Date post: 2017-11-08 03:25
Sometimes in psychological, physiological or medical research, individual subjects form the rows, time-the columns, and the cells form the treatments with an observation taken at each treatment cell. When this is done, and the measurements between each cell are to be compared, clear interpretation may be difficult because of possible carry-over effects of previous treatments (., earlier treatments affecting later ones).
The pre-test post-test control group design is also called the classic controlled experimental design. The design includes both a control and a treatment group. For example, if you wanted to gauge if a new way of teaching math was effective, you could:
Although this design is not frequently used in clinical studies, it is frequently used in both behavior and educational research and in medical studies involving the physical activities of patients (physical therapy, for example where the pre-measurement involves some sort of physical activity or testing). The additional cost of this design must be justified by the need for information regarding the possible effects of the pre-treatment measurement.
These types of issues crop up frequently, leading to the widespread acceptance of quasi-experimental designs especially in the social sciences. Quasi-experimental designs are generally regarded as unreliable and unscientific in the physical and biological sciences.
The One-Shot Design is highly useful as an inexpensive measure of a new treatment of the group in question. If there is some question as to whether any expected effects will result from the treatment, then a one-shot may be an economical route. In cases where other studies, or the cumulative knowledge in the field provide information about either pre-treatment baseline measurements or behavior, the effects of other kinds of treatments, etc., the experimenter might sensibly decide that it si not necessary to undertake a more extensive design. Simplicity, ease, and low cost represent strong potential advantages in the oft-despised one-shot.
For example, an experiment to test a new drug may have blocks of 755 males and 755 females. Each block contains 655 pairs, who are matched according to some criteria other than sex (like age, other medications, or health conditions). Each pair is then treated like a block, with each randomly assigned to receive the drug or a placebo. The following table shows experiment, where pair 6 could represent two healthy women age 79, pair 7 could represent two women age 79 with liver disease, pair 8 could contain two healthy women age 89, pair 9 could contain two women age 89 with liver disease, and so on.
This class of information refers to some rival hypothesis that threatens clear interpretation of the experiment. A common group of rivals threatens most experiments, particularly those using human subjects. Typically, the rival hypothesis asserts that something outside of the experiment proper produced the behavior or measurement of interest. To discover whether or not such rival events exert an influence, the designer must usually provide for one or more control groups. Typically, internal threats to validity include:
You can find this page online at: https:///science-fair-projects/competitions/experimental-design-for-advanced-science-projects
In scientific studies, experimental design is the gold standard of research designs. This methodology relies on random assignment and laboratory controls to ensure the most valid, reliable results. Although researchers recognize that correlation does not mean causation, experimental designs produce the strongest, most valid results. However, experimental design is often not practical for many studies in social science, education and business because researchers cannot, in many instances, exercise laboratory controls in natural-world settings or randomly assign subjects.
A factorial experimental design is used to investigate the effect of two or more independent variables on one dependent variable. For example, let 8767 s say a researcher wanted to investigate components for increasing SAT Scores. The three components are: