A power point presentation of Non-Probability Sampling. Report in Methods of Research in Education.

1. Reporter: Ma. Cora
Antonette E. Yu
Purposive
sampling
Convenience
sampling
Purposive
sampling
Quota
sampling
Voluntary
sampling
Snowball
sampling
Dimension
sampling
Determination of
sample size
Non- probability
Sampling

2. Non-Probability Sampling
Non-probability sampling is a sampling technique where the
odds of any member being selected for a sample cannot be
calculated. It’s the opposite of probability sampling, where
you can calculate the odds. In addition, probability sampling
involves random selection, while non-probability sampling
does not–it relies on the subjective judgment of the
researcher.
The odds do not have to be equal for a method to be considered probability
sampling. For example, one person could have a 10% chance of being selected
and another person could have a 50% chance of being selected. It’s non-
probability sampling when you can’t calculate the odds at all.
One major disadvantage of non-probability sampling is that it’s impossible to
know how well you are representing the population. Plus, you can’t
calculate confidence intervals and margins of error. This is the major reason
why, if at all possible, you should consider probability sampling methods first.

3. Non-Probability Sampling
Non-Probability
Sampling
Quota Sampling
Purposive
Sampling
Convenience
Sampling
Dimension
Sampling
Voluntary
Sampling
Snowball
Sampling

4. Convenience Sampling
Convenience sampling (also called accidental sampling
or grab sampling) is where you include people who are easy to
reach. For example, you could survey people from your
workplace or school, a club you belong to, or you could go to a
local mall and survey local shoppers. Although convenience
sampling is, like the name suggests–convenient–it runs a high
risk that your sample will not be representative of the
population. Sometimes, a convenience sample is the only way
you can drum up participants. According to UC Davis, it could
be “a matter of taking what you can get”.
Convenience sampling does have its uses, especially
when you need to conduct a study quickly or you are on a
shoestring budget. It is also one of the only methods you can use when
you can’t get a list of all the members of a population.
For example, let’s say you were conducting a survey for a company who
wanted to know what SM Supermall employees think of their wages. It’s
unlikely you’ll be able to get a list of employees, so you may have to resort
to standing outside of SM and grabbing whichever employees come out of
the door (hence the name “grab sampling”).

5. purposive Sampling
A purposive sample is where a researcher selects a sample
based on their knowledge about the study and population.
The participants are selected based on the purpose of
the sample, hence the name.
In this method of sampling the choice of sample items
depends exclusively on the judgment of the investigators.
That is, the investigators exercises their judgment in the
choice and includes those items in the sample.

6. Quota Sampling
Quota sampling is a type of judgment
sampling and is perhaps the most
commonly used sampling technique in
non-probability category. In a quota
sample, quotas are set up according to
some specified characteristics.
For example, in radio listening survey, the
interviewers may be told to interview 500
people living in a certain areas and that
out of every 100 persons interviewed 60
are housewife, 25 farmers and 15 children
under the age of 18.With in these quotas
the interviewer is free to select the people
to be interviewed.

7. Dimensional sampling
Dimensional Sampling is an extension to quota sampling. The
researcher takes into account several characteristics (e.g. gender,
income, residence and education. The researcher must ensure that
there is at least one person in the study representing each of the chosen
characteristics.
For example, it may be important to
include the responses of people of
different ages, then ensures that the
sample includes respondents from each of
the groups thus identified.

8. Voluntary sampling
A voluntary sample is one of the main types of non-
probability sampling methods. A voluntary sample is made
up of people who self-select into the survey. Often, these
folks have a strong interest in the main topic of the survey.
For example, in the recent
international pageant here in
the Philippines, social media
are busy looking for the next
woman to be crowned Miss
Universe 2017 through an on-
line poll.

9. Snowball sampling
Snowball sampling is known as network
or chain referral sampling.
The researcher identifies a small
number of respondents who possess a
specific set of characteristics of
interest. Then these respondents will
provide others who possess the same
characteristics set. This can be useful
for a population where sampling is
difficult (ex. Gang members, rape
victims or drug addicts)

10. Determination of sample size
The sample size is an important feature of any empirical study in
which the goal is to make inferences about a population from a sample.
In practice, the sample size used in study is determined based of the
expense of data collection, and the need to have sufficient statistical
power.
Different opinions have been expressed by experts for the selection
of sample size (i.e 5%,10% or 25% of the population). There are no
hard and fast rule can be laid down. However, according to the law of
large number, the largest the sample size, the better the estimation, or
the larger the sample, the closer the ‘true’ value of the population. It
may also be pointed out that the sample size should neither be too large
nor too small. It should be 'optimum' (efficiency, representativeness,
reliability and flexibility).

11. Calculating the Sample Size (ss)
Before we can calculate a sample size, we need to determine a few things
about the target population and the sample we need:
1. Population Size (N) — How many total people fit your demographic? For
instance, if you want to know about mothers living in the US, your population size
would be the total number of mothers living in the US. Don’t worry if you are unsure
about this number. It is common for the population to be unknown or approximated.
2. Margin of Error (e) — No sample will be perfect, so you need to decide
how much error to allow. The confidence interval determines how much higher or lower
than the population mean you are willing to let your sample mean fall.
If you’ve ever seen a political poll on the news, you’ve seen a confidence interval. It will
look something like this: “68% of voters said yes to Proposition Z, with a margin of
error of +/- 5%.”
3. Confidence Level (Z-score) — How confident do you want to be that the
actual mean falls within your confidence interval? The most common confidence
intervals are 90% confident, 95% confident, and 99% confident.

12. 4. Standard of Deviation (sd) — How much variance do you expect in
your responses? Since we haven’t actually administered our survey
yet, the safe decision is to use .5 – this is the most forgiving number
and ensures that your sample will be large enough.
Your confidence level corresponds to a Z-score. This is a constant value
needed for this equation. Here are the z-scores for the most common
confidence levels:
• 90% – Z Score = 1.645
• 95% – Z Score = 1.96
• 99% – Z Score = 2.326
Formula:
Sample Size = (Z-score)² * sd*(1-sd) / (e)²
assuming you chose a 95%)) confidence level, .5 standard deviation, and a
margin of error (confidence interval) of +/- 5%.
ss = ((1.96)² x .5(1-.5)/ (.05)²
= (3.8416 x .25) / .0025
= .9604 / .0025
= 384.16
385 respondents are needed

13. n = ___N_ __
1+ Ne2
Where n = sample size desired;
N = population size; and
e = desired margin or sampling error
Using this formula, what would be the sample size if the total population (N) is
2,000 and the margin of sampling error you allow is 5%?
The sample size maybe computed this way:
n = ____2000_____
1+(2000)(.05)2
n = 2000
1+5
n = 2000
6
n = 333 respondents
When using this formula, the population is assumed to be normally
distributed. When the population is small or poor, this does not apply.

14. * Sample size for specified marginof errors
• Assumption of normal approximation is poor and therefore the
sample size formula does not apply.