When designing an experiment, the purpose of random assignment is to ensure that
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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. Show
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. Random assignment or random placement is an experimental technique for assigning human participants or animal subjects to different groups in an experiment (e.g., a treatment group versus a control group) using randomization, such as by a chance procedure (e.g., flipping a coin) or a random number generator.[1] This ensures that each participant or subject has an equal chance of being placed in any group.[1] Random assignment of participants helps to ensure that any differences between and within the groups are not systematic at the outset of the experiment.[1] Thus, any differences between groups recorded at the end of the experiment can be more confidently attributed to the experimental procedures or treatment.[1] Random assignment, blinding, and controlling are key aspects of the design of experiments because they help ensure that the results are not spurious or deceptive via confounding. This is why randomized controlled trials are vital in clinical research, especially ones that can be double-blinded and placebo-controlled. Mathematically, there are distinctions between randomization, pseudorandomization, and quasirandomization, as well as between random number generators and pseudorandom number generators. How much these differences matter in experiments (such as clinical trials) is a matter of trial design and statistical rigor, which affect evidence grading. Studies done with pseudo- or quasirandomization are usually given nearly the same weight as those with true randomization but are viewed with a bit more caution. Benefits of random assignment[edit]Imagine an experiment in which the participants are not randomly assigned; perhaps the first 10 people to arrive are assigned to the Experimental group, and the last 10 people to arrive are assigned to the Control group. At the end of the experiment, the experimenter finds differences between the Experimental group and the Control group, and claims these differences are a result of the experimental procedure. However, they also may be due to some other preexisting attribute of the participants, e.g. people who arrive early versus people who arrive late. Imagine the experimenter instead uses a coin flip to randomly assign participants. If the coin lands heads-up, the participant is assigned to the Experimental group. If the coin lands tails-up, the participant is assigned to the Control group. At the end of the experiment, the experimenter finds differences between the Experimental group and the Control group. Because each participant had an equal chance of being placed in any group, it is unlikely the differences could be attributable to some other preexisting attribute of the participant, e.g. those who arrived on time versus late. Potential issues[edit]Random assignment does not guarantee that the groups are matched or equivalent. The groups may still differ on some preexisting attribute due to chance. The use of random assignment cannot eliminate this possibility, but it greatly reduces it. To express this same idea statistically - If a randomly assigned group is compared to the mean it may be discovered that they differ, even though they were assigned from the same group. If a test of statistical significance is applied to randomly assigned groups to test the difference between sample means against the null hypothesis that they are equal to the same population mean (i.e., population mean of differences = 0), given the probability distribution, the null hypothesis will sometimes be "rejected," that is, deemed not plausible. That is, the groups will be sufficiently different on the variable tested to conclude statistically that they did not come from the same population, even though, procedurally, they were assigned from the same total group. For example, using random assignment may create an assignment to groups that has 20 blue-eyed people and 5 brown-eyed people in one group. This is a rare event under random assignment, but it could happen, and when it does it might add some doubt to the causal agent in the experimental hypothesis. Random sampling[edit]Random sampling is a related, but distinct process. Random sampling is recruiting participants in a way that they represent a larger population. Because most basic statistical tests require the hypothesis of an independent randomly sampled population, random assignment is the desired assignment method because it provides control for all attributes of the members of the samples—in contrast to matching on only one or more variables—and provides the mathematical basis for estimating the likelihood of group equivalence for characteristics one is interested in, both for pretreatment checks on equivalence and the evaluation of post treatment results using inferential statistics. More advanced statistical modeling can be used to adapt the inference to the sampling method. History[edit]Randomization was emphasized in the theory of statistical inference of Charles S. Peirce in "Illustrations of the Logic of Science" (1877–1878) and "A Theory of Probable Inference" (1883). Peirce applied randomization in the Peirce-Jastrow experiment on weight perception. Charles S. Peirce randomly assigned volunteers to a blinded, repeated-measures design to evaluate their ability to discriminate weights.[3][4][5][6] Peirce's experiment inspired other researchers in psychology and education, which developed a research tradition of randomized experiments in laboratories and specialized textbooks in the eighteen-hundreds.[3][4][5][6] Jerzy Neyman advocated randomization in survey sampling (1934) and in experiments (1923).[7] Ronald A. Fisher advocated randomization in his book on experimental design (1935). See also[edit]
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What is the purpose of random assignment in an experiment?Random assignment is a procedure used in experiments to create multiple study groups that include participants with similar characteristics so that the groups are equivalent at the beginning of the study.
When designing an experiment the purpose of random assignment is to ensure that quizlet?The purpose of random assignment is to ensure that participants are unaware of the experimenter's hypothesis.
What is the most important reason for Random assignment?Random assignment prevents selection bias
So the allocation can no longer be biased since it is done at random, i.e. in a non-predictable way. This is in contrast with the real world, where for example, the sickest people are more likely to receive the treatment.
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