Chapter 7- Sampling- estimating the frequency of behaviors and beliefs

Question 1

External validity

Answer 1

Can the results of a study be generalized to some larger population of interest? Good external validity means that the sample is representative of the population

Question 2

How is external validity calculated for frequency claims?

Answer 2

To interrogate external validity for frequency claims, we ask whether people who responded to a survey (the sample) can adequately represent the larger population or can be generalized to another setting.

Question 3

Population

Answer 3

The entire set of people or products in which you are interested.

Question 4

Sample

Answer 4

A smaller set taken from that population

Question 5

Census

Answer 5

A census would include surveying every individual in the population- this usually isn’t necessary in research

Question 6

Population of interest

Answer 6

Researchers have to specify which population they want to generalize- this is the population of interest. Researchers are rarely looking at the world’s entire population. For example, if a sample of people rated a style of shoes on how well they fit, we might be interested in generalizing to the population of people who have worn those shoes

Question 7

Biased sample

Answer 7

Some members of the population of interest have a much higher probability than other members of being included in the sample.

Question 8

Unbiased sample

Answer 8

All members of the population have an equal chance of being included in the sample. Only unbiased samples allow us to make inferences about the population of interest

Question 9

When is a sample considered biased?

Answer 9

In a consumer survey or an online opinion poll, a biased sample would contain too many unusual people. Ex- the students who rate a professor on a website might tend to be the most angry or disgruntled and not represent the group of students. A researcher’s sample might include only one kind of people when the population of interest has more variety. Ex- a sample of only men when the population of interest contains men and women. Sometimes, the population of interest might actually be one specific group, like men. In this case, the researcher has to make sure that the same of men represents the general population of men

Question 10

Ways a sample might be biased (2)

Answer 10

1. Sampling only those who are easy to contact

2. Sampling only those who volunteer

Question 11

Which validity does a biased sample threaten?

Answer 11

External

Question 12

Convenience sampling

Answer 12

Using a sample of people who are easy to contact and readily available to participate. College professors conducting studies might tend to use college students, but these samples might not be representative of populations that are less educated, older, or younger. Researchers might also end up with a convenience sample if they are unable to contact a certain subset of people. They might not be able to study those who live far away or those who don’t show up to a study appointment

Question 13

Self-selection

Answer 13

When a sample is known to contain only people who volunteer to participate. When Internet users choose to rate something, like an Amazon product or a professor on RMP, they are self-selecting when doing so- people who take the time to rate things might have stronger opinions or might be more willing to share ideas with others. We can rule out this bias when participants are invited via random selection

Question 14

Probability sampling/random sampling

Answer 14

Every member of the population of interest has an equal and known chance of being selected for the sample, regardless of if they are convenient or motivated to volunteer.

Question 15

Nonprobability sampling

Answer 15

Involves nonrandom sampling and results in a biased sample

Question 16

Probability sampling techniques (5)

Answer 16

1. Simple random sample
2. Oversample
3. Stratified random sample
4. Systematic sample
5. Cluster sample or multistage sample

Question 17

Nonprobability sampling techniques (4)

Answer 17

1. Convenience sample
2. Quota sample
3. Purposive sample
4. Snowball sample

Question 18

Simple random sampling

Answer 18

Some examples- the names of every member of a population are written on a plastic ball, and a machine spits out the number of balls necessary for a sample. A number is assigned to each individual in a population and then certain ones are selected using a table of random numbers- researchers use software to generate random numbers

Question 19

Systematic sampling

Answer 19

Using a computer or a random number table, the researcher starts by selecting 2 random numbers (4 and 7, for example). If the population of interest is a roomful of students, the researcher would start with the fourth person in the room, counting off every seventh person until the sample is the desired size. The Mehl study is an example of this, since researchers sampled conversations every 12.5 minutes

Question 20

Why are simple random sampling and systematic sampling not frequently used?

Answer 20

Simple random sampling and systematic sampling can be difficult and time consuming. It can be almost impossible to find and enumerate every member of the population of interest, so the next 3 are variants of the basic technique.

