University of California San Diego
Designing, Running, and Analyzing Experiments
University of California San Diego

Designing, Running, and Analyzing Experiments

This course is part of Interaction Design Specialization

Taught in English

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Scott  Klemmer
Jacob O. Wobbrock

Instructors: Scott Klemmer

30,983 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

3.6

(585 reviews)

|

77%

Intermediate level
Some related experience required
15 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

17 quizzes

Course

Gain insight into a topic and learn the fundamentals

3.6

(585 reviews)

|

77%

Intermediate level
Some related experience required
15 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the Interaction Design Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 9 modules in this course

In this module, you will learn basic concepts relevant to the design and analysis of experiments, including mean comparisons, variance, statistical significance, practical significance, sampling, inclusion and exclusion criteria, and informed consent. You’ll also learn to think of an experiment in terms of usability, its participants, apparatus, procedure, and design & analysis. This module covers lecture videos 1-2.

What's included

2 videos1 reading1 quiz

In this module, you will learn how to analyze user preferences (or other tallies) using tests of proportions. You will also get up and running with R and RStudio. Topics covered include independent and dependent variables, variable types, exploratory data analysis, p-values, asymptotic tests, exact tests, one-sample tests, two-sample tests, Chi-Square test, G-test, Fisher’s exact test, binomial test, multinomial test, post hoc tests, and pairwise comparisons. This module covers lecture videos 3-9.

What's included

7 videos2 quizzes

In this module, you will learn how to design and analyze a simple website A/B test. Topics include measurement error, independent variables as factors, factor levels, between-subjects factors, within-subjects factors, dependent variables as responses, response types, balanced designs, and how to report a t-test. You will perform your first analysis of variance in the form of an independent-samples t-test. This module covers lecture videos 10-11.

What's included

2 videos2 quizzes

In this module, you will learn about how to ensure that your data is valid through the design of experiments, and that your analyses are valid by understanding and testing for certain assumptions. Topics include how to achieve experimental control, confounds, ecological validity, the three assumptions of ANOVA, data distributions, residuals, normality, homoscedasticity, parametric versus nonparametric tests, the Shapiro-Wilk test, the Kolmogorov-Smirnov test, Levene’s test, the Brown-Forsythe test, and the Mann-Whitney U test. This module covers lecture videos 12-15.

What's included

4 videos2 quizzes

In this module, you will learn about one-factor between-subjects experiments. The experiment examined will be a between-subjects study of task completion time with various programming tools. You will understand and analyze data from two-level factors and three-level factors using the independent-samples t-test, Mann-Whitney U test, one-way ANOVA, and Kruskal-Wallis test. You will learn how to report an F-test. You will also understand omnibus tests and how they relate to post hoc pairwise comparisons with adjustments for multiple comparisons. This module covers lecture videos 16-18.

What's included

3 videos2 quizzes

In this module, you will learn about one-factor within-subjects experiments, also known as repeated measures designs. The experiment examined will be a within-subjects study of subjects searching for contacts in a smartphone contacts manager, including the analysis of times, errors, and effort Likert-type scale ratings. You will learn counterbalancing strategies to avoid carryover effects, including full counterbalancing, Latin Squares, and balanced Latin Squares. You will understand and analyze data from two-level factors and three-level factors using the paired-samples t-test, Wilcoxon signed-rank test, one-way repeated measures ANOVA, and Friedman test. This module covers lecture videos 19-23.

What's included

5 videos2 quizzes

In this module, you will learn about experiments with multiple factors and factorial ANOVAs. The experiment examined will be text entry performance on different smartphone keyboards while sitting, standing, and walking. Topics include mixed factorial designs, interaction effects, factorial ANOVAs, and the Aligned Rank Transform as a nonparametric factorial ANOVA. This module covers lecture videos 24-27.

What's included

4 videos2 quizzes

In this module, you will learn about analyses for non-normal or non-numeric responses for between-subjects experiments using Generalized Linear Models (GLM). We will revisit three previous experiments and analyze them using generalized models. Topics include a review of response distributions, nominal logistic regression, ordinal logistic regression, and Poisson regression. This module covers lecture videos 28-29.

What's included

2 videos2 quizzes

In this module, you will learn about mixed effects models, specifically Linear Mixed Models (LMM) and Generalized Linear Mixed Models (GLMM). We will revisit our prior experiment on text entry performance on smartphones but this time, keeping every single measurement trial as part of the analysis. The full set of analyses covered in this course will also be reviewed. This module covers lecture videos 30-33.

What's included

4 videos2 quizzes

Instructors

Instructor ratings
4.0 (59 ratings)
Scott  Klemmer
University of California San Diego
8 Courses219,383 learners
Jacob O. Wobbrock
University of California San Diego
1 Course30,983 learners

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3.6

585 reviews

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RR
4

Reviewed on Feb 26, 2019

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4

Reviewed on Jun 16, 2016

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