The Principles of Experimental Research
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Factor levels are the "values" of that factor in an experiment. For example, in the study involving color of cars, the factor car color could have four levels: red, black, blue and grey.
- 1.1 Study Design: basic concepts;
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In a design involving vaccination, the treatment could have two levels: vaccine and placebo. Example: in the "new drug study" refer to Handout 1 , if we are also interested in the effects of age and gender on the recovery rate, then these observational factors; while the treatment new drug or old drug is an experimental factor. Exercise: How many different treatments are there for the above examples? Choice of treatments depends on the choice of: i the factors which are the important factors ;.
Example: linear trend implies two levels; quadratic trend implies three levels. Usually 3 to 4 equally spaced levels are sufficient. Example: In a study of two retirement systems involving the 10 UC schools, we could ask if the basic unit should be an individual employee, a department, or a University. Answer: The basic unit should be an entire University for practical feasibility.
If the goal of the study is the drug usage among Americans aging from 18 to 22, is this a good design? For many designed studies, the sample size is an integer multiple of the total number of treatments.
W.M.S. Russell and R.L. Burch
This integer is the number of times each treatment being repeated and one complete repitition of all treatments under similar experimental conditions is called a complete replicate of the experiment. When a treatment is repeated under the same experimental conditions, any difference in the response from prior responses for the same treatment is due to random errors. Thus replication provides us some information about random errors. If the variation in random errors is relatively small compared to the total variation in the response, we would have evidence for treatment effect.
Example: In a study of light effects on plant growth rate, two treatments are considered: brighter environment vs. However, there is only one growth chamber which can grow 20 plants at one time. Therefore the plants need to be grown in 10 different time slots. In addition to randomizing the treatments, it is important to randomize the time slots also.
This is because, the conditions of the growth chamber such as humidity, temperature might change over time. Therefore, growing all plants with brighter light treatment in the first 5 time slots and then growing all plants with darker light treatment in the last 5 time slots is not a good design. In a blocked experiment , heterogenous experimental units with known sources of heterogenity are divided into homogenous subgroups, called blocks, and separate randomized experiments are conducted within each block.
The issue of measurement bias arises due to unrecognizable differences in the evaluation process. Example: The knowledge of the treatment of a patient may influence the judgement of the doctor. The source of measurement bias can be reduced to concealing the treatment assignment to both the subject and the evaluator double-blind. The design of a study thus consists of making decisions on the following: The set of explanatory factors. The set of response variables. The set of treatments. The set of experimental units. The method of randomization and blocking.
Sample size and number of replications. The outcome measurements on the experimental units - the response variables. Examples: 1. This article characterizes some basic aspects of carrying out an archaeological experiment. Note from the editors: We believe that our readers, particularly the younger ones in the arena of experimental research, could profit from reflecting on some constant principles of scientific experimentation. Therefore, and with the permission of the author, we condensed his publications on the subject as follows.
When discussing in theory the strengths and weaknesses of experimental archaeology, or when observing in practice current projects, invariably some aspects of the following four key issues arise. What follow is the personal opinion and the experience of the author, presented in the form of very concentrated and simplified headlines. When does an experiment agree scientific standards as they are accepted today?
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When it is: 1. Experimenting is not learning by doing. What is common amongst all trustworthy experimental projects? When all seven basic stages are well executed: 2. The data base includes literature, archaeological originals and opinions of experts. If needed, make corrections.
What is needed to become an expert in experimental archaeology? Besides experience: 3.
9. Experimental designs
Just as a game may be won in different ways, so a problem may be solved by following different paths or strategies. What can lead quicker to success in which situations? Recommendable for routine experiments or when surprises and changes are surely not to be expected. A very useful speed-up strategy when large volumes of possibly useless numerical data could be gathered in the course the experiments or already when procuring the data base.
click Also a helpful strategy before deciding to use computer supported working methods, which tend to accumulate superfluous data, costing time and work. This very flexible approach is best when there is much interdependence between the seven basic activities to be expected or when the learning steps in one activity immediately influence the work in others. This is particularly suitable for questions which involve large systems or when different stages are likely to occur in a single problem.
This is for large scale projects and also as a defence against being side-tracked by secondary discoveries or secondary difficulties.