Access Resources

Loading...

來自 University of Maryland, College Park 的課程

Framework for Data Collection and Analysis

257 個評分

University of Maryland, College Park

Framework for Data Collection and Analysis

257 個評分

課程 1（共 7 門，Specialization Survey Data Collection and Analytics ）

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.

從本節課中

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.

Access Issues3:10

Access Resources3:44

與講師見面

Frauke Kreuter, Ph.D.
Professor, Joint Program in Survey Methodology
Adjunct Research Professor, Institute for Social Research
Mariel Leonard
Lecturer
Joint Program in Survey Methodology

>> Now a lot of the data that researchers collect, survey data,

are de-identified in the last segment when we talk about record linkage and

data analysis, but also in the imputation set up.

We'll point to ways on how to de-identify data,

these techniques can be used for that very same purpose.

And then the survey data or experimental data are put into data archives.

This data archives is certainly worth visiting, and

increasingly has administrative data in it as well.

Likewise here, the Data Archives in the U.S.,

ICPSR is a very large one for the social sciences,

super popular, has been around for years, and a similar one in Germany.

Now, this is my vocation bias, providing you with these examples.

I'm sure that you guys can for your own country, scoop out those datasets or

data archives and share that with the community taking this course.

What is interesting is that increasingly federal agency and

national institutes that collect data, well actually even organizations like

Federal Employment Agency in Germany that aren't part of the ones that collect

the data as part of their administrative processes, make these data available for

researchers in their own country or across the world.

My current half employer, the Institute for

Employment Research in Germany has put out these research data centers that allow

accessing increasingly remote access to the data that are housed there.

Obviously entirely de-identified,

or with strong restriction on who can do that and how.

But it is possible, and therefore, a rich resource for all of you when you do

an analysis, and I can only recommend visiting these various websites.

Data.gov is a website.

If you haven't come across it you should definitely visit, and

I'm aware that not all countries can access .gov websites.

I apologize for those that can't, and

I hope that will change for you in some form.

But other countries have similar databases as well.

This is a collection of government data available, and

Eurostat similarly puts out a lot of data.

Maybe not quite as many microdata that you can reanalyze for your own purposes, but

certainly tables and aggregate figures that you can download as well.

And the amount of stuff that's out there is updated all the time.

What's nice about these kind of websites is

they often pair the data up with message reports, so that you do really know

enough about the data generating process that we talked about before.

In general, there's a big movement to open

data, open data forum, open data access.

A lot of countries are increasingly subscribed to that because those running

certain agencies or making policy decision,

there are not enough people to analyze all these data.

They rely on you to actually take this data and

create good insights out of them.

Data without boundaries is an interesting initiative that tried to,

at least within Europe and beyond,

to make this access of datasets easier in the exchange as well.

探索我們的目錄

免費加入並獲得個性化推薦、更新和優惠。

Coursera 致力於普及全世界最好的教育，它與全球一流大學和機構合作提供在線課程。

© 2018 Coursera Inc. 保留所有權利。

通過 App Store 下載

通過 Google Play 獲取