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In your research, have you ever encountered one or more the following scenarios:
My guess is you've run into at least a few of these on multiple occasions (and maybe you didn't even know it!).
The bad news is that your skills from parametric tests (like ANOVA) are no good in practically all of the above scenarios.
Knowing even a few basic non-parametric stats will help you tackle these situations.
Learning non-parametrics is a quick way to double the number of tools in your stats tool belt.
Here, you'll learn some of the most common non-parametric statistics used across many different fields of research. After we review the fundamentals of a test, I show you, step-by-step, how to conduct, interpret, and report each test in SPSS.
Learning these new stats will also help you better understand the tests you already know how to run, and you’ll be ready to take on the next person that asks you why you chose to use a Kruskal-Wallis instead of a one-way ANOVA.
You’ll have lifetime access to 24+ videos totaling over 3 hours of instructional content on non-parametric statistics. As a bonus, I have an entire module showing you how to make quality box plots in SPSS that you can use in your research publications or professional presentations.
You can download all of the data sets we use in the examples and follow along or go through them on your own for practice.
If you aren’t satisfied with the course for any reason, it’s backed by the Udemy 30-day money back guarantee.
Not for you? No problem.
30 day money back guarantee.
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Certificate of completion.
|Section 1: Introduction|
Introduction and How to Use the CoursePreview
|Section 2: General Guidelines for Selecting Parametric vs. Non-parametric Tests|
General Guidelines and Characteristics of Non-Parametric Statistics
|Quiz 1||4 questions|
If you're on the fence about whether or not your know your measurement scales, quiz yourself here first. This quiz will test your knowledge on nominal, ordinal, interval, and ratio scales.
|Quiz 2||4 questions|
Tests knowledge on characteristics and assumptions of parametric vs. non-parametric stats.
|Section 3: Analyzing Distributions and Group Variances|
Introduction to Analyzing Distributions
Analyzing Distributions - Example 1 (Correlation)
Analyzing Distributions - Example 2 (Group Data)
Homogeneity (equality) of Variance: Levene's Test (Supplemental Lecture)
Analyzing Distributions and Variances
|Section 4: Mann-Whitney: Two Independent Groups|
Introduction to Mann-Whitney UPreview
Mann-Whitney U Test - Example 1
Mann-Whitney U Test - Example 2
|Section 5: Kruskal-Wallis: Three or More Independent Groups|
Introduction to Kruskal-Wallis
Kruskal-Wallis Test - Example
|Section 6: Wilcoxon: Two Related, Matched, or Repeated Measures|
Introduction to Wilcoxon
Wilcoxon Test - Example 1
Wilcoxon Test - Example 2
|Section 7: Friedman: Three or More Related/Repeated Measures|
Introduction to Friedman Test
Friedman Test - Example
|Section 8: Non-Parametric Correlation: Spearman's Rho|
Introduction to Spearman's Rank Correlation (Spearman's Rho)Preview
Spearman's Rho - Example 1
Spearman's Rho - Example 2Preview
|Section 9: BONUS: Graphing Non-Parametric Data|
Introduction to Box Plots
Boxplots in SPSS: 2 Groups
Boxplots in SPSS: More than 2 Groups
Boxplots in SPSS: Repeated Measures
|Section 10: Course Conclusion|
I’ve taught multiple courses of statistics and research methods at the university level and I am a statistical consultant for researchers and businesses from around the world. I'm a researcher myself and I've published numerous scientific, peer-reviewed articles in top journals across several disciplines. Importantly, I’ve tutored many people over the years in statistics, not only students, but full fledged professionals and PhDs alike.
I’ve encountered many different learning styles in my experience, and so my approach is to present concepts and information in several different ways to help students think about the topics from different perspectives to gain better understanding of the material.