Weighing Contribution: Scatter Plot Analysis

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In summary, the analysis of a thread here would be particularly dull. All posts would be on-topic; all contributors would score 100%.
  • #1
DaveC426913
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I have a list of people whose contribution I'm trying to analyze.
Some people contribute a lot but are off-topic.
Some contribute a little but are on-topic.

How might I weigh them to show an accurate idea of who are the lesser value contributors?

A small sample:
Code:
Contributor On~topic Off~topic Total ~ %On ~ %Off
A ~ ~ ~ ~ ~ ~ ~ 79 ~ ~ ~ 49 ~ ~ 128 ~ 61.7 ~ 38.3
B ~ ~ ~ ~ ~ ~ ~ 81 ~ ~ ~ 21 ~ ~ 102 ~ 79.4 ~ 20.6
C ~ ~ ~ ~ ~ ~ ~ 25 ~ ~ ~ 14 ~ ~ ~39 ~ 64.1 ~ 35.9
D ~ ~ ~ ~ ~ ~ ~ ~1 ~ ~ ~ ~0 ~ ~ ~ 1 ~100.0 ~ ~0.0
A contributed 128 comments but 49 of them are off-topic, that's only 61.7%.
D contributed only 1 comment, and it is on-topic, that's a 100% score.

But did D really contribute as effectively as A?

[EDIT]
I guess a scatter plot would work effectively, wouldn't it?
 
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  • #3
Heh. Different forum.
The analysis of a thread here would be particularly dull. All posts would be on-topic; all contributors would score 100%.

I did a scatterplot.

I don't think I should post it here - unless I obscure the member names.

But, yeah, a word count would be much better. Some highly on-topic posts are several screens long.
 
  • #5
You could also rate the reading level of the text and compare scores from the original post ie first is 1st year college followon posts that are of the same level get higher score than ones that too high or too low in grade.
 
  • #7
How about (# of on-topic contributions) - (some factor) * (# of off-topic contributions). This would reward people for contributing, and penalize the off-topic contributions.
 
  • #8
masked-thread-contributors.png

Here it is, full-size:
http://www.davesbrain.ca/miscpix/masked-thread-contributors.png
 

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What is "Weighing Contribution: Scatter Plot Analysis"?

"Weighing Contribution: Scatter Plot Analysis" is a statistical method used to analyze the relationship between two variables by plotting data points on a scatter plot and determining the strength and direction of the relationship.

Why is "Weighing Contribution: Scatter Plot Analysis" important?

"Weighing Contribution: Scatter Plot Analysis" allows scientists to visually and quantitatively analyze the relationship between two variables, which can provide valuable insights and inform further research or decision-making processes.

What are the steps to perform "Weighing Contribution: Scatter Plot Analysis"?

The steps to perform "Weighing Contribution: Scatter Plot Analysis" include: 1) Collecting data on the two variables of interest; 2) Plotting the data points on a scatter plot; 3) Determining the strength and direction of the relationship using a correlation coefficient; 4) Interpreting the results and drawing conclusions.

What are some limitations of "Weighing Contribution: Scatter Plot Analysis"?

Some limitations of "Weighing Contribution: Scatter Plot Analysis" include: 1) It only measures the relationship between two variables, not causation; 2) It assumes a linear relationship between the variables; 3) It can be influenced by outliers or missing data; 4) It may not be appropriate for all types of data.

How can "Weighing Contribution: Scatter Plot Analysis" be used in scientific research?

"Weighing Contribution: Scatter Plot Analysis" can be used in scientific research to identify and explore relationships between variables, to test hypotheses, and to inform further experiments or studies. It can also be used to visualize and communicate research findings to others.

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