research planning and design 5

Question 1

issues with small samples

Answer 1

• easily distorted by extreme values
• less certainty about the location of the true mean
• larger confidence intervals can mask real differences between groups/ conditions

Question 2

issues with large samples

Answer 2

• sensitive to extreme outliers
• detecting insignificant differences which are too small to be meaningful
• sampling bias may still persist
• can be unethical
• waste of resources
• burden on participants

Question 3

probability sampling methods

Answer 3

= used to make a precise statement about a specific population
- - simple random sampling
- stratified random sampling
- cluster sampling
- systematic sampling

Question 4

non- probability sampling methods

Answer 4

= not as sophisticated, more common
- convienience sampling
- purposive sampling
- snowball sampling
- quota sampling

Question 5

simple random sampling

Answer 5

= every member of the population has an equal chance of being selected

• representative sample, cheap and easy to implement, unbiased.
• needs information about entire population -> might not be possible

Question 6

stratified random sampling

Answer 6

= divide population into smaller sub-populations based on specific characteristics( age, gender) -> then random sampling between thes sub groups.

• diversity of sample is reflected, includes sub groups that can usually be underrepresented with small sample sizes
• requires detailed knowledge of population, unknown characteristics?,

Question 7

proportional vs disproportional

Answer 7

proportional = The size of the sample from each stratum is proportional to the size of the stratum in the overall population

disproportional = The size of the sample from each stratum does not match the proportions of the strata in the population

Question 8

cluster sampling

Answer 8

= divides a population into smaller groups or clusters -> cluster is selected randomly.

• time and cost efficient, large populations, high external validity
• too few clusters may be selected, difficulty defining clusters, might be biased if clusters are nor representative

Question 9

systematic sampling

Answer 9

= select members of the population at regular intervals eg every third person

• easy, time-efficient, using fixed interval reduces bias, good when you dont know details about full population
• not recommended for periodically ordered groups (employee shift patterns)

Question 10

convienience sampling

Answer 10

= take them where you find them, people who are easy to contact/ in geographical proximity/ volunteers

• participants readily available, quick and easy
• high risk of bias, self selection has issues with external validity

Question 11

purposive sampling

Answer 11

= obtaining a sample with pre-determined criteria

• theoretical generalisability
• prone to research bias/ no statisitical inference

Question 12

snowball sampling

Answer 12

= start with one or more initial participants, then continue to recruit on the basis of referrals from those initial participants -> until desired sample is reached

• addresses research Q, efficient for hard to find populations
• research bias, time intensive recruitment