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

Application of the Energy Equation01:04

Application of the Energy Equation

1.2K
The application of the energy equation to centrifugal pumps is a fundamental principle in fluid dynamics and engineering. In this scenario, the energy equation is used to calculate the flow rate of a centrifugal pump responsible for transferring water between two reservoirs at different elevations. The pump applies an energy input of 7500 joules per second, and the vertical difference between the lower and upper reservoirs is 10 meters. Additionally, the head loss due to friction and other...
1.2K
Application of the Linear Momentum Equation01:15

Application of the Linear Momentum Equation

429
The application of the linear momentum equation can be used to analyze the forces needed to hold a 180-degree pipe bend in place with flowing water. In this case, water flows through the bend with a constant cross-sectional area of 0.01 square meters and a flow velocity of 15 meters per second. The pressure at the entrance is 0.2 Megapascals and the pressure at the exit is 0.16 Megapascals.
The goal is to determine the force components in the x and y directions to hold the pipe in place. Since...
429
The Nernst Equation02:59

The Nernst Equation

47.0K
Nonstandard Reaction Conditions
The interconnection between standard cell potentials and various thermodynamic parameters such as the standard free energy change ΔG° and equilibrium constant K has been previously explored. For example, a redox reaction involving zinc(II) and tin(II) ions at 1 M concentration with Eºcell = +0.291 V and ΔG° = −56.2 kJ is spontaneous.
47.0K
Henderson-Hasselbalch Equation02:48

Henderson-Hasselbalch Equation

76.5K
The ionization-constant expression for a solution of a weak acid can be written as:
76.5K
Modeling with Differential Equations01:25

Modeling with Differential Equations

84
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
84
Chemical Equations03:10

Chemical Equations

81.6K
Chemical equations represent the identities and relative quantities of substances involved in a chemical reaction. The substances undergoing reaction are called reactants, and their formulas are placed on the left side of the equation. The substances generated by the reaction are called products, and their formulas are placed on the right side of the equation. Plus signs (+) separate individual reactant and product formulas, and an arrow (→) separates the reactant and product (left and right)...
81.6K

You might also read

Related Articles

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

Sort by
Same author

Guessing During Testing is a Person Attribute Not an Instrument Parameter.

Educational and psychological measurement·2025
Same author

From Development to Validation: Exploring the Efficiency of Numetrive, a Computerized Adaptive Assessment of Numerical Reasoning.

Behavioral sciences (Basel, Switzerland)·2025
Same author

Development of a Forced-Choice Personality Inventory via Thurstonian Item Response Theory (TIRT).

Behavioral sciences (Basel, Switzerland)·2025
Same author

Coaching inexperienced clinicians before a high stakes medical procedure: randomized clinical trial.

BMJ (Clinical research ed.)·2024
Same author

Types and Predictors of Service use Among Young Children Recommended to Receive Intensive Services After Initial Autism Spectrum Disorder Diagnosis.

Journal of autism and developmental disorders·2024
Same author

Calibrating Items Using an Unfolding Model of Item Response Theory: The Case of the Trait Personality Questionnaire 5 (TPQue5).

Evaluation review·2023

Related Experiment Video

Updated: Feb 5, 2026

Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish
14:43

Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish

Published on: July 18, 2020

8.6K

Assessing Construct Validity in Math Achievement: An Application of Multilevel Structural Equation Modeling (MSEM).

Georgios D Sideridis1,2, Ioannis Tsaousis3, Abdullah Al-Sadaawi4,5

  • 1Harvard Medical School, Boston Children's Hospital, Boston, MA, United States.

Frontiers in Psychology
|September 21, 2018
PubMed
Summary

Multilevel modeling reveals distinct math achievement structures at individual and university levels. Older universities show superior math performance, highlighting the importance of accounting for data hierarchy.

Keywords:
construct validitydiscriminant Validitylevel specific misfitmultilevel confirmatory factor analysismultilevel structural equation modelingnested models

More Related Videos

Multilevel Microdissection and Functional-Structural Profiling of Human Renal Arterial Branches
06:51

Multilevel Microdissection and Functional-Structural Profiling of Human Renal Arterial Branches

Published on: September 5, 2025

648
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.6K

Related Experiment Videos

Last Updated: Feb 5, 2026

Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish
14:43

Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish

Published on: July 18, 2020

8.6K
Multilevel Microdissection and Functional-Structural Profiling of Human Renal Arterial Branches
06:51

Multilevel Microdissection and Functional-Structural Profiling of Human Renal Arterial Branches

Published on: September 5, 2025

648
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.6K

Area of Science:

  • Educational Psychology
  • Quantitative Psychology
  • Higher Education Studies

Background:

  • Understanding math achievement requires appropriate statistical modeling.
  • Previous research often overlooks the hierarchical nature of educational data.
  • National mathematics examinations are crucial for university admissions in Saudi Arabia.

Purpose of the Study:

  • To determine the optimal factor structure of math competency.
  • To model math achievement at both individual (person) and institutional (university) levels.
  • To investigate the impact of university characteristics on math achievement.

Main Methods:

  • Multilevel Structural Equation Modeling (MSEM) applied to data from 2,881 students.
  • Analysis of a national mathematics examination with four sub-factors.
  • Evaluation of model fit using established psychometric criteria (Ryu & West, 2009; Gorsuch, 1983).

Main Results:

  • Aggregate data analysis showed both unidimensional and 4-factor models fit equally well; unidimensional was preferred for parsimony.
  • Multilevel analysis revealed different optimal structures: unidimensional at the university level and a 4-factor correlated model at the person level.
  • Older universities demonstrated superior math achievement, suggesting institutional age is a significant factor.

Conclusions:

  • Ignoring multilevel data structures can lead to inaccurate conclusions about math competency.
  • The optimal factor structure for math achievement differs between individual and institutional levels.
  • Institutional characteristics, such as age, play a role in student math performance.