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

229
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...
229
Longitudinal Research02:20

Longitudinal Research

12.4K
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...
12.4K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.9K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.9K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.4K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.4K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

293
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...
293
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

296
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
296

You might also read

Related Articles

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

Sort by
Same author

Individual Mobility across Clusters: The Impact of Ignoring Cross-Classified Data Structures in Discrete-Time Survival Analysis.

Multivariate behavioral research·2023
Same author

Understanding Open Access Data Using Visuals: Integrating Prospective Studies of Children's Responses to Natural Disasters.

Child & youth care forum·2021
Same author

The impacts of ignoring individual mobility across clusters in estimating a piecewise growth model.

The British journal of mathematical and statistical psychology·2020
Same author

An Investigation of Exposure Control Methods With Variable-Length CAT Using the Partial Credit Model.

Applied psychological measurement·2019
Same author

Residual Normality Assumption and the Estimation of Multiple Membership Random Effects Models.

Multivariate behavioral research·2018
Same author

Evaluation of a Noyce program: Development of teacher leaders in STEM education.

Evaluation and program planning·2018

Related Experiment Video

Updated: Sep 8, 2025

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults
09:37

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Published on: May 12, 2016

8.9K

A new way for handling mobility in longitudinal data.

Christopher J Cappelli1,2, Audrey J Leroux2, Congying Sun3

  • 1Center for Education Integrating Science, Mathematics, and Computing, Georgia Institute of Technology, Atlanta, GA, USA.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary

Applied researchers can now use the multiple membership growth curve model (MM-GCM) for complex multilevel data. This new model accurately accounts for student mobility, unlike methods that ignore or delete mobile students, preventing biased results.

Keywords:
HLMgrowth curve modelmobilitymultiple membership

More Related Videos

Author Spotlight: Using the MouseWalker to Quantify Locomotor Dysfunction in a Mouse Model of Spinal Cord Injury
07:28

Author Spotlight: Using the MouseWalker to Quantify Locomotor Dysfunction in a Mouse Model of Spinal Cord Injury

Published on: March 24, 2023

3.3K
Behavioral and Locomotor Measurements Using an Open Field Activity Monitoring System for Skeletal Muscle Diseases
06:52

Behavioral and Locomotor Measurements Using an Open Field Activity Monitoring System for Skeletal Muscle Diseases

Published on: September 29, 2014

53.9K

Related Experiment Videos

Last Updated: Sep 8, 2025

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults
09:37

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Published on: May 12, 2016

8.9K
Author Spotlight: Using the MouseWalker to Quantify Locomotor Dysfunction in a Mouse Model of Spinal Cord Injury
07:28

Author Spotlight: Using the MouseWalker to Quantify Locomotor Dysfunction in a Mouse Model of Spinal Cord Injury

Published on: March 24, 2023

3.3K
Behavioral and Locomotor Measurements Using an Open Field Activity Monitoring System for Skeletal Muscle Diseases
06:52

Behavioral and Locomotor Measurements Using an Open Field Activity Monitoring System for Skeletal Muscle Diseases

Published on: September 29, 2014

53.9K

Area of Science:

  • Social Sciences
  • Educational Research
  • Statistical Modeling

Background:

  • Applied researchers in social sciences frequently encounter multilevel data with non-exclusive clustering.
  • This data structure poses a statistical challenge for traditional analysis methods.
  • Accurate modeling is crucial for understanding complex relationships in such data.

Purpose of the Study:

  • To introduce and demonstrate the utility of a multiple membership growth curve model (MM-GCM).
  • To provide a flexible statistical tool for analyzing longitudinal multilevel data with non-exclusive clustering.
  • To address the limitations of existing methods when dealing with mobile units.

Main Methods:

  • Development and application of a multiple membership growth curve model (MM-GCM).
  • Utilized a real longitudinal educational dataset featuring students who changed schools.
  • Compared MM-GCM parameter estimates against 'final school'-GCM and 'delete'-GCM approaches.
  • Conducted a simulation study to assess the impact of ignoring student mobility.

Main Results:

  • The MM-GCM offers greater flexibility in modeling growth curve intercepts.
  • Ignoring student mobility in analyses (e.g., 'final school'-GCM, 'delete'-GCM) leads to significant bias.
  • Bias is particularly pronounced in cluster-level coefficients and variance components.

Conclusions:

  • The MM-GCM is a robust method for analyzing longitudinal multilevel data with student mobility.
  • Ignoring student mobility can severely distort statistical findings.
  • Applied researchers should adopt advanced models like MM-GCM for accurate analysis of complex data structures.