Chapter 10: Sample Surveys

Question 1

What are the 3 ideas of sampling?

Answer 1

1. Examine a part of the whole: A sample can give information about the population.
2. Randomize to make the sample representative.
3. The sample size is what matters. It’s the size of the sample - and not its fraction of the larger population - that determines the precision of the statistics it yields.

Question 2

What are some sampling methods?

Answer 2

1. Simple random sample (SRS)
2. Stratified samples
3. Cluster samples
4. Systematic samples
5. Multistage samples

Question 3

What are some causes of bias?

Answer 3

1. Voluntary response
2. Convenience samples
3. Bad sampling frames
4. Undercover age
5. Nonresponse bias
6. Response bias

Question 4

Define ‘Population’.

Answer 4

The entire group of individuals or instances about whom we hope to learn.

Question 5

Define ‘Sample’.

Answer 5

A (representative) subset of a population, examines in hope of learning about the population.

Question 6

Define ‘Sample survey’.

Answer 6

A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population. Polls taken to assess voter preferences are common sample surveys.

Question 7

Define ‘Bias’.

Answer 7

Any systematic failure of a sampling method to represent its population. It is almost impossible to recover from bias, so efforts to avoid it are well spent. Common errors include relying on voluntary response, undercoverage of the population, nonresponse bias and response bias.

Question 8

Define ‘Randomization’.

Answer 8

The best defense against bias is randomization, in which each individual is given a fair, random chance of selection.

Question 9

Define ‘Sample size’.

Answer 9

The number of individuals in a sample. The sample size determines how well the sample represents the population, not the fraction of the population sampled.

Question 10

Define ‘Census’.

Answer 10

A sample that consists of the entire population.

Question 11

Define ‘Population parameter’.

Answer 11

A numerically values attribute of a model for a population. We rarely expect to know the true value of a population parameter, but we do hope to estimate it from sampled data. For example, the mean income of all employed people in the country is a population parameter.

Question 12

Define ‘Statistic, sample statistic’.

Answer 12

Values calculated for samples data. Those that correspond to, and thus estimate, a population parameter are of particular interest. For example, the mean income of all employed people in a representative sample can provide a good estimate of the corresponding population parameter.

Question 13

Define ‘Representative’.

Answer 13

A sample is said to be representative if the statistics computed from it accurately reflect the corresponding population parameters.

Question 14

Define ‘Simple random sample (SRS)’.

Answer 14

A simple random sample of sample size n is a sample in which each set of n elements in the population has an equal chance of selection.

Question 15

Define ‘Sampling frame’.

Answer 15

A list of individuals from who the sample is drawn. Individuals who may be in the population of interest, but who are not in the sampling frame, cannot be included in any sample.

Question 16

Define ‘Sampling variability’.

Answer 16

The natural tendency of randomly drawn samples to differ, one from another. Sometimes - unfortunately - called sampling error, sampling variability is no error at all, but just the natural result of random sampling.

Question 17

Define ‘Stratified random sample’.

Answer 17

A sampling design in which the population is divided into several subpopulations, or strata, and random samples are then drawn from each stratum. If the strata are homogenous, but are different from each other, a stratified sample may yield more consistent results.

Question 18

Define ‘Cluster sample’.

Answer 18

A sampling design in which entire groups, or clusters, are chosen at random. Cluster sampling is usually selected as a matter of convenience, practicality or cost.

Question 19

Define ‘Multistage sample’.

Answer 19

A sampling scheme that involves multiple stages of random sampling, where at each successive stage, we sample from lists of ever smaller clusters (hierarchal in nature).

Question 20

Define ‘Systematic sample’.

Answer 20

A sample drawn by selecting individuals systematically from a sampling frame. When there is no relationship between the order of the sampling frame and the variables of interest, a systematic sample can be representative.

Question 21

Define ‘Pilot’.

Answer 21

A small trial run of a survey to check whether questions are clear. A pilot study can reduce errors due to ambiguous questions.

Question 22

Define ‘Voluntary response bias’.

Answer 22

Bias introduced to a sample when individuals can choose on their own whether to participate in the sample. Samples based on voluntary response are always invalid and cannot be recovered, no matter how large the sample size.

Question 23

Define ‘Convenience sample’.

Answer 23

A sample of individuals who are conveniently available. Convenience samples often fail to be representative because every individual in the population is not equally convenient to sample.

Question 24

Define ‘Undercoverage’.

Answer 24

A sampling scheme that biases the sample in a way that gives a part of the population less representation than it has in the population suffers from undercoverage.

Question 25

Define ‘Nonresponse bias’.

Answer 25

Bias introduced when a large fraction of those samples fails to respond. Those who do respond are likely to not represent the entire population. Voluntary response bias is a form of nonresponse bias. but nonresponse bias may occur for other reasons. For example, those who work during the day won’t respond to a telephone survey during working hours.

Question 26

Define ‘Response bias’.

Answer 26

Anything in a survey design that influences responses. One typical response bias arises from the wording of questions, which may suggest a favoured response.