Question

Question text

The Chancellor of the California State University System has recently indicated that classes in the Fall of 2020 will continue to be virtual to be able to cope with the Corona Virus. Assume that all CSULB business students take a student satisfaction survey each semester. A researcher wants to compare compare two random groups of students from Fall 2019 to Fall 2020 in their satisfaction scores. The Chancellor has indicated that student satisfaction will improve with virtual classes. The groups are called FSS2019 for Student Satisfaction scores for Fall, 2020 and FSS202- for Student Satisfaction Scores for Fall, 2020. The study will calculate the difference using a confidence level of 95%. The population standard deviation is known.

The researcher wants to make sure even though they take random samples, they get a group from each of the local community colleges and plans to use Anova to analyze the data. What type of design could ensure this?

Select one:

a. Randomized Block Design

b. Completely Randomized Design

c. Matched Samples

d. Random but Related Sampling

Answer 1

ANs:- The correct ans is (a) Randomized Block Design

In stratified sampling,  the researcher divide the sample into relatively homogeneous subgroups or stratas and then select some of the members from those stratas.Like stratified sampling, randomized block designs are constructed to reduce noise or variance in the data by dividing the sample into relatively homogeneous subgroups or blocks (analogous to “strata” in stratified sampling). Then, the experimental design you want to implement is implemented within each block or homogeneous subgroup. The key idea is that the variability within each block is less than the variability of the entire sample. Thus each estimate of the treatment effect within a block is more efficient than estimates across the entire sample. And, when we pool these more efficient estimates across blocks, we should get an overall more efficient estimate than we would without blocking.