Hempelmann Eg

7/28/2019 Hempelmann Eg

1/18

EUROSPHERE WORKING PAPER SERIES

Online Working Paper No. 13, 2008

Analysing Interview Data

Possibilities and challenges

Bjarte Folkestad

This paper can be downloaded without charge from:http://eurosphere.uib.no/knowledgebase/workingpapers.htm

ISSN 1890-59886


2/18

EUROSPHERE ONLINE WORKING PAPER SERIES

Title: Analysing interview data: Possibilities and challenges

Author(s): Bjarte FolkestadWorking Paper No.13This version: December 2008Webpage: http://www.eurosphere.uib.no/knowledgebase/workingpapers.htm

EUROSPHERE, 2008http://www.eurosphere.uib.no

2008 by Bjarte FolkestadAll rights reserved.Short sections of text, not to exceedtwo paragraphs, may be quoted withoutexplicit permission provided that full credit,including notice, is given to the source.

The views expressed in this paper do notnecessarily reflect those of the EUROSPHEREProject.

The statement of purpose for theEUROSPHERE Online Working Paper Series

is available from the EUROSPHERE working papers website,http://www.eurosphere.uib.no/knowledgebase/workingpapers.htm

Bjarte FolkestadUnifob Global / University of [email protected]

ISSN 1890-5986 (online)


3/18

Analysing Interview Data

Possibilities and challenges

Bjarte FOLKESTAD

This paper examines the possibilities and challenges in analysing qualitative

interview data. The main inspiration of this paper draws upon the work of

Anne Ryen (2002)1

and the seminal work of Matthew B. Miles and A. MichaelHuberman (1994). Based on their contributions I will present the major

epistemological perspectives in qualitative research. Furthermore the paper

presents different analysis techniques. To enhance the understanding of the

various analysis techniques this paper will include examples from my own

research project on Euroscepticism among political parties.2

The paper will also discuss issues concerning comparability of the data and

analysis. Since my project aim at exploring Euroscepticism among various

political parties in Nordic countries and Central Eastern Europe it is important

to consider various strategies for comparing and analysing interview data.

Why do interviews?

When a research project is conducting qualitative interviews there is (and

presumably must be) a reason for it. One of these reasons might be because it

provides a new insight into a social phenomenon. So when I want to explore

the content of Euroscepticism, conducting qualitative interviews with elites in

political parties might therefore been seen as one option within many that

gives insight into this phenomenon. This, of course, will require reflections on

the reasons for selecting elites and parties, which respondents should be

selected, and how. As the main focus here is on the data analysis, this paper

will not consider these issues thoroughly, but as it will be shown below, a

distinction between data analysis and data collection is sometimes hard to

draw, and it is therefore important to have this in mind when evaluating how

interview data can be analysed.

Interviews allow the respondents to reflect and reason on a variety of

subjects in a different way than say opinion polls or party manifestos. Thus

1Det kvalitative intervjuet: Fra vitenskapsteori til feltarbeid (The Qualitative Interview: fromphilosophy of science to fieldwork) is a methodological book in Norwegian. All quotes fromRyen 2002 are my own translation.

2 It should be noted that the data collection for my project are a part of the Eurosphere project

(www.eurosphere.uib.no) this means that I will not personally collect all the data for my ownproject. Furthermore the data collection will also consist of data that I will not use forexploring Euroscepticism.


4/18

EUROSPHERE WORKING PAPER No.13 FOLKESTAD

ISSN 1890-5986 2

when elites are asked about issues such as the EU, migration and citizenship

we can get a deeper insight in how they think and reflect.

It goes without saying that such data are hard to obtain. A thorough

interview guide must be developed; appointments must be scheduled;

travelling from place to place; and finally the interview must be realised face-

to-face. Although there are several issues that might affect the research inthese phases, the analysis part is perhaps the most contested part of qualitative

method:

The most serious and central difficulty in the use of qualitative data is that

methods of analysis are not well formulated. For quantitative data, there are

clear conventions the researcher can use. But the analyst faced with a bank of

qualitative data has very few guidelines for protection against self-delusion,

let alone the presentation of unreliable or invalid conclusion to scientific or

policy-making audiences. How can we be sure that an earthy, undeniable,

serendipitous finding is not, in fact, wrong? (Miles, 1979:591)

This paragraph neatly sums up much of my own thoughts when I was

surveying the literature for guidelines. This is not to say that there are no

guidelines in the literature, quite on the contrary. But in order to do

surgical reading and to find alternatives/suggestions for how to analyse

qualitative interview data at the same time as comparing across countries/

organisations I had to spend quite some time reading through the literature,

which again made my strategy of surgical reading quite flawed.

