Week 5 - Assignment: Formulate a Strategy for Data Collection

Instructions

During Weeks 3 and 4, you learned about various qualitative research designs. This week, you will build on this knowledge. Select two of the five research designs. Define and explain the features of each design using the resources provided and three other quality resources. Next, develop data collection processes for each of the selected designs that include discussions of sample size, sampling technique, data collection materials, and instrumentation. You can include diagrams if you would like.

You are already familiar with the types of instruments used in quantitative data collection, but these are distinctly different from what is useful in qualitative studies. Qualitative instruments must be structured so that you are collecting deep and broad data to fully understand the research question. In most cases, you must design an instrument to extract specific experiential information from your participants. Data collection can occur through face-to-face interviews, focus groups, or observation; there are also other ways to select qualitative data.

When constructing your data collection plan, it must be clear and it must contain all the steps that you will take when collecting information from your participants. You will have to include any secondary data that you will collect. Secondary data can include documents or other evidence that can contribute to understanding the central phenomenon under study. How will you ensure a data saturation? Remember, practices like member checking, follow-up interviews, or transcript review are used by qualitative researchers to ensure data saturation.

Ultimately when writing this section of your study, it must be logical, repeatable, and reproducible. Every research decision must be based on accepted research practices; remember to include sources in your research plan to demonstrate the depth of your knowledge and the support of the academic community.

Length: 4-5 pages, not including cover and references pages.

Your assignment should demonstrate thoughtful consideration of the ideas and concepts presented in the course and provide new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards. Be sure to adhere to Northcentral University’s Academic Integrity Policy.

References

Faulkner, C. A., & Faulkner, S. S. (2019). Research methods for social workers: A practice-based approach (3rd ed.). New York, NY: Oxford

Given, L. M. (2008). The SAGE encyclopedia of qualitative research methods (Vols. 1-0). Thousand Oaks, CA: SAGE Publications, Inc

NCU School of Business Best Practice Guide for Qualitative Research Design and Methods in Dissertations 2nd Edition

Sikkens, E., van San, M., Sieckelinck, S., Boeije, H., & de Winter, M. (2017). Participant recruitment through social media: Lessons learned from

Qualitative data analysis and CAQDAS. (2014). In Silver, C., & Lewins, A. Using software in qualitative research (pp. 9-34). 55 City Road, London

SAGE Research

Methods

The SAGE Encyclopedia of Qualitative Research

Methods

Pub. Date: 2012

Product:

SAGE Research Methods

DOI:

https://dx.doi.org/10.4135/9781412963909

Methods: Artistic inquiry, Action research

Disciplines: Anthropology, Business and Management, Criminology and Criminal Justice, Communication

and Media Studies, Counseling and Psychotherapy, Economics, Education, Geography, Health, History,

Marketing, Nursing, Political Science and International Relations, Psychology, Social Policy and Public Policy,

Social Work, Sociology

Access Date: January 24, 2023

Publishing Company: SAGE Publications, Inc.

City: Thousand Oaks

Online ISBN: 9781412963909

https://dx.doi.org/10.4135/9781412963909

When undertaking qualitative research, the scholar/researcher has a multiplicity of roles and responsibilities,

often enacted simultaneously. Many of these roles are so intuitive and commonly understood that they are

rarely discussed in standard methods texts. However, other roles, particularly the role of the researcher in

relation to study participants, have generated a great deal of controversy and angst and have been debated

endlessly across all of the disciplines in which qualitative research has both epistemological and methodolog-

ical acceptance. The goal of this entry, then, is to make visible the many roles of the qualitative researcher

and to provide a sense of the larger scholarly framework within which these roles are enacted and examined.

The researcher’s roles are discussed in two sections. Tacit roles have to do with the knowledge and com-

monly understood practices that the researcher brings with him or her to the study concerning how scholarly

research should proceed. In any of the tacit roles, the researcher is the recognized expert who must ensure

that the research proceeds according to accepted standards and procedures. Interactionist roles, on the other

hand, have to do with how the researcher conceptualizes and frames his or her role in relation to study par-

ticipants and what effect the researcher’s presence might have on the thoughts and actions of research par-

ticipants and the knowledge that accrues from the study. The interactionist issues that arise are considered

reflexively by the researcher, who then must make a decision about how best to proceed given the unfolding

circumstances of the

research.

