Sampling Methods & Size Calculations

Question 1

What are the types of probability sampling?

Answer 1

Simple random

Systematic random

Stratified random

Cluster random

Question 2

Advantages to simple random probability sampling

Answer 2

Easy

Large samples likely to be representative

No info needed prior to sampling

Each member of the population has equal chance of being selected

Question 3

Disadvantages to simple random probability sampling

Answer 3

Larger samples often needed to be representative

Comparisons between small subgroups may be difficult

Question 4

Describe systematic random probability sampling

Answer 4

A random sample, with a fixed periodic interval, is selected from a larger population.

Starting point is chosen at random.

Question 5

When can systematic random probability sampling be especially beneficial?

Answer 5

When don’t have access to complete list of population in advance.

Question 6

Advantages of systematic random probability sampling

Answer 6

Simple + more straight forward than simple random sampling

Low risk of error

Avoids risk of clustering groups

A numbered list of people isn’t required

Question 7

Disadvantages of systematic random probability sampling

Answer 7

Not all individuals have equal chance of selection

Assumes size of population can be determined

Small risk that researcher could influence the order of the list in their favour

Question 8

When is stratified random sampling used

Answer 8

When we want to guarantee that a certain characteristic of our sample is included in the sample itself

i.e age or gender

Question 9

What does stratified random sampling ensure

Answer 9

A proportional number of people from a subpopulation

Question 10

Advantages of stratified random sampling

Answer 10

Fewer people needed to achieve the same representativeness compared to simple random sample.

Can customise sample to be comparable to wider population.

Can make inferences about small subgroups.

Question 11

Disadvantages to stratified random sampling

Answer 11

Info is req before for particular stratification variables

Becomes difficult if theres too many stratification variables

Question 12

What would cluster random sampling be appropriate for?

Answer 12

School based intervention

Question 13

List the non-probability sampling methods

Answer 13

Convenience

Purposive

Quota

Question 14

Convenience sampling

Answer 14

Ind. selected for ease

No equal probability of being selected

Question 15

Advantages to convenience sampling

Answer 15

Quick + easy compared to all probability sampling

Question 16

When is convenience sampling mostly used?

Answer 16

For pilot studies

Question 17

Disadvantages to convenience sampling

Answer 17

Can’t estimate sampling error

Exclusion bias i.e gender

Can’t generalise from the sample to the population

Question 18

Purposive sampling

Answer 18

Ind are selected because they posses a particular characteristic of interest or they represent a specific group.

No equal probability of being selected

Question 19

What is purposive sampling mainly restricted to?

Answer 19

Qualitative studies

Question 20

Advantages of purposive sampling

Answer 20

Guarantees characteristics of interest will be selected.

Good for qualitative studies

Question 21

Disadvantages of purposive sampling

Answer 21

Can’t estimate sampling error

Exclusion bias, biased responses

Can’t generalise from the sample to the population

Question 22

Quota sampling

Answer 22

Sampling continues until the quota for a specific characteristic is achieved, after which no more people with that characteristic will be selected.

Question 23

What is quota sampling often used for?

Answer 23

Social surveys

Question 24

Advantages to quota sampling

Answer 24

Ensures the sample have some characteristics the same as the population.

Common in large social surveys.

Question 25

Disadvantages to quota sampling

Answer 25

Can’t estimate sampling error

Exclusion bias

Can’t generalise from sample to population

Question 26

What may it mean if a sample size is too small?

Answer 26

Underpowered

Question 27

What may a sample size that is too big mean for the study?

Answer 27

Becomes overpowered

Question 28

Ethical issues relative to sample sizes being too big

Answer 28

Unethical due to causing unnecessary inconvenience

Wastes time + money

Extremely unethical if exposing more people than required to pain/risk of injury.

Question 29

Ethical issues relative to sample sizes being too small

Answer 29

Could fail to identify a true meaningful effect as being statistically sig. different from ‘no effect’.

Unethical due to waste of resources, peoples time and could cause misleading conclusions.

Question 30

What does the sample size calculation derive from?

Answer 30

Desired power - Should not be less than 80%

Desired significance level - 0.05

Estimate of the true effect size

Question 31

What comes under the estimate of the true effect size

Answer 31

Means/SD = Cohens D

Proportions = Rel. risk or Odds Ratio

Question 32

When are cohens D or rel. risk or odds ratio used

Answer 32

Cohens D = Data on continuous scale

Rel. risk or odds ratio = Used for dichotomous outcome variables (i.e yes or no).

Question 33

Power

Answer 33

Probability that, for a given effect size, your study will find a statistically significant effect.