s:3521:"%T Data analysis of agroforestry experiments. 1. Workshop overview and session summaries 
%A Stern R D 
%A Allan E F 
%A Coe, R. 
%X These course notes are on the analysis of data from experiments. They result from a series of statistics training courses organized by ICRAF/World Agroforestry Centre. These courses were originally on the design and analysis of agroforestry experiments, but they have been used more widely than this. The first component was on the design of experiments. This analysis course assumes familiarity with the main concepts from the design course. A brief review is given in Session 1. The second component was on data entry and management. This is a key area because poor data management often limits the processing of data. In this analysis course the examples provided have been ’managed’ so that the concepts related to the analyses could be illustrated easily. We anticipate that an initial phase in the course preparation will be to organize datasets from participants similarly. Hence, the data management component though normally undertaken prior to this component, is not a necessary prerequisite. This course is divided into two parts. The first part is entitled The Everyday Toolkit and covers the concepts that we believe scientists should be able to understand fully and the corresponding analyses that they should be able to undertake unaided following the training. The second part is called Handling Complexities. This examines how experimental data can be processed where there are complications. These complications are divided into three broad types. The first is due to complexities in the design which may either be due to a complex treatment structure, to difficulties in the layout of the trial, or to the way data were measured. For example, a measurement of farmers’ responses may be on a 5-point scale ranging from very good to very poor. The analysis of this type of ’categorical data’ is described here. We do not consider on-farm trials as a special category and hence examples of them will be used throughout the course. However, their analysis is often complicated because of their combination of a complex layout (many farmers, with few plots per farm) and the nature of measurement. The complexities arise from the lack of control of factors that would be within the treatment structure in an on-station trial, and the fact that this lack of control occurs both within and between farms. The handling of these complexities is discussed in the course. The second type of complexity is that which is due to the particular field of application. Particular features of agroforestry trials include ’repeated measures’, both in time and space, and difficulties that arise from the need to measure multiple components (e.g. concerning both trees and crops) within each plot. Courses for other audiences need to replace this section, as each subject area has a set of problems and methods specific to it. As an example, we provide a parallel session that considers some of the complications that are commonly encountered in livestock experiments. Finally we consider complexities that arise because of the nature of the data. Coping with zeros in the data and missing values are among the topics considered here. Our main aim in this second part of the course is for scientists to be aware of the methods that now exist to handle complex data. These are methods where scientists, at least initially, might want to work jointly with statisticians. 
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