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

3.5K
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...
3.5K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

160
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...
160
Stratified Sampling Method01:16

Stratified Sampling Method

14.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
14.2K
Longitudinal Research02:20

Longitudinal Research

12.9K
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...
12.9K
Variability: Analysis01:11

Variability: Analysis

325
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
325
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

420
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...
420

You might also read

Related Articles

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

Sort by
Same author

A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).

BMC psychiatry·2026
Same author

A data-analytics framework for exploring regression associations in multivariate categorical data of firefighters' PTSD.

Journal of applied statistics·2026
Same author

Development of Sleep medications MYTH-understanding (SMYTH) and Sleep Medications Overexpectation of REsponses (SMORE) among the general population.

Sleep & breathing = Schlaf & Atmung·2026
Same author

Unveiling topic dependencies through a multilevel topic model: a hierarchical approach to enhanced interpretability.

Journal of applied statistics·2026
Same author

Machine learning approached a 14-item shortened version of the Positive And Negative Sleep Appraisal Measure (PANSAM-14).

Sleep & breathing = Schlaf & Atmung·2026
Same author

The Dysfunctional Self-Focus Attributes Scale-7 (DSAS-7): A Machine Learning-based Development of a Shortened Version of the DSAS.

Journal of medical systems·2026

Related Experiment Video

Updated: Nov 28, 2025

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

6.6K

Latent Class Analysis for Repeatedly Measured Multiple Latent Class Variables.

Saebom Jeon1, Tae Seok Seo2, James C Anthony3

  • 1Department of Marketing Bigdata, Mokwon University.

Multivariate Behavioral Research
|November 25, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces joint latent class profile analysis (JLCPA) to model complex associations between multiple latent variables over time. JLCPA effectively identifies sequential patterns in longitudinal data, offering a faster computational approach.

Keywords:
Drug-taking behaviorlongitudinal datamultivariate latent classesrecursive EMstage-sequential process

More Related Videos

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

7.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.3K

Related Experiment Videos

Last Updated: Nov 28, 2025

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

6.6K
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

7.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.3K

Area of Science:

  • Statistics
  • Psychology
  • Sociology

Background:

  • Analyzing stage-sequential shifts in multiple latent classes is complex due to unobservable patterns and interdependencies.
  • Existing methods struggle to directly identify associations and sequential patterns among multiple discrete latent variables.

Purpose of the Study:

  • To propose a novel multivariate latent class analysis for longitudinal data, termed joint latent class profile analysis (JLCPA).
  • To systematically identify associations among multiple discrete latent variables and their sequential patterns.
  • To develop a computationally efficient algorithm for parameter estimation in longitudinal latent class models.

Main Methods:

  • Developed joint latent class profile analysis (JLCPA) for longitudinal data.
  • Proposed a recursive formula for the Expectation-Maximization (EM) algorithm to enhance computational speed.
  • Utilized data from the National Longitudinal Survey of Youth 1997 (NLSY97).

Main Results:

  • The proposed JLCPA method effectively identifies associations and sequential patterns among multiple latent variables.
  • The recursive EM algorithm significantly reduces computation time compared to the standard EM method.
  • The study successfully investigated multiple drug-taking behaviors in early-onset drinkers across different life stages.

Conclusions:

  • JLCPA offers a robust framework for analyzing complex longitudinal associations between latent variables.
  • The efficient EM algorithm facilitates the application of JLCPA to large datasets.
  • This approach provides valuable insights into developmental trajectories of behaviors such as substance use.