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

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Units or people are selected by ‘chance’ or ‘probability’: guided by the Principle of Random Selection

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The researcher begins by establishing the sampling frame All units in the population have a chance of inclusion People have equal chance of inclusion

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Therefore, people know in advance the opportunity of inclusion in

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the sample Features of Probability Sampling: 

Uses rigorous rules and procedures: clear dos or don'ts

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Reduces bias: over-or-under representation

Probability Sampling 

Enhances accuracy/precision: produce a sample that reflects the population

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Results of probability sampling can be generalised to the target population

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Deals with large samples

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Demanding in terms of resources: o o o

o

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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

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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

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Practice: skills and experience of the researcher

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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’

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No equal chance for inclusion in the sample

Non Probability Sampling 

Good for exploratory studies: investigating the phenomenon that is not clearly known

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Allows the researcher to: 

To select units that will provide the information that is needed

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Gain insights into the phenomenon

Usability: 

Rules and procedures: easier to implement

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Small samples

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Time saving

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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

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Units or people are selected using recommendations by earlier units or people 

Stage 1: the researcher identifies an initial person in the desired population

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Stage 2: the researcher asks the initial person to recommend other people with the desired characteristics

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Stage 3: the researcher continues to assemble units until he or she has a sample size needed

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Usability:

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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

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Units or people are given an opportunity to choose to be part of the sample

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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

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Explains the characteristics of units required: age, gender, place, race and so on

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Stage 2: the researcher receives/assess the relevance of the units or people

Non Probability Sampling Self-Sampling 

Stage 3: Irrelevant units: Rejected

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Stage 4: Relevant units: Included in the sample

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Usability: 

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Allows the researcher to recruit people with special: 

Feelings about the study

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Interests in the problem

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Interests in the findings

Good for human trials: pharmaceutical industry

Non Probability Sampling Purposive Sampling 

Also called Judgemental or Selective or Subjective Sampling

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Inclusion in the sample depends on the judgment of the researcher

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The researcher selects people with a ‘purpose’ in mind 

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Purpose: understand the phenomenon

Stage 1: the researcher examines the characteristics of the units available

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Stage 2: the researcher makes judgement on which units to include in the sample

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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

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Inclusion in the sample depends on the judgment of the researcher

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Units or people are selected for inclusion because of their accessibility and proximity to the researcher

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The readily available units (Library)

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Usability:    

Fast Easy to implement Cost cutting Subjects are readily available

Non Probability Sampling Quota Sampling 

Units are selected proportionally to the target population

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Stage 1: the researcher identifies a population

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Stage 2: the researcher divides the population into groups (strata)

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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

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Helps to collect vital information more quickly

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Cuts costs

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Saves time

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Makes the population manageable

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Increases accuracy and quality of data: can check for distortions/bias

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Effective if a population is infinite

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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

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Objectives of the study

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Enough Resources: 

Time

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Finances

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Knowledge: academic information, concepts and principles on sampling methods

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Skills Thank You!

References 

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Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Thousand Oaks, CA: Sage. Creswell, J. W. (2012). Qualitative inquiry and research design: Choosing among five traditions (3rd ed.). Thousand Oaks, CA: Sage.Gray, G., & Guppy, N. (2007). Successful surveys: Research methods and practice (4th ed.). Toronto: Harcourt Canada. Creswell, J. W., & Plano Clark, V. L. (2007). Designing and conducting mixed methods research. Thousand Oaks, CA: Sage. Denzin, N. K. & Lincoln, Y. S. (1994). Introduction: Entering the field of qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research. Thousand Oaks, CA: Sage Publications. Glasser, B. G. & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago, IL: Aldine Publishing Company. Glesne, C. (1999). Becoming qualitative researchers: An introduction. New York: Longman. McNiff, J., & Whitehead, J. (2002). Action research: Principles and practice (2nd ed.). London; Routledge Falmer.

References 

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Mertens, D. M. & McLaughlin, J. A. (2004). Research and evaluation methods in special education. Thousand Oaks, CA: Corwin Press. Merriam, S. B. (1998). Qualitative research and case study applications in education. San Francisco, CA: Jossey-Bass Publishers. Merriam, S. B., & Associates. (2002). Qualitative research in practice: Examples for discussion and analysis. San Francisco: Jossey Bass. Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Thousand Oaks, CA: Sage. Morgan, D. L. (1988). Focus groups as qualitative research. Beverly Hills, CA: Sage Publications. Murname, R. J., & Willett, J. B. (2011). Methods matter: Improving causal inference in educational and social science research. New York: Oxford University Press.

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Patton, M. (2002). Qualitative research and evaluation methods. (3rd ed.). Thousand Oaks, CA: Sage Publications.

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Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Allyn & Bacon.

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Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage.