2.7 Sources of aggregated data

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From the course by The Hong Kong University of Science and Technology

Social Science Approaches to the Study of Chinese Society Part 2

9 ratings

The Hong Kong University of Science and Technology

Social Science Approaches to the Study of Chinese Society Part 2

9 ratings

This course is intended as a first step for learners who seek to become producers of social science research. It is organized as an introduction to the design and execution of a research study. It introduces the key elements of a proposal for a research study, and explains the role of each. It reviews the major types of qualitative and quantitative data used in social science research, and then introduces some of the most important sources of existing data available freely or by application, worldwide and for China. The course offers an overview of basic principles in the design of surveys, including a brief introduction to sampling. Basic techniques for quantitative analysis are also introduced, along with a review of common challenges that arise in the interpretation of results. Professional and ethical issues that often arise in the conduct of research are also discussed. The course concludes with an introduction to the options for further study available to the interested student, and an overview of the key steps involved in selecting postgraduate programs and applying for admission. Learners who complete the course will be able to make an informed decision about whether to pursue advanced studies, and should be adequately prepared to write an application for postgraduate study that exhibits basic understanding of key aspects of social science research paradigms and methodologies. Explore the big questions in social science and learn how you can be a producer of social science research. Course Overview video: https://youtu.be/QuMOAlwhpvU Part 1 should be completed before taking this course: https://www.coursera.org/learn/social-science-study-chinese-society

From the lesson

Evidence

Week 2 will discuss the kind sources social scientists use for research. By the end of this week, you should be able to identify some of these major sources and perhaps pinpoint some sources that can be used in your own study.

2.1 Evidence3:00

2.2 Survey data12:21

2.3 Public data10:12

2.4. Administrative and archival microdata12:38

2.5. Multi-generational microdata11:21

2.6 Aggregated data12:40

2.7 Sources of aggregated data7:44

2.8 Qualitative sources13:53

2.9 New sources11:21

2.10 Summary2:27

Meet the Instructors

Cameron Campbell
Professor of Social Science
Associate Dean of the School of Humanities and Social Science

[MUSIC]

Hi, in this module, I'm going to introduce some sources of aggregated data.

Places where you can obtain national level indicators relatively straightforwardly,

if you want to start experimenting or trying out with your own analysis.

Or fortunate in that now a days it's very easy to access aggregated data,

national level indicators or even subnational indicators for

provinces and regions via the Internet.

A few decades ago if anyone wanting to make use of this data,

typically have to go to way library and a major research university.

Or in a large city in go to the reference section to obtain statistical year

books or United Nations reports in publication and so forth.

And then transfer that data in to a computer to conduct analysis.

Now a days however, it's accessible to anyone with Internet

access and indeed you can download it on your phone.

Now one of the really important sources of aggregated of data,

perhaps your first visit should be to the World Bank website.

So the World Bank website has a rich trough of indicators,

development indicators on different aspects of economic health, education and

other performance that we consider relevant to economic development.

A related website is the organization for economic cooperation and development.

Which collects statistics of it's own again related to economic development,

but also including other aspects like labor, economics, education, and so forth.

Again everything is accessible at a website with a user friendly interface.

The United Nations has a rich array of demographic indices.

Birth rates, fertility rates, migration rates, marriage rates, and so forth.

As well as a lot of other development indicators, economic indicators, and

so forth related to the ones available at the World Bank or the OECD websites.

If you're interested specifically in health,

the World Health Organization has extensive data reported to it

by member countries on the prevalence of specific diseases.

And then other things that we think are important that are related to health.

For example, certain kind of health care spending,

healthcare infrastructure and so forth.

And then for the environment, there is the UNEP,

the United Nations Environmental Program.

Which is again a rich array of environmental indicators from

United Nations member countries.

Now of course, if you conduct an analysis,

you don't have to restrict yourself to using only one of these sources.

Now a days, it's very common for people to download data from multiple sources.

Because certain variables maybe only available at one location versus another.

So people download data from multiple sources, combine it and

then conduct analysis of the resulting aggregated data.

If you have a specific interest in mainland China, Hong Kong or Taiwan,

you're also very fortunate that now a days much of the key data

are accessible again via the web.

Not like 20 or 30 years ago where you would have to go to a research library,

a reference section to look at actual statistical year books and so forth.

For Mainland China,the National Bureau of Statistics should be your starting point.

There's a ritury of economic and other indicators for

Mainland China available at this website.

As well as links to a number of other government agencies in the Mainland

with more detailed data on specific topics.

If you're interested in Hong Kong the Census and

Statistics website says rich detail from the Hong Kong census as well as

other data collection efforts by the Hong Kong government.

You can even carry out simple tabulations online in your browser

of Hong Kong census data at this website.

There's also extensive detail on health statistics for

Hong Kong at the Hong Kong Health website.

And then for Taiwan, the government there has extensive

amounts of statistical information available at its website.

Again, covering topics like health, economics, education, and

again a number of other topics.

Now to make use of all of these data,

you'll have to learn more advanced methods in a statistics course.

I'll mention some of these methods now, but

you'll probably need additional training to really learn them properly.

The most basic approach is to analyze aggregated data using regression analysis.

This has been the main stay of the analysis of aggregated data since

the 1950s or even earlier.

A lot of pioneering studies that made use of newly available

international aggregated data in the 1950s.

For example, on the relationships between life expectancy and

per capita income make use of basic regression analysis.

Over the decades, people working on economic data especially

make use of advanced techniques in time series analysis.

So when we're looking at economic indicators over the years whether it's

unemployment rates, the gross domestic product, other economic indicators.

These indicators have unique properties that make it difficult to

analyze them solely with basic regression analysis techniques.

There are specific techniques we refer to as time series analysis techniques.

They are specifically used for

indicators that we have on a annual basis or a quarterly basis and

which account for the special properties of such data.

More recently as people has become interested in establishing cause and

effect in looking at relationships among variables in aggregated data.

There been a lot of studies that make use of instrumental variables,

we mention this in the earlier lectures.

Again the idea is to find some other variable that is a source of

exogenous variation in what we think is an independent variable.

And then use an instrumental variable analysis to

isolate the causal effect of variations in a proposed

independent variable in shaping variation in some outcome

variable which we think is the dependent variable.

Finally, many papers now for natural or quasi experiments,

policy changes, disasters, other sorts of shocks that allow for,

again, some claim about cause and effect.

Now of course, if you're going to make use of aggregated data,

using all of these techniques which ever technique that you use.

You need to be mindful of the potential problems with aggregated

data that I talked about at the end of the last module.

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