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15. AMB3 Quantitative Methods 2015
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Transcript of 15. AMB3 Quantitative Methods 2015
QUANTITATIVE METHODS
MDSC3200Affette McCaw-Binns, Community Health Section
Learning Objectives
After the lecture and with supplemental reading you should:
1. Distinguish between qualitative and quantitative methods of data collection
2. State the reasons why sampling is performed3. Distinguish between probabilistic and non-
probabilistic sampling methods 4. Describe probabilistic sampling methods
random, systematic, stratified, cluster, multi-stage
5. Describe non-probabilistic (convenience) sampling methods
6. State the advantages and disadvantages of each sampling method
7. State how each sampling method could bias study results
21/04/23McCaw-Binns
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Quantitative vs Qualitative Research Quantitative methods – document amount of the
problem How much of a problem there is Who numerically are affected
Study based on measures of quantity or frequency Findings described in numbers, not words
Qualitative methods – classify phenomena What, how, when, where – the essence/ambience The nature of the problem as people perceive it
Concepts, definitions, characteristics, symbols, descriptors Why groups differ What social/behavioural problems influence disease
and how21/04/23McCaw-Binns
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Quantitative data collection methods
Census – full count of total population Survey – sample of population
Study units selected from the population in such a way that the findings can be generalized to the total population
Ensure that sample selected is not biased This affects the type of conclusions made
Bias – Last defines bias as: “Any effect at any stage of investigation or
inference tending to produce results that depart systematically from the true values”
21/04/23McCaw-Binns
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Why sample? Not feasible to always measure the whole
population A subset, which is carefully chosen…
can be representative of the entire population
can provide valid information about the population
Everyday life consists of making generalizations from samples Example:
Population – medical/dental students knowledge of epidemiology
Sample - responses to quiz questions on epidemiology 21/04/23McCaw-Binns
5
General principles
Statistical techniques allow inferences to be made about populations once samples … Are representative of the parent population Are sufficiently large Achieve high response rate
A representative sample has all the characteristics of the population from which it is drawn and must be… Internally valid – measures what it is intended to
measure Externally valid – can be generalized to the wider
population from which the sample was drawn21/04/23McCaw-Binns
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“Population” terms
Population – group of people (institutions, cases or
objects) defined as under study by a researcher e.g. infants, hospitals, bats
Sample or target population – group or population from which a sample is
drawn Population – pregnant women in Jamaica Sample population – pregnant women in
KSA Would this sample be externally valid?
21/04/23McCaw-Binns
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Types of samples Non-probability – sample chosen in
haphazard fashion – of limited utility Convenience Snowball Quota
Probability – each unit in the total population has a known probability or chance of being selected Simple random Systematic Stratified Cluster
21/04/23McCaw-Binns
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Non-probability Convenience sample Also called incidental or haphazard or grab sample
grab who you can get e.g. stand at Queens Gate and asking questions of
passersby Sample based on availability No sampling frame needed Can be chosen systematically
every nth patient in the clinic today or first “n” patients Which approach is better and why?
Drawback:Drawback: Sample may not be representative Some units may be over-selected, other under-
selected or missed altogether Impossible to adjust for such distortions
Bad sample = incorrect conclusions!21/04/23McCaw-Binns
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Non-probability Quota sampling
Species of the convenience sample Composition of sample determined beforehand Sample units selected so that all categories of
specific characteristic is represented = Quotas, e.g.
Age – 55% young, 35% middle age, 10% old Gender – 48% male, 52% female
Interview as many people in each category until fill the quota for characteristic of interest
Drawback:Drawback: May still not be representative Some groups not represented Some groups over-represented21/04/23McCaw-Binns
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Non-probability Purposive sample Sample selected to fill specific purpose of
study e.g. Interested in full spectrum of beliefs on a
subject so people on the fringes deliberately included (over-represented)
Interested in attitudes to service at antenatal clinic so sampling done at peak times
Drawback:Drawback: External validity?
