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About Randomized Field Experiments Randomized field experiments allow researchers to scientifically measure the impact of an intervention on a particular outcome of interest.

What is a randomized field experiment? In a randomized experiment, a study sample is divided into one group that will receive the intervention being studied (the treatment group) and another group that will not receive the intervention (the control group). For instance, a study sample might consist of all registered voters in a particular city. This sample will then be randomly divided into treatment and control groups. Perhaps 40% of the sample will be on a campaign’s Get-Out-the-Vote (GOTV) mailing list and the other 60% of the sample will not receive the GOTV mailings. The outcome measured –voter turnout– can then be compared in the two groups. The difference in turnout will reflect the effectiveness of the intervention.

What does random assignment mean? The key to randomized experimental research design is in the random assignment of study subjects – for example, individual voters, precincts, media markets or some other group – into treatment or control groups. Randomization has a very specific meaning in this context. It does not refer to haphazard or casual choosing of some and not others. Randomization in this context means that care is taken to ensure that no pattern exists between the assignment of subjects into groups and any characteristics of those subjects. Every subject is as likely as any other to be assigned to the treatment (or control) group. Randomization is generally achieved by employing a computer program containing a random number generator. Randomization procedures differ based upon the research design of the experiment. Individuals or groups may be randomly assigned to treatment or control groups. Some research designs stratify subjects by geographic, demographic or other factors prior to random assignment in order to maximize the statistical power of the estimated effect of the treatment (e.g., GOTV intervention). Information about the randomization procedure is included in each experiment summary on the site.

What are the advantages of randomized experimental designs? Randomized experimental design yields the most accurate analysis of the effect of an intervention (e.g.,a voter mobilization phone drive or a visit from a GOTV canvasser, on voter behavior). By randomly assigning subjects to be in the group that receives the treatment or to be in the control group, researchers can measure the effect of the mobilization method regardless of other factors that may make some people or groups more likely to participate in the political process. To provide a simple example, say we are testing the effectiveness of a voter education program on high school seniors. If we allow students from the class to volunteer to participate in the program, and we then compare the volunteers’ voting behavior against those who did not participate, our results will reflect something other than the effects of the voter education intervention. This is because there are, no doubt, qualities about those volunteers that make them different from students who do not volunteer. And, most important for our work, those differences may very well correlate with propensity to vote. Instead of letting students self-select, or even letting teachers select students (as teachers may have biases in who they choose), we could randomly assign all students in a given class to be in either a treatment or control group. This would ensure that those in the treatment and control groups differ solely due to chance. The value of randomization may also be seen in the use of walk lists for door-to-door canvassers. If canvassers choose which houses they will go to and which they will skip, they may choose houses that seem more inviting or they may choose houses that are placed closely together rather than those that are more spread out. These differences could conceivably correlate with voter turnout. Or if house numbers are chosen by selecting those on the first half of a ten page list, they may be clustered in neighborhoods that differ in important ways from neighborhoods in the second half of the list. Random assignment controls for both known and unknown variables that can creep in with other selection processes to confound analyses. Randomized experimental design is a powerful tool for drawing valid inferences about cause and effect. The use of randomized experimental design should allow a degree of certainty that the research findings cited in studies that employ this methodology reflect the effects of the interventions being measured and not some other underlying variable or variables.

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This idea can be generalised: most potential causes of disease can be viewed as producing effects that are undesired, unintended, and unexpected [
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]. This becomes clear from classic success stories of epidemiologic research: e.g., before the links between smoking and lung cancer or asbestos and mesothelioma were known, people who exposed themselves to these risks were unaware of the consequences—which is why the risks could be investigated by observational studies.

The above reasoning should not lead to uncritical acceptance of all observational research about causes of diseases. A mental device to guide our judgement about new claims from observational research is to position the research on an “axis of haphazardness of exposure” ( Charlotte Olympia Kitty slingback pumps Wye2zC6a8l

Figure 1. Axis of Haphazardness of Exposure

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At one side there is research on genetic effects. This is the closest that observational research can come to randomisation. At the other end of the axis there is research contrasting, for example, the mortality of vegetarians to non-vegetarians. That contrast is completely non-haphazard: vegetarians have different social backgrounds, different education, different life styles, and may have taken up the habit because they are health-conscious. The differences in (self) assignment will bias the comparison, and it is known in advance that the bias will be next to intractable in the analysis, since its various components cannot be known in sufficient detail. Therefore, an assessment of the effect of vegetarian diets needs randomised trials, e.g., to show whether vegetarian diets decrease blood pressure.

Most observational research hovers somewhere between the extremes. Sometimes an observational researcher is quite close to the quasirandom haphazardness of genetic exposures, for example, when studying adverse effects in selected groups of patients where the adverse effect is unpredictable [ Etnies SCOUT Trainers blue/black/white 5IrRe
]. When confronted with a new exposure that is not that close to ideal haphazardness, it is useful to ask oneself whether the most important confounders can be listed, can be measured fairly accurately, and can be controlled for. If the answer to these questions is positive, that will lead to greater credibility of the results. If negative, as in the vegetarian example, we may attach no credibility to the results despite any attempts at statistical correction for confounders.

Many scientists believe that results from observational research are less credible because of the problem of subgroups and multiplicity of analysis: multiple looks at data for associations that were not the original aims of the data collection.

This problem can be conceptualised on an “axis of multiplicity” ( Nike White amp; Blue Air Max 1 Anniversary Sneakers gSMz7t
). At one extreme there are genome-wide analyses, where tens of thousands of single nucleotide polymorphisms (SNPs) are investigated for disease associations. The prior probability that some grain of explanation will come from any individual SNP is slim, say, one in 100,000 [ Nike Womens Benassi Duo Ultra Slide Sandal DgSLB8k7Z8
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]: the prior odds that the therapy that is tested is worthwhile are 50–50, and multiplicity of analysis is strictly not allowed. Thus, the axis of multiplicity is at the same time an axis of prior belief: the prior belief that some factor will be a causal explanation for a condition or that some therapy or treatment will work [ Balenciaga Green amp; Red VaporMax FK MOC 2 Sneakers lkGv6A0

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