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Randomization in an experiment refers to a random assignment of participants to the treatment in an experiment. OR, for instance we can say that randomization is assignment of treatment to the participants randomly.
For example: a teacher decides to take a viva in the class and randomly starts asking the students.
Here, all the participants have equal chance of getting into the experiment. Like with our example, every student has equal chance of getting a question asked by the teacher. Randomization helps you stand a chance against biases. It can be a case when you select a group using some category, there can be personal biases or accidental biases. But when the selection is random, you don’t get a chance to look into each participant and hence the groups are fairly divided.
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As mentioned earlier, randomization minimizes the biases. But apart from that it also provides various benefits when adopted as a selection method in experiments.
Randomization can be subject to errors when it comes to “randomly” selecting the participants. As for our example, the teacher surely said she will ask questions to random students, but it is possible that she might subconsciously target mischievous students. This means we think the selection is random, but most of the times it isn’t.
Hence, to avoid these unintended biases, there are three techniques that researchers use commonly:
In simple random sampling. The selection of the participants is completely luck and probability based. Every participant has an equal chance of getting into the sample.
This method is theoretically easy to understand and works best against a sample size of 100 or more. The main factor here is that every participants gets an equal chance of being included in a treatment, and this is why it is also called the method of chances.
Methods of simple random sampling:
Example: A teacher wants to know how good her class is in mathematics. So she will give each student a number and will draw numbers from a bunch of chits. This will include a randomly selected sample size and It won’t have any biases depending on teachers interference.
It is a method of randomly assigning participants to the treatment groups. A block is a group is randomly ordered treatment group. All the blocks have a fair balance of treatment assignment throughout.
Example: A teacher wants to enroll student in two treatments A and B. and she plans to enroll 6 students per week. The blocks would look like this:
Week 1- AABABA
Week 2- BABAAB
Week 3- BBABAB
Each block has 9 A and 9 B. both treatments have been balanced even though their ordering is random.
There are two types of block assignment in permuted block randomization:
Generate a random number for each treatment that is assign in the block. In our example, the block “Week 1” would look like- A(4), A(5), B(56), A(33), B(40), A(10)
Then arrange these treatments according to their number is ascending order, the new treatment could be- AAABB
This includes listing the permutations for the block. Simply, writing down all possible variations.
The formula is b! / ((b/2)! (b/2)!)
For our example, the block sixe is 6, so the possible arrangements would be:
6! / ((6/2)! (6/2)!)
6! / (3)! x (3)!
6x5x4x3x2x1 / (3x2x1) x (3x2x1)
720 / 36
20 possible arrangements.
The word “strata” refers to characteristics. Every population has characteristics like gender, cast, age, background etc. Stratified random sampling helps you consider these stratum while sampling the population. The stratum can be pre-defined or you can define them yourself any way you think is best suitable for your study.
Example: you want to categorize population of a state depending on literacy. Your categories would be- (1) Literate (2) Intermediate (3) Illiterate.
Steps to conduct stratified random sampling:
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