Determining matched pairs

  • Thread starter ultimatejester
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In summary, the conversation discusses a problem with determining if a study on the mean undergraduate debt of Hispanic students graduating in 2005 compared to Asian-American students graduating in 2005 should use a matched pair or independent t-test. The use of the word "undergraduate" is causing confusion and it is suggested to use an independent t-test with unequal variances. The question of controlling for other factors such as major or location is also brought up. Ultimately, there is enough evidence to suggest that Hispanic students have a lower mean undergraduate debt than Asian-American students.
  • #1
ultimatejester
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I am having a little problem tackling this problem. I can't seem to figure out if this is a matched pair or not. What we would like to study is "whether the mean undergraduate debt of Hispanic students graduating in 2005 is less than the mean undergraduate debt of Asian-American students graduating in 2005". The word undergraduate is causing the problem. I am leaning towards independent pairs but i thought i should consult here first.

Thanks.
 
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  • #2
Independent t-test with equal or unequal sample sizes (as the case may be).
 
  • #3
That is exactly what i thought. It is of unequal variances (Good thing i did the f-test). Anyways thanks for the help. Btw if some one does care, there is enough evidence to infer that the mean the mean undergraduate debt of Hispanic students graduating in 2005 is less than the mean undergraduate debt of Asian-American students graduating in 2005
 
  • #4
Are you controlling for the major/department/college/city/state, etc.? These characteristics may or may not be uniform across cultural backgrounds.
 

1. What is the purpose of determining matched pairs?

The purpose of determining matched pairs is to compare two sets of data that are related to each other, in order to see if there is a significant difference between the two sets.

2. How do you determine matched pairs?

To determine matched pairs, you first need to identify two sets of data that are related to each other. Then, you need to pair up the data points from each set based on a specific characteristic or variable. Finally, you can compare the paired data points to see if there is a significant difference between the two sets.

3. What are the advantages of using matched pairs in data analysis?

Using matched pairs in data analysis allows for more accurate and precise comparisons between two sets of data. It also helps to control for confounding variables and eliminate the effects of individual differences in the data.

4. What are some common examples of matched pairs in scientific research?

Some common examples of matched pairs in scientific research include before-and-after measurements in experiments, comparisons between twins or siblings, and comparing data from the same individual at different time points.

5. How do you determine if there is a significant difference between matched pairs?

To determine if there is a significant difference between matched pairs, you can use statistical tests such as the paired t-test or Wilcoxon signed-rank test. These tests will calculate a p-value, which indicates the likelihood of obtaining the observed difference between the two sets of data if there is no real difference. A low p-value (usually <0.05) indicates a significant difference between the matched pairs.

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