sampling Methods for Social and natural science research

2. Whether it is in Natural Science or Social
Science , most of the students will have to
do a project or assignment using some kind
of research
In the research process, sampling and data
collection is one of the vital components
This presentation will provide an
introduction to various sampling methods
that one could adopt in research in Social as
well as Natural Sciences

3. Process
The sampling process comprises several stages:
Defining the population of concern
Specifying a sampling frame, a set of items or events
possible to measure
Specifying a sampling method for selecting items or
events from the frame
Determining the sample size
Implementing the sampling plan
Sampling and data collecting
Reviewing the sampling process
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4. Population
All subjects (items/people) having the characteristic
the researcher wishes to understand.
As the time and resources are limited to get
information from all, it is required to identify a subset
or a representative sample of that population.

5. Sampling Frame
The sample which we believe to have the
elements/properties we are looking for
Is representative of the population

6. Sampling
A sample is a smaller but representative collection of
units from a population to determine the truths about
the population
Why sample
As time, resources and work are limited need to work
on something manageable but representative

7. Methods of data collection
i. Measurements
ii. Observations ( non- interviews)
iii. Personal interviews
iv. Type- structured or unstructured
v. Approach – direct or indirect
vi. Telephone interviews
vii. Mailing questionnaires

8. Types of Sampling
Quantitative sampling
 Sampling of biological material
 Plots, transects, quadrats etc.
Qualitative sampling
 Surveys, questionnaires, discussions, observations etc.

9. Sampling tools in biological data
collection

10. Quadrat Sampling
Transect Sampling

11. Some methods used in sociological
data collection
Surveys
Key informant interviews

12. Preparation of a questionnaire
with different categories
I. Quantity or information
II. In which year did you receive the membership of Knuckles
Environment Society?
III. Category
IV. Have you ever been or are you now involved in conservation
activities for the nature?
V. 1.Yes(currently) 2.yes (in the past) 3.Never
VI. List or multiple choices
VII. Do you think the time spend on nature protection programs as any
of the following?
1.A must 2.a necessity 3.a right 4.an investment
5.Waste of time 6.non of these

13. iv. Scale
How would you describe your parents’ attitude to nature
protection programs?
1.Very positive 2.positive 3.mixed/neutral 4.negative
5.very negative 6.not sure
v. Ranking
What do you see as the main purpose of your nature
protection activities? Please rank all these relevant in order
from 1.
Personal development/career development/ subject interest/
recreation/ fulfill ambition /keeping stimulated /other

14. vi. Complex grid/table
How would you rank the benefits of your study for each of
the following. Please rank each item.
For Very
positive
Positive Neutr
al
Negativ
e
Very
negative
Not
sure
you
Your family
Your
employer
country
community

15. Vii. Open ended
We would like to hear from you if you have any further
comments.

16. Ethical issues in data collection
Ethical issues concerning the participants….
I.Collecting information (time wasting)
II.Seeking consent
III.Providing incentives
IV.Seeking sensitive information
V.Possibility of causing harm to the participants
VI.Maintaining confidentiality

17. Ethical issues in data collection
Ethical issues relating to the researcher….
i.Avoiding bias
ii.Provision of deprivation of a treatment
iii.Using inappropriate research methodology
iv.Incorrect reporting
v.Inappropriate use of information

18. What is your population of interest?
To whom do you want to generalize
your results?
All doctors
School children
All Canadians
All Women aged 15-45 years
Other

19. 19
SAMPLING BREAKDOWN

20. SAMPLING…….
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TARGET POPULATION
STUDY POPULATION
SAMPLE

21. Types of SamplingProbability Sampling
Every unit in the population has a chance of being
selected in the sample
All sample units are given same weight
Also known as equal probability of selection
Non Probability Sampling
Some elements of the population have no chance of
selection hence non random sampling
Sampling is done based on a predetermined criteria

22. Non probability sampling includes
Accidental Sampling
Quota Sampling
Purposive Sampling

23. Example
We visit every household in a given street,
and interview the first person to answer the
door. In any household with more than one
occupant, this is a nonprobability sample,
because some people are more likely to answer
the door (e.g. an unemployed person who
spends most of their time at home is more
likely to answer than an employed housemate
who might be at work when the interviewer
calls) and it's not practical to calculate these
probabilities.

24. Types of Samples
Probability (Random) Samples
Simple random sample
Systematic random sample
Stratified random sample
Multistage sample
Multiphase sample
Cluster sample
Non-Probability Samples
Convenience sample
Purposive sample
Quota
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25. SIMPLE RANDOM SAMPLING
• Applicable when population is small, homogeneous
& readily available
• All subsets of the frame are given an equal
probability. Each element of the frame thus has
an equal probability of selection.
• It provides for greatest number of possible
samples. This is done by assigning a number to
each unit in the sampling frame.
• A table of random number or lottery system is
used to determine which units are to be selected.
• Estimates are easy to calculate.
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26. SIMPLE RANDOM SAMPLING
contd……..
Disadvantages
If sampling frame large, this method
impracticable.
Minority subgroups of interest in population
may not be present in sample in sufficient
numbers for study.
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27. SYSTEMATIC SAMPLING
Systematic sampling relies on arranging the
target population according to some ordering
scheme and then selecting elements at regular
intervals through that ordered list.
Systematic sampling involves a random start
and then proceeds with the selection of every
kth element from then onwards. In this case,
k=(population size/sample size).
A simple example would be to select every 10th
name from the telephone directory (an 'every
10th' sample, also referred to as 'sampling with
a skip of 10'). 27

29. SYSTEMATIC SAMPLING……
ADVANTAGES:
Sample easy to select
Suitable sampling frame can be identified
easily
Sample evenly spread over entire reference
population
DISADVANTAGES:
Sample may be biased if hidden periodicity in
population coincides with that of selection.
Difficult to assess precision of estimate from
one survey.
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30. Stratified Sampling
The sampling frame is organised into pre determined
strata
Sampling is done within the strata as an independent
sub population
Individual elements are randomly selected within it
As each stratum is treated as independent population
different sampling approaches can be applied to
different strata.