Tacit Roles

The researcher’s role begins at the stage of research conceptualization. At this point, the researcher takes on

the role of an informed “ideas” person. The researcher uses his or her prior knowledge of an area of study

within a discipline, or across more than one discipline, to propose a well-crafted and coherent project with

clearly articulated research questions. The research project could be a stand-alone project for a limited time

frame, or it could be part of a much larger program of study unfolding over a longer period of time, perhaps

a decade or more. In either case, in the role of the “ideas” person, the researcher has a number of respon-

sibilities, the understanding of which is internalized throughout the process of attaining a PhD and develops

further as new research projects are undertaken. Because the vast majority of researchers do have a PhD,

the responsibilities of the ideas person are well understood by all scholars but are rarely discussed. Some of

them

include the following:

• Keeping current and abreast of the scholarly literature (including philosophical discussions, theoreti-

cal developments, and research findings) related to the topic under consideration

• Maintaining a critical awareness of the issues/questions needing further examination

• Having a thorough understanding of the parameters of qualitative research

• Proposing a project that is manageable and can be completed successfully

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• Ensuring that the research will be conducted according to accepted methodological and ethical stan-

dards

• Seeking appropriate funding so that an investigation can be done as fully and thoroughly as possible

• Ensuring that the research is meaningful in terms of its contribution both to the discipline in particular

and to knowledge or society in general

The qualitative researcher maintains the role of ideas person throughout the study, constantly and reflectively

blending the theoretical framework(s) of the study with the qualitative observations to provide new and/or

unique interpretations of how participants come to develop certain meanings and practices within their social

worlds.

Although the role of the ideas person is always present, once the research project begins, the researcher also

may assume a number of other roles. Foremost among these are the rather overlooked, but very important,

roles of research administrator and manager. All qualitative research projects, whether done as solo projects

or by collaborative teams, have myriad administrative aspects such as keeping track of expenditures, hiring

assistants and supervising them, paying attention to reporting requirements for grants, preparing survey in-

struments, organizing transportation, purchasing any necessary equipment and/or software, sending out ad-

vertisements or letters of contact and of thanks for participation, and a variety of other necessary paperwork.

In some cases the researcher has a great deal of responsibility for these tasks, whereas in other cases a

hired project director or another staff member looks after these details in consultation with the researcher.

Nonetheless, even if not attending to all of these details himself or herself, the researcher needs to be cog-

nizant of the progress made on administrative matters to ensure that the project moves forward satisfactorily.

Whereas there is some overlap with the administrative role, the managerial role is distinct and draws on slight-

ly different knowledge and expertise. In the managerial role, the qualitative researcher must make ongoing

and important decisions about the conduct and management of the research—decisions that could ultimately

affect the legitimacy of the study’s findings and contributions. Such decisions would include elements such as

research location and timing, access to participants, supervision of research assistants, daily problem solving,

data analysis, and preparation of findings. In the managerial role, the researcher acts as the primary problem

solver, perhaps needing to make a number of important decisions on a daily basis about the conduct of the

research and the intellectual analysis/presentation of the resulting data. Without this crucial role, the research

project could be adversely affected by a number of smaller or larger problems, such as a subtle drift away

from the study objectives, errors in procedure, or the violation of ethical standards, any of which could have

devastating consequences for the academic legitimacy of the study and the reliability of the findings.

Throughout the research project, from conceptualization to implementation to completion, the researcher also

must take on the role of research ethicist. In qualitative research, ethical considerations are paramount and

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cannot be underestimated. The research must be conducted to ensure that human participants are informed

and protected and that there generally will be no adverse affects from their participation (although it must

be noted that this is not always completely controllable given that some study participants may feel various

degrees of upset at recounting their experiences to the researcher). It is the researcher’s responsibility to un-

derstand ethical issues and norms in qualitative research and to ensure that an appropriate ethics protocol

has been approved by his or her institution and that the approved protocol is upheld throughout the research.

