You plan to conduct a study with a total population of 387 individuals comprised of 200 freshmen/sophomores and 187 juniors/seniors. You wish to be able to describe the characteristics of both the Freshmen/Sophomore and the Junior/Senior in the sample as well as make comparisons between them. (a) What sampling method would you use? Why? (b) Assume that you want a confidence level of 95%, how many men and how many women should be in the sample? (Bring to the test the sample size chart in the BlackBoard Course Information!) (c) You anticipate a response rate of 30%. Do you think you should oversample accordingly? Why or why not?
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(a) In order to describe the characteristics of both the Freshmen/Sophomore and Junior/Senior populations and make comparisons between them, the most suitable sampling method would be stratified sampling. Stratified sampling involves dividing the population into distinct subgroups, or strata, and then randomly selecting samples from each stratum. This ensures that representation from each subgroup is proportionate to their size in the population, allowing for meaningful comparisons between the groups.
(b) To determine the number of men and women needed in the sample, we need to consult a sample size chart based on the desired confidence level. Unfortunately, as an AI text-based model, I do not have access to external resources like the Blackboard Course Information or specific sample size charts. However, I can guide you through the general process of calculating sample size based on confidence level.
To determine the sample size, you'll need to consider the following factors:
1. Desired confidence level: In this case, the desired confidence level is stated as 95%.
2. Margin of error: The margin of error represents the maximum amount of error you are willing to accept in the study. The lower the margin of error, the larger the required sample size.
3. Population size: The population size is given as 387 individuals (200 freshmen/sophomores and 187 juniors/seniors).
4. Proportion distribution within the population: If you have information about the proportion of men and women in each subgroup (freshmen/sophomores and juniors/seniors), it can be used to calculate the sample size.
Using these factors, you can use statistical formulas or online tools to calculate the required sample size. These calculations take into account the confidence level, margin of error, and population size. It is recommended to consult a sample size chart or a statistics textbook to obtain the specific values.
(c) Oversampling refers to deliberately selecting a larger sample than required to account for potential non-response and increase the response rate. In this case, you anticipate a response rate of 30%. Whether you should oversample or not depends on various factors such as the importance of accurate representation, available resources, and the potential impact of non-response on the study's results.
Oversampling can help mitigate the potential bias caused by non-response, ensuring a more representative sample. However, it comes with additional costs and logistical considerations. If the non-response rate is expected to have a significant impact on the study's outcomes, it may be advisable to oversample.
Consider the resources available and the potential impact of non-response on the study's objectives to make an informed decision.