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MED-106 Research Methods ✅ > 7.1: Systematic Error- Selection Bias & More ✅ > Flashcards

7.1: Systematic Error- Selection Bias & More ✅ Flashcards

1
Q

Random error

A

Error introduced solely by chance

Is inherent in the sampling process

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2
Q

Systematic error

A

Bias

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

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3
Q

Error in epidemiological studies

A

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

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4
Q

How is systematic error affected by sample size?

A

Bias always remains the same, regardless of sample size

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5
Q

Selection bias

A

Systematic error

Resulting from participants used not being representative of the source population

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

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6
Q

What greatly affects selection bias?

A

The sample method

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7
Q

Sampling methods

A

Random sampling

Systematic sampling

Non-probability sampling

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8
Q

Random sampling

A

(Also known as Probability sampling)

Sample selected by probabilistic methods

Allows strong statistical interference about the whole group

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9
Q

Probability sampling types

A

Simple random sampling

Stratified random sampling

Cluster sampling

Multi-stage sampling

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10
Q

Systematic sampling types

A

Simple systematic sampling

Proportional quota sampling

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11
Q

Systematic sampling

A

Sample selected according to simple, systematic rules

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12
Q

Non-probability sampling

A

Sample selected by convenience

Involved non-random selection based on convenience

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13
Q

Simple random sampling: overview

A

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

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14
Q

When is SRS mostly used?

A

In quantitative research

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15
Q

SRS steps

A
  1. Identify source population
  2. Set up sampling frame
  3. Decide on sample size
  4. Randomly select individuals from sampling size
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16
Q

SRS pros

A

Representative sample (if sample size is large enough)

Less costly and less time-consuming

Ideal for quantitative studies and test of hypothesis

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17
Q

SRS cons

A

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

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18
Q

Stratified random sampling: overview

A

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

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19
Q

Stratified random sampling: steps

A
  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
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20
Q

Stratified random sampling pros

A

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

Allows comparison of population sub-groups

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21
Q

Stratified random sampling cons

A

Time-consuming

Higher complexity might give rise to errors

22
Q

Cluster sampling overview

A

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)

23
Q

Difference between stratified and cluster

A

Stratified takes individuals from groups

Cluster takes the whole group

24
Q

Cluster sampling steps

A
  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
25
Q

Cluster pros

A

Good for large and diverse pops

Less costly and less time consuming

26
Q

Cluster cons

A

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

27
Q

Multi-stage sampling

A

Uses structure of natural clusters of individuals within the population

After randomly selecting clusters, there is a random selection of individuals within the cluster

28
Q

Multi-stage sampling

A
  1. Random selection of large clusters
  2. Random selection of smaller clusters within large clusters

3.Random selection of individuals within smaller clusters

29
Q

Multi-stage sampling pros

A

May improve sample representativeness

Less costly and less time consuming

30
Q

Multi-stage cons

A

Representativeness may be compromised if:

-too few clusters are selected
-clusters are too specific
-clusters contain too few individuals

31
Q

Systematic sampling

A

Sample selected by by simple systematic rule

Could be equivalent to simple random sample if there is no biasing pattern in selection process

32
Q

Systematic sampling steps

A
  1. Identify source population
  2. Set up sampling frame
  3. Decide on sample size
  4. Systematically select individuals from sampling frame
33
Q

Systematic sampling pros

A

More convenient alternative approach if random sampling isn’t possible

Faster and potentially cheaper

34
Q

Proportional quota sampling

A

Same principal as stratified random sampling

Strata filled by non-random sampling

35
Q

Proportional quota sampling

A
  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
36
Q

Proportional quota sampling: steps

A
  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)
37
Q

Proportional quota sampling pros

A

Acceptable convenient method if random sampling is not possible

Compared to systematic, could ensure original population structure (as it uses predefined population) strata

38
Q

Proportional quota sampling cons

A

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

39
Q

Convenience sampling

A

Most frequent non-probability sampling

Based on convenience

40
Q

Convenience samplings steps

A
  1. Identify source population
  2. Decide on sample
  3. Conveniently select individuals
41
Q

Examples of non-probabilistic sampling methods

A

Convenience

Purposive

Voluntary response

Snowball

42
Q

Advantages of convenience sampling

A

Cheap

Fast

Convenient (duh)

43
Q

Cons of convenience sampling

A

Representativeness of the sample will DEFINITELY be compromised

->as individuals are selected in non-random fashion

44
Q

How to decide which sampling method should be used?

A

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

45
Q

Which method is best for small samples?

A

Stratified random sampling

46
Q

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

A

Random sampling techniques

47
Q

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

A

Selection bias is operating to some extent

48
Q

Descriptive research

A

Prevalence of a disease in a population

Important to have perfectly representative sample as selection bias will greatly influence findings

49
Q

Analytic research

A

Investigating exposure-outcome association

Minor selection bias may not affect findings to a large extent

50
Q

What method should always be avoided?

A

Convenience sampling

MED-106 Research Methods ✅ (21 decks)

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