3.8. Response rates and follow-up

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Social Science Approaches to the Study of Chinese Society Part 2

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The Hong Kong University of Science and Technology

Social Science Approaches to the Study of Chinese Society Part 2

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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 want to talk about another important piece of

conducting a survey, response rates and for

longitudinal surveys attrition and follow up.

Response rates refer to the share of the population that was included in the sample

who actually complete the survey and yield usable data.

Even if sampling is done correctly,

differential response rates may introduce bias into the resulting data.

If certain types of sampled units were more or less likely to respond, the effect

on the final dataset may be the same as if they were over-sampled or under-sampled.

So, for example, if people of a particular social class,

particular race, are especially unlikely to respond in the survey,

they may be underrepresented in the final dataset.

Now, what can be even more subtle, and perhaps more problematic,

are situations where there are certain types of people that are less likely to

respond, but they don't have obvious characteristics that would allow us to

conduct some sort of reweighting to adjust for their under-response.

But that's a highly technical issue that I can't get into here.

Now, if failure to respond is purely random, that's less of a problem.

We just have a smaller dataset, but it may not be a biased dataset.

Now, the reason we need to pay attention to this is that around the world

response rates are declining.

Decades ago, response rates in many places were actually quite high.

Now, again,

in recent decades people are steadily less willing to participate in surveys.

This is especially true for telephone surveys.

A few decades ago, opinion polling,

other kinds of polling or using telephones were very successful.

Back in the 1970s, even in the 1980s, or early 1990s,

phone calls from telemarketers and scammers were relatively rare.

If somebody called you at home it was probably because they had

a serious purpose.

People were willing to answer the phone and

then even answer questions once the survey had been explained.

Nowadays of course, if you're like me,

you may not answer the phone if you don't recognize the number.

Or you may be immediately suspicious of anyone who's asking questions.

So the problem with response rates is especially serious when it comes to

telephone polling but there's more general problems, as well.

Now there is some controversy about the overall effects

of declining response rates and what's to be done about it.

And there's some studies suggesting that perhaps even low

response rates are not necessarily fatal for a study.

However, because it is generally impossible to determine

whether non-response is random, that is,

whether the people that refuse to participate differs systematically from

those who did participate, we still prefer higher response rates.

Because the reason that we can't figure out why non-respondents are different

from respondents is that by definition, they decline to participate in the survey.

We don't know anything about their characteristics.

So we don't know if they differ.

So generally, we do like higher response rates in a survey.

How do people maximize response rates?

In a multistage cluster sample, like we talked about early,

where we have perhaps respondents, or targeted respondents in the sample,

concentrated in particular blocks, or particular neighborhoods.

We can saturate those neighborhoods with information

about a survey ahead of the visits of the interviewers.

So people that are running very large surveys with large budgets,

they do a lot of preparatory work.

Once they've drawn the sample they know which blocks,

which neighborhoods they may be visiting.

They may first distribute flyers, they may even go door to door to say that

there's a survey on it's way and explain the survey to people.

Interviewers have to be trained to introduce themselves and the project.

So it's not simply sufficient to go in and

demand that people participate in an interview.

Typically there has to be some discussion of the origin of the project.

Interviewers typically carry identification.

They all include contact numbers for the leaders of the research project.

Which allow respondents or potential respondents to contact the professors that

are running the survey to confirm the details of the interviewer's explanation.

They may need to make repeated visits to the sampled households.

So rather than, say knocking once on a door and then moving on and

going to a different household if nobody is there, a survey with a large budget

may have interviewees going back day after day knocking again and again and

seeking other ways to contact people to try to maximize response.

Respondents may be offered cash or gifts as incentives.

Of course this is rarely a large amount of money.

It's more a sign of respect.

A token of appreciation for

the respondents taking time from their busy schedules to complete an interview.

Longitudinal surveys have some special considerations

when it comes to maximizing response rates.

And in particular when it comes to longitudinal surveys,

attrition is a major issue.

This refers to the phenomenon of people who

started out by participating in the survey.

They initially agreed to be interviewed.

They perhaps participated in the first wave, but

then they are lost in later waves.

This may happen because the respondents move and they can no longer be found or

because they simply decide to stop participating.

This is a real problem if it isn't random.

If it's random, then you just have a smaller sample.

But if it isn't random, you may be introducing biases in terms of the fact

that the people who are observed repeatedly may be different

from the people who were just observed once and then dropped out of the survey.

Researchers conducting longitudinal surveys

typically collect detailed contact information.

Not only for the respondents but for the friends of the respondents and

relatives of the respondents.

So that they have a wide network of people that they can try to contact

if they cannot locate the original respondent in a later wave.

So these are just a few of the basic considerations that come up when we worry

about response rates, attrition, and follow up in surveys.

In practice, some of these issues are more complex.

And you may need additional training or to do additional reading to really

get the details you need if you're going to conduct a study of your own.

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