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1.1 Study Design: basic conceptsUsually the goal of a study is to find out the relationships between certain explanatory factors and the response variables. The design of a study thus consists of making decisions on the following:
1.2 FactorsFactors are explanatory variables to be studied in an investigation. Examples: 1. In a study of the effects of colors and prices on sales of cars, the factors being studied are color (qualitative variable) and price (quantitative variable). 2. In an investigation of the effects of education on income, the factor being studied is education level (qualitative but ordinal). Factor levels 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. In a design involving vaccination, the treatment could have two levels: vaccine and placebo. Types of factors
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. 1.3 Treatments
Examples:
Exercise: How many different treatments are there for the above examples? Choice of treatments Choice of treatments depends on the choice of: (i) the factors (which are the important factors); (ii) levels of each factor.
Example: gender has two levels: female and male
Example: linear trend implies two levels; quadratic trend implies three levels. Usually 3 to 4 equally spaced levels are sufficient.
1.4 Experimental units
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.
Example: A study conducted surveys among 5,000 US college students, and found out that about 20% of them had uses marijuana at least once. If the goal of the study is the drug usage among Americans aging from 18 to 22, is this a good design?
1.5 Sample size and replicatesLoosely speaking, sample size is the number of experimental units in the study.
Replicates For many designed studies, the sample size is an integer multiple of the total number of treatments. 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.
Why replicates? 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. 1.6 Randomization
Example: In a study of light effects on plant growth rate, two treatments are considered: brighter environment vs. darker environment. 100 plants are randomly assigned to each treatment (all genetically identical). However, there is only one growth chamber which can grow 20 plants at one time. Therefore the 200 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. 1.7 BlockingIn 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.
1.8 Measurements of response variablesThe 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). Contributors
What are important considerations when designing an experiment?When designing experiments identify all of the potential variables in the system, control them, and vary only one variable at a time. Look for and eliminate all possible sources of error. Use the highest quality experimental methods, reagents, and instrumentation available.
What is not required in an experimental design?Blocking is an important compromise between randomization and control, but not required in an experimental design.
What are the 4 elements of experimental design?Section 2: Experimental Studies
True experiments have four elements: manipulation, control , random assignment, and random selection. The most important of these elements are manipulation and control.
What are the 3 components of an experimental design?In general, designs that are true experiments contain three key features: independent and dependent variables, pretesting and posttesting, and experimental and control groups.
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