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

Regression Analysis01:11

Regression Analysis

9.0K
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:
9.0K
Multiple Regression01:25

Multiple Regression

4.4K
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...
4.4K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.8K
Correlation and Regression00:53

Correlation and Regression

4.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
4.2K
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
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...
7.3K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.9K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.9K

You might also read

Related Articles

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

Sort by
Same author

Guidelines for meta-analyses and systematic reviews in urology.

BJU international·2025
Same author

Guidelines for Meta-Analyses and Systematic Reviews in Urology.

Urology·2025
Same author

Guidelines for Meta-analyses and Systematic Reviews in Urology.

European urology·2025
Same author

Guidelines for Meta-Analyses and Systematic Reviews in Urology.

The Journal of urology·2025
Same author

Advances in meta-analysis: A unifying modelling framework with measurement error correction.

The British journal of mathematical and statistical psychology·2024
Same author

Radiation Toxicity in Patients With Collagen Vascular Disease: A Meta-Analysis of Case-Control Studies.

International journal of radiation oncology, biology, physics·2021
Same journal

Doubly robust augmented weighting estimators for the analysis of externally controlled single-arm trials and unanchored indirect treatment comparisons.

Research synthesis methods·2026
Same journal

Prompt engineering of large language models for paper screening in medical meta-analyses and systematic reviews: A prospective comparative study - CORRIGENDUM.

Research synthesis methods·2026
Same journal

Evaluating the accuracy and speed of eight deduplication tools: A comparative study.

Research synthesis methods·2026
Same journal

A comparison of preprint search aggregators: comprehensive identification of preprints in the information retrieval stage of evidence syntheses.

Research synthesis methods·2026
Same journal

Meta-research on key metrics of preregistered scoping reviews.

Research synthesis methods·2026
Same journal

Facilitators and barriers to engaging patient partners in knowledge syntheses: A stage-based approach.

Research synthesis methods·2026
See all related articles

Related Experiment Video

Updated: Apr 11, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

10.0K

Synthesizing regression results: a factored likelihood method.

Meng-Jia Wu1, Betsy Jane Becker2

  • 1Loyola University Chicago, School of Education, Chicago, IL, USA.

Research Synthesis Methods
|June 9, 2015
PubMed
Summary
This summary is machine-generated.

Synthesizing regression results is challenging, but a new factored likelihood method accurately combines regression models with different predictors. This approach estimates missing correlations, enabling robust meta-analysis of linear models.

Keywords:
likelihoodlinear modelsmeta-analysisregressionsynthesis

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.1K
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

Related Experiment Videos

Last Updated: Apr 11, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

10.0K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.1K
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

Area of Science:

  • Statistics
  • Biostatistics
  • Quantitative Psychology

Background:

  • Regression methods are prevalent across scientific disciplines.
  • Existing methods for synthesizing regression results are limited.
  • There is a need for robust techniques to integrate findings from studies with varying predictors.

Purpose of the Study:

  • To introduce and evaluate a novel factored likelihood method for synthesizing regression models.
  • To address the challenge of combining regression results when studies include different sets of predictors.
  • To provide a method for estimating synthesized standardized slopes and correlations.

Main Methods:

  • Utilizes a factored likelihood approach, originally designed for missing data.
  • Employs reported correlations from individual studies.
  • Estimates missing correlations through a series of regression analyses.
  • Applies sweep operators for calculation of synthesized slopes.

Main Results:

  • The factored likelihood method demonstrated great accuracy and stability in Monte Carlo simulations under fixed-effects models.
  • The method successfully synthesizes correlations among variables, even when studies have different predictor sets.
  • An example illustrated the practical application of calculating synthesized slopes.

Conclusions:

  • The factored likelihood method offers a powerful and flexible tool for the meta-analysis of linear regression models.
  • The technique is applicable to various scenarios involving the synthesis of regression analyses with differing predictors.
  • The method provides a reliable way to combine regression findings, enhancing the synthesis of quantitative research.