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

Application scenarios for nonstandard log-linear models.

Patrick Mair1, Alexander von Eye

  • 1Department of Statistics and Mathematics, Vienna University of Economics and Business Administration, Vienna, Austria. patrick.mair@wu-wien.ac.at

Psychological Methods
|June 15, 2007
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

Low Self-Esteem Predicts Persistent Suicidal Ideation Across Adolescence and Young Adulthood: A 14-year Longitudinal Study.

Research on child and adolescent psychopathology·2026
Same author

X implies Y - Testing Hypotheses of Direction of Effect Using Configural Frequency Analysis.

Integrative psychological & behavioral science·2026
Same author

Charting age-related change in the architecture of fluid cognition.

Child development·2026
Same author

Using smartphone surveys to predict next-week suicide attempts.

Journal of psychopathology and clinical science·2026
Same author

Control of Type 1 and Type 2 Errors in Configural Frequency Analysis.

Journal for person-oriented research·2026
Same author

Longitudinal changes in T1w/T2w estimates of cortical myelin with age and pubertal timing.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
See all related articles

This study defines hierarchical, nonhierarchical, and nonstandard log-linear models. It illustrates applications and parameter interpretation using design matrices and coding schemes for better understanding of statistical models.

Area of Science:

  • Statistics
  • Statistical Modeling

Background:

  • Log-linear models are widely used in statistical analysis.
  • Understanding different types of log-linear models and their parameter interpretations is crucial for accurate analysis.

Purpose of the Study:

  • To define hierarchical, nonhierarchical, and nonstandard log-linear models.
  • To present application scenarios for nonhierarchical and nonstandard models.
  • To focus on the interpretation of model parameters, both formally and by magnitude.

Main Methods:

  • Definition and classification of log-linear models.
  • Utilization of design matrices to represent model hypotheses and parameter interpretability.
  • Discussion of coding schemes, specifically dummy coding and effects coding.

Related Experiment Videos

  • Illustration with data examples from existing literature.

    • Main Results:

    • Clear definitions provided for hierarchical, nonhierarchical, and nonstandard log-linear models.
    • Design matrices effectively illustrate parameter interpretability and model differences.
    • Coding schemes (dummy and effects) are discussed in context.
    • Practical data examples demonstrate model applications.

    Conclusions:

    • The article clarifies the distinctions and applications of various log-linear models.
    • Emphasizes the importance of parameter interpretation for robust statistical analysis.
    • Provides a practical guide for researchers using log-linear models.