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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...

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Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
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Updated: May 7, 2026

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

A marginal-mean ANOVA approach for analyzing multireader multicase radiological imaging data.

Stephen L Hillis¹

¹Departments of Radiology and Biostatistics, The University of Iowa, 3710 Medical Laboratories, 200 Hawkins Drive, Iowa City, IA 52242-1077, U.S.A.; Comprehensive Access and Delivery Research and Evaluation (CADRE) Center, Iowa City VA Health Care System, IA 52242-1077, U.S.A.

Statistics in Medicine

|September 17, 2013

Summary

The correlated-error ANOVA method is generalized using a marginal-mean ANOVA framework. This approach simplifies analysis for reader performance in medical imaging studies and extends to various study designs.

Keywords:

correlated ANOVA diagnostic radiology receiver operating characteristic (ROC) curve

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Area of Science:

Medical Imaging Analysis
Statistical Modeling
Radiology Research

Background:

The Obuchowski-Rockette (OR) correlated-error ANOVA method is established for analyzing reader performance in multireader, multicase (MRMC) radiological imaging.
The OR method's formal derivation is limited to the test-by-reader-by-case factorial study design.

Purpose of the Study:

To reframe the OR model within a marginal-mean ANOVA framework.
To provide an intuitive understanding and facilitate derivation of OR model components.
To enable straightforward generalization of the OR procedure to diverse study designs.

Main Methods:

The OR model is conceptualized as a marginal-mean ANOVA model.
An algorithm is presented for deriving OR-type analysis formulas for any balanced study design.
This approach leverages conventional ANOVA methods for accessibility.

Main Results:

The marginal-mean ANOVA framework offers intuitive motivation for the OR model and its constraints.
Simplified derivations are provided for OR test statistics, parameter estimates, distributions, and confidence intervals.
The approach allows for easy generalization of OR procedures to other study designs.

Conclusions:

The marginal-mean ANOVA perspective simplifies and extends the application of the OR method.
This framework enhances the accessibility and applicability of correlated-error ANOVA in reader performance studies.
The proposed method facilitates the analysis of MRMC data across various balanced study designs.