In the following I will try to depict some of the major epistemological

perspectives in qualitative research as well as exemplify some analysis

techniques.

What are our preferences?

Miles and Huberman (1994) argue that researchers must lay out their

epistemological starting points: It is good medicine, we think, for researchers

to make their preferences clear (ibid:4). A crucial point in making our

preferences clear is therefore to assess how we view the data that is collected.

This involves finding answers to questions like: how is the role of the

interviewer treated? Do the answers of the respondents represent the reality or

is the reality produced through the interaction between respondent and

interviewer?

While it is beyond the scope of this paper to give a full presentation of themain paradigms that exist within qualitative research today, a short summary/

review could be useful in order to get a better insight into how the analysis is

performed.

Based on Ryen (2002) and Silverman (2001) the table below present

some major paradigm within qualitative research.


5/18


ISSN 1890-5986 3

Table 1: Major paradigms in qualitative research3

Paradigm Characteristics Status of Data Methodology

Naturalistic Positivism

the social reality is real

important that theresearcher does not

affect/ influence thedata

quotes are marked

Focus on What-questions

Facts aboutbehaviour and

attitudes

Random

samplesStandard

questions

Tabulations

Etnometodology

Constructionism

search for productionof meaning reality is

produced through

interaction

focus on how-questions

Mutually

constructed

Unstructured

Open-ended

interviews

Emotionalism

purpose: to get on the

inside

focus on the subjectsown feelings both the

researchers and the

subjects feeling is

empirical material

Authentic

experience

Any interview

treated as

topic.

Post-modernism

worried about self-consciousness

research constructs thereality by producing

description on it.

-

De-

constructing

texts

Of these, the naturalistic paradigm is held to be the most dominant one. I

believe one of the reasons for this is the fact that this paradigm offers some

concrete solutions in how to analyse qualitative data. This paradigm calls for a

standardised procedure, with structured or semi-structured interviews, that

usually are pre-tested. This result in data ...that give us access to 'facts' about

the world (Silverman 2001:86). While being dominant it has been subject to

criticism from a range of researchers, which explains why there are several

alternatives in how to collect and analyse qualitative data. In particular the role

of the interviewer and how the interview/conversation are interpreted are

crucial points of departure when criticising the positivist paradigm. So whilethe positivist interviewer is objective the interviewer in the emotionalist

tradition is subjective. As Denzin put it:

I wish to treat the interview as an observational encounter. An encounter...

represents the coming together of two or more persons for the purpose of

focused interaction (Denzin, 1970:133 quoted in Silverman 2001:95).

The interviewees own feelings through the interview are thereby also

empirical material for analysis. Ethnometodologist/ constructionist are on the

3 This table is based upon Ryen (2002) chapter 4 and Silverman (2001) chapter 4. It should benoted that there are differences in the labelling of the paradigms. This is shown in the table forthe two first rows (Ryens labels are mentioned first).


6/18


ISSN 1890-5986 4

other hand are ...searching for produced meaning or conversation on how the

world is constituted by its members (Ryen 2002:64). This means that the

interview and the interaction between the respondent and interviewer are

important to consider.

Within the Post-modernist tradition (which is not mentioned by

Silverman), the issue of representation is crucial. Post-modernist de-constructstexts in order to re-present the reality. The research-procedure constructs the

reality by producing images of it. The reality is put in parenthesis and what is

left, is a world of images and representation (Ryen 2002:70).

The purpose of this short review is to pave way for the argument that both

single research projects (conducted by a single or small group of researchers)

and more multiple/ large projects (conducted by a large group) should make

some assumptions on how the data is treated.

I believe that there is much to the fact that the data is influenced by the

very interview situation itself. The quality of the data will vary not only from

country to country but also from interview to interview. For example factors

like where the interview is conducted (at the respondents office or in a publicspace like a restaurant or caf) might affect the data response. Also the

difference in elite structure (Bygnes, 2008), formality of the interview

situation, nervousness (both interviewer and interviewee) is factors that should

be accounted for. At the same time it would probably be a too time-consuming

process if factors like these should be built into the final data-report,

particularly in larger research projects and when comparing across cases/

countries is important.

A suggestion would therefore be to use the naturalistic/ positivist

perspective as a starting point for analysing the data. There are two reasons for

this: first of all because this perspective offers some clear guidelines for

analysing the data. Secondly these guidelines could be useful in enhancing the

comparability of the data, because it gives some standardisation of data

collection, reduction and analysis.

