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

Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
359
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

712
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...
712
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

596
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...
596
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

627
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
627
Methods of Medium Optimization01:28

Methods of Medium Optimization

70
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
70

You might also read

Related Articles

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

Sort by
Same author

Prevalence and Predictors of Self-Reported Adverse Experiences in Digital Meditation Training: 2 Randomized Controlled Trials.

JMIR mental health·2026
Same author

Multimodal assessments of therapist characteristics are largely unrelated to patient outcomes: A preregistered analysis.

Clinical psychological science : a journal of the Association for Psychological Science·2026
Same author

Trial-By-Trial Changes in Neural Indices of Performance Monitoring Uniquely Correspond to Behavioral Adjustments During a Flanker Task.

Human brain mapping·2026
Same author

Investigating the replicability of the social and behavioural sciences.

Nature·2026
Same author

Artificial Intelligence to Support Human-Provided Mental Health Treatment.

Annual review of clinical psychology·2026
Same author

When ELIZA meets therapists: A Turing test for the heart and mind.

PLOS mental health·2026
Same journal

Effect of third-wave cognitive behavioral therapy in adults with acquired brain injury: A systematic review and meta-analysis of randomized controlled trials.

Journal of consulting and clinical psychology·2026
Same journal

Effectiveness of stepped counseling on depression in distressed family caregivers of older adults: A randomized pragmatic trial (ReDiCare).

Journal of consulting and clinical psychology·2026
Same journal

Dysfunctional attitudes in cognitive-behavioral therapy and antidepressant pharmacotherapy for adult depression: A systematic review and meta-analysis of individual participant data.

Journal of consulting and clinical psychology·2026
Same journal

Complete delivery matters: Multilevel complier average causal effect analysis of universal mental health curricula in two randomized controlled trials in U.K. schools.

Journal of consulting and clinical psychology·2026
Same journal

Investigating craving as an indicator of early response to buprenorphine treatment among adults with opioid use disorder.

Journal of consulting and clinical psychology·2026
Same journal

Mental health benchmarks in the United States: An unheeded call to action.

Journal of consulting and clinical psychology·2026
See all related articles

Related Experiment Video

Updated: May 3, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K

Analyzing multiple outcomes in clinical research using multivariate multilevel models.

Scott A Baldwin1, Zac E Imel2, Scott R Braithwaite1

  • 1Department of Psychology, Brigham Young University.

Journal of Consulting and Clinical Psychology
|February 5, 2014
PubMed
Summary
This summary is machine-generated.

Multivariate multilevel models offer advanced data analysis for psychotherapy researchers, extending common growth models to analyze multiple outcomes simultaneously. These powerful statistical tools enhance clinical research by examining complex treatment effects across various measures.

More Related Videos

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

5.8K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.1K

Related Experiment Videos

Last Updated: May 3, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K
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

5.8K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.1K

Area of Science:

  • Psychology
  • Statistics
  • Clinical Research

Background:

  • Multilevel models are standard in intervention research.
  • Most intervention studies use multiple outcome measures.
  • Multivariate analysis methods are underutilized in this field.

Purpose of the Study:

  • To discuss multivariate extensions of multilevel models for psychotherapy researchers.
  • To demonstrate how these models can be applied to analyze multiple outcome measures in intervention studies.

Main Methods:

  • Utilized simulated longitudinal treatment data.
  • Extended common univariate growth models to a multivariate framework.
  • Developed methods to examine multivariate hypotheses involving fixed and random effects.

Main Results:

  • Demonstrated how multivariate models extend univariate growth models.
  • Showcased the examination of multivariate hypotheses regarding treatment effects across outcomes.
  • Illustrated the analysis of relationships between changes in different outcomes using random effects.

Conclusions:

  • Multivariate multilevel models provide a flexible and powerful approach for clinical research.
  • These models can significantly enhance the analysis of complex intervention data with multiple outcomes.