HOME Module List Site Index About This Site Glossary Resources Innovative Technology Center UT Statistics Courses
Using SAS Choose Design ANOVA Compare Means Regression Examples
Randomized Complete Block Design
  
Single Factor
     

Check that your experimental setup comforms to a
Randomized Complete Block Design with a Single Factor

You have a RCBD experimental design if groups of your experimental units differ, and each group has as many experimental units as there are treatments. There is at least one variable that you are aware of that makes the groups differ. There is variation to block on, and the groups are blocks.

You have a single factor treatment design (or no treatment design), if you consider your treatments to not be divisible into factors. The treatments are all "just treatments".

Plant example: You are applying treatments to plots (experimental units), and all plots are not similar in moisture, soil type, slope, fertility, etc. However, sub-groups of plots (four in iluustration above) are similar, and each sub-group is a block. This makes it an RCBD. The number of plots in the block has to equal the number of treatments, so blocks are "complete". You have 5 levels of nitrogen fertilizer. Each block will need to have 5 experimental units.

Animal example: You are applying treatments to animals (experimental units), and all animals are not similar in weight, age, breed, etc. However, you can identify sub-groups of animals that are similar, and each sub-group is a block. If blocks contain all treatments, this makes it an RCBD. You have 4 protein level diets. Each block will need to have 4 animals.


Begin SAS Analysis: RCBD with Single Factor

Or select another design

Home | Contact us | Module list & summary | Glossary/Terms | About this site | Stats courses | Links | Index