Analysing qualitative interviews

In the following part I will present some of the analysis techniques that are

applied in the literature. This part builds upon the examples and methods

described in Ryen (2002), Miles and Huberman (1994) and Erlandson et al.

(1993)

Before introducing the possible analysing techniques it should be notedthat the analysis phase in itself is a continuous process, and that we can not

easily distinguish the collection, reduction and analysis phases from each

other: data analysis does not occur in a vacuum (Erlandson et al.

1993:113). This is because researchers are continuously interacting between

the respondents and the research tools. For each interview that is conducted

more knowledge is possessed: not only about the phenomenon that is studied

in it self but also about the interview guide as well. As the researcher becomes

more experienced he or she will find several buttons to push in order to get

the information that we are searching for.

A technique that might not demand particular systematising than a

transcription is quote-research. Here we use quotes from interview asillustrative or confirming examples.


7/18


ISSN 1890-5986 5

For instance if we have the assumption that says that: Euroscepticism is a

position that views the EU as a market-liberal project. A quote like: I think

that EU is too market-liberal and I am therefore against the whole project

would then be taken as an example that would confirm this assumption. The

challenge here is to distinguish between journalism and science. While using

quotes to describe the data is not a problem in itself it becomes a problemwhen this is the only way of analysing the data (Ryen 2002:169).

Let us therefore consider the naturalistic/ positivist paradigm where a more

sophisticated technique has been sought developed. Lincoln and Guba argues

that the data Analysis involves taking constructions gathered from the

context and reconstructing them into meaningful wholes(Lincoln and Guba,

1985:333 quoted in Erlandson et al. 1993:116). This process has according to

Erlandson et al. (1993) four elements: 1)unitizing data, 2)emergent category

designation, 3)negative case analysis and 4)bridging, extending and surfacing

data.

Here we consider the two first steps: unitizing and categorizing which

follows a step-by-step procedure:

1. Read the first unit of data2. Read the second unit3. Proceed in this fashion until all units have been assigned to categories4. Develop category titles or descriptive sentences or both that distinguish

each category from the others

5. Start over

The data reduction here starts with the raw data which can be a section or the

entire answer to one question. This must then be split into entities which in

turn can be ordered into categories. In the figure 1 below I show this procedure

is done in practice. The example is taken from an interview with an MP from

the Norwegian Progress Party (Fremskrittspartiet). The quote is a word-for-

word transcription of what the respondent said when I asked about his own

vision of the future of EU. The quote has some unfinished sentences which

might show that the respondent was reflecting on the question (or talking

while thinking). It should also be noted that this is also a translation from

Norwegian to English. The text beginning with I: and in italics represent theinterviewer, whereas the text beginning with R: represent the respondent.


8/18


ISSN 1890-5986 6

Figure 1: An example of data reduction

I: In which direction should the EU develop itself in the future in your opinion, specifically concerning the relationship between th

regions

R: I am a kind of EU-minimalist. For the EU is about making life easier, a little more flexible and to realise the four freedoms that the

For me the EU should create a dynamic that make an economic and [pause] yes, make sure so happens. The EU that have moved in o

has made things worse, made it more difficult and at least at certain areas made it more troublesome. If I were to decide I would haveto be, in particular the thoughts that were dominating at the end of the 80s and beginning of the 90s. The chase in deepening the coo

more pain than gain for EU as an organisation.

I: So if the trend is more political power to Brussels you are against this?

R: Absolutely, Absolutely. I mean that all experience shows that the nation state, also in Europe has come to stay.

I am a kind of EU-

minimalist

EU is about

making life easier

Realise the

four freedoms

The EU that have moved in other

directions as well, and in my opinionthis has made things worse, made it

more difficult and at least at certain

areas made it more troublesome.

If I were to decide I

would have lead the EUback to basic. More of

what used to be, in

particular the thoughts

that were dominating at

the end of the 80s and

beginning of the 90s

Right trajectory of EU Wrong trajectory of EU


9/18


ISSN 1890-5986 7

This figure is an example of how data reduction can be done in practice. I have

not reduced all the data from the raw data because of space limitations. For

example one quote that I would have added is the fact that this respondent

thinks of the EU as an organisation. This I believe fit into the pattern of how

the respondent perceives the EU: namely as an economic organisation that

benefits the nation-states of Europe.I have reduced the quote to three categories: right and wrong trajectory of

the EU and a more ontological category, Nation-state. Whereas the two first

categories shows the answer the respondent is giving to the question (right and

wrong directions of the EU), the last category tells us something about what

kind of political organisation/ polity he views as the most proper. The question

is whether it is possible to extract more information from the data than what is

displayed here? As I noted above this quote is from a MP representing the

Progressive party. This is a party known for having a market-liberal/

libertarian economic profile. Combining this background information with the

answer, it becomes clear that the respondent does not want the EU to deal with

social policies. After all if you are against heavy taxation and officialredistribution of wealth in the first place (such as the national government),

why would you want an additional body to do the same thing (such as the

EU)?

