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

3.1K
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...
3.1K
Factorial Design02:01

Factorial Design

13.1K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.1K
Regression Analysis01:11

Regression Analysis

5.8K
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:
5.8K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

261
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
261
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.1K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.1K
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

You might also read

Related Articles

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

Sort by
Same author

A comparison of multivariate and univariate meta-analysis.

Behavior research methods·2026
Same author

The Wor2 phenotypic switching regulator controls biofilm formation in Candida auris.

NPJ biofilms and microbiomes·2026
Same author

Exploring the Use of Multiple Imputation for Handling Missing Covariates in Meta-Regression with Dependent Effect Sizes.

Multivariate behavioral research·2026
Same author

Missing Data Sensitivity Analyses for Alcohol Research.

Alcohol, clinical & experimental research·2026
Same author

A Self-Guided App-Based Mindfulness Intervention for Racially and Ethnically Minoritized Individuals Who Experience Discrimination-Related Mental Health Symptoms: Randomized Controlled Trial.

Journal of medical Internet research·2026
Same author

Global emergence and rapid spread of <i>Candidozyma auris</i> (syn. <i>Candida auris</i>): epidemiology, biology, and antifungal resistance.

Clinical microbiology reviews·2026

Related Experiment Video

Updated: Jul 29, 2025

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

10.2K

A factored regression model for composite scores with item-level missing data.

Egamaria Alacam1, Craig K Enders1, Han Du1

  • 1Department of Psychology, University of California, Los Angeles.

Psychological Methods
|May 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new factored regression method for handling missing data in composite scores. The approach is effective even with many items, outperforming traditional methods in simulations.

More Related Videos

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

9.2K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

831

Related Experiment Videos

Last Updated: Jul 29, 2025

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

10.2K
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

9.2K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

831

Area of Science:

  • Psychometrics
  • Behavioral Science Research

Background:

  • Composite scores are crucial in behavioral science, often using multi-item questionnaires.
  • Item-level missing data is a common challenge, impacting precision and statistical power.
  • Existing missing data methods can become overly complex, especially with high dimensionality.

Purpose of the Study:

  • To describe and evaluate a novel factored regression specification for composite scores with incomplete item responses.
  • To address the complexity and "curse of dimensionality" in missing data handling for psychometric models.
  • To offer an effective alternative for managing missing data in behavioral research.

Main Methods:

  • Utilized computer simulations to compare the proposed factored specification against multiple imputation and latent variable modeling.
  • Evaluated the method's performance under various conditions, including scenarios with a large number of items relative to sample size.
  • Applied the method to real-world data, demonstrating its practical utility with available software.

Main Results:

  • The proposed factored specification demonstrated effectiveness in handling item-level missing data for composite scores.
  • The approach proved robust even under extreme conditions, such as when the number of items exceeded the sample size.
  • Simulation results indicated the new method's potential to maximize precision and power in analyses.

Conclusions:

  • The novel factored regression approach offers a viable and effective solution for managing missing item responses in composite scores.
  • This method provides a valuable tool for behavioral scientists dealing with complex, high-dimensional datasets.
  • The described technique enhances the reliability and validity of composite scores in research applications.