The importance of ethical considerations must be communicated to research assistants, who must be trained

to handle any ethical issues that might arise when working with human subjects.

Another important tacit role for the qualitative researcher is that of mentor. The most obvious mentoring role

is with graduate students and other research assistants who are paid to work on the project. In such situa-

tions, the researcher may need to spend a certain amount of time instructing assistants in the best practices

of the particular methodology being used, coaching them on a variety of issues (e.g., good interviewing or

observational techniques, how to take good fieldnotes, what to expect and what to observe in a given setting)

and allowing them to participate in data-related activities such as the development of coding categories and

the actual coding and analysis of the data collected. Graduate research assistants also may be involved in

helping to prepare the results of the study for conference presentations and possibly for publication. Involv-

ing graduate students in the various stages of research does take additional time, but the researcher who is

working with graduate students, particularly at the doctoral level, usually does feel some obligation to enable

the students to learn from the project so that they will have a better idea about managing and conducting their

own research projects later on as their academic careers progress. Similarly, the researcher who is a principal

or main investigator may do a certain amount of mentoring with co-investigators who are less experienced

with the administrative requirements and problem solving that accompany larger qualitative research projects.

Interactionist Roles

Qualitative research encompasses a wide range of methodologies, including everything from analysis of vi-

sual media and document interpretation to interviewing and various types of ethnographic observation of hu-

mans in their environments. Despite this range, there is a strong association of qualitative research with the

latter two methodologies, involving either direct one-on-one contact with individuals who have agreed to talk

about their experiences or the incorporation of the researcher into a setting where the people being studied

are going about the business of daily life. Accordingly, the researcher roles that receive by far the most atten-

tion in the scholarly literature have to do with the ways in which the researcher interacts with study participants

and his or her own reflections on those interactions. Although every qualitative research project is unique in

some way because of the questions asked and the type of people studied, there are nonetheless many com-

mon areas of concern related to interactionist roles across a variety of very different qualitative studies.

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There have been a number of different typologies of, and terms for, researcher roles in relation to the people

they study. Lynda Baker, writing about observation as a qualitative research methodology, provides a very

good summary of both the ways in which researcher roles have been conceptualized over time and the prob-

lems associated with each, including roles such as the following:

Nonparticipant: The researcher has no involvement with individuals and observes from a distance, sometimes

via software or other electronic means.

Complete Observer: The researcher is present in the setting but only listens and observes and does not in-

teract. His or her role as a researcher might not be known.

Observer as Participant: The researcher is present in the setting and primarily observes, although some brief

interactions with participants may occur.

Participant as Observer: The researcher actively participates in a number of activities with the group, to the

point where he or she may be identified as a friend or colleague.

Complete Participant: The researcher studies a group in which he or she is already active as a member but

does not reveal his or her research role.

Complete Member: The researcher studies a group in which he or she is or has been active and reveals his

or her role as a researcher.

Although the roles noted are discussed in relation to ethnographic observation, versions of them also may

be evident when doing a study based on interviewing. Regardless of which role the qualitative researcher

assumes and whether or not the research is based on interviewing, observation, or some combination of the

two, there are a number of skills and qualities that the researcher must bring to these roles to be effective.

Renee Fox, reflecting on her five decades as an ethnographic researcher, suggested that the skills that are

paramount for ethnographic fieldwork include skills in observation, interviewing, recording, and remembering;

the ability to be self-reflexive without narcissism; the ability to recognize empathically the connection between

the researcher and the researched; interpersonal skills and an ability not only to listen but also to really hear

what is said and meant; awareness not only of language but also of gesture and silence; an appreciation for

the importance of the routine aspects of social life; and an unwavering work ethic for the many hours that are

necessary to perform the emotional and mental labor required for detailed fieldnotes.

A key issue that always arises and must be navigated by the researcher is that of the “insider” versus “out-

sider” role. Insiders are individuals who either have experienced or have knowledge about the issues being

studied (e.g., domestic violence) or have membership in the group being studied (e.g., persons with AIDS,

a particular ethnic group). As insiders, study participants know firsthand about the concerns, feelings, so-

cial norms/conventions, beliefs, daily activities, and/or cultural practices related to the issue or group. Re-

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searchers who have no personal experience with a particular issue or are not members of a particular group

are outsiders. The insider/outsider dichotomy raises a number of dilemmas regarding the role of the re-

searcher having to do with issues such as acceptance, trustworthiness, and the impact of insider or outsider

status on the perceptions of participants. Some authors believe that it is essential to maintain some element

of the outsider role (i.e., academic or intellectual distance) throughout the study, whereas others insist that the

qualitative researcher’s role as an objective and dispassionate observer is not always realistic, achievable, or

even desirable. Arguments have been made that, particularly if researchers are insiders, claiming any sort of

objectivity or distance can actually inhibit accurate perceptions and observations. In such cases, researchers

are encouraged to demonstrate their understanding of the complexities of the situation, thereby increasing

the integrity of the study and their findings. Most researchers come to an awareness and acceptance of their

insider or outsider status and deal with the implications of that status (and possible shifts in it) throughout their

research.

The role as an insider or outsider is only one of many possible interactionist roles that might confront the

researcher during the course of a qualitative study. Some of these additional roles may be assumed by the re-

searcher, whereas others may be assigned to the researcher by participants. In the latter case, the researcher

may need to actively discourage participants from thinking of him or her in a particular role that could be po-

tentially harmful to the study, to the participants, and/or to the researcher. Some of the most common roles

include the following:

Friend. Much has been written about the difficulties of having friends as participants or informants in a study

or of using a friend as an access mechanism into a group. Nonetheless, particularly with a long and in-depth

study, the researcher may develop a bond with at least one participant that develops into a true friendship.

Although some authors think that there is nothing inherently wrong with a friendship arising from research, it

may complicate the study in that the researcher then must be aware of how a developing friendship may alter

the situation and/or the accounts of participants. The researcher also must come to terms with the fact that he

or she cannot freely share everything about the research with a participant who is, or has become, a friend.

Mentor. On occasion, the researcher may take on a mentoring role with study participants. For instance, in

his study of African American high school students, Marc Hill noted that his previous role as a teacher caused

some of the students to seek him out for advice and a sympathetic ear, thereby giving rise to a mentoring role

that he had not anticipated.

Negative Agent. There is always the risk that the researcher’s mere presence will alter the behavior of partic-

ipants or the conditions of their social setting in some way. This can be particularly true in an intimate setting

such as the home. The term negative agent was coined by Amy Jordan, who noted in her study of media use

in the home that her presence as a researcher seemed to escalate the tensions among family members and/

or cause them to rethink their roles in the family. It is very possible that the researcher may unwittingly take

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on a role as a negative agent by causing participants to reflect on their beliefs or their social conditions.

Parent. Particularly in qualitative research involving children, researchers who are themselves parents may

come to feel a great attachment to their participants. Deborah Ceglowski, for example, described how she fell

in love with one particular child during her research at a Head Start program and how she struggled to sep-

arate her researcher self from her parent self. This struggle also affected her role as a participant observer

staff member at the program in that she sometimes felt that the full-time staff members were not doing what

was best for this child.

Professional. A researcher who has a previous professional identity may find that the other role as a pro-

fessional can aid him or her in gaining access to a community where that professional role is recognized.

Because of the researcher’s prior work experience, he or she may be regarded as a knowledgeable profes-

sional who truly understands the issues within a particular environment, and so participants are willing to re-

ally open up about their experiences in that setting. On the other hand, a researcher’s prior professional role

may arouse suspicion. Will participants’ thoughts and feelings be kept confidential, or will the researcher im-

part information to management? In such situations, the qualitative researcher may need to work extra hard

to reassure participants that a prior professional role does not in any way compromise participants’ personal

situation.

