Which of these statements is NOT true regarding a randomized block design experiment?
A.) The elements are randomly allocated to treatment and control groups.
B.) This design has an advantage of controlling for variables that might confound the response.
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C.) Elements are randomly selected from equal-sized blocks of the total population.
D.) The sample is divided into participants or subjects and then grouped by a variable of interest.
Answer; C.) Elements are randomly selected from equal-sized blocks of the total population.
What is A randomized block design
Randomized block design is the simplest of all experimental designs. Test treatments are allocated at random to blocks, and each block receives only one treatment. The advantage of this design is that any treatment effects are virtually guaranteed to be attributable to the treatments themselves and not to some extraneous factor. If a randomly allocated treatment does have an effect, then it is said to be statistically significant.
The disadvantages of block design lie in the numerous assumptions it makes about the way that you wish your experiment participants to behave – namely that they should behave identically when in different groups, which they often don’t. Block design is therefore often used only in “before and after” experiments, and in situations where the effects of many different treatments can be predicted with high probability.
Sometimes understanding the concept of block randomization may be tricky for statistics students. Statistics homework help is here to give you full statistics homework help. Our team of professional statistics tutors is qualified to complete all kinds of assignments that a statistics student may find difficult.
Purpose of Block Randomization
The purpose of using this kind of randomization is to eliminate the effects of individual differences among experimental units that might affect the outcome of the experiment. Also, this method is used to ensure that all observations are independent from each other, meaning that no two observations share the same value for an explanatory variable. When block randomization is used, every observation in a block has exactly one value for each explanatory variable.
Block randomization can be used when we have a large number of observations on one or more variables, and when we wish to evaluate the effects of different treatments on different groups. This can be a useful strategy when we want to use the complete dataset in the analysis.
Example of Randomized Block Design to Assess Patients With Alzheimer’s Disease
A group of medical students planned to research how a 13-week physical training program affected Alzheimer’s patients’ capacity to carry out daily tasks.
And because there are significant physical differences between men and women, the students chose to block on gender. This is because gender affects the test measurement outcome and it is not a worthwhile research variable in and of itself.
Therefore, it would be extremely helpful to block gender in order to eliminate its impact as a different explanation for the result.
The students chose 16 patients to participate in the study, 6 males and 10 females.
The following is how the blocks were made:
Step 1: Blocking
In accordance with gender, participants were split into two blocks.
Step 2: Randomization
At this step, each block’s training was distributed at random.
Step 3: Data Analysis
Tukey’s HSD and ANOVA were employed.
When should randomized block design be used?
You should use a randomized block design when:
1. The outcome is influenced by an unwelcome or boring variable.
E.g. The performance of a learning program may depend upon the level of interest that a student has toward the subject matter, and therefore, we might want to ensure that all observations are independent from each other.
2. You have a small number of observations on one or more explanatory variables.
3. You can measure this variable in at least two ways.
What happens if you don’t block?
If you don’t block, the error term absorbs all of the block variability, making it challenging to identify an effect when one is present.
.For example, the effect you’re interested in is due to the factor(s) and not to an unwanted variable
E.g. We might want to control for variability in the individual’s initial skill so that we can accurately measure the effectiveness of a given training program; however, this kind of control may not be optimal since we might end up ignoring all other factors that contribute toward learning outcomes, such as the person’s interest in the subject matter or his/her overall ability/competence. when one is present.
Limitations of the randomized block design
1. Not appropriate for large numbers of treatment
Block designs are limited to situations where you have a fairly small number of treatments. The number of blocks that are formed grows along with the number of blocking factors, nearing the sample size, meaning that the number of participants in each block would be quite low, which would be problematic for the randomized block design.
2. Challenge in selecting the number of blocks
Another limitation of this design is that it can be challenging to select the number of blocks. The number of blocks must be able to reflect the number of treatment levels but simultaneously, they also have to reach a good balance between control and statistical power.
3. The blocking variable is hard to find or measure
It can be difficult for the researcher to ensure that the variables are independent. If the researcher is faced with a situation where there are many blocking factors, then it can be difficult to find out which factor is affecting the outcome, and when we don’t find out, one possibility is that we might overlook an effect.