Скачать или смотреть Calculating the Correlation Between Two Variables on Different Scales by Class

Learn how to calculate the correlation between student scores and teacher assessments across different scales with R programming using dplyr and ggpubr.
---
This video is based on the question https://stackoverflow.com/q/70489994/ asked by the user 'Hani' ( https://stackoverflow.com/u/12948697/ ) and on the answer https://stackoverflow.com/a/70494238/ provided by the user 'M1DRG' ( https://stackoverflow.com/u/9903082/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Calculate correlation between two variables on two different scales by class

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding the Correlation Between Student Scores and Teacher Evaluations

In educational settings, the correlation between student performance and teacher evaluations can provide valuable insights into teaching effectiveness. Often, student scores can be measured on different scales compared to teacher assessments. This guide will guide you through the process of calculating the correlation between these two variables using R programming, specifically utilizing the dplyr and ggpubr libraries.

The Problem

You may find yourself in a situation where you have combined two datasets, each containing information on students’ average scores and teacher assessments on a class-wide scale. In our example, we have three variables:

Class (e.g., A, B, C)

Total1 (individual average score of each student)

Total2 (average teacher assessment for each class)

Given this dataset, the goal is to find out if there's a significant correlation between the average student scores (Total1) and the average teacher assessments (Total2) by class. You want to get both the Pearson correlation coefficient and the p-value.

Here is a glimpse of the data we are analyzing:

ClassTotal1Total2A4.96.7A3.86.7A4.26.7B4.57.2B3.97.2B4.17.2C3.56.5C4.46.5C3.66.5The Solution

To solve this problem effectively, we need to follow these steps:

Step 1: Setting Up Your Data

First, you need to create a data frame from your datasets. You can use the following code to set this up in R:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Calculating Mean Scores by Class

Next, we need to group our data by class and calculate the mean for Total1 and Total2. Here’s how you can do that:

[[See Video to Reveal this Text or Code Snippet]]

This will give you a summary data frame that looks like this:

ClassTotal1 MeanTotal2 MeanA4.36.7B4.177.2C3.836.5Step 3: Calculating the Correlation

Finally, you can calculate the correlation coefficient and p-value using the cor.test() function as follows:

[[See Video to Reveal this Text or Code Snippet]]

The output from this function will provide you with the correlation coefficient along with the p-value. Here’s an example of what the output might look like:

[[See Video to Reveal this Text or Code Snippet]]

In this example, the correlation coefficient is 0.5, indicating a moderate positive correlation between student average scores and teacher assessments. The associated p-value of 0.6667 suggests that the correlation is not statistically significant.

Conclusion

Understanding the correlation between student performance and teacher evaluations can shed light on areas needing improvement in the educational process. By following this structured approach and employing R's dplyr and ggpubr, you can effectively analyze and derive valuable insights from your data.

If you have any questions or need further clarification on any steps, feel free to reach out!