As the figure also shows there is a dotted line from the I am an EU

minimalist entity to the Nation-state category. I have included it because of

uncertainty of where to place this entity. On the one hand this might be a

statement where the respondent is arguing for the right trajectory of the EU,

but on the other hand this could also be a part of his argument for favouring

the nation-state. This shows one of the dilemmas the researcher is faced with

when analysing the data. The same entity can be placed in several categories

but if the researcher does this too often, it will undermine the value of

categorising. It does not make analytical sense if many entities exist in most

categories (Ryen 2002:153). This is one of the reasons why we have to repeat

this process several times, both within and across cases. We will therefore be

involved in a time-consuming yet creative process where new categories will

be introduced and old ones rejected.

Iterative analyses

While Miles and Huberman (1994) have some similarities with Erlandson et al

(1993) there are some differences in their focus. They distinguish threeprocesses in the analysis4:

1. Data reduction this starts at the very initial research phase whenconcepts and methods are developed and subjects/ phenomenon are

selected.

2. Data display seeking meaning on a limited part of the data(summaries, diagrams and text-matrices)

3. Conclusion comparing, contrasting, searching for patterns,triangulation etc.

4 These points are based on Ryen 2002:155.


10/18


ISSN 1890-5986 8

Miles and Huberman also spend time on discussing the inductive and

deductive approach in interview research design. According to them the

iterative analyses combine both:

After one has inductively identified a theme, one goes on to try verifying or

confirming the finding (deductive), which again gives an inductive loop.Huberman and Miles sees it as legitimate and useful to both start with

conceptual analytical categories, that is deductive, or to gradually develop

them, that is inductive (Ryen 2002:157)

This shows that there is a lot of potential in the data that we have collected.

For one I have at the initial phase an inductive approach to my own thesis. By

asking What is Euroscepticism? I seek to explore the content of

Euroscepticism among the respondents that are interviewed. At the same time

I travel into the field with a conceptual baggage. The categories (concerning

trajectory of the EU) that I suggested in the example above (figure 1), bear

some resemblances to how Kopecky and Mudde (2002) conceptualise supportand opposition to European integration. According to Kopecky and Mudde it

is useful to separate between scepticism towards EU on the one hand and

scepticism towards the general ideas of European integration on the other.

Building on an Eastonian (Easton, 1965:124ff.) system the purpose of

introducing these two kinds of scepticism is to distinguish between diffuse and

specific support. This in turn leads to a table consisting of four categories:

Table 2 Kopeck and Muddes conceptualisation of Euroscepticism

Euroenthusiasts(pro-integration, pro-trajectory) Europragmatists(anti-integration, pro-trajectory

Eurosceptics

(pro-integration, anti-trajectory

Eurorejects

(Anti-integration, anti-trajectory)

Source: Kopeck and Mudde (2002:303).

If this conception of Euroscepticism is our starting point, we must ask

ourselves whether this the data reduction that is done in figure 1 fits this table

or not. In other words can we place the respondent within one of the four cellsin the table? If we assume that the trajectory of the EU is more than economic

cooperation, then the respondent is quite clearly anti-trajectory. At the same

time the respondent does not seem to reject the idea of integration as a whole

(that is economic integration). It seems therefore plausible to categorise the

respondent as a Eurosceptic. What we must then consider, and this would thus

be a part an inductive loop, is if the concept Eurosceptics lacks precision.

We should for instance expect answers that are similar to the one I have

presented in figure 1, but almost turned upside down. That is, a respondent

that rejects the economic cooperation, but welcomes the social Europe. This

person would not be against integration, but we could not categorise the

respondent as pro-trajectory either (that is Euroenthusiasts in the table). In


11/18


ISSN 1890-5986 9

that sense we might end up categorising two respondents with quite different

view of what is the best way of organising Europe into the same category.

