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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

285
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...
285
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

12.9K
The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
12.9K
Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

675
Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
675
Variability: Analysis01:11

Variability: Analysis

543
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...
543
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

615
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%...
615
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

6.1K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
6.1K

You might also read

Related Articles

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

Sort by
Same author

Detecting Transition Points in the Slope-Intercept Relation in Linear Latent Growth Models.

Multivariate behavioral research·2025
Same author

Mediators that Matter: Psychological Distress, Developmental Assets, and Educational Outcomes among Black Youth.

Journal of educational psychology·2025
Same author

Sedimentation in Saudi Arabia's 574 reservoirs: Nationwide assessment using remote sensing and erosion modeling.

Journal of environmental management·2025
Same author

nmax and the quest to restore caution, integrity, and practicality to the sample size planning process.

Psychological methods·2025
Same author

Multiphase Structured Latent Curve Models for Count Response Data: A Re-Analysis of the Acquisition of Morphology in English.

Psychometrika·2025
Same author

Predicting Post-Fracture Recovery with Smartphone Mobility Data: A Proof-of-Concept Study.

The Journal of bone and joint surgery. American volume·2025

Related Experiment Video

Updated: Feb 16, 2026

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

7.3K

Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification.

Jeffrey R Harring1, Daniel M McNeish2, Gregory R Hancock1

  • 1Department of Human Development and Quantitative Methodology, University of Maryland, College Park.

Psychological Methods
|December 22, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces two methods to address omitted variables in structural models: a fixed parameter approach and a Bayesian random parameter approach. These techniques improve model accuracy when key variables are missing.

More Related Videos

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

3.8K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

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

Published on: January 31, 2014

17.2K

Related Experiment Videos

Last Updated: Feb 16, 2026

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

7.3K
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

3.8K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

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

Published on: January 31, 2014

17.2K

Area of Science:

  • Statistics
  • Psychometrics
  • Educational Psychology

Background:

  • Structural models are susceptible to external misspecification due to omitted variables.
  • Omitted variables can significantly distort model inferences and conclusions.

Purpose of the Study:

  • To present and evaluate two novel strategies for handling omitted variables in structural modeling.
  • To enhance the robustness of statistical inferences in the presence of potential model misspecification.

Main Methods:

  • A fixed parameter approach using a 'phantom variable' with pre-set parameter values.
  • A Bayesian random parameter approach, defining prior distributions for the phantom variable's parameters.

Main Results:

  • Both methods provide viable strategies for sensitivity analysis regarding omitted variables.
  • Demonstrated effectiveness on an applied example from educational psychology.

Conclusions:

  • External misspecification sensitivity analyses should be standard practice in measured and latent variable modeling.
  • These methods aid researchers in assessing the impact of potentially unobserved salient variables.