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

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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”

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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

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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?

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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?

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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

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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

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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

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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

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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

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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

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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

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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?

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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?

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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?

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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?

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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

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