Only need a response for two classmate:
Original Discussion:
Create a comprehensive explanation of the purpose and use of correlation analyses. Discuss potential relationships Teton Grand may want to explore via a correlation.
Please also consider what additional data you would suggest Teton Grand to collect / provide so they can explore new and different relationships than what we have been discussing in previous weeks. (For example, you might suggest they provide information on the teachers each student had in their courses).
When responding to peers react to their posts highlighting anything you find particularly interesting or hard to understand.
Response 1
The analysis of correlation is a valuable statistical technique commonly utilized to explore the relationship between two variables (Sarty, 2020). Its primary objective is to ascertain whether a correlation exists between the variables and, if so, to measure its strength and direction. Additionally, correlation statistics can identify patterns in data and provide insight into future trends (Sarty, 2020).
Conducting a correlation analysis would be beneficial for Teton Grand to gather further details about their program by exploring potential relationships between different variables. To accomplish this, Teton Grand may need to collect more data or provide greater detail about their current data. For instance, they could collect data on staff satisfaction levels and correlate it with exam scores to determine whether a relationship exists.
Another correlation analysis could involve exploring the relationship between attendance and student performance, as well as the format of the course (e-learning, on-ground, or blended) and its impact on final exam scores. By analyzing these relationships using correlation analysis, Teton Grand could determine whether any significant relationships exist between the variables.
Statistics: Correlation and Regression Analysis in SPSS
Response 2
The purpose of a correlation analysis is to estimate the association between two variables that are either scored on an interval, ratio, or ordinal scale (how2stats, 2011). In other words, it calculates the change in one variable due to the change in another, helping companies determine which variables they may want to further investigate. There are three main types of correlation analysis. Pearson’s correlation is most common and appropriate when data is measured on interval or ratio scales. For example, any linear variables such as age, height, weight, and temperature. Spearman’s rank correlation is appropriate when data is measured on an ordinal scale such as salary or education degrees. Kendall’s tau correlation is used to determine the strength of dependence between two variables.
Teton Grand may want to use Pearson’s correlation to explore the relationship between the day of class and learning format. This analysis paired with previous tests we have run can give more insight to the impact these variables have on each other and final exam scores. Spearman’s rank correlation can be used to explore the relationship between instructor years of experience and final exam score. New information on which classes instructors taught and in which formats could provide more valuable insights.
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