Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

Overview of Microsoft Excel as a Data Analysis Tool

Overview of Microsoft Excel as a Data Analysis Tool

Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...

Statistical Software for Data Analysis and Clinical Trials

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

Performing a Simple Data Analysis using MS-Excel Function

Performing a Simple Data Analysis using MS-Excel Function

Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...

Transcription Factors

Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...

Data Reporting and Recording

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...

You might also read

Related Articles

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

Sort by

Same author

A next generation of the schema therapy model of personality pathology: A cross-cultural and international study protocol.

PloS one·2026

Same author

Estimating Discrete Latent Variable Models Using Amortized Variational Inference.

Psychometrika·2026

Same author

Regularized Joint Maximum Likelihood Estimation of Latent Space Item Response Models.

Psychometrika·2026

Same author

A Tutorial on Estimating the Precision of Individual Test Scores for Anyone Constructing and Using Psychological Tests.

Psychometrika·2026

Same author

Autoencoders for Amortized Joint Maximum Likelihood Estimation of Confirmatory Item Factor Models.

Multivariate behavioral research·2025

Same author

R Package for Calculating Estimators of the Proportion of Explained Variance and Standardized Regression Coefficients in Multiply Imputed Datasets.

Applied psychological measurement·2025

Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026

Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026

Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026

Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026

Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026

Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 24, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Multiple Imputation of Missing Data in Moderated Factor Analysis.

Joost R van Ginkel¹, Dylan Molenaar²

¹Leiden University.

Multivariate Behavioral Research

|January 23, 2026

Summary

This summary is machine-generated.

Missing data in moderated factor analysis is challenging. A new multiple imputation method effectively handles missing moderator data, outperforming listwise deletion and predictive mean matching in accuracy and power.

Keywords:

full information maximum likelihood listwise deletion missing data moderated factor analysis multiple imputation predictive mean matching

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

Related Experiment Videos

Last Updated: Jan 24, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

Area of Science:

Psychometrics
Statistical modeling
Data analysis

Background:

Moderated factor analysis extends the common factor model with a continuous moderator variable.
Handling missing data on indicator variables is typically managed with full information maximum likelihood.
Missing data on the moderator variable presents a significant challenge, often necessitating listwise deletion.

Purpose of the Study:

To propose and evaluate a multiple imputation procedure for moderated factor analysis with missing moderator data.
To compare the performance of the proposed method against listwise deletion and predictive mean matching.

Main Methods:

Development of a moderated factor model-based multiple imputation technique.
Comparative analysis using simulated data under various missing data conditions.
Evaluation metrics included parameter estimate bias and statistical power.

Main Results:

The proposed multiple imputation procedure effectively handles missing moderator data.
Listwise deletion and predictive mean matching exhibited lower power and greater bias in parameter estimates.
Multiple imputation demonstrated superior performance compared to traditional methods.

Conclusions:

Multiple imputation is a robust and recommended approach for addressing missing moderator data in moderated factor analysis.
The proposed method offers improved accuracy and statistical power over listwise deletion and predictive mean matching.
This advancement facilitates more reliable analyses in the presence of complex missing data patterns.