The document discusses correlation tests, highlighting the difference between parametric and non-parametric statistics. It outlines appropriate statistical techniques for analyzing relationships between different types of variables, such as Pearson correlation for continuous variables and chi-square tests for nominal variables. Additionally, it presents services offered by a dissertation consulting company, Statistics Solutions, including various chapters of dissertation support.

1. Running and Interpreting Correlation
Tests
Justin D’Souza
Quantitative Specialist

2. Table of
Contents
Statistics Solutions Services
Correlation Tests
Intellectus Demo
Summary
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3. Services Offered
by Statistics
Solutions
• Topic Development
• Prospectus or Concept Papers
• Introduction Chapter
• Literature Review Chapter (identifying articles)
• Methodology Chapter (Quantitative/Qualitative).
• IRB forms
• Data entry templates
• Survey Monkey upload
• Results Chapter (Quantitative/Qualitative)
• Discussion Chapter
• Powerpoints for Defense
• Journal Publications
 Need help with your dissertation? Call 727-442-4290

4. Parametric and
Non-Parametric
Statistics
Parametric Techniques
 Parametric statistics are based on assumptions about the distribution of population from
which the sample was taken.
 Usually this “assumption” is that the data follows a normal (bell-shaped) distribution.
 Parametric techniques usually involve examining a continuous variable.
Non-Parametric Techniques
 Nonparametric statistics are not based on assumptions, that is, the data can be collected
from a sample that does not follow a specific distribution.
 A normal (bell-shaped) distribution is not required for non-parametric techniques.
 Non-parametric techniques usually involve examining nominal or ordinal variables.
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5. Relationship
between two
numerical
variables
Parametric Technique
 Pearson Correlation– Appropriate statistical analysis when testing for relationship between
two continuous-level variables.
 Example: Is there a significant relationship between mileage and horsepower?
Non-Parametric Technique
 Spearman’s Rho and Kendall’s Tau Correlation– Appropriate statistical analysis when
testing for relationship between two variables, when at least one of the variables is
measured ordinally.
 Example: Is there a significant relationship between mileage categories and
horsepower?
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6. Relationship
between
continuous and
dichotomous
variable
Parametric Technique
 Point-biserial Correlation– Appropriate statistical analysis when testing for
relationship between dichotomous and continuous-level variables.
 Example: Is there a significant relationship between transmission type of a
car (automatic vs manual) and mileage?
Non-Parametric Technique
 If you have a dichotomous variable and an ordinal variable, there is not a non-
parametric equivalent to the point-biserial correlation. Instead the approach will
need to be shifted to a Mann-Whitney U test to test for differences in ordinal
variable by dichotomous variable.
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7. Relationship
between two
nominal-level
variables
Non-Parametric Technique
 If you have are testing the relationship between two nominal-level variables, run
a chi-square test of independence
 Example: Is there a significant relationship between transmission type and
engine type?
Need help with your dissertation? Call 727-442-4290

8. Summary
 Determine what level of measurement of variables you are using. .
 Determine if data follow a normal distribution.
 Pinpoint analysis to address research questions.
 Use Intellectus Statistics to analyze your data and present interpretation.
Need help with your dissertation? Call 727-442-4290

9. Additional
Support
Statistics Solutions is a full-service dissertation consulting
company providing graduate students timely, editorial
support for their dissertations and scholarly projects
For information about our services, receive a
complementary 30-min consultation available Mon-Fri 9-5
ET
Contact us at info@statisticssolutions.com
Phone: 727-442-4290