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Randomization in experimental design

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What is randomization in experimental design?

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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Why is randomization in experimental design important?

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 prevents biases and makes the results fair. 
  • It makes sure that the groups made for conducting an experiments are as similar as possible to each other so that the results come out as accurate as possible. 
  • It also helps control the lurking variables which can affect the results to be different from what they are supposed to be. 
  • The sample that is randomly selected is meant to be representative of the population and since it doesn’t involve researcher’s interference, it is fairly selected. 
  • Randomizing the experiments helps you get the best cause-effect relationships between the variables. 
  • It makes sure that the random selection is done from all genders, casts, races and the groups are not too different from each other. 
  • Researchers control values of the explanatory variable with a randomization procedure. So, if we see a relationship between the explanatory variable and response variables, we can say that it is a causal one.

What are different types of randomization techniques in experimental design?

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:

  • Simple Random Sampling

SIMPLE RANDOM SAMPLING

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:

  • Lottery– Like the old ways, the participants are given a number each. The selection is done by randomly drawing a number from the pot. 
  • Random numbers– Similar to the lottery method, this includes giving numbers to the participants and using random number table.

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. 

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  • Permuted Block Randomization

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:

  1. Random number generator

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

  1. Permutations

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. 

  • Stratified Random Sampling

STRATIFIED RANDOM SAMPLING

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: 

  1. Define the target audience.
  2. Identify the stratification variables and decide the number of strata to be used.
  3. Using a pre-existent sampling frame or by creating a frame that includes all the information of the stratification variable for the elements in the target audience.
  4. Make changes after evaluating the sampling frame depending on its coverage.
  5. Each stratum should be unique and should cover each and every member of the population. 
  6. Assign a random, unique number to each element.
  7. Define the size of each stratum according to your requirement. 
  8. The researcher can then select random elements from each stratum to form the sample. 

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