Selecting Research Participants: Chapter 5

Question 1

population

Answer 1

group sharing some common characteristics

Question 2

sample population

Answer 2

• subset of the population
• people selected for the study

Question 3

representative sample goal

Answer 3

• to select samples that are similar to the populations
• if the sample represents the population, the results of the study can be generalized to the population
• refer to PowerPoint 5, slide 4 for diagram

Question 4

target population

Answer 4

group defined by the researcher’s specific interests

Question 5

accessible population

Answer 5

• easily available segment of a target population
• researchers typically select their samples from this type of population

Question 6

representative sample

Answer 6

sample which has the same characteristics as the population

Question 7

bias in regard to representativeness

Answer 7

• bias is a major threat to representativeness
• biased samples characteristics are very different from the population
• bias arises from sampling bias

Question 8

ways to get a biased sample

Answer 8

• Sampling only those who are easy to contact, like a convenience sampling
• sampling only those who volunteer, like self-selection (volunteering) sampling

Question 9

sample size

Answer 9

• large sample will probably be more representative than a small one
• minimum of 10 participants is required for statistical purposes

Question 10

power analysis

Answer 10

• to determine the sample size needed to obtain the expected results with a given degree of confidence

Question 11

Law of large numbers

Answer 11

the larger the sample size, the more likely it is that values obtained from the sample are similar to the actual values for the population

Question 12

categories of sampling

Answer 12

• non-probability sampling
• probability sampling

Question 13

non-probability sampling issues

Answer 13

• exact size of the population is NOT known, and it is NOT possible to list all the individuals in the population
• probability each individual has to be selected in the sample is UNKNOWN
• selection process is NOT unbiased
• greater risk of producing a biased sample than probability sampling

Question 14

probability sampling

Answer 14

• simple random sampling
• systematic random sampling
• stratified random sampling
• proportionate stratified random sampling
• cluster random sampling
• multistage random sampling

Question 15

simple random sampling

Answer 15

• equality: each individual has an equal chance of selection.
• independence: choice of one individual does not influence the probability of choosing another individual.
• E.g., Draw names out of a hat, use a
  random number table

Question 16

systematic random sampling

Answer 16

sample members from a larger population are selected according to a random starting point and a fixed, periodic interval
* Entire population is enumerated in a list
* Random starting point
* Every nth person

Question 17

stratified random sampling

Answer 17

• population is divided into subgroups (strata); equal numbers are then randomly selected from each of the subgroups.
• guarantees that each subgroup will have adequate representation
• overall sample is usually not representative of the population
• useful when the goal is to make comparisons among subgroups

Question 18

proportionate stratified random sampling

Answer 18

• population is subdivided into strata.
• number of participants from each stratum is selected randomly.
• proportions in the sample correspond
  to the proportions in the population.
• ensures the sample will be representative of the
  population

Question 19

cluster random sampling

Answer 19

• clusters (preexisting groups) instead of individuals are randomly selected from a list of all the clusters that exist within the population
• all members of the selected clusters comprise the sample
• easy method for obtaining a large, relatively random sample

Question 20

multistage random sampling

Answer 20

• random sampling at multiple stages
• effective in choosing a sample that is representative of a widely dispersed population
  E.g.:
  Canadian university professors’
  opinions on student literacy
• Stage 1: Randomly select universities
  across Canada
• Stage 2: Randomly select departments
  within universities
• Stage 3: Randomly select professors
  within departments

Question 21

methods of simple random sampling

Answer 21

• sampling with replacement
• sampling without replacement

Question 22

issues with simple random sampling

Answer 22

It is possible (although usually unlikely) to obtain a very distorted sample because of chance

Question 23

systematic random sampling: how to figure out n?

Answer 23

Select a random sample of 100 participants from a
population of 50,000.
* Step 1: Target population must be placed in a list
* Step 2: Select the interval: 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑠𝑖𝑧𝑒 /
𝑁𝑠𝑎𝑚𝑝𝑙𝑒 𝑠𝑖𝑧𝑒, 𝑛
= 500
* Step 3: Randomly pick a number from 1 to 500, e.g.,
342
* Step 4: Start with 342 and pick every 500th person
on the list after that number

Question 24

issues with systematic random sampling

Answer 24

ensures a high degree of representativeness, though it may violate the principle of independence

Question 25

issues with stratified random sampling

Answer 25

- does not adequately represent proportions found in the population - individuals in the population have different probabilities of being selected

Question 26

issues with proportionate stratified random sampling

Answer 26

- requires a lot of work - may make it difficult or impossible to compare subgroups within strata

Question 27

issues with cluster random sampling

Answer 27

- selections are not independent - E.g., students in one classroom are more likely to be similar to each other than students in another classroom)

Question 28

issues with multistage random sampling

Answer 28

- cost- and time-effective - could be issues with nonindependence

Question 29

probability random sampling issues

Answer 29

- methods can be unrealistic - too much time and effort - may not be practical or possible - need to know the population (i.e., have a list of all members of the population)

Question 30

non-probability random sampling

Answer 30

- Convenience Sampling - Quota Sampling - Purposive Sampling - Snowball Sampling

Question 31

convenience sampling

Answer 31

- individual participants are obtained by selecting those who are available and willing - easy method - less expensive, time efficient than probability sampling methods - select a reasonably representative sample and clearly describe the selection process to help correct the problems with this form of sampling

Question 32

issues with convenience sampling

Answer 32

- a weak form of sampling - probably biased, especially if participants volunteer to participate

Question 33

quota sampling

Answer 33

- subgroups are identified to be included - quotas are established for individuals to be selected through convenience from each subgroup - reflect proportions in population - allows controlling the composition of a convenience sample - easy, economical, and time-efficient

Question 34

issues with quota sampling

Answer 34

- sample is probably biased - not randomly selected

Question 35

purposive sampling

Answer 35

- known as judgmental, selective, or subjective sampling * requires prior knowledge about the purpose of their study in order to know who to select and how to approach eligible participants

Question 36

snowball sampling

Answer 36

- begin with someone who meets the criteria for inclusion in your study - then ask them to recommend others who they may know who also meets the criteria - most used in hard-to-reach populations

Question 37

cluster random sampling example

Answer 37

looking at a school, and each cluster is a classroom we pick among the clusters