7.1: Systematic Error- Selection Bias & More ✅

Question 1

Random error

Answer 1

Error introduced solely by chance

Is inherent in the sampling process

Question 2

Systematic error

Answer 2

Bias

Introduced by man-made actions relating to the conduct of the study

Question 3

Error in epidemiological studies

Answer 3

Random bias DECREASES with increase in sample size

If total population is used, random bias = 0

95% confidence interval becomes narrower increases with sample size

Question 4

How is systematic error affected by sample size?

Answer 4

Bias always remains the same, regardless of sample size

Question 5

Selection bias

Answer 5

Systematic error

Resulting from participants used not being representative of the source population

->leads to a bias sample, which gives rise to bias estimates

Question 6

What greatly affects selection bias?

Answer 6

The sample method

Question 7

Sampling methods

Answer 7

Random sampling

Systematic sampling

Non-probability sampling

Question 8

Random sampling

Answer 8

(Also known as Probability sampling)

Sample selected by probabilistic methods

Allows strong statistical interference about the whole group

Question 9

Probability sampling types

Answer 9

Simple random sampling

Stratified random sampling

Cluster sampling

Multi-stage sampling

Question 10

Systematic sampling types

Answer 10

Simple systematic sampling

Proportional quota sampling

Question 11

Systematic sampling

Answer 11

Sample selected according to simple, systematic rules

Question 12

Non-probability sampling

Answer 12

Sample selected by convenience

Involved non-random selection based on convenience

Question 13

Simple random sampling: overview

Answer 13

Most straight-forward

All individuals in the sampling frame have the same probability of being selected, independently of all others

Given a larger sample size, ensures individuals are representative of source population

Question 14

When is SRS mostly used?

Answer 14

In quantitative research

Question 15

SRS steps

Answer 15

1. Identify source population
2. Set up sampling frame
3. Decide on sample size
4. Randomly select individuals from sampling size

Question 16

SRS pros

Answer 16

Representative sample (if sample size is large enough)

Less costly and less time-consuming

Ideal for quantitative studies and test of hypothesis

Question 17

SRS cons

Answer 17

May be impractical if sampling frame is too large or pop is geographically diverse

If a large sample is used, could be time consuming or costly

Question 18

Stratified random sampling: overview

Answer 18

Same as SRS but within strata of the population

Size of the random sample should be proportional to the specific stratum size in the population

Question 19

Stratified random sampling: steps

Answer 19

1. Indemnify source population
2. Set up sampling frame
3. Decide on sample size
4. Decide on pre-defined population strata
5. Based on overall proportions of the population, calculate how many people should be sampled from each subgroup
6. Randomly select individuals to fill strata

Question 20

Stratified random sampling pros

Answer 20

Allows for more precise conclusions by ensuring every subgroup is properly represented in the sample

Allows comparison of population sub-groups

Question 21

Stratified random sampling cons

Answer 21

Time-consuming

Higher complexity might give rise to errors

Question 22

Cluster sampling overview

Answer 22

Based on natural clusters of individuals within the population
e.g. hospitals, schools, streets, city districts etc…

Involves taking a random sample from these clusters

Sampling frame is a list of all clusters

If the clusters are large, one of the above techniques may be used to choose sample (SRS or stratified random sampling)

Question 23

Difference between stratified and cluster

Answer 23

Stratified takes individuals from groups

Cluster takes the whole group

Question 24

Cluster sampling steps

Answer 24

1. Identify source population
2. Set up sampling frame (compromised of clusters)
3. Decide on sample size (number of clusters and individuals)
4. Randomly select clusters form sampling frame

Question 25

Cluster pros

Answer 25

Good for large and diverse pops Less costly and less time consuming

Question 26

Cluster cons

Answer 26

Substantial differences between clusters can cause errors Difficult to ensure the sampled clusters really represent the whole pop Representativeness may be compromised if: -too few clusters selected -clusters are too specific -cluster contain too few individuals

Question 27

Multi-stage sampling

Answer 27

Uses structure of natural clusters of individuals within the population After randomly selecting clusters, there is a random selection of individuals within the cluster

Question 28

Multi-stage sampling

Answer 28

1. Random selection of large clusters 2. Random selection of smaller clusters within large clusters 3.Random selection of individuals within smaller clusters

Question 29

Multi-stage sampling pros

Answer 29

May improve sample representativeness Less costly and less time consuming

Question 30

Multi-stage cons

Answer 30

Representativeness may be compromised if: -too few clusters are selected -clusters are too specific -clusters contain too few individuals

Question 31

Systematic sampling

Answer 31

Sample selected by by simple systematic rule Could be equivalent to simple random sample if there is no biasing pattern in selection process

Question 32

Systematic sampling steps

Answer 32

1. Identify source population 2. Set up sampling frame 3. Decide on sample size 4. Systematically select individuals from sampling frame

Question 33

Systematic sampling pros

Answer 33

More convenient alternative approach if random sampling isn’t possible Faster and potentially cheaper

Question 34

Proportional quota sampling

Answer 34

Same principal as stratified random sampling Strata filled by non-random sampling

Question 35

Proportional quota sampling

Answer 35

1. Indemnify source population 2. Set up sampling frame 3. Decide on sample size 4. Decide on pre-defined population strata 5. Select individuals to fill strata non-randomly

Question 36

Proportional quota sampling: steps

Answer 36

1. Identify source population 2. Set up sampling frame 3. Decide on the sample size 4. Decide on pre-defined population strata 5. Select individuals to fill strata (non-randomly)

Question 37

Proportional quota sampling pros

Answer 37

Acceptable convenient method if random sampling is not possible Compared to systematic, could ensure original population structure (as it uses predefined population) strata

Question 38

Proportional quota sampling cons

Answer 38

The representativeness may be compromised -as individuals aren’t selected randomly as individuals are not selected randomly

Question 39

Convenience sampling

Answer 39

Most frequent non-probability sampling Based on convenience

Question 40

Convenience samplings steps

Answer 40

1. Identify source population 2. Decide on sample 3. Conveniently select individuals

Question 41

Examples of non-probabilistic sampling methods

Answer 41

Convenience Purposive Voluntary response Snowball

Question 42

Advantages of convenience sampling

Answer 42

Cheap Fast Convenient (duh)

Question 43

Cons of convenience sampling

Answer 43

Representativeness of the sample will DEFINITELY be compromised ->as individuals are selected in non-random fashion

Question 44

How to decide which sampling method should be used?

Answer 44

Depends on -aim of study -nature of source population -sample size -other practical issues

Question 45

Which method is best for small samples?

Answer 45

Stratified random sampling

Question 46

Which method(s) is best to minimise selection bias?

Answer 46

Random sampling techniques

Question 47

What do we always assume when using non-random sampling?

Answer 47

Selection bias is operating to some extent

Question 48

Descriptive research

Answer 48

Prevalence of a disease in a population Important to have perfectly representative sample as selection bias will greatly influence findings

Question 49

Analytic research

Answer 49

Investigating exposure-outcome association Minor selection bias may not affect findings to a large extent

Question 50

What method should always be avoided?

Answer 50

Convenience sampling