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**Non-Parametric Analysis**

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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 1Introduction and How to Use the Course03:51

Section 2: General Guidelines for Selecting Parametric vs. Non-parametric Tests

Lecture 2General Guidelines and Characteristics of Non-Parametric Statistics14:55

Quiz 1Measurement Scales4 questions

Quiz 2Parametric vs. Non-Parametric Tests4 questions

Section 3: Analyzing Distributions and Group Variances

Lecture 3Introduction to Analyzing Distributions08:38

Lecture 4Analyzing Distributions - Example 1 (Correlation)15:38

Lecture 5Analyzing Distributions - Example 2 (Group Data)13:41

Lecture 6Homogeneity (equality) of Variance: Levene's Test (Supplemental Lecture)13:36

Quiz 3Analyzing Distributions and Variances3 questions

Section 4: Mann-Whitney: Two Independent Groups

Lecture 7Introduction to Mann-Whitney U09:48

Lecture 8Mann-Whitney U Test - Example 109:15

Lecture 9Mann-Whitney U Test - Example 210:11

Section 5: Kruskal-Wallis: Three or More Independent Groups

Lecture 10Introduction to Kruskal-Wallis07:11

Lecture 11Kruskal-Wallis Test - Example16:18

Section 6: Wilcoxon: Two Related, Matched, or Repeated Measures

Lecture 12Introduction to Wilcoxon09:58

Lecture 13Wilcoxon Test - Example 108:18

Lecture 14Wilcoxon Test - Example 210:44

Section 7: Friedman: Three or More Related/Repeated Measures

Lecture 15Introduction to Friedman Test09:31

Lecture 16Friedman Test - Example19:15

Section 8: Non-Parametric Correlation: Spearman's Rho

Lecture 17Introduction to Spearman's Rank Correlation (Spearman's Rho)10:53

Lecture 18Spearman's Rho - Example 105:26

Lecture 19Spearman's Rho - Example 208:24

Section 9: BONUS: Graphing Non-Parametric Data

Lecture 20Introduction to Box Plots02:49

Lecture 21Boxplots in SPSS: 2 Groups12:45

Lecture 22Boxplots in SPSS: More than 2 Groups02:45

Lecture 23Boxplots in SPSS: Repeated Measures04:25

Section 10: Course Conclusion

Lecture 24Conclusion01:03

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