Designing, Running, and Analyzing Experiments
- Offered byCoursera
Designing, Running, and Analyzing Experiments at Coursera Overview
Duration | 15 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Designing, Running, and Analyzing Experiments at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 7 of 8 in the Interaction Design Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 15 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Portuguese (Brazilian), Vietnamese, German, Russian, English, Spanish
Designing, Running, and Analyzing Experiments at Coursera Course details
- You may never be sure whether you have an effective user experience until you have tested it with users. In this course, you?ll learn how to design user-centered experiments, how to run such experiments, and how to analyze data from these experiments in order to evaluate and validate user experiences. You will work through real-world examples of experiments from the fields of UX, IxD, and HCI, understanding issues in experiment design and analysis. You will analyze multiple data sets using recipes given to you in the R statistical programming language -- no prior programming experience is assumed or required, but you will be required to read, understand, and modify code snippets provided to you. By the end of the course, you will be able to knowledgeably design, run, and analyze your own experiments that give statistical weight to your designs.
Designing, Running, and Analyzing Experiments at Coursera Curriculum
Basic Experiment Design Concepts
01. What You Will Learn in this Course
02. Basic Experiment Design Concepts
ALL COURSE MATERIALS
Understanding the Basics
Tests of Proportions
03. Description of a Study of User Preferences
04. Getting Started with R and RStudio
05. Exploring Data and a First Test of Proportions
06. Understanding and Reporting Your First Statistical Test
07. Exact Tests, Asymptotic Tests, and the Binomial Test
08. More One-Sample Tests of Proportions
09. Two-Sample Tests of Proportions
Understanding Tests of Proportions
Doing Tests of Proportions
The T-Test
10. Experiment Design Concepts in a Simple A/B Test
11. Analyzing a Simple A/B Test with a T-Test
Understanding Experiment Designs
Doing Independent-Samples T-Tests
Validity in Design and Analysis
12. Designing for Experimental Control
13. Data Assumptions and Distributions
14. Testing for ANOVA Assumptions
15. Mann-Whitney, a Nonparametric T-Test
Understanding Validity
Doing Tests of Assumptions
One-Factor Between-Subjects Experiments
16. Description of a Study for a Oneway ANOVA
17. Analyzing and Reporting a Oneway ANOVA
18. Kruskal-Wallis, a Nonparametric Oneway ANOVA
Understanding Oneway Designs
Doing Oneway ANOVAs
One-Factor Within-Subjects Experiments
19. Description of a Study for a Oneway Repeated Measures ANOVA
20. Counterbalancing Repeated Measures Factors
21. Long-Format and Wide-Format Data Tables
22. The Paired T-Test and Wilcoxon Signed-Rank Test
23. Analyzing a Repeated Measures ANOVA and Friedman Test
Understanding Oneway Repeated Measures Designs
Doing Oneway Repeated Measures ANOVAs
Factorial Experiment Designs
24. Description of a Study for a Factorial ANOVA
25. Understanding Interaction Effects
26. Analyzing a Factorial ANOVA
27. The ART, a Nonparametric Factorial ANOVA
Understanding Factorial Designs
Doing Factorial ANOVAs
Generalizing the Response
28. Introduction to Generalized Linear Models
29. Analyzing Three Generalized Linear Models
Understanding Generalized Linear Models
Doing Generalized Linear Models
The Power of Mixed Effects Models
30. Introduction to Mixed Effects Models
31. Analyzing a Linear Mixed Model
32. Analyzing a Generalized Linear Mixed Model
33. Course in Review
Understanding Mixed Effects Models
Doing Mixed Effects Models
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