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

Feedback Inhibition00:46

Feedback Inhibition

57.1K
Biochemical reactions are occurring constantly in cells, converting starting substances to different products, usually with the help of enzymes that speed the reactions. Without enzymes, it would take far too long for most reactions to occur to be useful to the cell!
57.1K
Modeling with Differential Equations01:25

Modeling with Differential Equations

66
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...
66
Chemical Equations03:10

Chemical Equations

81.3K
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)...
81.3K
Feedback Loops01:01

Feedback Loops

64.3K
In most cases, excessive hormone production is prevented by negative feedback—a loop that starts with a stimulus inducing the release of a particular substance, like a hormone, to maintain a certain level before triggering a signal that results in a decrease in further release of the hormone.
64.3K
The Nernst Equation02:59

The Nernst Equation

46.8K
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.
46.8K
Thermochemical Equations02:55

Thermochemical Equations

35.9K
For a chemical reaction (the system) carried out at constant pressure – with the only work done caused by expansion or contraction – the enthalpy of reaction (also called the heat of reaction, ΔHrxn) is equal to the heat exchanged with the surroundings (qp).
35.9K

You might also read

Related Articles

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

Sort by
Same author

Posttraumatic stress in German volunteer lifeguards: evidence for the building block effect.

BMC public health·2026
Same author

Analyzing the Temporal Structure of Proactive Coping: An Integrative Approach.

Journal of personality·2026
Same author

Assessing three altruism facets by economic games and self-report: a multitrait-multimethod investigation.

Scientific reports·2026
Same author

Optimizing the Short Dark Triad Scale Using an Ant Colony Optimization Algorithm.

Assessment·2026
Same author

Missing Data Handling via EM and Multiple Imputation in Network Analysis using Glasso and Atan Regularization.

Multivariate behavioral research·2025
Same author

Longitudinal associations between well-being, hair cortisol, and self-reported health.

Applied psychology. Health and well-being·2024
Same journal

Proficiency order invariance of MLE, MAP, EAP, and WLE in item response theory.

The British journal of mathematical and statistical psychology·2026
Same journal

Bias and precision in true-score estimation.

The British journal of mathematical and statistical psychology·2026
Same journal

Polychoric correlations under the assumption of elliptical latent traits.

The British journal of mathematical and statistical psychology·2026
Same journal

Regularized reduced rank regression for mixed predictor and response variables.

The British journal of mathematical and statistical psychology·2026
Same journal

A multiple-choice SDT model for cognitive diagnosis models.

The British journal of mathematical and statistical psychology·2026
Same journal

Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling.

The British journal of mathematical and statistical psychology·2026
See all related articles

Related Experiment Video

Updated: Jan 30, 2026

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.2K

Analysing multisource feedback with multilevel structural equation models: Pitfalls and recommendations from a

Jana Mahlke1, Martin Schultze2, Michael Eid1

  • 1Division of Methods and Evaluation, Department of Educational Science and Psychology, Freie Universität Berlin, Germany.

The British Journal of Mathematical and Statistical Psychology
|January 30, 2019
PubMed
Summary
This summary is machine-generated.

This study determined minimum sample sizes for validating multisource feedback using multilevel structural equation models. Accurate standard error estimation requires 400 self-ratings or four ratings from peers/subordinates.

Keywords:
convergent and discriminant validitymultilevel structural equation modellingmultisource feedbackmultitrait-multimethod analysissimulation study

More Related Videos

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.4K
Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
15:05

Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation

Published on: May 20, 2020

9.3K

Related Experiment Videos

Last Updated: Jan 30, 2026

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.2K
Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.4K
Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
15:05

Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation

Published on: May 20, 2020

9.3K

Area of Science:

  • Organizational Psychology
  • Psychometrics
  • Quantitative Research Methods

Background:

  • Multisource feedback instruments, such as 360-degree feedback, are crucial for assessing employee performance and development.
  • Validating these instruments often involves complex statistical models to ensure reliability and validity.
  • Multilevel structural equation models (MSEM) are the preferred method for analyzing such data structures.

Purpose of the Study:

  • To determine the minimal required sample sizes for a specific non-standard MSEM incorporating self-ratings and ratings from multiple sources (peers, subordinates).
  • To investigate the impact of sample size on the accuracy of parameter and standard error estimation in this model.
  • To evaluate the influence of convergent and discriminant validity on model estimation accuracy.

Main Methods:

  • A Monte Carlo simulation study was employed to systematically vary sample sizes.
  • A non-standard MSEM was utilized, distinguishing between level-2 (self-ratings) and level-1 (others' ratings) variables.
  • Model fit was assessed using a corrected level-specific standardized root mean square residual (CLSRMR).

Main Results:

  • Model parameters were accurately estimated even with small sample sizes (100 self-ratings, 2 ratings from peers/subordinates).
  • Precise standard error estimation required larger samples: 400 self-ratings or at least four ratings from peers/subordinates.
  • Smaller sample sizes primarily biased standard errors related to common method factors, with trade-offs observed between self- and other-ratings.

Conclusions:

  • The study provides crucial sample size recommendations for researchers using this specific MSEM for multisource feedback validation.
  • Researchers should be cautious about standard error bias with smaller sample sizes, particularly concerning common method factors.
  • The corrected CLSRMR is recommended for model fit analysis, and the χ² test statistic should be interpreted with caution.