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.
Este curso faz parte do Programa de cursos integrados Design de interaçãoDesign de Interação
Idealização, Execução e Análise de Experimentos
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18,392 já inscritos!
Programa de cursos integrados Design de interaçãoDesign de InteraçãoUniversidade da Califórnia, San Diego
Informações sobre o curso
14,393
14,393Habilidades que você terá
ExperimentExperimental DesignStatistical ModelR ProgrammingStatistics
Programa - O que você aprenderá com este curso
Semana
11 hora para concluir
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 usability, its participants, apparatus, procedure, and design & analysis. This module covers lecture videos 1-2.
2 vídeos (total de (Total 19 mín.) min), 1 leitura, 1 teste
1 leituras
ALL COURSE MATERIALS10min
1 exercício prático
Understanding the Basics16min
Semana
22 horas para concluir
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 vídeos (total de (Total 41 mín.) min), 2 testes
7 videos
04. Getting Started with R and RStudio2min
05. Exploring Data and a First Test of Proportions7min
06. Understanding and Reporting Your First Statistical Test5min
07. Exact Tests, Asymptotic Tests, and the Binomial Test2min
08. More One-Sample Tests of Proportions9min
09. Two-Sample Tests of Proportions11min
2 exercícios práticos
Understanding Tests of Proportions36min
Doing Tests of Proportions30min
Semana
31 hora para concluir
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 vídeos (total de (Total 25 mín.) min), 2 testes
2 videos
11. Analyzing a Simple A/B Test with a T-Test10min
2 exercícios práticos
Understanding Experiment Designs20min
Doing Independent-Samples T-Tests30min
Semana
42 horas para concluir
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 vídeos (total de (Total 41 mín.) min), 2 testes
4 videos
13. Data Assumptions and Distributions14min
14. Testing for ANOVA Assumptions9min
15. Mann-Whitney, a Nonparametric T-Test2min
2 exercícios práticos
Understanding Validity24min
Doing Tests of Assumptions30min
Semana
51 hora para concluir
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 vídeos (total de (Total 14 mín.) min), 2 testes
3 videos
17. Analyzing and Reporting a Oneway ANOVA9min
18. Kruskal-Wallis, a Nonparametric Oneway ANOVA3min
2 exercícios práticos
Understanding Oneway Designs12min
Doing Oneway ANOVAs30min
Semana
62 horas para concluir
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 vídeos (total de (Total 49 mín.) min), 2 testes
5 videos
20. Counterbalancing Repeated Measures Factors8min
21. Long-Format and Wide-Format Data Tables3min
22. The Paired T-Test and Wilcoxon Signed-Rank Test14min
23. Analyzing a Repeated Measures ANOVA and Friedman Test15min
2 exercícios práticos
Understanding Oneway Repeated Measures Designs20min
Doing Oneway Repeated Measures ANOVAs30min
Semana
73 horas para concluir
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 vídeos (total de (Total 40 mín.) min), 2 testes
4 videos
25. Understanding Interaction Effects5min
26. Analyzing a Factorial ANOVA12min
27. The ART, a Nonparametric Factorial ANOVA14min
2 exercícios práticos
Understanding Factorial Designs20min
Doing Factorial ANOVAs30min
Semana
81 hora para concluir
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 vídeos (total de (Total 25 mín.) min), 2 testes
2 videos
29. Analyzing Three Generalized Linear Models18min
2 exercícios práticos
Understanding Generalized Linear Models20min
Doing Generalized Linear Models30min
Semana
92 horas para concluir
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 vídeos (total de (Total 35 mín.) min), 2 testes
4 videos
31. Analyzing a Linear Mixed Model11min
32. Analyzing a Generalized Linear Mixed Model10min
33. Course in Review2min
2 exercícios práticos
Understanding Mixed Effects Models24min
Doing Mixed Effects Models30min
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21%
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Melhores avaliações
This was really useful. The course was well structured and provided excellent real-life examples that are easily transferrable to other scenarios. Keep it up!
Have to watch three times to keep up because it is really hard for people without former experiences... Anyway it is worth it
Instrutores
Scott Klemmer
ProfessorCognitive Science & Computer Science
Jacob O. Wobbrock
ProfessorThe Information School
Sobre Universidade da Califórnia, 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.
Sobre o Programa de cursos integrados Design de interaçãoDesign de Interação
Você aprenderá como desenvolver tecnologias deixam seus usuários satisfeitos, e não frustrados. Aprenderá também como gerar idéias de design, técnicas de prototipagem rápida para os novos projetos, e como usar protótipos para obter feedback dos seus companheiros de equipe, clientes e usuários. Você também vai aprender princípios do design visual, da percepção e cognição que conferem ao projeto características eficazes de interação.
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