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

Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

586
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
586
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

365
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
365
Longitudinal Studies01:26

Longitudinal Studies

481
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
481
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.3K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.3K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

483
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
483
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

565
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...
565

You might also read

Related Articles

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

Sort by
Same author

Influence of sire fertility on the metabolism of in vitro produced embryos.

Reproduction & fertility·2026
Same author

Associations between subclinical bovine respiratory disease, growth patterns, and the nasal and fecal microbiota in dairy replacement heifers: a retrospective study.

Frontiers in veterinary science·2026
Same author

Physiologic and Perceptual Responses During Resistance Exercise With Self-Selected and Nonpreferred Music.

Journal of strength and conditioning research·2025
Same author

Characterisation of the bacterial and archaeal microbiota in fresh colostrum collected from a single, spring-calving dairy herd.

PloS one·2025
Same author

Temporal establishment of the colon microbiota in Angus calves from birth to post-weaning.

PloS one·2025
Same author

Metabolic profiling identifies fertility markers in bull sperm†.

Biology of reproduction·2025
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Jan 18, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.7K

Ruling out Latent Time-Varying Confounders in Two-Variable Multi-Wave Studies.

David A Kenny1, D Betsy McCoach2

  • 1Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut, United States.

Multivariate Behavioral Research
|June 3, 2025
PubMed
Summary
This summary is machine-generated.

Researchers can now test for unmeasured confounders in multi-wave studies using the Latent Time-Varying Covariate (LTVC) model. This method helps rule out alternative explanations for observed associations between variables, strengthening causal inference.

Keywords:
Causal InferenceLatent ConfounderLongitudinal DataStructural Equation Modeling

More Related Videos

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

10.7K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K

Related Experiment Videos

Last Updated: Jan 18, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.7K
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

10.7K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K

Area of Science:

  • Psychometrics
  • Quantitative Psychology
  • Causal Inference

Background:

  • Estimating causal cross-lagged effects in multi-wave designs is crucial but challenged by unmeasured time-varying confounders.
  • Existing methods lack a strategy to definitively rule out such confounding.
  • This gap hinders the accurate interpretation of longitudinal associations.

Purpose of the Study:

  • To propose and validate a novel strategy for testing the influence of unmeasured time-varying covariates.
  • To introduce the Latent Time-Varying Covariate (LTVC) model for this purpose.
  • To enhance the rigor of causal inference in two-variable, multi-wave studies.

Main Methods:

  • Development of the Latent Time-Varying Covariate (LTVC) model, testable with data from three or more waves.
  • Assessing model fit to determine if a time-varying covariate can explain observed covariation.
  • Imposing stationarity constraints to improve statistical power for detecting effects.

Main Results:

  • The LTVC model effectively tests whether unmeasured time-varying covariates account for all covariation between variables.
  • Model fit indicates the plausibility of confounding, challenging causal interpretations.
  • Stationarity constraints enhance the power of the LTVC model, especially for smaller effects.

Conclusions:

  • The LTVC model provides a critical tool for researchers to probe and potentially rule out confounding by unmeasured time-varying factors.
  • This approach strengthens the validity of causal cross-lagged effect estimations in longitudinal research.
  • The study offers methods to address a key limitation in analyzing multi-wave data.