ObjectivesBy the end of this lesson, you will be able to... Show
For a quick overview of this section, watch this short video summary: Designed ExperimentsBefore we can talk about what to design an experiment, we first need to know what an experiment is in a statistics context. A designed experiment is a controlled study in which one or more treatments are applied to experimental units (subjects). The experimenter then observes the effect of varying these treatments on a response variable. You can see already that we've got quite a few terms. You may want to get the definition sheet that we started back in Section 1.1. experimental unit - person or object upon which the treatment is applied treatment - condition applied to the experimental unit response variable - the variable of interest factors - variables which affect the response variable To help clarify all this terminology, let's consider a simple example: Example 1 Consider the study we looked at in Example 3 in Section 1.2. It was from the New England Journal of Medicine and concerned the low-carb Atkins diet. If you need a refresher, here's a link to the summary of the report in the New England Journal of Medicine. If you'd like more detail, there's a copy of the full article through the New England Journal of Medicine. Focus on the "Methods" section for details on the experimental design and sampling procedure. Once you've reread the articles, try to determine the experimental units, response variable, treatment, and factors from the study. When you're ready, click on the links in the table below to reveal the answer.
As is mentioned in your text, many designed experiments are double-blind. This means that neither the subjects nor the experimenters know who is receiving which treatment. Typically, subjects are assigned to two groups, with one receiving the treatment (like a new medical drug), while the other receives a placebo. This can be key to avoid researcher bias. Suppose, for example, that the previous study was done by the Atkins Institute and researchers new who was on which diet. Don't you think they'd be tempted to try to influence the results somehow? In some cases, though, a single-blind experiment is preferable. One good example of this might be a study involving a heart medication. In this case, the doctors involved should be aware of who is taking the drug, and who is taking the placebo. The Steps in Designing an ExperimentStep 1: Identify the problem or claim to be studied. Step 2: Determine the factors affecting the response variable. Step 3: Determine the number of experimental units. Step 4: Determine the level(s) of each factor.
Step 5: Conduct the experiment. Step 6: Test the claim. OK, now that we have the basic process down, let's look at an example using various designs. We're going to focus on three particular experimental designs - completely randomized, matched-pairs, and randomized block. Your textbook also goes through all three following an example of the effect of fertilizers on plant growth. I'm going to do something similar, but using a different example. Example 2 Suppose we want to determine the effect of using the practice exams on student exam scores. If we do a survey of students and determine which have used the practice exam and which haven't, we might not really know if the practice exam made a difference. Can you see why? OK, I have an idea. Essentially, it's because "better" students will typically be more willing to use the practice exam. Since the students self-selected whether or not to use it, we don't know if they did better because of the practice exam, or just because they were better students to begin with. Let's start our design process. Step 1:
Identify the problem or claim to be studied. Step 2: Determine the factors affecting the response variable. Step 3: Determine the number of experimental units. Step 4: Determine the level(s) of each factor.
That's the basics. Now on to the experiment itself. Completely Randomized DesignA completely randomized design is when each experimental unit is assigned to a treatment completely at random. (This is similar to simple random sampling.) In this design, we would randomly select 60 students and randomly split them into two groups with 30 each. One group does not take the practice exam, while the other does. We have the two groups then take the actual exam and we compare results. Here's a visual: Matched-Pairs DesignA matched-pair design is when the experimental units are paired up and each of the pair is assigned to a different treatment. There are a couple ways to do matched-pairs - we could find people who are very similar somehow, and have one do the practice exam and the other not. Unfortunately, there are so many factors affecting performance on the exam, this pretty impractical. Another way to do a matched-pair design is to have the same individual before and after the treatment. In this case, we could do just that - give the exam, have students study the practice exam, and then give the exam again. The problem with this design is that we don't know if the improvement (if any) is from the practice exam or just from seeing the material again. A better plan would be to have all individuals take the exam as a "pre-test", then have 30 students take the practice exam, while the rest do not. Then we have the students all take the exam again, and we compare the "before" and the "after". Randomized Block DesignA randomized block design is used when the experimental units are divided into homogeneous groups called blocks. Within each block, the experimental units are randomly assigned treatments. (This is similar to stratified sampling.) Student maturity is a huge factor in college success. Another idea might be to split our sample by academic year - those in their first year versus those in their second. Essentially, we're stratifying the sample, and then doing a completely randomized design on each of the "strata". What is the name of the experimental design where neither the experimenter nor the participant know which group they are in?Definition. The double-blind design describes an experimental procedure in which neither the participant nor the experimenter are aware of which group (i.e., experimental or control) each participant belongs to.
What is the procedure where neither the subject nor the experimenter know which group is receiving which treatment?A double-blind study is one in which neither the participants nor the experimenters know who is receiving a particular treatment. This procedure is utilized to prevent bias in research results. Double-blind studies are particularly useful for preventing bias due to demand characteristics or the placebo effect.
Is an experiment in which neither the experimenter nor the participants know if the subjects are in the experimental or the control group?Explanation: Control is the way that experimenters try to minimize the effects of any other variables besides the IV and DV. A double blind experiment is when neither the experimenter nor the subjects know which subjects are in the experimental and which are in the control group.
What are the 4 types of experimental design?Four major design types with relevance to user research are experimental, quasi-experimental, correlational and single subject. These research designs proceed from a level of high validity and generalizability to ones with lower validity and generalizability. First, a note on validity.
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