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 Experiment Videos

Annotation: correspondence analysis

B S Everitt1

  • 1Institute of Psychiatry, London, U.K.

Journal of Child Psychology and Psychiatry, and Allied Disciplines
|November 18, 1997
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A Monte Carlo Investigation Of The Likelihood Ratio Test For The Number Of Components In A Mixture Of Normal Distributions.

Multivariate behavioral research·2016
Same author

A Note on Parameter Estimation for Lazarsfeld's Latent Class Model using the EM Algorithm.

Multivariate behavioral research·2016
Same author

A Monte Carlo Investigation of the Likelihood Ratio Test for Number of Classes in Latent Class Analysis.

Multivariate behavioral research·2016
Same author

Analysis of drop-out data in treatment trials.

The British journal of psychiatry : the journal of mental science·1999
Same author

Mixture model mapping of the brain activation in functional magnetic resonance images.

Human brain mapping·1999
Same author

The use of multivariate statistical methods in psychiatry.

Statistical methods in medical research·1998
Same journal

Co-development of ADHD symptoms and emotional problems from childhood to adulthood: predictors and developmental outcomes.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same journal

Individualized EEG functional connectivity predicts clinical symptoms in ADHD, dyslexia, and their comorbidity.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same journal

Mechanism-based subtypes of problematic use of the internet and corresponding neurobehavioral characteristics among children and adolescents.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same journal

Personally meaningful life events from adolescence to young adulthood: a longitudinal natural language processing analysis.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same journal

A double-blind randomized controlled trial of personalized upper-alpha neurofeedback in children with ADHD.

Journal of child psychology and psychiatry, and allied disciplines·2026
Same journal

Self-administered single-session interventions for mental health in young people: A systematic review and meta-analysis.

Journal of child psychology and psychiatry, and allied disciplines·2026
See all related articles

Correspondence analysis visually explores relationships between categorical variables. This graphical method offers clearer data insights than traditional numerical analysis, aiding researchers in understanding complex data structures.

Area of Science:

  • Statistics
  • Data Analysis
  • Social Sciences

Background:

  • Investigating relationships between categorical variables is crucial in many research fields.
  • Traditional methods like chi-squared statistics can be complex to interpret.
  • Graphical representations can enhance data understanding.

Purpose of the Study:

  • To highlight the utility of correspondence analysis for exploring categorical data.
  • To demonstrate how graphical displays improve data interpretation.
  • To provide context for the application of correspondence analysis.

Main Methods:

  • Correspondence analysis (CA) is employed as a statistical technique.
  • Graphical visualization of variable relationships is central to the method.

Related Experiment Videos

  • Comparison with traditional numerical analysis and chi-squared statistics is implied.

    • Main Results:

    • Correspondence analysis provides readily interpretable graphical insights into variable associations.
    • The method facilitates a more intuitive understanding of data structure compared to numerical tables.
    • Practical experience is beneficial for fully appreciating its capabilities.

    Conclusions:

    • Correspondence analysis is a valuable tool for uncovering patterns in categorical data.
    • Its graphical output simplifies the interpretation of complex relationships.
    • The technique can be integrated with other statistical models for robust analysis.