An Introduction to Qualitative
Research
Non Probability Sampling Methods
Postgraduate Research Seminar
School of Management, Information Technology & Governance (SMITG)
Given Mutinta, PhD
1 November, 2013
UKZN INSPIRING
Sampling Terminologies
SAMPLING: is a process of selecting a small
portion or part of the population to represent the
entire or target population
Population
Target Population
POPULATION: is the entire collection of units
or people in a given area the study will be
conducted
Sample
TARGET POPULATION: is the entire collection
units or people a researcher is interested in
SAMPLING FRAME: a list of all units or people
in a population from which a sample is selected
Sampling
Frame
SAMPLE: a subset of the entire population
selected to participate in the study
UNIT: a basic element or a person in a sample
or population
Unit or element or
person
Sampling Terminologies
SAMPLING BIAS: the over-or-under
representation of a group of the population in
terms of characteristics needed
SAMPLE SIZE: total number of units or people
selected to participate in the study
SAMPLING ERROR: is the difference between
population value and sample value
Population
Target Population
Sample
Sampling
Frame
PARAMETERS: these are characteristics of
the entire population
Unit or element or
person
Sampling Methods
Probability
Non Probability
Probability Sampling

Also called as Random or Quantitative Sampling

Units or people are selected by ‘chance’ or ‘probability’: guided by the
Principle of Random Selection

The researcher begins by establishing the sampling frame
All units in the population have a chance of inclusion
People have equal chance of inclusion

Therefore, people know in advance the opportunity of inclusion in


the sample
Features of Probability Sampling:

Uses rigorous rules and procedures: clear dos or don'ts

Reduces bias: over-or-under representation
Probability Sampling

Enhances accuracy/precision: produce a sample that reflects the
population

Results of probability sampling can be generalised to the target
population

Deals with large samples

Demanding in terms of resources:
o
o
o
o

Time
Finances
Knowledge: a researcher needs appropriate academic
information/concepts/principles
Skills: a researcher needs appropriate abilities
Good for systematic empirical studies:
o
Whose objective is to: quantify data or measure incidences
Non Probability Sampling

Also called Judgment or Non-Random or Qualitative Sampling

Units or people are selected based on the judgment of the
researcher:(Chaotic/Liberty to defile/credible and reliable)

Theory: tested knowledge on how to sample a population: academic
information, rules, concepts on how to sample a population


Practice: skills and experience of the researcher

Evolutionary nature of research: researcher is conscious of the now
Since selection is dependent on the judgment of the researcher:

Selection: is by ‘choice’ not ‘chance’

No equal chance for inclusion in the sample
Non Probability Sampling

Good for exploratory studies: investigating the phenomenon that is not
clearly known


Allows the researcher to:

To select units that will provide the information that is needed

Gain insights into the phenomenon
Usability:

Rules and procedures: easier to implement

Small samples

Time saving

Cheaper
Features of Probability and Non
Probability Sampling
Non Probability
Probability
 Informed by mathematical theory:
rigorous rules and procedures
 Selection is by chance: principle of
random selection
 Judgmental theory
 Detailed sampling frame
 Works without a sampling frame
 Chance is known in advance
 Chance is greater but not known
 Equal chance
 Dependent on the researcher
 True representative sample
 Reliable
 Results generalised
 Insight into
 Needs a lot of resources (TFSK)
 Little
 Large samples
 Small samples
 Selection is by choice: principle of
judgement
 Not ‘diametric opposition’: antagonistic but complementary
or overlapping
Non Probability Sampling
Snowball Sampling

Inclusion in the sample depends on the judgment of the researcher

Units or people are selected using recommendations by earlier units
or people

Stage 1: the researcher identifies an initial person in the desired
population

Stage 2: the researcher asks the initial person to recommend other
people with the desired characteristics

Stage 3: the researcher continues to assemble units until he or she has a
sample size needed

Usability:

Allows the researcher to gain access to populations that are:



Hard-to-reach: students sex workers
Hidden: gangsterism/satanism
Snowball analogy
Non Probability Sampling
Self-Sampling

Inclusion in the sample depends on the judgment of the researcher

Units or people are given an opportunity to choose to be part of the
sample

Stage 1: the researcher starts by announcing to the target population
the need for people to participate in the study: radio, print media etc.

Clearly explains: the nature of the study/what it involves

Explains the characteristics of units required: age, gender,
place, race and so on

Stage 2: the researcher receives/assess the relevance of the units
or people
Non Probability Sampling
Self-Sampling

Stage 3: Irrelevant units: Rejected

Stage 4: Relevant units: Included in the sample

Usability:


Allows the researcher to recruit people with special:

Feelings about the study

Interests in the problem

Interests in the findings
Good for human trials: pharmaceutical industry
Non Probability Sampling
Purposive Sampling

Also called Judgemental or Selective or Subjective Sampling

Inclusion in the sample depends on the judgment of the researcher

The researcher selects people with a ‘purpose’ in mind


Purpose: understand the phenomenon
Stage 1: the researcher examines the characteristics of the units
available

Stage 2: the researcher makes judgement on which units to include in
the sample

Stage 3: Units with relevant characteristics are selected: to answer the
research questions achieve the purpose of the study
Non Probability Sampling
Convenient Sampling

Also called Accidental or Grab or Opportunity Sampling

Inclusion in the sample depends on the judgment of the
researcher

Units or people are selected for inclusion because of their
accessibility and proximity to the researcher

The readily available units (Library)

Usability:




Fast
Easy to implement
Cost cutting
Subjects are readily available
Non Probability Sampling
Quota Sampling

Units are selected proportionally to the target population

Stage 1: the researcher identifies a population

Stage 2: the researcher divides the population into groups (strata)

Calculates the proportion of a group to the target population
For example:
Target population of 1, 000 students;
 600 male students (60% of the total target population)
 400 female students (40% of the total target population)
 My sample will be made up of 60% males and 40% females
 Desired sample size was 100 students, this would mean our sample
should include 60 male students and 40 female students
Usability:
 Cheap
 Fast
 Simple

Sampling Process
1
Define the target population
2
Determine the sampling frame
3
Specify the units or people
4
Select the sampling method
5
6
Determine the sample size
Select the sample
Why Sampling?

Helps to produce a sample

Helps to collect vital information more quickly

Cuts costs

Saves time

Makes the population manageable

Increases accuracy and quality of data: can check for distortions/bias

Effective if a population is infinite

Reduces problems of hiring staff
“The proof of the pudding is in eating” Asmal
Sauple
Issues to consider when
Selecting Sampling Methods

Nature of the research problem

Objectives of the study

Enough Resources:

Time

Finances

Knowledge: academic information, concepts and principles on
sampling methods

Skills
Thank You!
References
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