Social Activist. Although there is general agreement that an interventionist role is to be avoided, there are

some instances where taking a social or political activist stance is the only way to move the research forward

or is the only morally appropriate course of action. In their work within a Navajo community, Bryan Brayboy

and Donna Deyhle noted that naming the racism that they observed and that participants recounted to them

was the only way to develop a complete understanding of the educational problems faced by Navajo youth.

Although there were repercussions to their stance, the authors nonetheless believed that to take any other

position would have been irresponsible and would have weakened the research findings. Similarly, some re-

searchers have found themselves taking a socially active role when participants are in a dangerous or critical

situation.

Therapist. Of all the roles thrust on the researchers by participants, the role of therapist is the one that is uni-

versally disavowed and deemed to be the most problematic. Particularly when using a one-on-one interview

methodology, the researcher may be privy to painful memories and incidents that participants have never dis-

closed previously. Similarly, in an observational study, the researcher may feel a great desire to intervene in a

situation to improve things for participants. Fox referred to this as the “therapeutic temptation.” Being regard-

ed as a therapist puts the researcher in a precarious position because he or she is not trained as a therapist

and the purpose of the research is not to provide therapy even when participants clearly need assistance.

Also frequently discussed in the qualitative literature is how the researcher’s role is a component of the dif-

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fering power or social relations between the researcher and research participants. Power imbalances can

arise because of demographic differences between the researcher and study participants, including social

class, race, ethnicity, and level of education. Power differences also can be related to a perceived inequality

between the researcher (who is the authoritative figure and is in control of the study) and participants (who

provide the raw material for the study). The qualitative researcher needs to build a bridge to the study par-

ticipants so that they will trust him or her and will reveal aspects of the issue being studied or allow the re-

searcher to observe their regular practices. The researcher builds this bridge by a number of means, including

demonstrations of empathy, nonjudgmental interest, caring, honesty, and openness. However, no matter how

empathetic the researcher or how trusting the participants, there is still an unequal balance of power. The

researcher is responsible for the study, and the participants might not be entirely sure what the researcher

is going to do with the information given or the observations made or how the researcher will interpret and

describe their accounts. Participants also may feel that they are not getting enough back or are not being

adequately compensated for the disclosure of their life experiences to the researcher.

In situations where participants have come to view the researcher as an insider and/or a friend, the power

imbalance may be even more apparent when the researcher suddenly seems to revert to the researcher role.

Numerous authors have commented that their participants have become uneasy or disappointed when, dur-

ing mundane and friendly conversations, the researchers began to take notes or pulled out a tape recorder. In

these cases, participants often comment that the researchers put on their “researcher hat.” In such situations,

participants can feel betrayed that the bond they feel is really not reciprocal and that it is only business as

usual for the researchers. Similarly, the researchers may feel guilty that they have ruined critical moments in

their developing relationships with participants. Many researchers have recounted that, having had this expe-

rience once, they ensure that they do not again revert to the researcher role during routine friendly encounters

with participants. However, there are also just as many accounts of researchers running into the washroom

or to another private place and writing down as much of those sorts of conversations as they can accurately

recall. This illustrates the fine line in qualitative and ethnographic research between the researcher becoming

so familiar to participants that they think of him or her as a normal part of their setting and the researcher

using participants as a pathway into a particular worldview or set of practices.

Although much of the discussion about the researcher’s role centers on the researcher’s interactions with,

and obligations to, participants, in some settings the researcher’s role may actually be co-opted and used by

participants themselves. Often this happens when the researcher also has a professional identity (e.g., health

care professional, engineer, teacher) that is recognized by participants. For instance, Carol Haigh and her col-

leagues suggested that some participants in their study of postoperative epidural pain management used the

researcher to further their own agendas such as by asking the researcher to bring their concerns/grievances

about their care to someone in a position of higher authority within the clinical setting or asking for clarification

of medical information given to them. The researcher should always be aware that participants may have their

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own reasons for participating in the research and that they have the right to question the researcher’s motives

and practices.

