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.
Overview
Syllabus
- Research Designs and Data Sources
- The first course in the specialization provides an overview of the topics to come. This module walks you through the process of data collection and analysis. Starting with a research question and a review of existing data sources, we cover survey data collection techniques, highlight the importance of data curation, and discuss some basic features that can affect your data analysis when dealing with sample data. Issues of data access and resources for access are introduced in this module.
- Measurements and Analysis Plan
- In this module we will emphasize the importance of having a well-specified research question and analysis plan. We will provide an overview over the various data collection strategies, a variety of available modes for data collection and some thinking on how to choose the right mode.
- Quality Framework
- In this module you will be introduced to a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also helps 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.
- Application of TSE Framework to Existing Surveys
- In this module we introduce a few surveys across a variety of topics. For each we highlight data collection features. The surveys span a variety of topics. We challenge you to think about alternative data sources that can be used to gather the same information or insights.
Taught by
Frauke Kreuter, Ph.D.
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Reviews
4.0 rating, based on 3 Class Central reviews
4.2 rating at Coursera based on 744 ratings
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This course greatly helped me in polishing and/or fine-tuning my understanding on the science and implementation of survey research, especially on topics/issues/considerations that usually give little attention to when participating in such research…
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A good overview of surveys and data collection methods. The Total Survey Error framework is good, but its application isn't shown as strongly as it should be. Regardless this is a good primer for future material.
