CH. 7 - SAMPLING

Explain why a larger sample is not necessarily more externally valid than a smaller one.

When a phenomenon is rare, we do need a large sample in order to locate enough instances of that phenomenon for valid statistical analysis. But for most variables, when researchers are striving to generalize from a sample to a population, the size of a sample is in fact much less important than how that sample was selected. When it comes to the external validity of the sample, it's how, not how many.

D) Biased Sample

Which of the following four terms is not synonymous with the other? A) Generalizable Sample B) Externally Valid Sample C) Representative Sample D) Biased Sample

C) A stratified random sample of 120

Which of the following samples is most likely to generalize to its population of interest? A) A convenience sample of 12,000 B) A quota sample of 120 C) A stratified random sample of 120 D) A self-selected sample of 120,000

Probability Sampling

A category name fro random sampling techniques, such as simple random sampling, stratified random sampling, and cluster sampling, in which a sample is drawn from a population of interest so each member has an equal and known chance of being included in the sample. Also called random sampling.

Stratified Random Sampling

A form of probability sampling; a random sampling technique in which the researcher identifies particular demographic categories of interest and then randomly selects individuals within each category. EX: A group of researchers might want to be sure their sample of 1,000 Canadians includes people of South Asian descent in the same proportion as in the Canadian population (which is 4%). Thus, they might have two categories (strata) in their population: South Asian Canadians and other Canadians. In a sample of 1,000, they would make sure to include at least 40 members of the category of interest (South Asian Canadians). Importantly, how- ever, all 1,000 members of both categories are selected at random.

For what type of claim will it be most important for a researcher to use a representative sample?

A representative sample is very important with frequency claims because external validity is a priority.

Sample

The group of people, animals, or cases used in a study; a subset of the population of interest.

Snowball Sampling

A variation on purposive sampling, a biased sampling technique in which participants are asked to recommend acquaintances for the study.

When might researchers decide to use a non-probability sample, even though a probability sample would ensure external validity?

Even though you need a probability sample to support a frequency claim, many associations and causes can still be accurately detected even in a non-probability sample (external is not as important). Random assignment is prioritized over random sampling when conducting an experiment. EX: Let's say a driver uses the Waze navigation app to report heavy traffic on a specific highway. This driver is not a randomly selected sample of drivers on that stretch of road. However, traffic is the same for everybody, conscientious or not, so even though this driver is a nonrandom sample, her traffic report can probably generalize to the other drivers on that road.

A) Estimating the proportion of U.S. teens who are depressed

Externally valid samples are more important for some research questions than others. For which of the following research questions will it be most important to use an externally valid sampling technique? A) Estimating the proportion of U.S. teens who are depressed B) Testing the association between depression and illegal drug use in U.S. teens C) Testing the effectiveness of support groups for teens with depression

What are four ways of selecting a non-probability sample? What types of people might be more likely to be selected in each case?

1) Convenience Sampling People: Individuals that are easy to reach, such as psychologists studying students on their own campus. 2) Purposive Sampling People: Individuals that fall into a particular area that is being studied. For example, researchers studying the effectiveness of a specific intervention to quit smoking would only seek smokers for their sample by posting flyers at a local tobacco store (not random). 3) Snowball Sampling People: Individuals fall into a particular area that is being studied, then recruit others that they know (EX: support group) whom fall into that same category to be studied. 4) Quota Sampling People: Individuals belonging to a subset of the population of interest (EX: 80 Latinos, 80 African Americans, 80 Asians).

What are five techniques for selecting a probability sample of a population of interest? Where does randomness enter into each of these five selection processes?

1) Simple Random Sampling Randomness: From the start, you take a random sample from the entire population of interest. 2) Cluster Sampling Randomness: Clusters of participants within the population of interest are elected at random, then sample everyone in each cluster. 3) Multistage Sampling Randomness: Involves two or more random samples, where you first randomly sample clusters, and then randomly sample people in the the selected cluster. 4) Stratified Random Sampling Randomness: The researcher identifies particular categories of interest and then randomly selects individuals within each category. 5) Oversampling Randomness: A variation of stratified sampling in which researcher intentionally overrepresents one of the groups. 6) Systematic Sampling Randomness: A computer to random number table is used and the researcher selects two random numbers.

Quota Sampling

A biased sampling technique in which a researcher identifies subsets of the population of interest, sets a target number for each category in the sample, and nonrandomly selects individuals within each category until the quotas are filled.

