Quality of Data

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En provenance du cours de University of Maryland, College Park

Framework for Data Collection and Analysis

301 notes

University of Maryland, College Park

Framework for Data Collection and Analysis

301 notes

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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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.

Quality of Data5:01

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

Welcome in module three in our course, Framework for

Data Collection and Analysis.

In this third module, we'll introduce

a quality perspective on data products.

A good resource to start wherever you are in the world,

is the EUROSTAT Handbook on Quality Assessment Methods and Tools.

It's a beautiful document that has a lot of general guidelines

on how to think about data quality and quality of a statistic that you produce.

This visualization here shows you elements of quality management systems.

And each of these pieces has a corresponding principal for

the European Statistics Code of Practice.

You can download this document freely and I highly recommend taking a look,

in particular, if you're involved in statistic production, the gamut setting.

Shorter summary you find here in

a piece by Paul Beamer in Public Opinion Quarterly.

It catches the most important

categories to think about when you think about data quality.

One is accuracy.

And accuracy is something this total survey error framework,

that we're gonna introduce in a moment, is designed to minimize.

It's really all honed in on accuracy.

However, there are other elements to quality as well.

For example, you want your data to be credible.

So, it does matter who puts out the data and

whether this is seen as a trustworthy entity by the community.

Not just people using surveys, but government officials,

public spokespersons, private companies, enterprises, any of those.

Another aspect of quality is comparability.

In particular, if you're interested in long time series or

comparing data across countries to make investment decisions or policy decisions,

you want to make sure you know that you're comparing similar things.

They can be accurate and good, each in their own way.

But, they might lack comparability,

because different things are measured, for example.

So that's something to keep in mind.

Usability is another aspect.

Is the data documentation and

accessibility in a way organized that does allow easy use?

Relevance is an important one.

We don't wanna invest money in collecting data or designing data and

even scraping found data, that lack relevance.

You are in charge of demonstrating that relevance or

at least having that communication with stakeholders on the relevance.

Accessibility, I already mentioned, and we discussed this earlier

the importance of the data foundation and the research data centers.

Timeliness is an interesting aspect.

Some statistics have fix reporting rates, we'll show you an example in module four.

Data delivery's on a schedule is helpful.

And some of the data sources that you might wanna use don't have this aspect.

Also completeness, is the data rich enough, so that you can

really follow through with the analysis objective that we discussed in module two?

And are the estimates coherent?

Can you combine those data and the results with other data sources.

Was there mismatch that will automatically confuse any user of US statistics.

The various frameworks here.

One is that of Continuous Quality Improvement.

Also discussed in Paul Beamer's paper.

And a suggestion on making sure that you have quality always at

the forefront when you produce your statistic or the data.

First piece here is, preparation of a workflow diagram that can

help to visualize the process and identify key process variables.

Those process variables can be identified and monitored over time,

and if need be, changes introduced to improve the process.

You wanna make sure that you have control over your process,

and for those of you who really want to dive deep into that literature,

there's a whole segment on process control, quality control.

All these keywords here are represented in a flowchart that Morganstein and

Marker developed.

Quite useful to think about and to look at.

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