Question 21

Cluster sampling

Answer 21

An option when people are already divided into arbitrary groups. Clusters of participants within a population of interest are randomly selected, and then all individuals in each selected cluster are used. Example- studying all public high schools (clusters) in a state. A researcher could randomly select 100 of those schools and then select students from each school

Question 22

Multistage sampling

Answer 22

Two random samples are selected, a random sample of clusters and then a random sample of people within those clusters. High school example- the researcher would select 100 schools randomly, and then select a random sample of students from each of the 100 schools.

Question 23

Stratified random sampling

Answer 23

Stratified random sampling- the researcher purposefully selects particular demographic categories (strata) and then randomly selects individuals within each of the categories, proportionate to their assumed membership in the population. Example- researchers want to make sure their sample of 1,000 Canadians includes people of South Asian descent in the same proportion as the Canadian population (4%). In a sample of 1,000, they would select 40 people of South Asian descent. All members of the sample would be selected randomly, including the South Asian individuals.

Question 24

How is stratified random sampling different from cluster sampling? (2)

Answer 24

1. Strata are meaningful categories (like ethnic or religious groups), while clusters are more arbitrary.
2. The final sample size of the strata reflect their proportion in the population, while clusters aren’t selected with such proportions in mind.

Question 25

Oversampling

Answer 25

The researcher intentionally over represents one or more groups. With the Canadian example, researchers might want to include more than 40 South Asian people to get a more precise estimate. However, the increased proportion of this group is still selected randomly. The final survey results will be adjusted so the oversampled group is weighted to their actual proportion in the population

Question 26

Weighting

Answer 26

Researchers can adjust the data so categories of people that were underrepresented in the sample can be better represented.

Question 27

Random

Answer 27

In research, random has a precise meaning- occurring without any order or pattern

Question 28

Random sampling

Answer 28

Researchers create a sample using some random method, like drawing names from a hat, so that each member of the population has an equal chance of being in the sample. Random sampling enhances external validity

Question 29

Random assignment

Answer 29

Only used in experimental designs. Individuals are randomly assigned to an experimental or control group. Enhances internal validity by ensuring that the experimental and control group have the same types of people in them, which controls for alternative explanations of outcomes.

Question 30

When would researchers use an unrepresentative sample?

Answer 30

In cases where external validity is not vital to a study’s goals, researchers might be content with a nonprobability sampling technique. Convenience sampling is most common but not the only option

Question 31

Purposive sampling

Answer 31

When researchers only want to study a particular group, they can recruit these participants in a nonrandom way. Limiting a sample to one group doesn’t necessarily make it purposive. Recruiting smokers by posting flyers at a tobacco store would make recruitment non random and therefore uses purposive sampling- this is not representative of all the smokers in the area

Question 32

Snowball sampling

Answer 32

When participants are asked to recommend a few acquaintances for the study until the sample is large enough. This sampling is unrepresentative because people are recruited via social networks, which are not random

Question 33

Quota sampling

Answer 33

The researcher identifies subsets of the population of interest and then sets a target number for each category in the sample. Next, the researcher samples from the population of interest non-randomly until the quotas are filled. Participants are generally selected through convenience or purposive sampling

Question 34

Which validity is a priority for a frequency claim?

Answer 34

External validity is a priority- it is very important in order for frequencies for a specific population to be accurate

Question 35

What is the best way to interrogate a frequency claim?

Answer 35

You usually can’t directly check the accuracy of a frequency claim because it’s usually impossible to survey every member of a certain population. The best way to interrogate a frequency claim is to examine the method the researchers used. As long as it was a probability sampling technique, you can be more confident in the external validity of the result.

Question 36

If a frequency claim uses a nonprobability sample, what should you consider?

Answer 36

Researchers might not have the funds to obtain random samples for their studies and their priorities lie elsewhere. If a frequency claim is not based on a probability sample, you need to consider whether the reason for the sample’s bias is relevant to the claim.

Question 37

How does a larger sample size affect external validity?

Answer 37

Larger samples are not more externally valid than smaller samples- how the sample was selected is more important. Large samples will sometimes be necessary in order to locate enough instances of a phenomenon, however.

Question 38

What sample size is usually adequate?

Answer 38

When researchers do use random sampling, 1000-2000 people are usually all that they usually need, even for large populations

Question 39

How does sample size affect the margin of error?

Answer 39

As a polling sample gets larger, its margin of error becomes more precise. However, the external validity of the poll comes from its sampling method, not the sample size