À propos de ce cours : 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.
Les destinataires de ce cours : This class is at the level of advanced undergraduates or first-year graduate students in fields related to human-computer interaction, computer science, interaction design, user experience design, user-centered design, or human-technology behavior studies.
Créé par : University of California San Diego
Enseigné par : Scott Klemmer, Professor
Cognitive Science & Computer Science
Enseigné par : Jacob O. Wobbrock, Professor
The Information School
| Informations de base | Cours 7 de 8 dans Interaction Design Specialization |
| Niveau | Intermediate |
| Engagement | 9 weeks, 8-10 hours/week |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
Basic Experiment Design Concepts
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 its participants, apparatus, procedure, and design & analysis. This module covers lecture videos 1-2.
2 vidéos, 1 lecture
- Reading: All Course Materials
- Vidéo: 02. Basic Experiment Design Concepts
Noté: Understanding the Basics
SEMAINE 2
Tests of Proportions
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.
7 vidéos
- Vidéo: 04. Getting Started with R and RStudio
- Vidéo: 05. Exploring Data and a First Test of Proportions
- Vidéo: 06. Understanding and Reporting Your First Statistical Test
- Vidéo: 07. Exact Tests, Asymptotic Tests, and the Binomial Test
- Vidéo: 08. More One-Sample Tests of Proportions
- Vidéo: 09. Two-Sample Tests of Proportions
Noté: Understanding Tests of Proportions
Noté: Doing Tests of Proportions
SEMAINE 3
The T-Test
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.
2 vidéos
- Vidéo: 11. Analyzing a Simple A/B Test with a T-Test
Noté: Understanding Experiment Designs
Noté: Doing Independent-Samples T-Tests
SEMAINE 4
Validity in Design and Analysis
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 their 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.
4 vidéos
- Vidéo: 13. Data Assumptions and Distributions
- Vidéo: 14. Testing for ANOVA Assumptions
- Vidéo: 15. Mann-Whitney, a Nonparametric T-Test
Noté: Understanding Validity
Noté: Doing Tests of Assumptions
SEMAINE 5
One-Factor Between-Subjects Experiments
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.
3 vidéos
- Vidéo: 17. Analyzing and Reporting a Oneway ANOVA
- Vidéo: 18. Kruskal-Wallis, a Nonparametric Oneway ANOVA
Noté: Understanding Oneway Designs
Noté: Doing Oneway ANOVAs
SEMAINE 6
One-Factor Within-Subjects Experiments
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, oneway repeated measures ANOVA, and Friedman test. This module covers lecture videos 19-23.
5 vidéos
- Vidéo: 20. Counterbalancing Repeated Measures Factors
- Vidéo: 21. Long-Format and Wide-Format Data Tables
- Vidéo: 22. The Paired T-Test and Wilcoxon Signed-Rank Test
- Vidéo: 23. Analyzing a Repeated Measures ANOVA and Friedman Test
Noté: Understanding Oneway Repeated Measures Designs
Noté: Doing Oneway Repeated Measures ANOVAs
SEMAINE 7
Factorial Experiment Designs
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.
4 vidéos
- Vidéo: 25. Understanding Interaction Effects
- Vidéo: 26. Analyzing a Factorial ANOVA
- Vidéo: 27. The ART, a Nonparametric Factorial ANOVA
Noté: Understanding Factorial Designs
Noté: Doing Factorial ANOVAs
SEMAINE 8
Generalizing the Response
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.
2 vidéos
- Vidéo: 29. Analyzing Three Generalized Linear Models
Noté: Understanding Generalized Linear Models
Noté: Doing Generalized Linear Models
SEMAINE 9
The Power of Mixed Effects Models
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.
4 vidéos
- Vidéo: 31. Analyzing a Linear Mixed Model
- Vidéo: 32. Analyzing a Generalized Linear Mixed Model
- Vidéo: 33. Course in Review
Noté: Understanding Mixed Effects Models
Noté: Doing Mixed Effects Models
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Créateurs
University of California San Diego
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
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