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

Methods of Medium Optimization01:28

Methods of Medium Optimization

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...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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 from...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Introduction and Methods of Leveling01:26

Introduction and Methods of Leveling

Leveling is a surveying procedure used to determine elevation differences between distant points. Elevation refers to the vertical distance above or below a reference datum, typically mean sea level (MSL). In the United States, elevations are often referenced to the mean sea level station at Father Point Rimouski along the St. Lawrence Seaway. To make the datum accessible, permanent markers are established throughout the region. These markers, called benchmarks, have known elevations. If the...
Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...

You might also read

Related Articles

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

Sort by
Same author

New methodological and software tools for probing moderation in intrinsically nonlinear models.

Behavior research methods·2026
Same author

Association of community sociodemographic and tuberculosis-related factors with variability in community-level tuberculosis stigma in South Africa: an ecological analysis from the MISSED TB Outcomes study.

BMJ open·2026
Same author

Single-Level Bifactor Models as Implicit Multilevel Factor Models Without a Bifactor Structure.

Multivariate behavioral research·2026
Same author

Determining the power of a 1-sided z-test given only the power of the corresponding 2-sided test.

Journal of behavioral medicine·2025
Same author

Community variability in TB-related stigma in South Africa: an ecologic analysis from the MISSED TB Outcomes Study.

medRxiv : the preprint server for health sciences·2025
Same author

Experiments in daily life: When causal within-person effects do (not) translate into between-person differences.

Psychological methods·2025
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

Related Experiment Video

Updated: Jun 9, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

A general multilevel SEM framework for assessing multilevel mediation.

Kristopher J Preacher1, Michael J Zyphur, Zhen Zhang

  • 1Department of Psychology, University of Kansas, 1415 Jayhawk Boulevard, Lawrence, KS 66045-7556, USA. preacher@ku.edu

Psychological Methods
|September 9, 2010
PubMed
Summary
This summary is machine-generated.

Multilevel structural equation modeling (MSEM) offers a unified framework for mediation analysis with nested data. This approach overcomes limitations of multilevel modeling (MLM) by accommodating Level-2 outcomes and distinguishing between- and within-level effects.

More Related Videos

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Related Experiment Videos

Last Updated: Jun 9, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Area of Science:

  • Psychology
  • Statistics
  • Social Sciences

Background:

  • Multilevel modeling (MLM) is commonly used for nested data analysis.
  • Existing MLM approaches have limitations in testing mediation with Level-2 outcomes.
  • Current MLM methods may conflate between- and within-level indirect effects.

Purpose of the Study:

  • To present a unified multilevel structural equation modeling (MSEM) framework for mediation analysis.
  • To overcome limitations of MLM in multilevel mediation.
  • To integrate existing and new multilevel mediation models.

Main Methods:

  • Developed an integrative 2-level MSEM mathematical framework.
  • Demonstrated the framework using applied examples and software code.
  • Compared MSEM with traditional MLM approaches for mediation analysis.

Main Results:

  • MSEM accommodates mediation pathways with Level-2 outcomes.
  • MSEM provides distinct estimates for between- and within-level indirect effects.
  • MSEM offers a more flexible and comprehensive approach than MLM.

Conclusions:

  • MSEM provides a superior framework for multilevel mediation analysis compared to MLM.
  • The MSEM framework integrates various multilevel mediation models.
  • Using MSEM can lead to different substantive conclusions than MLM.