Jove
Visualize
Contact Us

Related Concept Videos

Convenience Sampling Method00:55

Convenience Sampling Method

10.8K
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...
10.8K
Stratified Sampling Method01:16

Stratified Sampling Method

14.4K
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...
14.4K
Systematic Sampling Method01:17

Systematic Sampling Method

12.4K
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...
12.4K
Sampling Plans01:23

Sampling Plans

861
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...
861
Surveys02:16

Surveys

16.6K
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
16.6K
Data Collection by Survey01:07

Data Collection by Survey

8.5K
The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
8.5K

You might also read

Related Articles

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

Sort by
Same author

Trauma Nurses' Perceptions of Burnout, Value, and Administrative Interventions During COVID-19.

Journal of trauma nursing : the official journal of the Society of Trauma Nurses·2026
Same author

Shared Decision-Making Within Families About Returning to Sport After Recovery From Concussion: Exploring Parent and Adolescent Perspectives.

The Journal of adolescent health : official publication of the Society for Adolescent Medicine·2026
Same author

Efficacy of codesigned COVID-19 booster vaccine promotion materials for long-term care staff: a cluster-randomized trial.

BMC public health·2026
Same author

Preventing Severe Hypoglycemia in Type 2 Diabetes: Randomized Controlled Trial of Proactive Care With Versus Without Psychoeducation.

Journal of general internal medicine·2026
Same author

Equitable Discharge Teaching During COVID-19: Paediatric Emergency Nurses' Perspectives From Qualitative Interviews.

Journal of advanced nursing·2026
Same author

Physician experiences with clinical uncertainty in the trauma setting: making clinical guidance accessible to those in need.

Trauma surgery & acute care open·2026
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 Experiment Video

Updated: Jan 8, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

990

Beyond saturation: A qualitative framework for operationalizing respondent sampling (Q-FORS) for data adequacy.

Clarissa Hsu1, Delaney Glass2, Stephanie Cruz3

  • 1Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA; University of Washington, School of Public Health, Seattle, WA, USA.

Social Science & Medicine (1982)
|December 14, 2025
PubMed
Summary

This study introduces the Qualitative Framework for Operationalizing Respondent Sampling (Q-FORS) to address data adequacy in qualitative health research. Q-FORS offers a practical approach to determining sample size, respecting diverse qualitative methodologies.

More Related Videos

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

35.8K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.2K

Related Experiment Videos

Last Updated: Jan 8, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

990
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

35.8K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.2K

Area of Science:

  • Social Sciences
  • Health Research Methodology

Background:

  • Qualitative research sample size determination differs from quantitative methods.
  • Saturation is often problematically applied as a universal standard in qualitative health research.
  • Existing qualitative methods have varied approaches to sample size and data adequacy.

Purpose of the Study:

  • To introduce the Qualitative Framework for Operationalizing Respondent Sampling (Q-FORS).
  • To provide a practical framework for determining data adequacy in qualitative research, particularly in health.
  • To offer a transparent method for accounting for sampling choices in qualitative studies.

Main Methods:

  • Developed the Q-FORS framework based on a narrative review of qualitative sample size and saturation literature.
  • Focused on data adequacy as the central concept for qualitative sample determination.
  • Demonstrated Q-FORS application with two qualitative research examples.

Main Results:

  • Q-FORS clarifies existing literature by emphasizing data adequacy over a one-size-fits-all saturation standard.
  • The framework is applicable across the research lifecycle, from proposal to reporting.
  • Q-FORS respects the epistemological underpinnings of qualitative research.

Conclusions:

  • Q-FORS provides a pragmatic and epistemologically sound approach to qualitative sampling in health research.
  • This framework enhances transparency and rigor in qualitative data collection and analysis.
  • Researchers can utilize Q-FORS to justify sample size and data adequacy decisions.