Related Experiment Video
Updated: Aug 7, 2026

09:28
A Within-Subject Experimental Design using an Object Location Task in Rats
Published on: May 6, 2021
"An analysis-of-variance model for intrasubject replicaiton design": some additional comments
1Stanford University.
Journal of Applied Behavior Analysis
|January 1, 1974
Summary
Fixed effects ANOVA is inappropriate for single-subject studies. Time series analysis, accounting for serial correlation, is recommended for more accurate single-subject research findings.
Area of Science:
- Behavioral Science
- Psychology
- Research Methodology
Background:
- The use of fixed effects Analysis of Variance (ANOVA) for single-subject research designs has been a topic of methodological discussion.
- Previous applications, such as Gentile, Roden, and Klein (1972), and Hartmann's proposed one-way ANOVA model, have been scrutinized for their suitability.
Purpose of the Study:
- To evaluate the appropriateness of fixed effects ANOVA models for single-subject data analysis.
- To identify limitations of existing ANOVA approaches in single-subject research.
- To recommend a more suitable statistical method for analyzing single-subject data.
Main Methods:
- Critically reviewed the fixed effects ANOVA procedure as applied to single-subject designs.
- Examined Hartmann's one-way fixed-effect ANOVA model proposal.
- Considered alternative statistical approaches for single-subject data.
Main Results:
- The fixed effects ANOVA procedure is deemed inappropriate for single-subject data analysis.
- Hartmann's one-way fixed-effect ANOVA model also presents limitations for single-subject research.
Conclusions:
- Standard ANOVA methods are not suitable for single-subject research due to inherent data characteristics.
- Time series analysis, which explicitly addresses serial correlation, is a more appropriate statistical technique for single-subject data.
Related Concept Videos
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Group Design
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
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...
What is an ANOVA?
The Analysis of Variance or ANOVA is a statistical test developed by Ronald Fisher in 1918. It is performed on three or more samples to check for equality between their means.
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples should be randomly and...
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples should be randomly and...
Statistical Methods to Analyze Parametric Data: ANOVA
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.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
What is ANOVA?
The Analysis of Variance or ANOVA is a statistical test developed by Ronald Fisher in 1918. It is performed on three or more samples to check for equality between their means.
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples be randomly and independently...
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples be randomly and independently...

