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SPSS Practice: Correlation Testing To best prepare for the assignment in this unit, you must become familiar with some basic statistical skills related to correlation testing. Although there are...

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SPSS Practice: Correlation Testing

To best prepare for the assignment in this unit, you must become familiar with some basic statistical skills related to correlation testing. Although there are several tests that you could choose from, such as Pearson, Spearman, Kendall, and Biserial, you will only need to understand the basic differences for two slightly different correlation tests: Pearson correlation and Spearman correlation.

Before performing any inferential statistical analysis, it is common for a researcher to look for relationships among the various variables for which data has been collected.

For example, in our scenario, we want to find out if a change to a vegetarian diet from a typical American omnivorous diet will have an effect on emotional well-being. Before we come to that part of the analysis, however, it is a good idea to see if there are other factors that might influence the study results. It is entirely possible that there may be some hidden influence on the outcome that is related to age or BMI. We will explore just one statistical detective method that can be used to address this issue.

Instructions

For this discussion, refer to the helpful links in Resources and use the Alaska study’s Emotional Well-Being Corrected data set to perform the following analyses for only three variables that have interval/ratio data: Age, BMI and Baseline SF-36 Scores:

Pearson Correlation
  1. Assess the selected variables for outliers and normal distribution and report which type of statistical correlation testing would be the most appropriate.
  2. Create a scatterplot for each selected combination of the above variables to identity the graphic nature of the relationship.
  3. Perform a Pearson Correlation test on the following, regardless of whether the data distribution looks normal: relationship between Age and BMI, then relationship between BMI and Baseline SF-36 scores.
  4. Report the results as the magnitude of the relationship (correlation coefficient) and direction of the relationship (positive or negative).
Spearman Correlation
  1. Perform a Spearman Correlation test regardless of whether the data distribution looks normal for the same two-variable combinations.
  2. Report the results.
Comparison
  1. Explain the differences between the Pearson Correlation and the Spearman Correlation, including when to use each test, advantages, and disadvantages of each.
  2. Describe one or two of the challenges you found while performing these exercises and how you resolved the issues. Where appropriate, provide the address of any website that helped you.

Remember to refer to the guidelines in the FEM as you prepare your post.

Response Guidelines

Read and respond to the posts of your peers according to the guidelines in the FEM.

Address one of more of the following in your response:

  • How do the challenges and resolutions of your peers compare to yours?
  • How did the comparison between the Pearson Correlation and the Spearman Correlation of your peers compare to yours?

Learning Components

This activity will help you achieve the following learning components:

  • Prepare data for analysis.
  • Identify the chi-square test of independence.
  • Perform a chi-square test of independence.
  • Interpret the overall clinical meaning and limitations of the relationship of two variables, based on a correlation analysis and literature regarding age and stress.
  • Write about statistical concepts clearly, accurately, and professionally.
Answered Same Day Aug 21, 2021

Solution

Gajula Nagasai answered on Aug 23 2021
109 Votes
Statistical Evaluation:
Outlier test: Outlier is an i
egular observation which is far from the data outlier will change the shape of results
Outlier test:
From the above three boxplots for the three variables we observed that there are no outliers presented in the data that means there is no evidence that the data deviates from normality that means the data follows approximately normal distributions
The basic methods in statistics to test the co
elation between two variables is
Pearson co
elation test
Spearman Rank co
elation
Kandle rank co
elation
From the above the 3 methods the appropriate and common method to test the co
elation coefficient is Pearson Co
elation test
Scatter plots:
The above scatter plot explains that there is no significance difference between Age and Bassline SF-36 well Being score
The above scatter plot explains that there is no significance difference between Age and BMI
The above scatter plot explains that there is no significance difference between BMI and Bassline SF-36 well Being score
Pearson co
elation and interpretation of results:
Relation ship between Age and BMI
    Co
elation Coefficients
     
    AGE
    BMI
    AGE
    1
     
    BMI
    0.098003
    1
There is no significant impacting co
elation between Age and BMI the positive sign in co
elation coefficient explains that whereas a weak positive co
elation between 2 variables
Relationship between Bassline SF-36 Well-Being Score and BMI:
    Co
elation coefficients
     
    BMI
    Bassline SF-36 Well-Being Score
    BMI
    1
     
    Bassline SF-36 Well-Being Score
    -0.07196903
    1
There is no significant impacting co
elation between Bassline SF-36 Well-Being Score and BMI the negative sign in co
elation coefficient explains that whereas a weak negative co
elation between 2 variables.
Spearman’s rank co
elation:
    Age...
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