3.2. Surveys and sampling

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En provenance du cours de The Hong Kong University of Science and Technology

Social Science Approaches to the Study of Chinese Society Part 2

7 notes

The Hong Kong University of Science and Technology

Social Science Approaches to the Study of Chinese Society Part 2

7 notes

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

À partir de la leçon

Sampling

By the end of Week 3, you should be able to understand why RANDOM SAMPLING is important in a survey, outline the most common approaches to sampling and discuss key considerations when choosing a sampling strategy for your study.

3.1. Overview: Sampling2:56

3.2. Surveys and sampling13:21

3.3. Probability sampling12:38

3.4. Clustered sampling17:22

3.5. Stratification, oversampling13:59

3.6. Respondent-driven sampling7:50

3.7(a) Conducting a survey - Preparation10:13

3.7(b) Conducting a survey - Designing a Questionnaire9:04

3.8. Response rates and follow-up7:22

3.9. Summary2:22

Rencontrer les enseignants

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 talk about surveys and

sampling in an introductory way.

I'm going to talk about where surveys came from and some of the approaches to

sampling that were used before the modern era of random or

probability based sampling started, which I'm going to talk about in later modules.

So, surveys seek to generalize to some population from a sample.

Sampling refers to the process of selecting the units within the population

that we're interested in to include in a sample that we are going to survey.

The goal is to produce a sample that is representative of the larger population.

In other words, we would like our sample, its characteristics, to resemble

that of the larger population from which the sample is drawn.

That way, if we do some calculations based on the sample,

we should be able to generalize to the population as a whole.

Sampling's actually also relevant for studies that are not surveys, but

in some cases make use of very large amounts of data, too much data to,

say analyze, in which case some subset has to be extracted.

I won't be getting much into that in the course of this lecture.

Now, the origins of surveys were in 19th century in Victorian England.

Social reformers sought to understand the conditions of the urban poor.

These surveys typically focused on income and

expenditure, and things like housing and so forth.

People were very interested in the plight of the people living in

the crowded neighborhoods of London and other major cities,

and essentially understanding how they fared or managed when they were earning

relatively low wages and then had to spend much of their income on basic necessities.

The samples were large, because at that time, people thought that

a large sample was better, and they typically consisted of all the residents

of a selected community that had been somehow identified as quote, typical.

The limitations, however, of trying to generalize from quote,

typical communities, quickly became apparent.

So people began looking for ways to draw larger samples from

broader cross sections of a country, or at least a large region.

One early approach, which again the limitations were recognized fairly

quickly, was convenience sampling.

One example is starting in 1916, the Literary Digest,

at the time a famous magazine in the United States, began conducting

polls to predict the outcomes of the U.S. Presidential elections.

They tabulated responses from millions of cards mailed to subscribers,

automobile owners, and telephone users across the United States.

So they were able to achieve great geographic coverage of the United States,

but, as we'll see later, at something of a cost.

The reason that they used automobile owners and telephone users was that at

that time in the United States, they were one of a small number of populations for

which there were comprehensive listings that could be used to contact people with.

Now the results, amazingly enough from this rather haphazard approach to

sampling, basically, sending out postcards to millions of people and

asking them to indicate a preference and mail them back, were actually correct,

until 1936, when they incorrectly predicted a victory for

Alf Landon over Roosevelt.

Now, you probably know that this prediction was wrong,

because most of you have probably never hear of Alf Landon.

Roosevelt, obviously, remained as president for some years afterward.

The sample turned out to be biased and unrepresentative.

It turned out that automobile owners, telephone users, and

subscribers to the Literary Digest were among the, on average,

wealthier people in the United States at the time, and

more likely to vote Republican and vote for Alf Landon.

So I refer to this as a convenience sample,

because essentially the emphasis was on ease of contact.

That is, a sample that was constructed in a way that maximized the ease of

reaching out and recruiting respondents, without much care to ensuring

that they were representative in the way we think about representative nowadays.

Another approach to sampling,

which was a bit more systematic, was purposive sampling.

Many, if not most of the late 19th or

early 20th surveys, used what we now think of as purposive sampling.

For example, I mentioned earlier,

the studies in the 19th century that focused on particular urban districts.

So urban districts or

rural communities were selected because they were thought to be, in some way,

typical or representative of a class of such communities or districts.

This turns out, we now know, to be fairly problematic, because it turns out that

almost any neighborhood, any village, is never really truly typical.

Every village or community is special.

It has features that distinguish it and make it difficult to

generalize from its experience to, for example, an entire country.

But at the time, there were logistical and other reasons to take this approach.

