Randomising participants’ placement into different treatment groups is termed a random assignment in experimental research.
Simple random assignment ensures that each participant of the controlled experiment has equal chances of being placed in either the experiment or the control group. Therefore, experimental studies that employ simple random assignment are also termed completely randomised designs.
Random assignment plays a vital role in experimental studies to ensure that the groups are comparable before the study starts and any differences that might occur between the groups are only a result of random factors.
Random assignment helps to enhance the internal validity of a study, and therefore its importance cannot be overlooked in experimental studies.
During experiments, researchers manipulate the independent variable and observe the changes and effects on the dependent variables. In contrast, the other variables are controlled to ensure they don’t interfere with the results. It can be done by using different levels of the independent variable for the different participant groups.
Such a design is called an independent measure design or between-groups.
Example of different levels of independent variables
A clinical study is conducted to investigate the effect of iron supplements in the diet on the body’s energy levels. In this case, the iron supplements’ effect is considered the independent variable while the energy level is the dependent one.
To experiment, researchers divide the participant into three groups, each receiving a different level of the independent variable:
With the help of random assignment, researchers can eliminate the possibility of systematic and biased differences in the treatment groups.
Without random assignment, researchers would not be able to rule out any alternative explanations that they might find in their results.
Example of non-random assignment
While conducting the experimental study on the effects of iron supplements on energy levels, you use flyers to recruit participants for the study. You distributed the flyers in your local gym, coffee shop and shopping mall. After shortlisting the participants for your sample, a haphazard method was used to sort the participants into groups like;
If such an assignment is resorted to, it will be challenging to determine if the participant characteristics are uniform across all groups. For instance, participants from the gym might exhibit higher levels of energy due to their active habits. On the other hand, participants, from the mall, for instance, might display lower signs of energy levels, which can introduce a bias in your study.
If results point to high energy levels in the heavy dosage group, there would not be a way to ascertain if the results are solely impacted by the independent variable, i.e. iron supplement consumption or if the healthy lifestyle of the participants played a role in the conclusion.
Though, random assignment helps to level out the differences between the treatment groups; it cannot make them equivalent every time. Differences might arise from extraneous variables or simply by chance.
Random variation in most cases is low between the groups and is therefore acceptable, especially in the case of a large sample. To sum up, researchers should always use random assignment to form their treatment groups for the experimental study wherever it is possible, both ethically and sensibly.
Random sampling and random assignment are essential concepts in research studies; however, it is essential to understand the difference between them.
Random sampling finds used in several experimental studies; however, random assignment is used only for between-subjects experiments.
While some studies require the use of both random sampling and random assignment, some studies require the use of only one or the other.
Random sampling helps to make stronger statistical inferences as it helps to enhance the external validity of the study by ensuring an unbiased and representative sample of the population.
Example of random sampling
You are researching new interventions to boost employee engagement for a large organisation.
Since you have access to all the employees, you assign them a number and use a simple random sample for data collection. Then, using a random number generator, you choose 300 employees for your study.
It allows you to confidently infer that the results will be applicable for the rest of the employees since a random sample has been used to conduct the study.
Random assignment helps to eliminate systematic bias or differences among the treatment groups to enhance the study’s internal validity. In addition, it helps you to attribute the outcomes to the independent variable.
Example of random assignment
Your experimental design includes observing two treatment groups
These include the following:
Random assignment should be used to place the participants into the different treatment groups. This can be done by assigning each participant a number and using a random number generator to sort them out into either the control or the experiment group.
It allows you to confidently deduce whether employee engagement has been boosted by the team-building interventions or not and not as a result of any bias between the groups.
There are several ways random assignment can be done. The most common is assigning participants a unique number and sorting them out into different treatment groups using a random number generator.
This type of random assignment is quite powerful since it gives the participants an equal chance of being placed in either the control or the experimental groups.
In experimental designs that are more complicated, random assignment is used only after the participants are placed in specific blocks according to a characteristic trait. A larger sample is required for such grouping into blocks if you are looking to achieve high statistical power.
For instance, participants are placed into blocks based on a similar characteristic or trait, like graduate vs undergraduate students and then use random assignment within each block to sort participants for your treatment groups. This helps you conclude if the characteristics played a role in altering the study’s outcome.
In the case of experimental matched designs, you create predefined blocks and then match up the participants based on the characteristics for each block. Then, the participants can be randomly assigned to the different conditions in the experiment within each matching group or pair and compare the outcomes.
There are some cases where random assignment is not ethical or relevant, and therefore the assignment of groups is done differently.
Random assignment is the process of randomly sorting participants into treatment groups for an experimental study to eliminate any systematic bias or differences in the groups that might influence the outcome of the study.
Random selection is the process of randomly selecting participants from a whole population for an experimental study, while the random assignment is the process of randomly placing the said participants into treatment groups to eliminate any difference between the groups.
Random assignment should always be used with independent measures or between-groups designs, consisting of a control group and one or more experimental groups.
Participants can be easily placed in groups with the help of random assignments. For example, each participant can be given a unique number, and a random number generator or lottery can be used to sort the participant into the respective groups. Other methods of random assignment can include a coin flip or a dice roll.
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