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The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables. Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study.
Adaptive Designs
Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs. The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes. Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century.
Independent variable (IV)
Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples. This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end. Randomization is important in an experimental research because it ensures unbiased results of the experiment.
Experimental research design example
During research, you observe one or more groups after applying a treatment to test whether the treatment causes any change. However, these conditions are unethical or impossible to achieve in some situations. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.
A team of psychologists is interested in studying how mood affects altruistic behavior. They are undecided however, on how to put the research participants in a bad mood, so they try a few pilot studies out. The group that receives the experimental treatment is the experimental group.
Below are some typical examples of experimental designs, with their type indicated. Simply put, the dependent variable is what is measured to assess the experimental outcome. In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means.
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In essence, a lurking variable is a third variable that is not measured in the study but may change the response variable. Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. Quantitative research designs tend to be more fixed and deductive, with variables and hypotheses clearly defined in advance of data collection. A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.
This means that each condition of the experiment includes a different group of participants. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same. In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible. These are pre-experimental research design, true experimental research design, and quasi experimental research design. A pre-experimental research study is a basic observational study that monitors independent variables’ effects. Typically, the researcher designs the treatment and randomly assigns subjects to control and treatment groups.
Only when this is done is it possible to certify with high probability that the reason for the differences in the outcome variables are caused by the different conditions. Therefore, researchers should choose the experimental design over other design types whenever possible. However, the nature of the independent variable does not always allow for manipulation.
For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors. However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand. Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.
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Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn. In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables. When done using true experimental design, causality can be infered, which allows researchers to provide proof that an independent variable affects a dependent variable.
There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve. Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.
The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables. To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period. Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail.
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