Randomized Comparative Experiment
A Randomized Comparative Experiment is a randomized assignment comparative experiment that follow a randomized controlled experiment design (with randomized controlled experiment treatment group(s) and a randomized controlled experiment control group(s)).
- It can be managed by a Randomized Comparative Experiment System.
- It can be designed by a Randomized Comparative Experiment Design Task.
- It can be analyzed by a Randomized Comparative Experiment Evaluation Task.
- It can (typically) be more costly to perform than a Post-hoc Analysis on Observational Data.
- It can (typically) assume that any differences between the two groups are due either to the Treatment or to Random Variation. *** The Treatment can be considered Effective if the difference is Statistically Significant and not by Chance ..
- It can range from being a Two-Group Randomized Experiment to being a Multi-Group Randomized Experiment.
- It can range from being a Subject-level Randomized Experiment(RCT) to being a Cluster-Randomized Experiment(GRT).
- It can range from being a Non-Blind Randomized Controlled Experiment to being a Double-Blind Randomized Controlled Experiment.
- It can range from being a Placebo-Controlled Randomized Experiment to being an A-B Randomized Experiment.
- It can range from being a Single-Factor per Treatment Controlled Experiment to being a Multi-Factor per Treatment Controlled Experiment.
- It can range form being a Parallel Randomized Experiment to being a Repeated Measures Randomized Experiment.
- It can range from being a Purely Randomized Controlled Experiment to being a Block Randomized Controlled Experiment.
- It can be categorized by a Randomized Trial Assessment, such as the CONSORT 2010 Checklist.
- In medical studies where the intervention is the administration of drugs, for example, the control group is known as the placebo group because a neutral substance (placebo) is administered to the control group without the subjects (or researchers) knowing if it is an active drug or not.
- a Randomized Experiment with Baseline and Post-Treatment Measures.
- a Champion-Challenger Experiment.
- See: Matched Control Experiment, Evidence-Based Practice.
- Amy Gallo. (2016). “A Refresher on Randomized Controlled Experiments.” In: HBR, MARCH 30, 2016
- QUOTE: ere are the basic steps:
- Decide what your dependent variable of interest is (remember there might be more than one). In our oil well example, it’s the speed or efficiency with which you drill the well.
- Determine what the population of interest is. Are you interested in understanding whether the new bit works in all of your wells or just specific types of ones?
- Ask yourself, What is it we’re trying to do with this experiment? What is the null hypothesis — the straw man you’re trying to disprove? What is the alternative hypothesis? Your null hypothesis in this case might be, “There is no difference between the two bits.” Your alternative hypothesis might be, “The new drill bit is faster.”
- Think through all of the factors that could spoil your experiment — for example, if the drill bits are attached to different types of machines or are used in particular types of wells.
- Write up a research protocol, the process by which the experiment gets carried out. How are you going to build in the controls? How big of a sample size do you need? How are you going to select the wells? How are you going to set up randomization?
- Once you have a protocol, Redman suggests you do a small-scale experiment to test out whether the process you’ve laid out will work. “The reason to do a pilot study is that you’re most likely going to fall on your a**, and it hurts less when it’s called a pilot study,” he jokes. With an experiment like the drill bit one, you may skip the pilot because of the cost and time involved in drilling a well.
- Revise the protocol based on what you learned in your pilot study.
- Conduct the experiment, following the protocol as closely as you can.
- Analyze the results, looking for both planned results and keeping your eyes open for unexpected ones.
- QUOTE: ere are the basic steps:
- (Dimitrov & Rumrill, 2003) ⇒ Dimiter M. Dimitrov, and Phillip D. Jr Rumrill. (2003). “Pretest-posttest Designs and Measurement of Change.” In: WORK: A Journal of Prevention, Assessment and Rehabilitation, 20(2).
- QUOTE: … RD = randomized design (random selection and assignment of participants to groups and, then, random assignment of groups to treatments). With the RDs discussed in this section, one can compare experimental and control groups on (a) posttest scores, while controlling for pretest differences or (b) mean gain scores, that is, the difference between the posttest mean and the pretest mean. Appropriate statistical methods for such comparisons and related measurement issues are discussed later in this article.