Research Methodology Workshop - Quantitative and Qualitative
1. Research Methodology Workshop
Quantitative & Qualitative
Dr. Philip Vaughter, UNU-IAS
ProsPER.Net Young Researchers’ School
10th March 2017
Vietnam National University – Ho Chi Minh City
2. Why the big deal about
methodology?
• A clear methodology section is one of the most
critical parts of a research proposal
• It is also the part that even the most experienced
researchers have difficulty in writing
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3. Why the big deal about
methodology?
• Purpose of a methodology section
I. To explain how the data was generated
II. To explain how the data was analyzed
When writing a methodology, it is critical to provide
enough information so that others can repeat the
experiment/study and reproduce the results, or
understand the context the results were generated
in so that the audience can judge whether your
conclusions are valid.
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4. Some tips for your methodology
section
• When writing a methodology section, it is best to
be direct and precise
• Try and operationalize any terminology you use
so that your audience can follow your
methodology clearly
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5. Steps in writing your
methodology
1) Research Background
1) Goals and Objectives of the Research
1) Propose Methodology
1) Determine Sources of Data
1) Develop a Timeline
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6. 1) Research Background
• This is a reference back to literature review on the
given topic, explaining what methodologies have
been used by other researchers to examine the
same or similar topics.
• These methodologies do not necessarily have to
be incorporated into the study, but it is a good
idea to acknowledge them and explain why or
why not they are appropriate for the given study.
• Note: This is an important early step, but is not
research, it is review
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7. 2) Goals and Objectives of
Research
• In this section, lay out the research objective of
the given study
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8. 3) Propose Methodology
• Present a rationale for why a given methodology
was chosen to investigate the field of interest.
• Helpful tip: present a flow chart of some form of
visual aid to illustrate the methodology being
presented
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9. 4) Determine Sources of Data
• Explicitly state what your sources of data will be.
Note, if you are collecting data on people or animals,
you will need to get ethical approval both from the
institution you are researching and potentially the
jurisdiction you will be researching in.
• Explicitly state what type of data you will be collecting
from your sources.
• Explain how this data will be gathered, including type
of sampling techniques or equipment used in the
collection process.
• If any measurements or categorizations are made
during data collection, explain briefly how these were
made. 9
10. 5) Develop a Timeline
• Develop a timetable for completion of the various
stages of work for the methodology (e.g.,
methodology development, data
collection/fieldwork, data processing, analysis,
results interpretation, etc.)
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11. Types of Data, Types of Research
Methods
Typically, researchers choose from three
methodological approaches:
I. Quantitative
II. Qualitative
III. Mixed
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12. Quantitative Research Methods
• Quantitative research methods are
characterized by the collection of information
which can be analyzed numerically
• Results are typically presented using
statistics, tables, and/or graphs
• Because quantitative data is numeric, the
collection and analysis of representative
samples is commonly used
• The more representative the sample, the more
likely that the quantitative analysis will reflect
results that can be generalized
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13. Quantitative Research Methods
• However, even if sample is representative,
quantitative data can be useless unless the
data collection instruments are appropriate,
well designed, and clearly explained to the
users of the data
• Example: Data collected using poorly
designed questionnaires may solicit a huge
amount of data, but result in much of it being
unusable because it is impossible to
generalize
• All too often, designers of data collection tools
frame qualitative questions quantitatively and
vice versa
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14. Quantitative Research Methods
• Strengths of Quantitative Data Analysis
Numeric estimates
Opportunity for relatively uncomplicated data
analysis
Data which are verifiable
Data which are comparable between different
communities and locations
Data which do not require analytical judgment
beyond consideration of how information will
be presented in the dissemination process
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15. Quantitative Research Methods
• Weaknesses of Quantitative Data Analysis
Gaps in information – ex., data which are not
included in collection cannot be included in
analysis
Labor intensive data collection process
Often, limited participation by populations
affected in the information collection process
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16. Qualitative Research Methods
• Qualitative research is by definition
exploratory
• It is used when we don’t know what to expect,
how to define an issue, or there is a lack of
understanding of why and how variables are
affected
• Qualitative data is useful for both exploring
both groups and individual entities, and can
generate case studies and summaries rather
than lists of numeric data
• Qualitative data are often textual observations that
portray attitudes, perceptions, or intentions
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17. Qualitative Research Methods
• Qualitative methods and analysis provide
added value in exploring intangible factors
• Ex., cultural expectations, gender roles, individual
feelings
• Sample size must be big enough to assure
inclusion of most or all of the variance in the
data
• Often times, the number of sample sites,
groups, or categorizations becomes obvious
as assessment progresses and new
categories, themes, and explanations stop
emerging from the data (theoretical
saturation)
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18. Qualitative Research Methods
• Strengths of Qualitative Data Analysis
Rich and detailed information
Perspectives that can include specific cultural
and social contexts (the human voice)
Inclusion of diverse cross-section
Data collection which can be carried out with
limited resources
Data collection which can be carried out with
limited respondents
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19. Qualitative Research Methods
• Weaknesses of Qualitative Data Analysis
Results un data which are not objectively
verifiable
Requires a labor intensive analysis process
(categorization, recording, etc.)
Needs skilled data collectors for consistency
and nuance
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