Multiple independent sample testing with proportions

This allows for comparison of multiple independent groups when the measurements are proportions. In summary, when dealing with proportions, you can use probit or logit with dummy variables to compare multiple independent groups, rather than using repeated t-tests.
  • #1
jamesv87
7
0
Hey everyone,

I'm working on my undergraduate thesis in biology and am having problems analyzing the data from my experiments. I'm wondering if anyone knows how to compare multiple independent groups when the measurements are proportions. The problem is that many of my values are 1 and 0 with relatively few in between. I've tried an arcsine transformation but it does not really help the distribution (Does it matter whether I use degrees or radians?). I've used Kolmogorov-Smirnov tests to compare between two groups at a time, and do find significance, which is reassuring, but I don't know if this is the best way to go about analyzing all of my data. Would I also be violating some rule of science if I did this, similar to using a repeated series of t-tests?

Your feedback is much appreciated!
 
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  • #2
You can use probit or logit, with the left hand variable being the proportions pooled across groups, and the right hand variables a set of "dummy" variables that indicate each respective group.
 

FAQ: Multiple independent sample testing with proportions

1. What is multiple independent sample testing with proportions?

Multiple independent sample testing with proportions is a statistical method used to compare the proportions of two or more groups or populations. It is often used in scientific research to determine if there is a significant difference between the proportions of certain characteristics or outcomes in different groups.

2. How is multiple independent sample testing with proportions different from other types of statistical tests?

Multiple independent sample testing with proportions differs from other types of statistical tests, such as t-tests or ANOVA, because it specifically compares the proportions of a characteristic or outcome in different groups, rather than the means or averages of numerical data.

3. What data is needed for multiple independent sample testing with proportions?

In order to conduct multiple independent sample testing with proportions, you will need to have the number of individuals or samples in each group, as well as the number of individuals in each group who have a certain characteristic or outcome of interest.

4. How is the significance of the results determined in multiple independent sample testing with proportions?

The significance of the results in multiple independent sample testing with proportions is typically determined by calculating a p-value. This value represents the probability of obtaining the observed results if there is no true difference between the groups. A p-value less than 0.05 is typically considered statistically significant.

5. What are the potential limitations of multiple independent sample testing with proportions?

One potential limitation of multiple independent sample testing with proportions is that it assumes the samples are independent and randomly selected. If this assumption is not met, the results may not be accurate. Additionally, this type of testing does not account for other variables that may influence the outcome, and can only determine if there is a significant difference between the groups, not the cause of the difference.

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