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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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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...
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Longitudinal Studies01:26

Longitudinal Studies

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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...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Comparing the Survival Analysis of Two or More Groups01:20

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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...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Updated: May 22, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Introduction to the Best Practice Recommendations for Longitudinal Latent Transition Analysis.

E Whitney G Moore1, Alessandro Quartiroli2,3, Todd D Little4

  • 1East Carolina University, Greenville, North Carolina, USA.

International Journal of Psychology : Journal International De Psychologie
|March 15, 2025
PubMed
Summary
This summary is machine-generated.

Finite mixture models identify subgroups from data. This tutorial explains latent transition analysis (LTA) for studying changes between these subgroups, offering best practices for researchers.

Keywords:
LTAMplusdrinking behaviourlongitudinal datamixture models

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Area of Science:

  • Quantitative Psychology
  • Statistical Modeling

Background:

  • Finite mixture models identify latent subgroups from diverse data.
  • Methodological advancements in finite mixture modeling are ongoing.
  • Latent transition analysis (LTA) is a key technique for subgroup analysis.

Purpose of the Study:

  • To provide a tutorial on Latent Transition Analysis (LTA).
  • To guide researchers in rigorously answering questions about transitions between latent classes.
  • To promote adherence to best practices in LTA.

Main Methods:

  • Finite mixture modeling framework.
  • Latent transition analysis (LTA) applied to longitudinal data.
  • Illustrative example using three-timepoint sport psychology data on collegiate student-athletes' health behaviors.

Main Results:

  • The tutorial details LTA's purpose, analysis steps, interpretation, and reporting.
  • Supplemental materials include Mplus syntax, decision processes, results, tables, and figures.
  • Demonstrates practical application of LTA in a specific research context.

Conclusions:

  • LTA is a valuable tool for understanding dynamic subgroup membership.
  • This tutorial enhances the applicability and approachability of LTA for researchers.
  • Best practices for LTA are crucial for rigorous and reliable findings.