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Summary Everything you need to know for the 2nd exam of RMCP, MPA year 1

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Vrije Universiteit Amsterdam (VU)

This is a summary of everything you need to know for the 2nd exam of RMCP (Reseach Methods for analyzing Complex Problems), a course in the Master's programme MPA (Management, Policy Analysis, and Entrepreneurship in health and life sciences) at VU Amsterdam. Written in English.

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research methods for analyzing complex problems

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1. College aantekeningen - Samenvatting research methods for analyzing complex problems deeltentamen i
2. Samenvatting - Everything you need to know for the 2nd exam of rmcp, mpa year 1
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HC’s – Research Methods for Analyzing Complex Problems

HC’s - Research Methods for Analysing Complex Problems
HC 7 – Qualitative data analysis
Data analysis in qualitative research starts when you design your research; you create your lenses
through you will look at your data. So analysis starts during the design fase. Data analysis is behind
the scenes; audience/commissioner wont see any of it. You can tell a story with your data. Try to
make a story that is catchy; so it will not be lost by other stories. You need to analyse in a rigorous,
logical way, you give the data meaning. You are interpreting the data. You should be guiding the
audience. What type of items do you see? Coding = labelling, categorizing. Thematic structuring =
talking about the function/colour/ways, how you would structure the data. Theorizing = what
linkages do you see? Theorize about it.

Qual is not just interviews, a lot of things can be qual. analysed! Literature, paintings, reports,
summaries, etc.

Assumptions about Qual research:
- Reality is socially constructed
o You try to understand how others construct the reality
- Emic (insiders point of view)
o Emic = insider. Tries to understand the other persons point of view
- Variables are complex, interwoven, and difficult to measure
o This has to do with the context of complex problems; different perspectives, different
stakeholders, different disciplines, etc. Be aware of this; you wont give the full truth
- The researcher is his/her own instrument
- No standardized procedures
o Many guidelines and principles, but unfortunately no standardized procedures
- Personal involvement and partiality
o We are personally involved, be aware of this! E.g. by the tone of your voice
- Empathic understanding
o Compassion understanding is preferred

Characteristics of Qual researcher:
- Reflexibility
o Think abstractly
o Step back and critically analyse situations
o Recognize the tendency towards bias
- Openness
o Be flexible and open to helpful criticism
▪ Be flexible when some frameworks you thought of end up not to be working
o Appreciative inquiry; allow the participant to feel powered by the fact that they are being
interviewed by you
- Sensitivity
o Sensitive to the words, interpretations and actions of respondents

When do you start to think of analysis? During the research design phase! During this design phase
you are devising different frameworks, interview guides; constructing ways of looking and ways of
understanding. You are also busy with analysis during the data collection phase. For example by

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,HC’s – Research Methods for Analyzing Complex Problems

asking probing questions (ask for clarification), or by co-constructing the meaning. And ofcourse
afterwards during the (desk) analysis phase, afterwards. This is when the coding of your responses
happens, you (re)construct relevant (relationships between)
topics and themes, and organize around core generalizations or
ideas.

First you notice the concepts. New concepts or ones you already
had in your theory/framework. Next you will collect examples of
these concepts. Different respondents might have different
examples. And finally you will analyse these concepts in order to
find commonalities.

Different types of analysis
There are different types or degrees of analysis. There are no clear boundaries in the types of
analysis. Different researchers might mean different things with the same name. In our type of
research, we see content- and thematic analysis most often.
- Content analysis
o The purpose is to describe the characteristics of the document’s content by More descriptive
examining who says what, to whom, and with what effect, and make inferences (often) more a
(may obtain quantitative elements) structured a prior
defined
o Counting how many people said a certain thing, how many arguments that that …,
codes/categories
etc.; more quan. elements.
o Deductive research
- Thematic analysis
o Thematic analysis as an independent qualitative descriptive approach; mainly
described as a method for identifying, analysing and reporting patterns (themes)
within data
o You are a bit more in depth compared to content analysis, looking for patterns by
exploration More open to
- Grounded theory interpretation
o The construction of theory through the open analysis of data. This is very open,
you start really from the data.
o Inductive research
Make sure you describe what kind of analysis you use, and what it contains. Maybe refer to other
articles with the same kind of analysis.

Codes
Codes = word(s) or short phrase(s) that represent(s) the essence or key attribute of narrative/verbal
information. Codes can be a word, multiple words, one phrase or multiple. It’s the essence or key
information that you are looking for. They are necessary to organize the data. They are used to
categorize the data, make sense of it. Coding is the process of organizing the data into ‘chunks’
(segments) that are alike. Most often you make a code guide beforehand with your research team;
what are we going to label in the text?

A coding guide; make sure to develop this structure. Not just providing the name of the codes, but
also what do you mean, collectively as a group, with this definition. You can also include an
illustrative code (with this code, we mean …). It provides guidance for when and how to use the

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, HC’s – Research Methods for Analyzing Complex Problems

codes. It will evolve throughout the analysis (refining); new codes might emerge, or different
meanings. And you continuously have discussions with your research team. In the beginning of your
research you already can find some codes; the basis on which your questions are based on.

Sometimes the codebook starts with more overarching themes, more abstract. Within these themes,
there are codes, and maybe even sub-codes.

Quotations bring the reader to the reality of the situation. It is a skill to choose the good quotes; you
want a few strong quotes in your report. They support your analysis and findings. They are
illustrative, and can cover a range of issues. For example when you have two stakeholders with
opposing views, put a quote of each stakeholder. But think of the anonymity!

Qualitative analysis steps
1. Data curation → transcribe the data (interviews, field notes, etc.)
2. Collect – code – collect – code etc. (familiarization with the data)
3. Read and re-read. Suspend initial interpretation. Focussed reading and start with open coding
4. Close examination, label text with keywords. Reviewing and axial coding
5. Modify codes, remove duplications, hierarchical order, integrate theory. Generate theory
6. Look for connections that emerge from the data

With deductive research, you start with a theory that you are going to test. With inductive research,
you are working towards the development of a theory. In deductive, you are testing the hypothesis,
so you are colouring it in, your participants have choices. You have a structured interview, a very
thematical look at the data; counting how many people said certain things. The more inductive you
go, the more open you are to the participant’s understanding of the world. The data collection and
analysis are very interrelated. Iterative process of coding; transcripts > concepts > categories.
Inductive research is not very appropriate for a 5 month research (internship).

Vertical analysis is on the storyline of one interview. For each interview you take the essence, the
storyline; what did this person believe is so important/values/believe? It can help you understand
how this person (interviewee) reasons. Sometimes this core is the summary. Vertical analysis is
focused on understanding the essence of individual interviews; ‘the narrative’ (or other data). You
need the unique line of arguing of individuals and their priorities.

Horizontal analysis compares multiple interviews, e.g. on the same topic/theme. You look for one
theme (theme A) throughout all the interviews. Then you do the same for theme B, and theme C.
Next you can look what has been said about theme A, theme B and theme C. It is focused on
aggregation and comparison of content of data across different interviews (or other data). Pay
attention to diversity (both majority and minority of views count).

You need both; vertical and horizontal analysis. It’s a process of going back and forward. If you only
use one, you are blinded for the bigger picture.

Downsides of horizontal analysis are: it reduces the narratives of individuals to parts. It may lead to
overemphasising elements, because comments are decontextualized (perhaps A was not important
in the interviews). It may overlook conflicting remarks within interviews. It neglects the unique line of
arguing of individuals (and the sum thereof).

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