Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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...

Reliability and Validity

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

Accuracy and Errors in Hypothesis Testing

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...

You might also read

Related Articles

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

Sort by

Same author

Tracing the Right Path: Determination of Large Pedigree Segmentation and Relatedness.

Behavior genetics·2026

Same author

Game, Set, and Match: A Scoping Review of Matching Characteristics for Control and Intervention Groups in Adaptive Behavioral Interventions for Physical Activity or Healthy Eating Designs for Populations with Overweight and Obesity.

Behavioral medicine (Washington, D.C.)·2026

Same author

The typical and atypical development of dynamic self-regulation and coregulation of respiratory sinus arrhythmia in mothers and children across early childhood.

Child development·2026

Same author

Penalized Subgrouping of Heterogeneous Time Series.

Multivariate behavioral research·2026

Same author

Extending reliability to intensive longitudinal data with the Kalman filter.

The British journal of mathematical and statistical psychology·2026

Same author

Simultaneous detection of gradual and abrupt structural changes in Bayesian longitudinal modelling using entropy and model fit measures.

The British journal of mathematical and statistical psychology·2026

Same journal

Maximum Likelihood and Bayesian Estimation in Cross-Domain Latent Growth Curve Modeling: The Impact of Reliability, Sample Size, and Missing Data.

Structural equation modeling : a multidisciplinary journal·2026

Same journal

Dynamic Modeling with Intensive Longitudinal Data: One-Step and Two-Step DSEM Approaches.

Structural equation modeling : a multidisciplinary journal·2026

Same journal

Accommodating Continuous Time Metrics within the Discrete-time Latent Change Score Model Using Definition Variables.

Structural equation modeling : a multidisciplinary journal·2025

Same journal

Does Cluster-Robust Estimation Provide Within-Study Effects? A Comparison of Individual Participant Data Methods in MASEM.

Structural equation modeling : a multidisciplinary journal·2025

Same journal

Two-Step Multilevel Latent Class Analysis in the Presence of Measurement Non-Equivalence.

Structural equation modeling : a multidisciplinary journal·2025

Same journal

Unsupervised Model Construction in Continuous-Time.

Structural equation modeling : a multidisciplinary journal·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 18, 2025

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

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

Published on: July 3, 2020

Measurement Model Misspecification in Dynamic Structural Equation Models: Power, Reliability, and Other

Hyungeun Oh¹, Michael D Hunter¹, Sy-Miin Chow¹

¹Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802.

Structural Equation Modeling : a Multidisciplinary Journal

|June 25, 2025

Summary

This summary is machine-generated.

Measurement error in Dynamic Structural Equation Models (DSEMs) causes significant parameter bias, even with high reliability. Careful model specification is crucial for accurate analysis of intensive longitudinal data (ILD).

Keywords:

Dynamic Structural Equation Models Intensive Longitudinal Data Measurement Error Within-person Dynamic Process

More Related Videos

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Related Experiment Videos

Last Updated: Sep 18, 2025

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

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

Published on: July 3, 2020

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Area of Science:

Psychometrics
Quantitative Psychology
Statistical Modeling

Background:

Dynamic Structural Equation Models (DSEMs) are powerful for intensive longitudinal data (ILD).
The impact of measurement structure misspecification in DSEMs is not well understood.
Reliability and model complexity can influence DSEM results.

Purpose of the Study:

To investigate the effects of measurement error and misspecification in DSEMs.
To evaluate how reliability conditions and model complexity impact parameter estimation.
To provide practical recommendations for DSEM design and analysis.

Main Methods:

Conducted Monte Carlo simulations to assess DSEM performance under misspecification.
Varied reliability conditions, number of participants, and time points.
Compared single-indicator and multiple-indicator DSEM measurement structures.

Main Results:

Omitting measurement errors caused severe dynamic parameter bias, irrespective of reliability.
Increased sample size and time points reduced but did not eliminate bias.
Single-indicator DSEMs with composite scores performed similarly to multiple-indicator DSEMs.

Conclusions:

Measurement misspecification is a critical issue in DSEMs, leading to biased dynamic parameters.
Design choices, including the number of indicators, significantly affect DSEM results.
Recommendations and tools are provided for improving DSEM reliability and power analysis.