Non-probability sampling methods do not allow researchers to determine sampling errors because selections are non-random. There are several types of non-probability sampling including convenience sampling, purposive sampling, quota sampling, and snowball sampling. When determining sample size, researchers must consider the study goals, desired precision, confidence level, population variability, and anticipated response rate to obtain a sample that accurately represents the target population.

1. Non Probability Sampling &
Determining Size of Sample

2. Non Probability Sampling
• Non probability sampling is any sampling method
where some elements of the population
have no chance of selection (these are sometimes
referred to as 'out of coverage„ / 'under covered'), or
where the probability of selection can't be accurately
determined.
• It involves the selection of elements based on
assumptions regarding the population of
interest, which forms the criteria for selection.
• Hence, because the selection of elements is non
random, non probability sampling does not allow the
estimation of sampling errors.

3. Probability Sampling V/S Non Probability
Sampling
• Non probability sampling does not involve random selection and
probability sampling does.
• With a probabilistic sample, we know the odds or probability
that we have represented the population well. With non
probability samples, we may or may not represent the
population well
• In general, researchers prefer probabilistic or random sampling
methods over non probabilistic ones, and consider them to be
more accurate and rigorous.
• However, in applied social research there may be circumstances
where it is not feasible, practical or theoretically sensible to do
random sampling.

4. Methods of Non Probability Sampling
 Accidental, Haphazard or Convenience
Sampling
Purposive Sampling or Judgmental
Sampling
Quota Sampling
Snowball Sampling

5. Accidental, Haphazard or Convenience Sampling
• A type of non probability sampling which
involves the sample being drawn from that part
of the population which is close to hand.
• That is, a population is selected because it is
readily available and convenient.
• It may be through meeting the person or
including a person in the sample when one
meets them or chosen by finding them through
the internet or phone.

6. Accidental, Haphazard or Convenience Sampling
Several important considerations for researchers using
convenience samples include:
• Are there controls within the research design or experiment
which can serve to lessen the impact of a non-random
convenience sample, thereby ensuring the results will be
more representative of the population?
• Is there good reason to believe that a particular
convenience sample would or should respond or behave
differently than a random sample from the same
population?
• Is the question being asked by the research one that can
adequately be answered using a convenience sample?

7. Purposive or Judgmental 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.
• Purposive sampling can be very useful for situations
where you need to reach a targeted sample quickly and
where sampling for proportionality is not the primary
concern.
• With a purposive sample, you are likely to get the
opinions of your target population, but you are also likely
to overweight subgroups in your population that are more
readily accessible.

8. Quota Sampling
In quota sampling, you select people non randomly
according to some fixed quota. There are two types
of quota sampling :
• Proportional Quota: Represent major characteristics
of population by proportion. E.g. 40% women and 60%
men. Also have to decide the specific characteristics
for the quota. E.g.
gender, age, education, race, religion etc.)
• Non – Proportional Quota: Specific minimum size of
cases in each category. Not concerned with upper
limit of quota. Smaller groups are adequately
represented in sample.

9. Snowballing Sampling
• In snowball sampling, you begin by identifying
someone who meets the criteria for inclusion in your
study.
• You then ask them to recommend others who they
may know who also meet the criteria.
• This method would hardly lead to representative
samples, there are times when it may be the best
method available.
• Snowball sampling is especially useful when you are
trying to reach populations that are inaccessible or
hard to find.

10. Overview of All Sampling Methods
• Convenience Sampling: Use who's available.
• Purposive Sampling: Selection based on purpose.
• Quota Sampling: Keep going until the sample size
is reached.
• Proportionate Quota Sampling: Balance across
groups by population proportion.
• Non Proportionate Quota Sampling: Study a
minimum number in each sub-group.
• Snowball Sampling: Get sampled people to
nominate others.

11. Determining Size of Sample
There are 5 steps in deciding size of sample:
o Determining Goals
o Determine desired Precision Of Results
o Determine confidence level
o Estimate Degree of Variability
o Estimate the Response Rate

12. Determining Goals
• Know the size of the population with which you‟re
dealing. If your population is small (200 people or
less), it may be preferable to do a census of
everyone in the population, rather than a sample.
• Decide the methods and design of the sample
you‟re going to draw and the specific attributes
or concepts you‟re trying to measure.
• Know what kind of resources you have available

13. Determine desired Precision of Results
• The level of precision is the closeness with which the
sample predicts where the true values in the
population lie.
• The difference between the sample and the real
population is called the sampling error.
• For example, if the value in a survey says that 65% of
farmers use a particular pesticide, and the sampling
error is ±3%, we know that in the real-world
population, between 62% and 68% are likely to use this
pesticide.
• This range is also commonly referred to as the margin
of error.

14. Determine the Confidence Level
• The confidence level involves the risk you‟re willing
to accept that your sample is within the average or
“bell curve” of the population.
• A confidence level of 90% means that, were the
population sampled 100 times in the same manner, 90
of these samples would have the true population
value within the range of precision and 10 would be
unrepresentative samples.
• Higher confidence levels require larger sample sizes
• If the confidence level chosen is too low, results will
be “statistically insignificant”.

15. Estimate the Degree of Variability
• Variability is the degree to which the attributes or
concepts being measured in the questions are
distributed throughout the population.
• A heterogeneous population divided more or less 50%-
50% on an attribute or a concept, will be harder to
measure precisely than a homogeneous
population, divided say 80%-20%.
• Therefore, the higher the degree of variability one
expect the distribution of a concept to be in target
audience, the larger the sample size must be to
obtain the same level of precision.

16. Estimate The Response Rate
• The base sample size is the number of responses you
must get back when you conduct your survey.
• Since not everyone will respond, it is needed to
increase sample size, and perhaps the number of
contacts attempt to account for these non-responses.
• To estimate response rate that one is likely to get, one
should take into consideration the method of survey
and the population involved.
• Direct contact and multiple contacts increase
response, as does a population which is interested in
the issues, involved, or connected to the institution
doing the surveying, or, limited or specialized in
character

17. Some tips
“Determining sample size”
Rules of thumb:
* anything ≥ 30 cases
* smaller population needs greater
sampling intensity
* type of sample
Statistical rules:
* level of accuracy required
* a priori population parameter
* type of sample

18. Why Sample Size Matters?
• Too large → waste time, resources and
money
• Too small → inaccurate results
• Generalize ability of the study results
• Minimum sample size needed to estimate
a population parameter.

19. Thank You