Non-Parametric Analysis

A Step-by-Step Guide to Non-Parametric Statistics in 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 basicnon-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.

What are the requirements?

• 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.

What am I going to get from this course?

• Over 24 lectures and 4 hours of content!
• 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

What 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.

Curriculum
Section 1: Introduction
Lecture 1 Introduction and How to Use the Course 03:51
Section 2: General Guidelines for Selecting Parametric vs. Non-parametric Tests
Lecture 2 General Guidelines and Characteristics of Non-Parametric Statistics 14:55
Quiz 1 Measurement Scales 4 questions
Quiz 2 Parametric vs. Non-Parametric Tests 4 questions
Section 3: Analyzing Distributions and Group Variances
Lecture 3 Introduction to Analyzing Distributions 08:38
Lecture 4 Analyzing Distributions - Example 1 (Correlation) 15:38
Lecture 5 Analyzing Distributions - Example 2 (Group Data) 13:41
Lecture 6 Homogeneity (equality) of Variance: Levene's Test (Supplemental Lecture) 13:36
Quiz 3 Analyzing Distributions and Variances 3 questions
Section 4: Mann-Whitney: Two Independent Groups
Lecture 7 Introduction to Mann-Whitney U 09:48
Lecture 8 Mann-Whitney U Test - Example 1 09:15
Lecture 9 Mann-Whitney U Test - Example 2 10:11
Section 5: Kruskal-Wallis: Three or More Independent Groups
Lecture 10 Introduction to Kruskal-Wallis 07:11
Lecture 11 Kruskal-Wallis Test - Example 16:18
Section 6: Wilcoxon: Two Related, Matched, or Repeated Measures
Lecture 12 Introduction to Wilcoxon 09:58
Lecture 13 Wilcoxon Test - Example 1 08:18
Lecture 14 Wilcoxon Test - Example 2 10:44
Section 7: Friedman: Three or More Related/Repeated Measures
Lecture 15 Introduction to Friedman Test 09:31
Lecture 16 Friedman Test - Example 19:15
Section 8: Non-Parametric Correlation: Spearman's Rho
Lecture 17 Introduction to Spearman's Rank Correlation (Spearman's Rho) 10:53
Lecture 18 Spearman's Rho - Example 1 05:26
Lecture 19 Spearman's Rho - Example 2 08:24
Section 9: BONUS: Graphing Non-Parametric Data
Lecture 20 Introduction to Box Plots 02:49
Lecture 21 Boxplots in SPSS: 2 Groups 12:45
Lecture 22 Boxplots in SPSS: More than 2 Groups 02:45
Lecture 23 Boxplots in SPSS: Repeated Measures 04:25
Section 10: Course Conclusion
Lecture 24 Conclusion 01:03







For More Rare Movies Check out my blog!

No Mirrors Please