverse LEP.
    • Challenges included articulating LEP responsibilities and navigating selection tensions inherent in maximum variance sampling.

    Conclusions:

    • Investing in relationships with trusted community partners is crucial for recruiting diverse patient and caregiver advisors.
    • A systematic LEP formation process, emphasizing maximum variance sampling, can yield a diverse and representative panel.
    • Successful LEP formation requires intentionality in recruitment, clear communication of roles, and strategic use of community partnerships.