These reflections I believe show one of the advantages of doing qualitative

interviews. Analysing the data in the way shown above, is useful particularly

when doing single-case analysis or an analysis of a certain phenomenon such

as political party elites perception of European integration. Also it helps tofamiliarise oneself with the data. However in order to do cross-country

comparison this might not be sufficient enough. One reason for that is that the

data reduction might vary from country to country and that we will not end up

with the same categories. While this finding would be interesting in itself it

would complicate the comparability of the data. This means that for larger

research projects, and when comparison is an important goal one must try to

find other alternatives of reducing and analysing the interview data. On the

other hand I do believe that the analysis-technique suggested might be useful

in a larger comparative project. For instance this could be an interesting

explorative process that will strengthen the analysis later in the project. In

particular this can be helpful when for instance comparing the data reports toideal types (more on this below). Also as I have shown with the iterative

analysis process, the interaction between the data and concepts could give way

for improving concepts and theories.

Phenomenology

As stated above this paper is not aiming of given an exhaustive description of

the various paradigms that exist in qualitative research. I have deliberately

taking the naturalist/ positivist paradigm as a starting point for illustrating how

we can go about when analysing interview data. This was justified because we

here have some clear guidelines for analysing data. However it should be

noted that there are other paradigms that also offer more structured ways of

analysing data, and phenomenological research is one of them. Kvale and

Brinkman (2009) gives their presentation of this perspective:

This kind of interview seeks to obtain descriptions of the interviewees lived

world with respect to interpretation of the meaning of the described

phenomena. It comes close to an everyday conversation, but as a professional

interview it has a purpose and involves a specific approach and technique; it

is semi-structured it is neither an open everyday conversation nor a closed

questionnaire (Kvale and Brinkmann, 2009:27).

For the examples I have used above (e.g. Figure 1), it makes sense to claim

that we are approaching the lived world of politicians. So when asking a

politician about what direction the EU should move I am engaging in a

conversation that for most politicians is an important theme in their life and

work. Using examples from Giorgi (1975), Kvale and Brinkman (2009)

suggest five steps for analysing interview in a phenomenological tradition:

1. Read through the whole interview to get a sense of the whole2. The researcher determines the natural meaning units3. The natural meaning unit is restated as simply as possible4. Interrogating the meaning units in terms of the specific purpose of the

study


12/18


ISSN 1890-5986 10

5. Essential non-redundant themes of the entire interview are tied togetherinto descriptive statement

I will not go into detail by using examples to show how analysing interviews

in this way can be done. But as we can see from this short review of

phenomenology is that there are some similarities to both the naturalistic andethnomethodological tradition. For one we see that data reduction is an

important key in analysing the data. Also we see that the focus meaning is

central is many of the paradigms presented above.

Comparing interview data

In my own research project comparative research is one of the core goals.

Interestingly enough Ryen does not acknowledge this as the main intention of

qualitative interview:

The main intention of qualitative interview is not to compare cases/ units butto get access to actions and events that are viewed as relevant for the

research/ study. Access to the single respondent and the way he or she views

the world is central (Ryen 2002:85).

Miles and Huberman (1994) on the other hand claim that cross-case analysis

could be of value. This is because it enhances generalisability as well as

deepening the understanding and explanation of a phenomenon. That being

said they also recognise that the goal of generalisability is disputed among

researchers (doing qualitative analysis).

One factor that could enhance the comparability of the data is when the

collected interviews are, at least initially, rather standardised. This involvesthat the interviewer has a fixed questionnaire that is adhered to in a rather

strict fashion, and that there are clear guidelines for selection organisations

and respondents within these. It could also involve pre-testing of the

interview-guide/ questionnaire to enhance the validity of data. In this respect

Miles and Huberman calls for a clarification of whether to use a variable- or

case-approach. Ragins (1987) summary of these approaches or strategies is

worth quoting at length:

The two strategies are surprisingly complementary. The Case-oriented

strategy is best suited for identifying invariant patterns common to relatively

small sets of cases; the variable-oriented strategy is best suited for assessingprobabilistic relationships between features of social structures, conceived as

variables, over the widest possible population of observations. The main

weakness of the case-oriented strategy is its tendency toward particularizing

(often while pretending to great generality - for example, a theory of ethnic

political mobilization based on one case); the main weakness of the variable-

oriented strategy is its tendency towards abstract and sometimes vacuous,

generalizations (Ragin, 1987:69).

