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

Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Variation01:19

Variation

6.8K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
6.8K
Regression Analysis01:11

Regression Analysis

5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

73.7K
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. 
73.7K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.4K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
10.4K

You might also read

Related Articles

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

Sort by
Same authorSame journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same author

Generalized estimating equations to estimate the ordered stereotype logit model for panel data.

Statistics in medicine·2020
Same author

Rethinking the Interpretation of Item Discrimination and Factor Loadings.

Educational and psychological measurement·2019
Same author

Simplified Estimation and Testing in Unbalanced Repeated Measures Designs.

Psychometrika·2018
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
Same journal

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same journal

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.

Psychometrika·2026
Same journal

Revelle's Beta: The Wait Is Over-Computation Becomes Possible.

Psychometrika·2026
Same journal

On dimensional implication graphs.

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 2025

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

2.1K

Using External Information for More Precise Inferences in General Regression Models.

Martin Jann1, Martin Spiess2

  • 1Department of Psychology, University of Hamburg, Von-Melle-Park 5, 20146,  Hamburg, Germany. martin.jann@uni-hamburg.de.

Psychometrika
|February 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method, generalized method of moments with external moments, to improve empirical research. This approach enhances psychological research by reducing variance and narrowing confidence intervals for more precise findings.

Keywords:
external informationgeneralized method of momentsimprecise probabilities

More Related Videos

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.3K
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

3.4K

Related Experiment Videos

Last Updated: Jul 2, 2025

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

2.1K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.3K
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

3.4K

Area of Science:

  • Statistics
  • Psychological Research

Background:

  • Empirical research often utilizes external information like study results, meta-analyses, and expert knowledge.
  • Psychological research can leverage external data for theory building and hypothesis generation.
  • Existing statistical techniques, such as Bayesian prior distributions, incorporate external information into the estimation process.

Purpose of the Study:

  • To introduce and discuss the benefits of generalized method of moments with external moments (GMEM) in empirical research.
  • To provide analytical formulas for estimators and their variances in multiple linear regression using GMEM.
  • To introduce a robustification method for GMEM against external moment misspecification using imprecise probabilities.

Main Methods:

  • Derivation of analytical formulas for estimators and variances in multiple linear regression.
  • Implementation of these formulas in an R function for applied use.
  • Simulation study to analyze the effects of various external moments.
  • Development of a robust approach using imprecise probabilities to address misspecification of external moments.

Main Results:

  • Analytical formulas for GMEM in multiple linear regression were derived.
  • A simulation study demonstrated the effects of different external moments.
  • A novel robustification technique against external moment misspecification was introduced.
  • Application to a dataset showed reduced variances and narrower confidence intervals for predicting premorbid intelligence quotient.

Conclusions:

  • Generalized method of moments with external moments offers a valuable technique for enhancing empirical and psychological research.
  • The proposed robustification method improves the reliability of GMEM when external information is imprecise.
  • GMEM leads to more precise estimations, as evidenced by reduced variances and narrower confidence intervals in predictive modeling.