Research 101: Sampling Techniques in Research

1. Research 101: Sampling Techniques
Harold Gamero

2. Sampling
Definition
Process by which a subgroup of a population is selected to make observations of a
population to make observations and statistical and statistical inferences.
• Collecting information from an entire population is virtually impossible
• Sample must be truly representative of the population of interest (universe of units of
analysis)
• Inadequate or biased samples are the main cause of erroneous inferences.
• Can be selected through randomized procedures (probabilistic sampling) or non-random
criteria procedures (non-probability sampling).

3. •Group to which the results are to be generalized.
•These are all the units of analysis that exist.
•It is the part of the population that is accessible and identifiable.
•Usually in a contact list.
•Unit of analysis vs. sampling unit.
•Fragment of the population that is analyzed by the researcher.
•It can be of two types: probabilistic or non-probabilistic.
Sampling process
Population
Sampling
frame
Sample

4. Non-probability sampling
• Technique in which some units of the population have zero chance of selection or where
the probability of selection cannot be accurately determined.
• Selection based on certain non-random criteria.
• Does not allow the estimation of sampling errors
• Information from a sample cannot be generalized back to the population.
• Types of nonprobability sampling techniques include convenience sampling, quota
sampling, expert sampling, snowball sampling.
• Best fit for qualitative researches.

5. Non-probability sampling
Elements of the sample
are chosen because they
are close to hand,
readily available, or
convenient.
Convenience sampling Quota sampling Expert sampling Snowball sampling
Any non-random
sampling, where the set
is segmented into
mutually exclusive
subgroups, representing
the population
stratification.
Respondents chosen
based on their expertise
on the phenomenon
being studied.
Identification of a few
respondents that match
the criteria, and then ask
them to recommend
others they know who
also meet the criteria.
For example:
If you stand outside a
shopping center and
hand out questionnaires
or interview them as
they walk in or out.
Proportional QS
Proportion of sample
matches the population
stratus.
Non-proportional QS
Not respecting
proportions.
Useful when:
Credible opinions about
a complex topic are
needed.
Only way to study
hard-to-reach
populations or when no
sampling frame is
available.

6. Probability sampling
• Technique in which every unit in the population has a chance (non-zero probability) of being
selected (statistically).
• This chance can be accurately determined, making the sample good for statistical analysis
and inferences.
• The different types of probability sampling techniques include Simple random sampling,
systematic sampling, stratified sampling, cluster sampling, matched-pairs sampling, and
multi-stage sampling.
• Best fit for quantitative researches.

7. Probability sampling
Definition
Sampling technique in which all the units of the population have the same probability
of the population have the same probability
of being chosen.
• All probability samples have two characteristics in common:
a) All units have probabilities of being chosen (never zero).
b) The selection process is somewhat random.

8. Probability sampling
•It consists of the random selection of elements of the sampling frame.
Simple
•The sampling frame is ordered according to some criterion.
•The first element is chosen randomly and the following ones under the ratio K=N/n.
Systematic
•The sampling frame is divided into mutually exclusive subgroups.
•Simple random sampling is then applied to each subgroup.
Stratified

9. Probability sampling
•When the population is widely dispersed geographically.
•The population is divided into clusters and then all the units in these clusters are
studied.
By clusters
•When it is desired to compare two subgroups of the population under some
criterion.
Matched-pairs
•Sampling involving two or more types of sampling at different stages.
Multi-stage

10. Sampling statistics
Variable (Age)
Frequency
(Students)
18 19 20 21 22 23 24 25 26 27 28

11. Sampling statistics
Variable (Age)
1 answer
Frequency
(Students)
18 19 20 21 22 23 24 25 26 27 28

12. Sampling statistics
Variable (Age)
43 responses
Frequency
(Students)
18 19 20 21 22 23 24 25 26 27 28

13. Sampling statistics
Frequency
(Students)
249 responses
(Sample)
Variable (Age)
18 19 20 21 22 23 24 25 26 27 28

14. Sampling statistics
Variable (Age)
Frequency
(Students)
249 responses
(Sample)
18 19 20 21 22 23 24 25 26 27 28

15. Sampling statistics
Variable (Age)
Frequency
(Students)
Normal distribution
18 19 20 21 22 23 24 25 26 27 28

16. Sampling statistics
Positive asymmetry Negative asymmetry

17. Sampling statistics
Platicurtosis Leptokurtosis

18. Sampling statistics
Population
Sample 1
Sample 2
Sample 3
_
X=21
_
X=23
_
X=25
_
X=23
__
Xs
Normal
Distribution
of Samples
.
.
.

19. Sampling statistics
Population
(Parameters)
Sample
(Statistics)
Average age
= 23 years old
Average age
= 25 years old
Standard
Error

20. Sampling statistics
Sample
(Statistics)
Individual
Response
Average age
=
25 years old
Age
=
21 years old
Standard
deviation

21. Sampling statistics
Largest standard deviation Lower standard deviation

22. Sampling statistics
• Standard deviation, variance and sum of squares mean the same thing:
a) The fit of the mean to the sample data.
b) Data variability.
c) How well the statisticians represent the sample data.
d) The amount of error in statistical calculations.

23. Sampling statistics
Confidence intervals
Variable (Age)
Frequency
(Students)
µ ± 3σ = 99%*.
µ ± 2σ = 95%*.
µ ± 1σ = 68%*.
*Probability of the
parameter being within the
confidence interval of the
statistic
18 19 20 21 22 23 24 25 26 27 28

24. Thank you.
Harold Gamero