There are many different strategies for case-oriented strategies, but those

suggested in Miles and Huberman seems to leaning towards testing the cases

against a theoretical framework or groups/ families. Many researchersapproach cross-case comparison by forming types or families. You inspect


13/18


ISSN 1890-5986 11

cases in a set to see whether they fall into clusters or groups that share certain

patterns or configuration (Miles and Huberman, 1994:174). This bears

resemblances to the method involving ideal types which I shall return to

below. In a variable-oriented strategy the inner-dynamic of the case are

replaced with a search for patterns and themes that cut across the cases. This

means that the data reduction is taken a step further and (perhaps) morepressure is put on the researcher in terms of interpreting the answers so that

they can be reduced into variables.

Before moving on to the ideal type method we need to ask ourselves: What

is a case? Gerring (2004) defines a case study as: an intensive study of a

single unit for the purpose of understanding a larger class of (similar) units

(ibid:342). He then makes the following clarification:

A population is comprised of a sample (studied cases) as well as

unstudied cases. A sample is comprised of several units, and each unit is

observed at discrete points in time, comprising cases. A case is comprised

of several relevant dimensions (variables), each of which is built upon anobservation or observations.

Thus using my project as an example it is possible to set up the following

table:

Table 3: Population, units and casesPopulation European party elites

Units Sample of different European organisations (e.g. political parties)

Cases Respondents from the political parties

On the basis of this table we could however identify a number of cases. In the

first place we have the individual interviews which can represent a single case.

Secondly we have the organisations that each of these individuals represent.

Thirdly the respondent can represent larger communities than organisations

be it party-families (such as social democrats), regions (Central Eastern

Europe) or nation-states.

It is therefore important to have this in mind when finalising the data

report. Should for instance the data report cover a summary of findings/answers from each respondent or should it be a summary of the entire

organisation as a whole? If the latter is preferred: should one have a variable

oriented approach where the focus on the main pattern and themes that exist

within this organisation? Another question that arises is what to do with

answers and respondents that are not fitting the pattern? In my own data

collection I experienced several times when the respondents claimed to be at

odds with what the party they represented meant about an issue. This is of

course anticipated, after all representing a party does not entail that one agrees

with every issue the party claims position. The challenge is how we can

include such findings in the data report, when/ if we are expected to

summarise the patterns for the party/ organisation as a whole?


14/18


ISSN 1890-5986 12

Perhaps a more fruitful way of clarifying these considerations is to

distinguish between units of observation and units of analysis. In the first case

the units of observation is the single respondents themselves (i.e. the

interviews). However in order to enhance the data collection we should also

include data about the organisations/ countries that the respondents represent.

Background and institutional data collection would also contribute to the unitsof observation. Considering units of analysis we proceed in the same way as

with the units under observation (individuals, organisations, regions and

countries). What is important to notice in this respect is that the researcher

must be aware of on what level the observation (data collection) and analysis

is situated. That being said, it is not always easy in an interview to distinguish

between an organisation and individual. Often when interviewing

spokespersons, employee or MPs representing a party on will often find

answers like: We in the Conservatives believe that, and My personally

opinion is that. So if we do not explicitly ask for personal or normative

opinions of the respondent then we must be aware of such distinctions. In

perhaps more interesting cases one can also find statements like: Well on thisissue I disagree with my own party, in my opinion.... Depending on the

research such answers could be compared to other data sources such as party

programmes, election manifestoes and so on.

This would be a part of the data triangulation, an issue which I have not

elaborated on so far. When triangulating: the researcher seeks out several

different types of sources that can provide insights about the same events

relationships (Erlandson et al., 1993:115). For a comparative project

additional data sources are important in increasing the validity of the findings.

As mentioned above in figure 1 it is important to know the background/

ideology of the party that the respondent is representing. So if a research

project about the EU includes Norway as one of the countries, the saliency of

the EU-issue should be included as important data. An example would be

whether the party representatives are unleashed by the party on this issue or

if the representatives are forced by the party whips to take a position that they

themselves do not adhere to.