Purposive Sampling

A biased sampling technique in which only certain kinds of people are included in a sample.

Non-Probability Sampling

A category name for nonrandom sampling techniques, such as convenience, purposive, and quota sampling, that result in a biased sample.

Oversampling

A form of probability sampling; a variation of stratified random sampling in which the researcher intentionally overrepresents one or more groups. EX: Perhaps a researcher wants to sample 1,000 people, making sure to include South Asians in the sample. Maybe the researcher's population of interest has a low percentage of South Asians (say, 4%). Because 40 individuals may not be enough to make accurate statistical estimates, the researcher decides that of the 1,000 people she samples, a full 100 will be sampled at random from the Canadian South Asian community. In this example, the ethnicities of the participants are still the categories, but the researcher is oversampling the South Asian population: The South Asian group will constitute 10% of the sample, even though it represents only 4% of the population.

Self-Selection

A form of sampling bias that occurs when a sample contains only people who volunteer to participate.

Population

A larger group from which a sample is drawn; the group to which a study's conclusions are intended to be applied. Also called population of interest.

Cluster Sampling

A probability sampling technique in which clusters of participants within the population of interest are selected at random, followed by data collection from all individuals in each cluster. EX: If a researcher wanted to randomly sample high school students in the state of Pennsylvania, for example, he could start with a list of the 952 public high schools (clusters) in that state, randomly select 100 of those high schools (clusters), and then include every student from each of those 100 schools in the sample.

Systematic Sampling

A probability sampling technique in which the researcher uses a randomly chosen number (N), and counts off every (Nth) member of a population to achieve a sample. EX: Using a computer or a random number table, a researcher starts by selecting two random numbers—say, 4 and 7. If the population of interest is a roomful of students, the researcher would start with the fourth person in the room and then count off, choosing every seventh person until the sample was the desired size.

Multistage Sampling

A probability sampling technique involving at least two stages: a random sample of clusters followed by a random sample of people within the selected clusters. EX: Referring to the earlier example, the researcher starts with a list of high schools (clusters) in the state and selects a random 100 of those schools. Then, instead of selecting all students at each school, the researcher selects a random sample of students from each of the 100 selected schools.

C) A sample of 25 dog owners selected at random from New York City pet registration records

A researcher's population of interest is New York City dog owners. Which of the following samples is most likely to generalize to this population of interest? A) A sample of 25 dog owners visiting dog-friendly New York City parks B) A sample of 25 dog owners who have appointments for their dogs at veterinarians in the New York City area C) A sample of 25 dog owners selected at random from New York City pet registration records D) A sample of 25 dog owners who visit New York City's ASPCA website

Unbiased Sample

A sample in which all members of the population of interest are equally likely to be included (usually through some random method), and therefore the results can generalize to the population of interest. Also called representative sampling.

Biased Sample

A sample in which some members of the population of interest are systematically left out, and as a consequence, the results from the sample cannot generalize to the population of interest. Also called unrepresentative sample.

Census

A set of observations that contains all members of the population of interest.

Which of these samples is more likely to be a representative of a population of 100,000? A) A snowball sample of 50,000 people B) A cluster sample of 500 people

B) A cluster sample of 500 people

Why are convenience, purposive, snowball, and quota sampling not examples of representative sampling?

Because non of theme involve selecting participants at random.

Convenience Sampling

Choosing a sample based on those who are easiest to access and readily available; a biased sampling technique.

In your own words, define the word random in the research methods context. Then describe the difference between random sampling and random assignment.

Random: Occurring without any order or pattern. Researchers use random sampling to create s ample using some random method, such as drawing names from a hat or using a random-digit phone dialer, so that each member of the population has an equal chance of being in the sample; enhances EXTERNAL validity. Random assignment is used only in experimental designs. When researchers want to place participants into two different groups (treatment, comparison), they usually assign them at random; enhances INTERNAL validity.

Simple Random Sampling

The most basic form of probability sampling, in which the sample is chosen completely at random from the population of interest (e.g., drawing names out of a hat). EX: Imagine that each member of the population of interest has his or her name written on a plastic ball. The balls are rolled around in a bowl, then a mechanism spits out a number of balls equal to the size of the desired sample. The people whose names are on the selected balls will make up the sample.

Random Assignment

The use of a random method (e.g., flipping a coin) to assign participants into different experimental groups.