I. Observational Study vs. Experiment
Two types of studies that are commonly used in statistics are observational and experimental studies. There are distinct differences between the two types.1. Observational Studies:
In an observational study, the sample population being studied is measured, or surveyed, as it is. The researcher observes the subjects and measures variables, but does not influence the population in any way or attempt to intervene in the study. There is no manipulation by the researcher. Instead, data is simply gathered and correlations are investigated. Since observational studies do not manipulate any variable, the results can only allow the researcher to claim association, not causation (not a cause-and-effect conclusion).
2. Controlled Experiment:
Unlike an observational study, an experimental study has the researcher purposely attempting to manipulate the variables. The goal is to determine what effect a particular treatment has on the outcome. Researchers take measurements or surveys of the sample population. The researchers then manipulate the sample population in some manner. After the manipulation, the researchers re-measure, or re-survey, using the same procedures to determine if the manipulation possibly changed the measurements. Since variables are controlled in a designed experiment, the results allow the researcher to claim causation (a cause-and-effect conclusion).When one variable causes change in another, we call the first variable the explanatory variable. The affected variable is called the response variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable may be called treatments. An experimental unit is a single object or individual to be measured.
The main principles we want to follow in experimental design are:
- Randomization
- Replication
- Control
A. Randomization
In order to provide evidence that the explanatory variable is indeed causing the changes in the response variable, it is necessary to isolate the explanatory variable. The researcher must design their experiment in such a way that there is only one difference between groups being compared: the planned treatments. This is accomplished by randomization of experimental units to treatment groups. When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups. At this point the only difference between groups is the one imposed by the researcher. Different outcomes measured in the response variable, therefore, must be a direct result of the different treatments. In this way, an experiment can show an apparent cause-and-effect connection between the explanatory and response variables.
B. Replication
The more cases researchers observe, the more accurately they can estimate the effect of the explanatory variable on the response. In a single study, we replicate by collecting a sufficiently large sample. Additionally, a group of scientists may replicate an entire study to verify an earlier finding. Having individuals experience a treatment more than once, called repeated measures is often helpful as well.C. Control
The power of suggestion can have an important influence on the outcome of an experiment. Studies have shown that the expectation of the study participant can be as important as the actual medication. In one study of performance-enhancing drugs, researchers noted:Results showed that believing one had taken the substance resulted in [performance] times almost as fast as those associated with consuming the drug itself. In contrast, taking the drug without knowledge yielded no significant performance increment.
It is often difficult to isolate the effects of the explanatory variable. To counter the power of suggestion, researchers set aside one treatment group as a control group. This group is given a placebo treatment–a treatment that cannot influence the response variable. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments. Of course, if you are participating in a study and you know that you are receiving a pill which contains no actual medication, then the power of suggestion is no longer a factor. Blinding in a randomized experiment preserves the power of suggestion. When a person involved in a research study is blinded, he does not know who is receiving the active treatment(s) and who is receiving the placebo treatment. A double-blind experiment is one in which both the subjects and the researchers involved with the subjects are blinded.
Placebo - An inactive treatment that has no real effect on the explanatory variable (ex. sugar pill or saline injection).
Blinding - A procedure where the participants in a study are kept unaware of whether they are in the treatment group or the control group.
Double Blinding - The act of blinding both the subjects of an experiment and the researchers who work with the subjects
Example 1
Researchers want to investigate whether taking aspirin regularly reduces the risk of heart attack. Four hundred men between the ages of 50 and 84 are recruited as participants. The men are divided randomly into two groups: one group will take aspirin, and the other group will take a placebo. Each man takes one pill each day for three years, but he does not know whether he is taking aspirin or the placebo. At the end of the study, researchers count the number of men in each group who have had heart attacks.Identify the following values for this study: population, sample, experimental units, explanatory variable, response variable, treatments.
Solution:
The population is men aged 50 to 84.
The sample is the 400 men who participated.
The experimental units are the individual men in the study.
The explanatory variable is oral medication.
The treatments are aspirin and a placebo.
The response variable is whether a subject had a heart attack.
III. Ethics
The widespread misuse and misrepresentation of statistical information often gives the field a bad name. Some say that “numbers don’t lie,” but the people who use numbers to support their claims often do.Professional organizations, like the American Statistical Association, clearly define expectations for researchers. There are even laws in the federal code about the use of research data.
When a statistical study uses human participants, as in medical studies, both ethics and the law dictate that researchers should be mindful of the safety of their research subjects. The U.S. Department of Health and Human Services oversees federal regulations of research studies with the aim of protecting participants. When a university or other research institution engages in research, it must ensure the safety of all human subjects. For this reason, research institutions establish oversight committees known as Institutional Review Boards (IRB). All planned studies must be approved in advance by the IRB. Key protections that are mandated by law include the following:
- Risks to participants must be minimized and reasonable with respect to projected benefits.
- Participants must give informed consent. This means that the risks of participation must be clearly explained to the subjects of the study. Subjects must consent in writing, and researchers are required to keep documentation of their consent.
- Data collected from individuals must be guarded carefully to protect their privacy.
https://pressbooks.lib.vt.edu/introstatistics/chapter/experimental-design-and-ethics/
https://www.khanacademy.org/math/statistics-probability/designing-studies/types-studies-experimental-observational/a/observational-studies-and-experiments
https://mathbitsnotebook.com/Algebra2/Statistics/STSurveys.html
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