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

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...

Factorial Design

Factorial Design

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...

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

One-Way ANOVA

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

Sample Size Calculation

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

You might also read

Related Articles

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

Sort by

Same author

Correction: Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study.

Journal of medical Internet research·2024

Same author

Exploring the Effect of the Dynamics of Behavioral Phenotypes on Health Outcomes in an mHealth Intervention for Childhood Obesity: Longitudinal Observational Study.

Journal of medical Internet research·2023

Same author

Regularized Exploratory Bifactor Analysis With Small Sample Sizes.

Frontiers in psychology·2020

Same author

A Comparison of Three Empirical Reliability Estimates for Computerized Adaptive Testing (CAT) Using a Medical Licensing Examination.

Frontiers in psychology·2018

Same author

Measuring theory of mind in schizophrenia research: Cross-cultural validation.

Schizophrenia research·2018

Same author

Consistent Partial Least Squares Path Modeling via Regularization.

Frontiers in psychology·2018

Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026

Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026

Same journal

Psychometric functions from multiple responses : Dedicated to the memory of Colin L. Mallows.

Behavior research methods·2026

Same journal

Low-cost, open-source, full-stack software and Arduino-based hardware for control of commercially available animal behavior systems.

Behavior research methods·2026

Same journal

PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data.

Behavior research methods·2026

Same journal

Talking surveys: How photorealistic embodied conversational agents shape response quality, engagement, and satisfaction.

Behavior research methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 3, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Exploratory factor analysis for small samples.

Sunho Jung¹, Soonmook Lee

¹Ulsan National Institute of Science and Technology, School of Technology Management, Ulsan, Korea. sjung@unist.ac.kr

Behavior Research Methods

|March 25, 2011

Summary

This summary is machine-generated.

Regularized exploratory factor analysis is recommended for small sample sizes (below 50). This method outperforms traditional maximum likelihood factor analysis and principal component analysis when data is near singular.

More Related Videos

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Related Experiment Videos

Last Updated: Jun 3, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

Psychometrics
Statistical analysis

Background:

Factor analysis is crucial for understanding complex data structures.
Traditional methods include maximum likelihood factor analysis and principal component analysis.
The impact of small sample sizes on these methods is not well understood.

Purpose of the Study:

To compare the performance of three exploratory factor analysis approaches under small sample conditions.
To evaluate regularized exploratory factor analysis against traditional methods.

Main Methods:

A simulation study was conducted.
An empirical example was used for validation.
Performance was assessed in small sample size scenarios (N<50) with near-singular covariance matrices.

Main Results:

Regularized exploratory factor analysis demonstrated superior performance compared to traditional methods.
The benefits were most pronounced with small sample sizes and near-singular data.

Conclusions:

Regularized exploratory factor analysis is a valuable alternative for small sample psychometric research.
It offers improved reliability and accuracy when dealing with limited data and specific data characteristics.