Université du Maryland, College Park

Framework for Data Collection and Analysis

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Cours 1 sur 7 dans 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.

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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

Summary0:58

Rencontrer les enseignants

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

Okay, so the last step here in this Data Output and Access piece,

that I wanna tell you about is mostly on the access part, for now.

Because having access to a lot of data is really what makes

this time exciting for us.

As data analysts, or data scientists, or statisticians or

whatever you wanna call the people that deal with the data.

So much data is publicly available and can be used for the public good.

But of course, there are privacy issues and

challenging ones that we haven't solved yet.

In this book, you find a variety of chapters dealing with the tension

between privacy on the one hand and public goods that can be created on the other.

Between the convenience of having Google tell me that it's about

time to leave my house, so that I will be at work in time,

versus private companies knowing everything about me.

And maybe that data being then later on used for accusations or for

denial, for certain products or services or what have you.

Particularly on insurances and loans,

that is a debate that you see heavily flaring up.

There is a tension between

identifiability and reach.

There is a tension that, even so I might decide not to

put my data out there to Facebook or other places.

Other people that do, with Facebook one can probably say a minority at this

point anymore, but other people that do reminds me a point about me and

therefore privacy is not longer guaranteed either.

There is attention between informed consent, asking people whether they

are billing, and purposefully providing the data.

And the fact that many of these consent statements are really confusing.

I wonder how many of you have ever read when they use their smartphones or

any other electronic device that collects data.

Through all the privacy statements, and really know what that means,

and understood.

Then there's the tension between privacy and data quality,

maybe knowing that these data will be collected, people stop misreporting,

age for example would be an easy one.

But other information probably as well.

There certainly is a tension on which country you talk to, and

I will be interested to hear from you guys on the discussion forum how that is.

That notion of privacy in your country.

Whether people are willingly giving the data up for the public good.

Or if they are more worried about that being abused,

like the Germans are with this tension between big brother and big mother.