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

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...

You might also read

Related Articles

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

Sort by

Same journal

Large fish eggs may lose their edge as temperature rises.

Oecologia·2026

Same journal

Secondary consumption of mycorrhizal fungi by two endangered marsupial carnivores: the spotted-tailed quoll (Dasyurus maculatus) and Tasmanian devil (Sarcophilus harrisii).

Oecologia·2026

Same journal

Unveiling the microhabitat puzzle: how spatial heterogeneity shapes cave invertebrate biodiversity across scales.

Oecologia·2026

Same journal

Soil microbial drought history affects physiological response of select tree species to drought stress.

Oecologia·2026

Same journal

Unveiling the effects of interspecific competition: ecological consequences of competitive release after damming on Salvelinus curilus populations in a three-salmonid species coexistence system.

Oecologia·2026

Same journal

Orchid bee diversity responds positively to forest cover and landscape heterogeneity in the Brazilian Savanna.

Oecologia·2026

Search research articles

Related Experiment Video

Updated: Mar 6, 2026

Fat Preference: A Novel Model of Eating Behavior in Rats

Fat Preference: A Novel Model of Eating Behavior in Rats

Published on: June 27, 2014

On the statistical analysis of multiple-choice feeding preference experiments.

John R Lockwood Iii¹

¹Carnegie Mellon University, Department of Statistics, Baker Hall 132, Pittsburgh, PA 15213-3890, USA e-mail: jlock@stat.cmu.edu, , , , , , US.

|March 18, 2017

Summary

Stopping rules in feeding preference experiments can lead to random food consumption. A new analysis method may misinterpret preference data due to confounding total consumption with relative food choices.

Keywords:

Key words Preference testing Multivariate analysis Parametric procedures Relative consumption Stopping rules

More Related Videos

Assessment of Social Transmission of Food Preferences Behaviors

Assessment of Social Transmission of Food Preferences Behaviors

Published on: January 25, 2018

A Standardized Protocol for Preference Testing to Assess Fish Welfare

A Standardized Protocol for Preference Testing to Assess Fish Welfare

Published on: February 22, 2020

Related Experiment Videos

Last Updated: Mar 6, 2026

Fat Preference: A Novel Model of Eating Behavior in Rats

Fat Preference: A Novel Model of Eating Behavior in Rats

Published on: June 27, 2014

Assessment of Social Transmission of Food Preferences Behaviors

Assessment of Social Transmission of Food Preferences Behaviors

Published on: January 25, 2018

A Standardized Protocol for Preference Testing to Assess Fish Welfare

A Standardized Protocol for Preference Testing to Assess Fish Welfare

Published on: February 22, 2020

Area of Science:

Behavioral Ecology
Quantitative Biology

Background:

Experimental stopping rules dictate experiment termination conditions.
Multiple-choice feeding-preference experiments are common in ecological and behavioral studies.
Existing analysis methods may not adequately account for variations in total consumption.

Purpose of the Study:

To investigate the impact of stopping rules on feeding-preference experiments.
To evaluate a recently proposed data analysis method for such experiments.
To propose an alternative analytical procedure that accounts for random total food consumption.

Main Methods:

Surveyed stopping rules in multiple-choice feeding-preference experiments.
Analyzed a recently proposed statistical method for preference data.
Developed and presented an alternative multivariate index for preference quantification.

Main Results:

Stopping rules in surveyed experiments resulted in random total food consumption.
The proposed analysis method may confound differences in food preferences with total consumption.
An alternative procedure is presented that is appropriate under all stopping regimes.

Conclusions:

Standard stopping rules in feeding-preference studies can introduce bias.
A novel analytical approach is needed to accurately quantify relative food preferences.
The proposed multivariate index offers a robust alternative for preference analysis.