Identifying the Structure of the Experimental Design

Abstract

In many areas of scientific research, complex experimental designs are now routinely employed. The statistical analysis of data generated when using these designs may be carried out by a statistician; however, modern statistical software packages allow such analyses to be performed by non-statisticians. For the non-statistician, failing to correctly identify the structure of the experimental design can lead to incorrect model selection and misleading inferences. A procedure, which does not require expert statistical knowledge, is described that focuses the non-statistician's attention on the relationship between the experimental material and design, identifies the underlying structure of the selected design, and highlights any potential weaknesses it may have. These are important precursors to the randomization and subsequent statistical analysis and can be easily overlooked by a non-statistician. The process is illustrated using a generalization of the Hasse diagram and has been implemented in a program written in R.

• Crossed Factors
• Hasse Diagram
• Nested Factors

Additional information

Notes on contributors

Simon T. Bate

Dr. Bate is Principal Statistician in Statistical Sciences Europe, GlaxoSmithKline. His email address is [email protected]

Marion J. Chatfield

Mrs. Chatfield is Statistics Manager in Statistical Sciences Europe, GlaxoSmithKline. Her email address is [email protected]