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

Longitudinal Studies01:26

Longitudinal Studies

203
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...
203
Longitudinal Research02:20

Longitudinal Research

12.1K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.1K
Regression Analysis01:11

Regression Analysis

6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K
Multiple Regression01:25

Multiple Regression

3.1K
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...
3.1K
Two-Way ANOVA01:17

Two-Way ANOVA

2.7K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.7K
One-Way ANOVA01:18

One-Way ANOVA

8.1K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
8.1K

You might also read

Related Articles

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

Sort by
Same author

Evidence-based practice attitude scale for Latinx mental health professionals: a novel application of confirmatory factor analysis.

Implementation science communications·2026
Same author

Guidance for the Methodological Challenges of Polytobacco Use in Tobacco Regulatory Science.

Public health reports (Washington, D.C. : 1974)·2025
Same author

Dynamic fit index cutoffs for treating likert items as continuous.

Psychological methods·2025
Same author

Apolipoprotein E, Executive Function, and Falls across Cognitive Status: A Cross-Sectional Study.

Dementia and geriatric cognitive disorders·2025
Same author

Qualitative Mediation Analysis: an Important Method for Exploring Mediating Mechanisms in Prevention Science.

Prevention science : the official journal of the Society for Prevention Research·2025
Same author

Exploring how many categories are needed to model ordinal intensive longitudinal data as continuous with dynamic structural equation models.

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: Aug 16, 2025

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

7.0K

Intensive longitudinal mediation in Mplus.

Daniel McNeish1, David P MacKinnon1

  • 1Department of Psychology, Arizona State University.

Psychological Methods
|December 22, 2022
PubMed
Summary
This summary is machine-generated.

This tutorial introduces dynamic structural equation models (DSEMs) for analyzing intensive longitudinal data in behavioral research. DSEMs offer advanced mediation modeling for time-varying and person-specific effects, overcoming limitations of traditional methods.

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

6.4K
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

4.0K

Related Experiment Videos

Last Updated: Aug 16, 2025

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

7.0K
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

6.4K
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

4.0K

Area of Science:

  • Behavioral Science
  • Psychology
  • Data Science

Background:

  • Traditional longitudinal mediation models often use panel data with sparse measurements.
  • Intensive longitudinal data (ILD) from digital devices present unique structural challenges.
  • Existing methods may not fully capture the nuances of ILD in behavioral research.

Purpose of the Study:

  • To address limitations of traditional longitudinal mediation models with ILD.
  • To introduce dynamic structural equation models (DSEMs) as a suitable framework for ILD mediation.
  • To provide a practical guide for applying DSEMs to intensive longitudinal mediation.

Main Methods:

  • Review of limitations in traditional longitudinal mediation for ILD.
  • Introduction and application of four DSEM-based intensive longitudinal mediation models (stationary, person-specific, dynamic, cross-classified).
  • Demonstration using a mobile health intervention for binge eating disorder with annotated Mplus code.

Main Results:

  • DSEMs effectively model time- and person-varying indirect effects in ILD.
  • Four distinct DSEM approaches offer flexibility for complex longitudinal mediation.
  • The tutorial provides practical guidance and code for empirical researchers.

Conclusions:

  • DSEMs provide a powerful and flexible approach for mediation analysis with intensive longitudinal data.
  • Researchers can leverage DSEMs to better understand dynamic processes in behavioral research.
  • This work facilitates advanced longitudinal mediation modeling with modern data collection methods.