In other situations, especially when it came to surveys in the early 20th century,

individual subjects were chosen based on observed characteristics,

with the goal of composing a representative sample.

Let me give you an example.

So, one form purposive sampling was quota sampling.

George Gallup successfully predicted the winner of the U.S.

Presidential elections in 1936, 1940 and 1944 using a very small samples,

much smaller than the ones in the Literary Digest polls.

But he used quota sampling, which again was being advocated

as a certainly an alternative to convenient sampling, and in general,

a acceptable way of drawing a sample.

So in quota sampling, targets were set for

the number of respondents with specified characteristics in the hope that they

would then compose a sample that was representative of the larger population.

So the focus was on age, sex and race, and other observed characteristics.

The resulting sample, in principle, should match the larger population

in terms of these specified characteristics.

Let's look at a hypothetical example of how quota sampling works to help

give some clarity.

So, imagine that we have a population and we're interested in composing

a sample where the quotas are set on three characteristics of the population,

age, sex and race.

So in this population, half of the population is age 21 to 50.

The other half is 50 and above.

Half the population is male, half the population is female.

90% of the population is white, 10% is black.

So if those are the three variables upon which we want to compose our quotas,

we would take these shares and then use them to work out the shares

of the population that were in each combination of categories.

So, for example, if we're looking at females, white, age 21 to 50,

50% times 50% times 90% would give you 22.5%.

Or black females age 50 and above, 2.5%,

which comes out of 50% times 50% times 10%.

So we have the shares of the population, you might say,

in each cell, each combination of age, sex, and race.

These are percentages.

Now, assuming that we want a sample with 1,000 people,

we can apply these percentages to 1,000 to

figure out what our quota would be in terms of going out to compose our sample.

So, for example, 22.5% of 1,000 would

be 225 white females who are aged 21 to 50,

25 black females aged 50 and above.

So these quotas would then be given to interviewers and

they would go out into the field and start looking for people that matched

the characteristics for each set of quoats until they filled up each cell.

So, they might keep looking for white males, age 21 to 50, and once they

had 225 that they had interviewed, they would declare that cell complete.

That the quota had been satisfied and then perhaps move on.

So this leaves a lot of discretion to the interviewer and it can lead to problems.

For example, the easiest way to fill a quota might be to go to some place where

you might, for example, find a lot of white males aged 21 to 50,

perhaps outside the gates of a factory if it were the United States in the 1940s.

So perhaps you could fill your quota in a few hours, but of course, you might be

looking at a very homogeneous group of white males aged 21 to 50 if you did that.

So, this is an example of setting a quota and

then how the instructions were actually given.

So, quota sampling turned out to have problems.

So in 1948, Gallup, who'd used quota sampling with relatively small

samples to correctly predict the Presidential elections before 1948,

incorrectly predicted Dewey as the winner of 1948 Presidential

election in the United States.

You can probably guess that that prediction was wrong because you

probably never heard of Dewey.

In fact, Truman won that election.

It turned out that the sample that Gallup was using was heavily biased and

it partly reflected the problems with quota sampling.

One problem was that quota sampling left,

again, too much discretion to the interviewer.

When you looked at the specified targets, the interviewer would have

discretion to pick people that they felt easy to approach.

So if they were told to, for example,

find a certain number of adult white males, they might look for

the ones that were dressed the best, or perhaps in particular neighborhoods,

because they might feel that they would be easiest to approach and interview, and

they might neglect people that were dressed in a more working class fashion or

perhaps in neighborhoods that they didn't want to visit.

So, you can easily end up with bias on other characteristics like,

for example, socioeconomic status or

something else, even if you satisfied the dictates of the quota.

The other issue, actually there were others.

But one of the other issues in 1948 was that the census information

that were used to compose the quotas was badly out of date.

From 1948, to figure out the shares of the population in each of the categories,

they used census data from 1940.

Now, the United States had changed a lot between 1940 and 1948.

It had been in a war.

There had been a massive migration of people from rural areas to urban areas to

take up jobs in wartime production, and these people stayed in the cities.

There was also improvements in education, a lot of other changes.

So the shares that were calculated from the 1940 data

were not necessarily relevant anymore in 1948.

So, rural groups were heavily overrepresented in the quotas for 1948.

So, Gallup and other pollsters got 1948 wrong.

Truman won the election.

And this somehow marked the end of quota sampling as a widely accepted

approach to sampling for surveys that were intended to generalize to a population.

It paved the way for the eventual rise of probability or random sampling,

which we're going to talk about in the remaining modules, and

which essentially dominated starting in the 1950s.

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