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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/designexperiments
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课程名称：设计、运行和分析实验 课程概述：在本课程中，您将学习如何设计以用户为中心的实验，如何运行这些实验，以及如何分析实验数据，以评估和验证用户体验。通过实际案例，您将深入理解实验设计和分析中存在的问题。课程中将使用R统计编程语言分析多个数据集，您无需提前具备编程经验，但需要阅读、理解并修改提供的代码片段。完成课程后，您将能够熟练设计、运行和分析自己的实验，为您的设计提供统计依据。 课程大纲： 1. **基本实验设计概念** 本模块介绍实验设计和分析的基本概念，包括均值比较、方差、统计显著性、实际显著性、抽样、纳入与排除标准以及知情同意。您将学习如何从可用性、参与者、设备、程序及设计与分析的角度思考实验。 2. **比例检验** 本模块教授如何使用比例检验分析用户偏好（或其他统计数据）。您将熟悉R和RStudio，包括独立和依赖变量、变量类型、探索性数据分析、p值、渐近检验、确切检验、一样本检验、双样本检验、卡方检验、G检验、费舍尔确切检验等。 3. **t检验** 本模块内容涉及简单网站A/B测试的设计与分析，包括测量误差、独立变量作为因子、因子水平等。您将执行第一次独立样本t检验的方差分析。 4. **设计与分析的有效性** 本模块讨论如何通过实验设计确保数据的有效性以及通过理解和验证假设来确保分析的有效性，涵盖实验控制、混杂变量、生态有效性等主题。 5. **单因子间接实验** 本模块聚焦于单因子间接实验，分析不同编程工具的任务完成时间，使用多种统计检验方法如独立样本t检验和方差分析等。 6. **单因子重复测量实验** 本模块探讨单因子重复测量实验，包括在智能手机联系人管理器中搜索联系人的实验，学习如何使用配对样本t检验和重复测量方差分析等方法。 7. **因子实验设计** 本模块介绍多因子实验和因子方差分析，分析在不同状态下（坐、立、走）使用智能手机键盘的文本输入性能。 8. **响应的推广** 本模块涉及分析非正态或非数值响应的广义线性模型（GLM），并重新分析之前的实验。 9. **混合效应模型的力量** 本模块介绍线性混合模型（LMM）和广义线性混合模型（GLMM），通过对文本输入性能的实验进行更全面的分析，复习课程内涵盖的分析内容。 此课程将为您提供在UX、IxD和HCI领域进行实验设计与分析的实用技能，帮助您更好地理解用户体验。
Name:Basic Experiment Design Concepts
Description: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.
Name:Tests of Proportions
Description: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.
Name:The T-Test
Description: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.
Name:Validity in Design and Analysis
Description: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.
Name:One-Factor Between-Subjects Experiments
Description: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.
Name:One-Factor Within-Subjects Experiments
Description: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.
Name:Factorial Experiment Designs
Description: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.
Name:Generalizing the Response
Description: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.
Name:The Power of Mixed Effects Models
Description: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.
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.
