Chapter 5: Selecting Research Participants

Question 1

target population

Answer 1

population that generally share at least one characteristic

Question 2

accessible population? cons?

Answer 2

portion of the target population consisting of individuals who are accessible to be recruited

cons: must be cautious about generalizing results to target population. the accessible population may differ too greatly from target populations and thus the conclusions based on study results only apply to the study population./

Question 3

representativeness

Answer 3

degree to which a sample mirrors/ resembles the population of interest

Question 4

biased sample

Answer 4

characteristics of individuals in the sample are DIFFERENT from those of the target population.

Question 5

sampling bias

Answer 5

when researchers create a biased sample by selectively choosing desirable participants (ex/ by choosing subjects with slight heat conditions to test a heart drug instead of incorporating people with severe cardio problems–> would make the drug look like its super effective)

Question 6

Law of Large Numbers

Answer 6

the larger the sample size, the more likely values taken from the sample will match actual values for population.

Question 7

relationship between representativeness and sample size

Answer 7

representativeness increases in relation to the square root of the sample size.

Question 8

Probability sampling:

Answer 8

sample when each element has a known probability of inclusion; investigator has no discretion regarding the inclusion or exclusion of an element.

Question 9

conditions of probability sampling

Answer 9

1) exact popultaion must be known
2) list of individuals in population must be known
3) each individual must have a specified probability of selection
4) random selection: every possible outcome is equally likely

Question 10

Non probability sampling

Answer 10

odds of selecting a particular individual is not known because the researcher does not know the population size, or the list of the popultion members are not known

participants are thus selected based on availability or researchers judgement

Question 11

non probability sampling leads to a ______ risk of producing a biased sample

Answer 11

an increased risk of producing a bias sample

Question 12

probability sample methods

Answer 12

1) simple random sampling
2) Systematic sampling
3) stratefied random samplin
4) proportionate stratified random sampling
5) cluster sampling
6) combined strategy sampling

Question 13

non-probability sampling methods

Answer 13

1) convenience sampling

2) quota sampling

Question 14

simple random sampling

Answer 14

when all members of the population of interest have an equal and independent chance of being seslcted

Question 15

pros and cons of SRS

Answer 15

pros; totally random, effective and practical way to create a REPRESENTATIVE SAMPLE, can estimate sampling error

cons; impossible without a complete up to date list of members of the population, expensive if population is dispersed, impractical if the population is extremely large, may not give samples you need (minorities) or equal # of people (males vs females)–> it is possible to obtain a distorted sample

Question 16

systematic sampling?

Answer 16

type of probability sampling; list all the individuals in the population, randomly selecting a starting plan, and selecting every nth element from a list of the total population.

Question 17

is independence princriple still intact in systematic sampling?

Answer 17

no, but there is still a high degree of representativeness, and spread sample more evenly amongst the population

Question 18

stratefied random sampling? process?

Answer 18

used when populatin consists of identifyable GROUPS but is homogenous in the characteristics you are studying, allows adequate representation of all strata.

process:

1) identify subgroups
2) select an equal random sample from each subgroup
3) combines subgroups into one sample

Question 19

pros and cons of stratefied random sampling?

Answer 19

pros; guarantees sample will contain relatively large group representing each subgroup (ex/ equal number of males and females), results of each statum may be of interest and can be analyzed separately, or compared with each other, reduced sampling error

cons;

• may not have enough information to stratify
• more complex to organize and analyze results
• difficult to measure sampling error
• may produce DISTORTED PICTURE of overall population
  - groups are represented equally in a sample but likely not equally distributed in the popultaion

Question 20

proportionate stratified random sampling?

Answer 20

allows the proportion of individuals with a given characteristic n the population to be reflected in the sample–> used in political surveys, more intensive and more work.

Question 21

Cluster sampling?

Answer 21

population members clustered in pre-existing and natural groups, and random clusters are chosen.

Question 22

example of cluster sampling?

Answer 22

a researcher wants to study 3rd graders, and needs 300 3rd grade students.
-instead of choosing 300 students in the city (target population) one by one, researcher can choose 10 classrooms at random, each with 30 students.

Question 23

pros and cons of cluster sampling?

Answer 23

pros; quick and easy, treatments can be done in groups

cons; less likely to represent whole population, imprecise if homogenous clusters, larger sampling error than SRS

Question 24

convenience sampling? pros and cons?

Answer 24

type of non-probability sampling methods; data collected are from participants that are accessible, available and willing to participate in the study.

pros; easy, less expensive, less time consuming
cons; weak form of smapling, no attempt to know the target population, no attempt to use random selection because its based on willingness to participate, little control of reprsentativeness.

Question 25

Quota sampling

Answer 25

type of non-probability sampling methods; availability sampling with constraint that there is a SET quote for each category–> kind of like stratified random sampling.