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

300
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...
300
Traits and States01:17

Traits and States

358
Personality traits represent consistent patterns in behavior, thoughts, and emotions, reflecting an individual's tendencies across various situations. For example, extraversion, a well-known trait, manifests in individuals as talkative, energetic, and enthusiastic behaviors. These traits are stable over time, offering a reliable framework for predicting how people might act in different contexts. However, they do not define every moment of an individual's life. In contrast to traits,...
358
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

120
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...
120
Longitudinal Research02:20

Longitudinal Research

12.8K
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...
12.8K
Structuralism01:26

Structuralism

2.6K
Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
Titchener's approach to structuralism was unique. He...
2.6K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

715
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
715

You might also read

Related Articles

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

Sort by
Same author

Experience sampling methods require more than numbers.

Communications psychology·2026
Same author

Assessing the internal consistency reliability of ecological momentary assessment measures: Insights from the WARN-D study.

Psychological assessment·2025
Same author

An investigation into in-sample and out-of-sample model selection for nonstationary autoregressive models.

The British journal of mathematical and statistical psychology·2025
Same author

Predicting dropout in intensive longitudinal data: Extending the joint model for autocorrelated data.

Psychological assessment·2025
Same author

The many reliabilities of psychological dynamics: An overview of statistical approaches to estimate the internal consistency reliability of intensive longitudinal data.

Psychological methods·2025
Same author

Bayes factors for two-group comparisons in Cox regression with an application for reverse-engineering raw data from summary statistics.

Journal of applied statistics·2025

Related Experiment Video

Updated: Nov 5, 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

6.5K

Using structural equation modeling to study traits and states in intensive longitudinal data.

Sebastian Castro-Alvarez1, Jorge N Tendeiro1, Rob R Meijer1

  • 1Department of Psychometrics and Statistics.

Psychological Methods
|May 20, 2021
PubMed
Summary
This summary is machine-generated.

Structural equation models (state-trait SEMs) offer an alternative for analyzing intensive longitudinal data by modeling traits and states. The trait-state-occasion (TSO) model demonstrated superior performance in simulation and empirical studies.

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
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

Related Experiment Videos

Last Updated: Nov 5, 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

6.5K
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
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

Area of Science:

  • Psychometrics
  • Longitudinal Data Analysis
  • Structural Equation Modeling

Background:

  • Traditional time series and multilevel models have limitations in analyzing intensive longitudinal data, failing to directly address trait-state conceptualizations and measurement error.
  • State-trait SEMs, rooted in latent state-trait (LST) theory, represent traits and states as latent variables, offering a more nuanced approach.
  • Standard state-trait SEMs can face overparameterization and nonconvergence issues with increasing measurement occasions due to wide data format requirements.

Purpose of the Study:

  • To evaluate the suitability of state-trait SEMs for intensive longitudinal data.
  • To compare the performance of traditional single-level state-trait SEMs against their multilevel counterparts.
  • To assess specific models: multistate-singletrait (MSST), common and unique trait-state (CUTS), and trait-state-occasion (TSO).

Main Methods:

  • A simulation study was conducted to compare different state-trait SEMs.
  • Three specific state-trait SEMs (MSST, CUTS, TSO) were analyzed in both single-level and multilevel formulations.
  • An empirical application was included to validate findings from the simulation study.

Main Results:

  • The multilevel versions of state-trait SEMs were investigated for their efficacy with long-format data.
  • The trait-state-occasion (TSO) model exhibited the best performance across both simulated and empirical datasets.
  • The study identified the TSO model as a robust choice for analyzing intensive longitudinal data.

Conclusions:

  • State-trait SEMs are valuable for examining the psychometric properties of questionnaires in intensive longitudinal research.
  • The TSO model is highlighted as a particularly effective approach for this type of data.
  • Further research is needed to address existing limitations, potentially through extensions to more general modeling frameworks.