Comparing Non-Uniform Data Sets

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Discussion Overview

The discussion revolves around the challenge of normalizing and summarizing non-uniform data sets from multiple reviewers evaluating student assignments based on three criteria. Participants explore methods for handling varying numbers of reviews per assignment and the implications for data representation, particularly in the context of visualizing results through histograms or other chart formats.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant suggests averaging the scores given by different teachers for each student to obtain a single grade for criteria A, B, and C.
  • Another participant proposes using medians instead of averages, arguing that only whole numbers are meaningful in this context.
  • A different viewpoint compares the situation to the Collaborative Filtering problem, suggesting that a grid representation could effectively display the scores for each criterion based on the number of reviewers.
  • The original poster expresses concern about the implications of scaling reviews and the potential loss of precision in results.
  • Participants discuss the large number of students involved, indicating that the scale of the data may influence the choice of summarization method.

Areas of Agreement / Disagreement

There is no consensus on the best method for normalizing the data, with participants proposing different approaches such as averaging, using medians, or employing grid representations. The discussion remains unresolved regarding the most appropriate technique to handle the varying number of reviews.

Contextual Notes

Participants highlight limitations related to the non-linear nature of the grading scale and the challenges posed by the large number of students and reviews. There is also uncertainty regarding the meaningfulness of numerical values in the context of the analysis.

JPierce
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I'm not sure my title is very descriptive, but I tried my best. I also hope I am posting this in the right forum. If not, please let me know. (I thought it might be better posted in the social sciences forum.)

I have a project where I am analyzing the results of multiple reviewers on a set of items. I am unsure as to the proper method of normalizing the data.

In essence, here is the problem:

We have a large stack of assignments turned in by students, with one assignment turned in by each student. Teachers analyzed each assignment according to three criteria, which I will call A, B, and C. Teachers values for each of these criteria on a scale from 1 to 5. However, this scale is not linear, so A=4 is not twice as "big" as A=2.

I simply want to display a summary of the results using (say) a histogram. I have no interest in calculating summary statistics because the numerical values for each criterion are purely denumerable -- that is, A = 2.4 (which could correspond to say grade level) is meaningless.

So far, so good. But some of the assignments were reviewed by up to five teachers. Others were reviewed by only one.

So one assignment turned in by (say) Jimmy may have the following reviews from five individual teachers:

A = 3; B = 1; C = 4
A = 3; B = 2; C = 4
A = 3; B = 1; C = 2
A = 2; B = 3; C = 4
A = 3; B = 2; C = 4

Another assignment turned in by Mary may only have A = 3; B = 1; C = 4 as measured by a single teacher.

So, how do we handle the fact that some assignments have more reviews than others? We could just scale up the number of reviews to a common value. In other words, we could pretend that Mary turned in five identical assignments, that is,

A = 3; B = 1; C = 4
A = 3; B = 1; C = 4
A = 3; B = 1; C = 4
A = 3; B = 1; C = 4
A = 3; B = 1; C = 4

Somehow that doesn't seem quite right. And it would foul up the precision of the results.

Another idea is to whittle down the number of reviewers to 1 for each assignment, but I have no good criteria for selecting the one sample to keep.

Any ideas?

If I have left out important info, just let me know.
 
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Most reasonable would seem that every student gets an A, B and C grade and each grade would be the average of the grades that the different teachers gave.

So that you would have:

Jimmy: A=2.8, B=1.8, C = 3.6
Marry: A=3.0, B=1.0, C=4.0
 
JPierce said:
I have no interest in calculating summary statistics because the numerical values for each criterion are purely denumerable -- that is, A = 2.4 (which could correspond to say grade level) is meaningless.

Oh, I am not really sure what you mean here, but it seems to indicate that only whole numbers are meaningful. In that case take medians instead of averages. So that you would have:

Jimmy: A=3, B=2, C=4
Marry: A=3, B=1, C=4
 
JPierce said:
...We have a large stack of assignments turned in by students, with one assignment turned in by each student. Teachers analyzed each assignment according to three criteria, which I will call A, B, and C. Teachers values for each of these criteria on a scale from 1 to 5. However, this scale is not linear, so A=4 is not twice as "big" as A=2.

I simply want to display a summary of the results using (say) a histogram...But some of the assignments were reviewed by up to five teachers. Others were reviewed by only one.

It sounds kind of similar to the Collaborative Filtering problem made famous by the Netflix Prize, but here if the number of students is not too large it should be possible to present all the data with a few charts.

For example the scores for criteria A could be represented by an Nx5 grid: number the students 1 to N according to some overall rating (such as average total score) and make the colour/brightness of cell (i,j) according to how many reviewers gave student i score j. Then repeat for criteria B and C (so all the data is in 3 grid charts or they could be combined into one with RGB colour mix). I don't know if any packages specifically do this type of chart but it should be possible in Excel using conditional formatting.
 
Thanks for all suggestions.

The number of students involved is very large -- tens of thousands.

I will look into the Netflix problem.

Using the median might work.

If anyone has more suggestions, please offer them. I will continue reading responses.
 

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