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

Cluster Sampling Method01:20

Cluster Sampling Method

13.7K
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...
13.7K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

403
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
403
Survival Tree01:19

Survival Tree

236
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
236

You might also read

Related Articles

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

Sort by
Same author

Assessing the relationships between frailty, sarcopenia and Alzheimer's disease biomarkers: a scoping review.

Ageing research reviews·2026
Same author

Author Correction: Random access and semantic search in DNA data storage enabled by Cas9 and machine-guided design.

Nature communications·2026
Same author

Prevalence of frailty and its association with cognition in preclinical Alzheimer's disease: a cross-sectional analysis of baseline data from the A4 study.

Age and ageing·2026
Same author

Reconciling links between diversity and population stability across global plant communities.

The New phytologist·2026
Same author

Vicarious trauma primes innate immunity and reconfigures human brain networks.

bioRxiv : the preprint server for biology·2026
Same author

Safety and feasibility of a low-cost laparoscope in a porcine model.

Surgical endoscopy·2025

Related Experiment Video

Updated: Nov 17, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Evaluating three stuttering assessments through network analysis, random forests and cluster analysis.

David Ward1, Ronan Miller1, Alexandre Nikolaev2

  • 1School of Psychology and Clinical Language Sciences, University of Reading, Reading, RG6 6AL, UK.

Journal of Fluency Disorders
|February 11, 2021
PubMed
Summary
This summary is machine-generated.

This study analyzed stuttering assessment tools, finding weak links between severity measures and quality of life/belief scales. This suggests a potential gap in current stuttering assessments.

Keywords:
Cluster analysisNetwork scienceRandom forestsStuttering assessment

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

342

Related Experiment Videos

Last Updated: Nov 17, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

342

Area of Science:

  • Speech and Language Pathology
  • Psycholinguistics
  • Behavioral Science

Background:

  • Stuttering involves complex interactions between cognitive and behavioral factors.
  • Assessing stuttering requires instruments that capture perceived impact and severity.
  • Understanding these relationships is crucial for effective intervention.

Purpose of the Study:

  • To investigate the relationships within and between three common stuttering assessment protocols.
  • To explore the nonlinear interactions of cognitive and behavioral variables in stuttering.
  • To identify potential gaps in current assessment tools for stuttering.

Main Methods:

  • Utilized data from 26 participants.
  • Applied network analysis, random forests, and cluster analysis.
  • Compared scores from Stuttering Severity Index (SSI-IV), Overall Assessment of the Speaker's Experience of Stuttering (OASES), and Unhelpful Thoughts and Beliefs About Stuttering (UTBAS) scales.

Main Results:

  • Network analysis showed weak interactions between SSI-IV and OASES/UTBAS.
  • Random forest analysis indicated strong relationships between OASES and UTBAS.
  • Cluster analysis suggested potential revisions for OASES and UTBAS scales.

Conclusions:

  • The combination of statistical methods provided a deeper evaluation of the assessment protocols.
  • A fractured network among SSI-IV, OASES, and UTBAS may hinder comprehensive understanding of stuttering.
  • There is a potential need for an assessment tool that integrates behavioral and social aspects of stuttering.