31.  Advantages
 Ensures proportionate representation of the sample
 E.g.. If we want to represent the minority sub groups adequately
this can be done by this
 Drawbacks
 When there are many strata to be used, the sampling size per
group may be larger than other methods
 Stratifying variable may be related to some but not to others and
may lead to complications
 If equal no of samples taken from all the stratified groups, less
representative ones could be over sampled if not careful.

32. STRATIFIED SAMPLING…….
32
Draw a sample from each stratum

33. CLUSTER SAMPLING
Cluster sampling is an example of 'two-stage
sampling' .
 First stage a sample of areas is chosen
 Second stage a sample of respondents within
those areas is selected.
 Population divided into clusters of homogeneous
units, usually based on geographical contiguity.
Sampling units are groups rather than individuals.
A sample of such clusters is then selected.
All units from the selected clusters are studied.
Cuts down on the cost of travel and other administrative costs
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34. Difference Between Strata and Clusters
Although strata and clusters are both non-
overlapping subsets of the population, they differ in
several ways.
All strata are represented in the sample; but only a
subset of clusters are in the sample.
With stratified sampling, the best survey results
occur when elements within strata are internally
homogeneous. However, with cluster sampling, the
best results occur when elements within clusters are
internally heterogeneous
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35. Activity
In estimation of immunization coverage in a
province, data on seven children aged 12-23
months in 30 clusters are used to determine
proportion of fully immunized children in the
province.
Give reasons why cluster sampling is used in this
survey.

36. Non Probability Sampling methods

37. CONVENIENCE SAMPLING
Sometimes known as grab or opportunity sampling or accidental
or haphazard sampling.
A type of nonprobability sampling which involves the sample being
drawn from that part of the population which is close to hand.
That is, readily available and convenient.
The researcher using such a sample cannot scientifically make
generalizations about the total population from this sample
because it would not be representative enough.
 For example, if the interviewer was to conduct a survey at a
shopping center early in the morning on a given day, the people
that he/she could interview would be limited to those given there
at that given time, which would not represent the views of other
members of society in such an area, if the survey was to be
conducted at different times of day and several times per week.
This type of sampling is most useful for pilot testing.
In social science research, snowball sampling is a similar technique,
where existing study subjects are used to recruit more subjects
into the sample.
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38. 38
CONVENIENCE SAMPLING…….
 Use results that are easy to get
38

39. QUOTA SAMPLING
 The population is first segmented into mutually exclusive
sub-groups, just as in stratified sampling.
Then judgment used to select subjects or units from
each segment based on a specified proportion.
For example, an interviewer may be told to sample 200
females and 300 males between the age of 45 and 60.
It is this second step which makes the technique one of
non-probability sampling.
 In quota sampling the selection of the sample is non-
random.
For example interviewers might be tempted to interview
those who look most helpful. The problem is that these
samples may be biased because not everyone gets a
chance of selection. This random element is its greatest
weakness and quota versus probability has been a matter
of controversy for many years
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40. Judgmental sampling or
Purposive sampling
- The researcher chooses the sample based on
who they think would be appropriate for the
study. This is used primarily when there is a
limited number of people that have expertise in
the area being researched
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41. PANEL SAMPLING (Time Series)
 Method of first selecting a group of participants through a
random sampling method and then asking that group for the same
information again several times over a period of time.
Therefore, each participant is given same survey or interview at
two or more time points; each period of data collection called a
"wave".
This sampling methodology often chosen for large scale or nation-
wide studies in order to gauge changes in the population with
regard to any number of variables from chronic illness to job
stress to weekly food expenditures.
 Panel sampling can also be used to inform researchers about
within-person health changes due to age or help explain changes in
continuous dependent variables such as spousal interaction.
 There have been several proposed methods of analyzing panel
sample data, including growth curves.
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42. Selecting sample sizes
Selecting sample size is a function of
Study goals
Degree of precision required
Design type
Budget
Other (ethical etc.)

43. Selecting the sample sizeA simple formula for this is as follows;
n = N/1+N*(e)2
Where
n=sample size
N = population size
e=the confidence level we like to work with (eg. If it is
95% then the error is 5% (0.05); if it is 99% then the
error is 1% (0.01)

44. The larger the population variability larger the
sample size to get an accurate reading
If the population is mostly homogenous the sample
size can be small

45. Example:
It is required to identify a presence of a disease in a
population. The number of the population that we
need to get information is 2500. We would like to
have the confidence level is 95%. Then the sample
size would be
N=2500/1+ (2500)*(0.05)2
=344

46. Eg. Investigating the level of
biodiversity in a natural forests
Using either plots or transects, the sampling needs to
be increased until the number of plant species
becomes no more

47. Describe physical/biological and sociological
experiments separately taking some
examples
For examples
Biological experiments – can show how to use the
plots/transects and give reasons for using them – this
is for non moving objects such as plants.
For moving objects – circular plots with time series
observations
For social experiments – other methodologies can be
used such as interviews, observations, key informant
surveys, focal groups discussions etc. – elaborate this

48. Cheers
Maxwell
maxran1@yahoo.co
m