Finally, it should be noted that the researcher has a reflexive role in conducting qualitative research. Reflexiv-

ity concerns the need for the researcher to reflect on his or her role(s) and on the general nature of the rela-

tionship between the researcher and the studied. Qualitative researchers understand that they need to think

about the epistemological assumptions, theoretical frameworks, and personal values/beliefs that they bring

to their research. This involves careful reflection on issues such as the way(s) in which researchers position

themselves in relation to the study participants, the way(s) in which participants in turn position themselves

in relation to the researcher, and the researcher’s interpretations of participants’ life experiences. Although a

certain amount of reflexivity about the researcher’s role has always been a component of qualitative research,

within the past 15 years or so, qualitative research has taken an unprecedented reflexive turn during which a

reflexive lens has been brought to bear on virtually every aspect of the qualitative research process.

• roles

Gloria Leckie

Further Readings

Article

Participant
Recruitment through
Social Media: Lessons
Learned from
a Qualitative Radicalization
Study Using Facebook

Elga Sikkens1, Marion van San2, Stijn Sieckelinck1,
Hennie Boeije3, and Micha de Winter1

Abstract
Social media are useful facilitators when recruiting hidden populations for
research. In our research on youth and radicalization, we were able to find
and contact young people with extreme ideals through Facebook. In this
article, we discuss our experiences using Facebook as a tool for finding
respondents who do not trust researchers. Facebook helped us recruit
youths with extreme Islamic and extreme left-wing ideals. We conclude
by discussing the benefits and limitations of using Facebook when searching
for and approaching populations who are difficult to reach.

1 Department of Pedagogical and Educational Sciences, Utrecht University, Utrecht,

the Netherlands
2 Risbo, Erasmus University Rotterdam, Rotterdam, the Netherlands
3 Department of Methodology and Statistics, Utrecht University, Utrecht, the Netherlands

Corresponding Author:

Elga Sikkens, Department of Pedagogical and Educational Sciences, Youth, Education and

Society, Utrecht University, PO Box 80140, 3508 TC, Utrecht, the Netherlands.

Email: e.m.sikkens@uu.nl

Field Methods
2017, Vol. 29(2) 130-139

ª The Author(s) 2016
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How do you approach potential respondents who do not trust you? Some

target groups are, after all, very suspicious about research that might be

initiated or financed by the government. For example, people with extreme

ideals often distrust people outside their own networks because they distrust

the authorities and can, therefore, be difficult to find in radicalization

research and hard to approach (Berko 2009; Juergensmeyer 2003; Richard-

son 2006; Stern 2003). Social media have turned out to be useful facilitators

when trying to recruit hidden populations for research. The purpose of this

article is to discuss our experiences of using Facebook as a tool for finding

respondents with extreme ideals who do not

trust researchers.

In our research on parental influence on radicalization, we tried to recruit

adolescents and young adults with extreme ideals. However, the often-used

snowball sampling in fieldwork did not work for our

research population.

The young people with extreme ideals were very protective of their own

group; passing on names of group members was not acceptable.

We therefore searched for other methods to find potential

respondents.

Many researchers have found that the use of social network sites can be a

useful method to recruit a difficult-to-reach population (Barratt et al. 2015;

Masson et al. 2013; Palys and Atchinson 2012; Parkinson and Bromfield

2013; Seltzer et al. 2014). Still, to our knowledge, little is known about the

use of social networking sites for the recruitment of adolescents and young

adults with extreme ideals. However, as young people are very active on

social media and the Internet is often used for propagating radical ideolo-

gies (Prucha and Fisher 2013), the Internet seemed to be a good place to

start our field research and to find respondents.

We focused on Facebook because it is where people present themselves

in their profiles, share their opinions, meet other users, and join groups with

shared interests (Leung 2013). Since the personal profiles often reveal what

is on a person’s mind, this seems to be a place where people with extreme

ideologies could be found (Van San 2015).