In the psychosis paper, the target population was sampled from community care facilities
Why were cases and controls so different? 21/04/23McCaw-Binns
11
Non-probability Snowball sampling
Also called chain referral sampling People already sampled are asked to identify
others who meet the sample criteria Useful for hard-to-find groups, e.g.
persons with rare characteristics or ‘unusual’ behaviours and who tend to move in closed circles
(drug users, MSMs, CSWs, centenarians)
Drawback:Drawback: Sample may not be representative
21/04/23McCaw-Binns
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Non-probability Caution: use sparingly Non-probability samples useful in very
preliminary pilot-type studies Want to know how difficult clients respond
e.g. very bright, behavioural challenges May be useful in some qualitative studies May be useful in some biological studies
where each member of the population is expected to have the same characteristics
Drawback:Drawback: External validity
21/04/23McCaw-Binns
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Probability sampling
The probability of an individual being included in the sample is known
Parent population is defined (sampling frame needed)
Each sampling unit has the same probability or chance of being selected
Designed to be representative of the population and hence results generalizable to the wider population, if done correctly
Drawback:Drawback: May be expensive and difficult to do
21/04/23McCaw-Binns
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Probability Simple random sample
Every individual in the population has an equal/known chance of being selected
Selection has independence – selection of one unit does not affect selection of the others
Chance is only factor influencing sample selection
Requirement/Drawback: Sampling frame
List of all study units available ahead of time or One can be easily compiled 21/04/23McCaw-Binns
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Drawing a random sample
Prepare sampling frame List of all individuals, units, events etc in the
population, e.g. Voters list Census population Registered students Births Health centres etc.
Determine sample size sampling ratio, e.g. 10%, 1%
Devise system to draw sample, e.g. Lottery method (‘grab bag’) Table of random numbers
21/04/23McCaw-Binns
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Table of random numbers
39634634 62349 740884088 65564 16379 19713 3915353 14595595 35050 404690469 27478 44526 67331 9336565 30734 734 71571 837223722 79712 25775 65178 0776363 64628628 89126 912541254 24090 25752 03091 3941111 42831831 95113 435113511 42082 15140 34733 68076 76 80583583 70361 410471047 26792 78466 03395 17635 35 00209209 90404 994579457 72570 42194 49043 2433030 05409409 20830 019111911 60767 55248 79253 12317 17 95836836 22530 91785 1785 80210 34361 52228 33869 69 65358358 70469 871497149 89509 72176 18103 5516969
21/04/23McCaw-Binns
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Simple Random Sampling
Drawbacks:Drawbacks: Representativeness not guaranteed,
especially if sample is small While ideal, rarely possible in the
real world Sampling frame often non-existent May be too costly or inefficient to locate
selected individuals
21/04/23McCaw-Binns
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Systematic sample
A “system” is used to select subjects according to a simple systematic rule Names which begin a certain letter Those who attend clinic on Tuesday Every nth person selected
“n” determined from the sampling ratio expressed as “k” (the sampling interval)
Individuals chosen at regular intervals e.g. every 5th unit from the sampling frame
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Probability Systematic sample I
Process: Decide on the sampling fraction (e.g.
5%= 1 in 20) List the population in order Randomly select starting point based on
the sampling fraction (e.g. number between 1 and 20)
Start at or near the beginning of list and select every nth person (e.g. 20th) based on the sampling fraction
21/04/23McCaw-Binns
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Systematic vs. simple random sample AdvantagesAdvantages:: Less time consuming/easier to perform Doesn’t require sampling frame ahead of time
e.g. persons registering at hospital on a given day Equivalent to simple random sampling only if
the sampling frame has no systematic pattern
DDisadvantageisadvantage:: Risk of bias if sampling interval coincides with
systematic variation in the sampling frame, e.g.
a) Selecting days and choosing an interval of 7b) Every 5th house sampled
Uniform blocks of 5 houses and 5th house on corner lot → only corner lot houses included
Why are these problems?21/04/23McCaw-Binns
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Probability Stratified sampling I
Odd variations can occur by chance
Simple random sampling does not ensure that the proportion of individuals with certain characteristics in the sample will be same as reference population, e.g.
Sample → 40% males, 60% females (by chance)
Population= 48% males, 52% females Some group naturally over/under-represented
Examples?
21/04/23McCaw-Binns
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Why stratified sampling? Reduces sample variation with respect to strata
can ensure balanced numbers in major sub-groups in sample
ensures equal representation of characteristic which may be unevenly distributed within a population
If sampling ratio same for each strata: proportionate allocation
If sampling ratio differs by strata e.g. oversample sparsely populated strata to improve sample
size disproportionate stratified sampling
Advantage:Advantage: Ensures that proportion of individuals sampled with a
characteristic of interest is adequately represented in the sample
21/04/23McCaw-Binns
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Selecting a stratified sample
Process: Divide sampling frame (population) into
strata or subsets, based on characteristic of interest; e.g. sex
Sample independently within strata if random sampling used = stratified random
sample if systematic sampling done = stratified
systematic sample May have to ‘over’ sample sparsely
populated strata to have enough subjects to study
21/04/23McCaw-Binns
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Cluster sampling Similar to simple random and systematic except unit
of selection is a group or cluster of individuals Sampling frame = clusters, not individuals in the clusters Sampling unit = cluster
Probability proportional to size (PPS): Probability of selecting a sampling unit (e.g., village,
district, HC) proportional to the size of its population Useful when the sampling units vary considerably in size
Assures that those in larger sites have same probability of getting into the sample as those in smaller sites, and vice versa
What does this imply re number of highly populated versus sparsely populated clusters selected?
