Non-Parametric Analysis
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Non-Parametric Analysis

A Step-by-Step Guide to Non-Parametric Statistics in SPSS
4.4 (23 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
352 students enrolled
Created by Drew Birnie
Last updated 9/2015
Current price: $12 Original price: $20 Discount: 40% off
4 days left at this price!
30-Day Money-Back Guarantee
  • 4 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I Learn?
  • Quickly grasp basic principles of each test with this straightforward approach
  • Learn when to use non-parametric vs. parametric tests
  • Be able to select, conduct, interpret, and display results from non-parametric tests using SPSS.
  • Evaluate the distribution and the variance (variability) of a data set both graphically and statistically
  • Learn how to report non-parametric results in APA format
  • Step-by-step instructions on how to conduct the most common types of non-parametric tests, including Mann-Whitney U, Kruskal-Wallis, Wilcoxon Signed Rank, Friedman Test, and Spearman's Rho.
  • Learn to create and modify quality box plots commonly used to display non-parametric data
View Curriculum
  • At least some familiarity with basic statistics: distributions, hypothesis testing, p-values, confidence intervals, etc.
  • SPSS is definitely recommended, especially if you want to follow along and analyze the sample data sets yourself. However, since we go over the principles of each test in separate lessons from the examples, it is certainly possible to gain a solid understanding of the subject material without the use of SPSS.

In your research, have you ever encountered one or more the following scenarios:

  • survey data
  • non-linear data
  • “chunked” data (1-4 cm, 2-5 cm, >5 cm…)
  • qualitative judgments measured on a ratings scale
  • data that don’t follow a normal distribution (this is a big one)
  • data that violate assumptions of ANOVA (what were those assumptions again…?)

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.

Who is the target audience?
  • This course is best suited to students who have at least some basic knowledge of statistics. Intermediate to advanced students, who have a good grasp of conducting parametric statistics, can augment their skills by learning how to select, conduct, interpret, and display non-parametric statistics in SPSS.
  • In each lesson, we begin with a video and supplementary material to introduce the principles of a non-parametric test. We then use separate videos to go over examples of each test, which allows students to focus their time on the material they need most. So, whether you're new to non-parametric tests and want an end-to-end guide in tackling the subject, or you want to learn how to conduct and interpret the tests in SPSS, this course is certainly adaptable to your needs.
Compare to Other SPSS Courses
Curriculum For This Course
24 Lectures
1 Lecture 03:51
General Guidelines for Selecting Parametric vs. Non-parametric Tests
1 Lecture 14:55
General Guidelines and Characteristics of Non-Parametric Statistics

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.

Measurement Scales
4 questions

Tests knowledge on characteristics and assumptions of parametric vs. non-parametric stats.

Parametric vs. Non-Parametric Tests
4 questions
Analyzing Distributions and Group Variances
4 Lectures 51:33
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
3 questions
Mann-Whitney: Two Independent Groups
3 Lectures 29:14

Mann-Whitney U Test - Example 1

Mann-Whitney U Test - Example 2
Kruskal-Wallis: Three or More Independent Groups
2 Lectures 23:29
Introduction to Kruskal-Wallis

Kruskal-Wallis Test - Example
Wilcoxon: Two Related, Matched, or Repeated Measures
3 Lectures 29:00
Introduction to Wilcoxon

Wilcoxon Test - Example 1

Wilcoxon Test - Example 2
Friedman: Three or More Related/Repeated Measures
2 Lectures 28:46
Introduction to Friedman Test

Friedman Test - Example
Non-Parametric Correlation: Spearman's Rho
3 Lectures 24:43
BONUS: Graphing Non-Parametric Data
4 Lectures 22:44
Introduction to Box Plots

Boxplots in SPSS: 2 Groups

Boxplots in SPSS: More than 2 Groups

Boxplots in SPSS: Repeated Measures
Course Conclusion
1 Lecture 01:03
About the Instructor
Drew Birnie
4.4 Average rating
23 Reviews
352 Students
1 Course
Academic, Tutor, and Consultant

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.