Ideal Types and deductive approaches

As the example I have presented above has a certain inductive character we

should also consider more deductive oriented strategies. An option that might

be more appropriate is the use of ideal types:

In this method, a phenomenon under study is first defined based on an

existing theory. The features of this phenomenon, which this theory

presupposes to exist, are delineated through deductive reasoning. Then,

empirical cases proximity (similarity) to or distance (difference) from this

theoretical ideal type is measured or described. (Sicakkan, 2006: 90)

This way of analysing social phenomena is well-known in social science with

Max Weber standing out as the main contributor (Ritzer, 2000). Perhaps the

most recognised example is his ideal-type of the bureaucracy where he among

other things shows the rationalisation of the world (which was an effect of

bureaucratisation). This study is an example of how an ideal-typical model of

a certain phenomena can be used to study the effects of large societal


15/18


ISSN 1890-5986 13

structures. Sicakkan discusses the problems of using singular ideal-types with

showing that this might lead to to a lack of significant information about

the phenomenon under study (Sicakkan, 2006: 91). He suggests two solutions

to overcome this challenge:

comprehensive inductive conceptualization comprehensive deductive conceptualization

In the first case all empirical cases are treated equal in order to conceptualise a

given phenomenon. In the second we deploy multiple theoretical ideal-types in

which empirical cases are measured against these. To specify this method of

multiple ideal-types we can use an example from a quantitative study, where

Sicakkan (2005) classified responses to immigration, in a Norwegian survey:

1. Identification of several relevant contesting normative theories2. construction separate attitude models based on each selected theory

3. comparing cases with these attitude models and classifying them intogroups with respect to their differences from or similarities to these

models

4. constructing a single scale or multiple scales accounting for both thedifferences between the groups found in 3 and for the differences

between the cases within each group

Here we can argue that point 1 and 2 in this procedure is activated prior to the

data collection. Thus we identify and construct the models we intend to use in

the analysis, and then develop the appropriate research tools that will be used

in point 3 and 4. What I believe can be one of the benefits in comparing the

data towards theoretical models is that answers that might seem inconsistent

might contain a sound logic in these models. As Sicakkan notes A subject can

bear both positive and negative attitudes to immigrants in different

dimensions(Sicakkan, 2005:56). As I have experienced in my own data

collection, it is not unusual that respondents can speak furiously against

European integration because they do not accept the loss of sovereignty for

their own country, and then later in the same interview speak warmly about

EUs eastward enlargement in 2004.

While the method involving ideal types has a different starting point, one

of the aims in the analysis remains the same compared to analysis by

Erlandson et al. (1993) and Miles and Huberman (1994) that is to categoriseand make sense of the collected data.

To exemplify this way of analysing interview we could therefore first start

with an expected answer scheme/ table:

Table 4: Ideal types and expected findings

Ideal type A Ideal type B Ideal type C

Question/ theme 1 A.1 B.1 C.1

Question / theme 2 A.2 B.2 C.2

Question/ theme 3 A.3 B.3 C.3


16/18


ISSN 1890-5986 14

The cells (A.1, A.2 etc) would consequently include expected findings that are

expected according to the ideal type. It goes without saying that the expected

findings are not to be found empirical, since the ideal types are not empirical.

This means that while some responses from the interviews might fit into the

cells the interview/ respondent/ organisation as a whole will not. We will not

find organisations that are truly representing Ideal type A or B, but we willmost likely find organisations that have similarities and differences to the

various ideal types. For a more concrete example of this table we can view a

shortened version of Sicakkans five ideal-typical models of response to

immigration5:

Table 5: Five Ideal-Typical Models of Response to Immigration (shortened)

Libertarian Liberalist Republican Communitarian Tribal

Immigrants

right to vote

in localelections

after 3-

years

residence

AgreePartlyagree

Partlydisagree

Disagree Disagree

Segregation

as conflict

resolution

DisagreePartly

disagreeDisagree Agree Disagree

Developing ideal-types for the phenomenon of Euroscepticism per se has

provided some difficulties. One reason for this is that we are dealing with

attitudes that can be related to stances on rather specific issues (e.g. rejectingEuropean integration as a whole because of certain fisheries policies). In other

words we are lacking some sense of generalness and as Ritzer notes: Ideal

types should be neither too general nor too specific (Ritzer, 2000:116). In this

case Euroscepticism is too specific. The strategy will therefore be to lower the

level of abstraction and create some expected types of Euroscepticism, that are

based on a) theoretical concepts and b) initial findings from the interviews

(finding key characteristics inductively).

Summary

This paper helps to avoid what Kvale calls the 1,000-page question: Howshall I find a method to analyze the 1,000 pages of interview transcripts I have

collected(Kvale, 1996:176). The question should be avoided at least after the

data collection is finished. If you have collected 1,000 pages without

considering how to analyse them, then you have probably made quite a few

epistemological and methodological errors along the way.

As this paper has shown giving both general and specific

recommendations for choosing analysis techniques is not easy nor is it always

desirable. This is largely because we have already chosen some paths at the

outset of our research. In that way no research is truly inductive in the sense

5This is a shortened version of Table I in Sicakkan 2005:54. A review of the ideal-typicalmodels is given in the same article.


