关于此课程： This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data sources likely matches your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. Furthermore this course will provide you with a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also help you to identify errors associated with different data sources. You will learn some metrics to quantify each potential error, and thus you will have tools at hand to describe the quality of a data source. Finally we will introduce different large scale data collection efforts done by private industry and government agencies, and review the learned concepts through these examples. This course is suitable for beginners as well as those that know about one particular data source, but not others, and are looking for a general framework to evaluate data products.
教学方： Frauke Kreuter, Ph.D., Professor, Joint Program in Survey Methodology
Adjunct Research Professor, Institute for Social Research
教学方： Mariel Leonard, Lecturer
Joint Program in Survey Methodology
| 基本信息 | 课程 1（共 7 门，Survey Data Collection and Analytics Specialization ） |
| 级别 | Intermediate |
| 承诺学习时间 | 4 weeks of study, 1-2 hours/week |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
- Leyendo: Course Overview
- Leyendo: Readings and Resources List
- 视频: Research Question Design
- Cuadro de aviso de la discusión: Discussion Prompt: Your own experience
- 视频: Types of Data
- 视频: Examples of Found Data
- 视频: Visualizing the Data Generation Process
- 视频: Data Curation
- 视频: Data Analysis
- 视频: Access Issues
- 视频: Access Resources
- 视频: Summary
- Cuadro de aviso de la discusión: Discussion Prompt: Privacy
- Leyendo: Handouts
- Leyendo: AAPOR (2015)
- Leyendo: Couper (2013)
- 视频: Issues with Inductive Reasoning
- 视频: Planning on What You Want to Observe
- 视频: Planning on How to Collect Data
- 视频: New Modes
- 视频: Web and Google
- 视频: Choosing a Mode
- Leyendo: Handouts
- Leyendo: Jäckle et al. (2015)
- 视频: Quality of Data
- 视频: Inference
- 视频: Survey Life Cycle from a Design Perspective - Measurement
- 视频: Survey Life Cycle from a Design Perspective - Representation
- 视频: Survey Lifecycle from a Process Perspective
- 视频: Survey Lifeycle from a Quality Perspective
- 视频: Survey Lifecycle from a Quality Perspective (II) - Metrics
- 视频: Survey Lifecycle from a Quality Perspective (III) - Coverage and Sampling
- Leyendo: Handouts
- Leyendo: Groves (2011)
- Leyendo: Groves & Lyberg (2010)
- 视频: NCVS
- 视频: NSDUH
- 视频: SCA
- 视频: NAEP
- 视频: BRFSS
- 视频: CES
- Cuadro de aviso de la discusión: Discussion Prompt: Alternative Data Sources
- 视频: SHARE
- 视频: ESS
- Cuadro de aviso de la discusión: Discussion prompt: In your country
- Leyendo: Handouts
- Leyendo: Davidov (2008)
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
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| 访问课程材料 | 可用 |
| 访问评分的材料 | 可用 |
| 收到最终成绩 | 可用 |
| 获得可共享的课程证书 | 可用 |
Very clear and organised structure
Clear examples to support learning objectives
Attractive use of voice
One point for improvement: Do not film while the lecturer is still refinding breath
Helped me to gain a sense of what the survey is and the overall data collection process using surveys
Overall I found the course to be really useful, and I feel confident moving into the next module. I would have benefitted from having more practical examples given to help some of the terminology sink in a little better, but I think that speaks more to my unfamiliarity with the language than with the lecturer's methods.
very enjoyable. Excellent scene-setter for more in depth treatments of other aspects of survey-led quant research.