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When is Cluster Sampling preferred
If difficult to draw a simple random sample, e.g.
No complete sampling frame Logistically the process may be unwieldy, e.g.
Persons scattered over wide geographic area And, there is a list of groupings of study
units, e.g. Villages, schools, polling divisions, health centres
Then, these groupings can be randomly selected
Each unit = group of persons or units = cluster21/04/23McCaw-Binns
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Provisos
Remember …The unit of selection is the clusterCharacteristics of members within a cluster may be correlatedLarge clusters can bias results if individual characteristics pooledBetter to have many small clusters than few large clusters
21/04/23McCaw-Binns
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Selecting a cluster sample
Identify the sub-groups or clusters of the population
Not necessarily homogeneous as strata are Draw a random sample of clusters
Each cluster = persons/units in geographic area Then, select either:
All persons in cluster Random sample of persons in each cluster Only persons meeting pre-identified criteria, e.g.
All households with children 0-4 years Every nth household with an adult 30-79 years old 15 systematically selected adolescents 15-19 years
21/04/23McCaw-Binns
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Why use cluster sampling?
AdvantagesAdvantages Useful for national surveys Less expensive, time consuming Study population easier to locate
Not scattered geographicallyDisadvantagesDisadvantages May→ errors if the disease, attribute or variable
studied is clustered in the population, e.g. typhoid, Chikungunya Why would such diseases be clustered in space?
Initial survey may establish if there is clustering of characteristics
May be able to adjust for effect of clustering in data analysis21/04/23McCaw-Binns
29
Weighting
If strata are sampled proportionately the sample can be combined and treated like a simple random sample
In disproportionate sampling different weights must be applied to the strata before data are combined
21/04/23McCaw-Binns
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PROBABILITY Multi-stage sampling Population is sampled in stages, e.g. In two-stage sample
“Primary sampling units” selected e.g. schools, electoral districts
Individuals then selected from primary sampling units e.g. health centres, AN clinics, women 20-34
wks gestation Any method of sampling used at each stage
21/04/23McCaw-Binns
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Summary 1: Lesson exercises You want a sample of 10% of students in
the class How would you draw a:
Simple random sample Stratified random sample
What would be your stratification variable? Cluster sample
What natural clusters do we now have in the class?
What is the unit of randomization?
21/04/23McCaw-Binns
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Summary 2: Sample A
The book “Women and Love” (Shere Hite, 1987) was based on the author’s findings in response to a survey. She send out 100,000 questionnaires to women's organizations and 4.5% were returned
What kind of sampling was this? What problems do you see with this
approach and the conclusions from this book?
21/04/23McCaw-Binns
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Summary 2: Sample A
The book “Women and Love” (Shere Hite, 1987) was based on the author’s findings in response to a survey. She send out 100,000 questionnaires to women's organizations and 4.5% were returned
What kind of sampling was this? What problems do you see with this
approach and the conclusions from this book?
21/04/23McCaw-Binns
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Summary 3: Sample B
An interviewer is interested in learning about interest in civil unions in the gay community in Jamaica. They identified a few persons and asked them to suggest others who could be approached to take part in this study
What kind of sampling is this? Why was this the best approach to take
for this study?
21/04/23McCaw-Binns
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Summary 4: Sample C
Investigators wish to assess the association between dietary practices and attitudes towards lifestyle and nutrition counseling among medical students on all 3 UWI campuses.
How might they select a sample for this study?
What factors should they consider in deciding how to choose their sample? Location? Gender? Differences in class
size? Would the campus
specific gender distribution be an issue?
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McCaw-Binns
Campus2008/9
Total -FMS
Males
Females
Cave Hill 188 60(32%)
128(68%)
Mona 3081
887(29%)
2194(71%)
St Augustine
2007
725(36%)
1282(64%)
Summary 5: sampling
Do samples always have to be representative of the entire population? Samples should be representative of the target
population to whom one wishes to generalize ones findings. If only interested in children however there is no need to sample both adults and children.
How could the population sampled affect your conclusions? If samples are biased findings cannot be safely
generalized to the parent population from which is was drawn, leading to potentially erroneous conclusions
21/04/23McCaw-Binns
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