17/18


ISSN 1890-5986 15

that we have absolutely no idea of what to expect before starting our scientific

endeavour. At the very beginning when we select or research phenomenon we

have made some assumption and expectation of what we could possibly expect

to find.

Still I believe there is much to the fact that there is an interaction between

inductive and deductive approaches when analysing qualitative interview data.And, depending on the phenomenon under study, it would therefore be very

interesting to seek to combine the two analysis techniques as suggested above

(naturalistic/positivistic and ideal types).

By making our preferences clear throughout the research we will not only

save time (and avoid the 1,000-page question) but also making the analysis

and results more understandable to both the reader and the researcher him-/

herself. Since the comparative approach that I have selected for my own

project is important, I also believe that a standardisation of the interview data

both in the analysis and collection is equally important. This is creates several

challenges that can not only be met with standardisation, but also with

triangulation and close collaboration if the research is conducted by severalresearchers. Yet at the same time the standardisation should not be based on a

deductive approach alone. As this paper has shown, there is an interaction

between the different research phases and the researcher. This in turn creates

many opportunities for development a creative and innovative research design

that was not anticipated prior to the data-tool development and data-collection.

Based on the considerations in this paper I believe that a combination of

the standardised analysis/ collection and measurement against Ideal types/

theoretical models on the one hand and more inductive analysis on the other

are fruitful for exploring Euroscepticism among political parties on the

outskirts of Europe.


18/18


ISSN 1890-5986 16

References

Bygnes, S. (2008) Interviewing People-Oriented Elites. The Eurosphere Online

Working Paper Series, Working Paper No. 10 / 2008.

Denzin, N. K. (1970) The Research Act in Sociology: a Theoretical Introduction to

Sociological Methods, London, Butterworths.Easton, D. (1965) A Framework for Political Analysis, Englewood Cliffs, N.J.,

Prentice-Hall.

Erlandson, D. A., Harris, E. L., Skipper, B. L. & Allen, S. D. (1993) Doing

Naturalistic Inquiry: a Guide to Methods, Newbury Park, Calif., Sage.

Gerring, J. (2004) What is a Case Study and What is it good for? American Political

Science Review, 98, 341-354.

Giorgi, A. (1975) An Application of Phenomenlogical Method in Psychology. in

Giorgi, A., Fischer, C. & Murray, E. (Eds.) Duquesne Studies in

Phenomenological Psychology. Pittsburg, PA, Duquesne University Press.

Kopecky, P. & Mudde, C. (2002) The Two Sides of Euroscepticism: Party Positions

on European Integration in East Central Europe. European Union Politics 3,297-326

Kvale, S. (1996) Interviews: An Introduction to Qualitative Research Interviewing,

Thousand Oaks, Calif., Sage.

Kvale, S. & Brinkmann, S. (2009) Interviews: Learning the Craft of Qualitative

Research Interviewing, Los Angeles, Calif., Sage.

Lincoln, Y. S. & GUBA, E. G. (1985) Naturalistic Inquiry, Beverly Hills, Calif.,

Sage.

Miles, M. B. (1979) Qualitative Data as an Attractive Nuisance Problem of Analysis

Administrative Science Quarterly, 24, 590-601.

Miles, M. B. & Huberman, A. M. (1994) Qualitative Data Analysis: an Expanded

Sourcebook, Thousand Oaks, Calif., Sage.Ragin, C. C. (1987) The Comparative Method: Moving Beyond Qualitative and

Quantitative Strategies, Berkeley, Calif., University of California Press.

Ritzer, G. (2000) Sociological Theory, New York, McGraw-Hill.

Ryen, A. (2002)Det Kvalitative Intervjuet: fra Vitenskapsteori til Feltarbeid, Bergen,

Fagbokforl.

Sicakkan, H. G. (2005) Senses That Make Noise & Noises That Make Sense. Three

Techniques for Scaling Attitudes to Immigration. Norwegian Journal of

Migration Research, 5, 42-73.

Sicakkan, H. G. (2006) The European politics of citizenship and asylum: a

comparative analysis of the relationship betweeen citizenship models and the

legal-institutional frames of asylum determination in West Europe. Bergen,Department of Comparative Politics, University of Bergen.

Silverman, D. (2001) Interpreting Qualitative Data: Methods for Analysing Talk,

Text and Interaction, London, Sage.

Hempelmann Eg

Documents

Transcript of Hempelmann Eg