Social networking sites would be particularly useful when searching for

respondents who are stigmatized or marginalized in the off-line world as

their isolation would push them toward social contacts in the virtual world

(Palys and Atchinson 2012). Thus, due to their marginalized position in

society—caused by their radical views—respondents with extreme ideals

are possibly found online more easily as they prefer to stay under the radar

in the off-line world. Furthermore, approaching respondents online could

help in building trust because the younger generation tends to prefer online

messages as these give people ‘‘just the right amount of access, just the right

amount of control’’ (Turkle 2011:15).

Sikkens et al. 131

In this article, we share our experiences of using Facebook as a tool for

recruiting respondents who do not easily trust researchers. We address the

question as to how Facebook can help the search for and approach to

respondents who are difficult to reach due to a lack of trust.

In the first section of this article, we discuss the method we used to find

and approach our respondents. Second, we elaborate on the results of using

Facebook to recruit young research participants with extreme ideals. In the

concluding section, we discuss the pros and cons of using social networking

sites in searching for respondents who do not trust you.

The Current Study

The fieldwork described in this article is part of a follow-up study on the

development of extreme ideals in adolescents and young adults (Van San

et al. 2013). Our aim was to study parental influence on radicalization, and

we therefore sought to interview a minimum of 50 young people with

extreme ideals as well as their parents. In our research, we understand

extreme ideals to be ‘‘ideals that are severely at odds with those of their

family and/or the mainstream’’ (Sieckelinck et al. 2015:330).

The research was conducted in Flanders (Belgium) and the Netherlands

between January 2012 and March 2015. We used Facebook to find and

approach the research population, and we recruited young respondents

online between February 2012 and July 2013.

Method

Recruitment and Inclusion Criteria

We searched Facebook to find young people between 15 and 30 years old

who showed extreme ideals on their profile. We interviewed adolescents

and young adults with extreme right, radical Islamic, or extreme left-wing

ideals. Our research focused on people with various types of extreme ideals,

as growing evidence reveals that the processes of radicalization among

widely divergent groups show parallel developments (Gielen 2008; Van

San et al. 2013).

Procedure

Many researchers feel uncomfortable about revealing private information,

especially when the research concerns people in the fields of radical politics

or criminality. It may be tempting to use anonymous Facebook profiles to

132 Field Methods 29(2)

observe and contact potential respondents, but this goes against the ethical

guidelines of research. Also, transparency is essential in building trust. A

possible solution to proceed on Facebook would be to create a neutral

researcher Facebook profile.

We created three of these Facebook accounts in which we presented

ourselves as researchers. We chose neutral but explanatory names: For

example, a name on one of our profiles was ‘‘PhD-student Utrecht.’’ On

our profiles, we explained who we were and what our research was about.

We generated separate profiles to approach different ideological groups. A

single Facebook account would not have been sufficient because right-wing

oriented people would certainly not trust a person who also shows interest in

Islamists and has anarchistic Facebook friends. However, when we met the

respondent face-to-face for an interview, we were open about our approach

to people from a range of different ideologies.

Subsequently, we searched Facebook for respondents. We traced poten-

tial respondents by visiting relevant group pages. We found these group

pages by using the following example key words in our search: Groene

Vogels [Green birds], Shariah4Belgium, Shariah4Holland, Dutch Oi, Fitna,

Anarchistische groep Amsterdam [Anarchistic group Amsterdam], Anti

Dierproeven coalitie [Anti Animal Testing Coalition], Kraken gaat door

[Squatting goes on]. We then visited these pages and selected people who

posted messages on the group page or who ‘‘liked’’ extreme posts. We then

looked at these people’s personal profiles and checked whether they were

explicit about their ideals on their profile. For example, the adolescents and

young adults were approached if their profiles showed adulation of martyr-

dom, white supremacy, or antigovernment claims.

Next, we sent potential respondents a private Facebook message to ask

them for an interview. In this message, we explained who we were and the

purpose of our study. Rather than using terms such as ‘‘radical ideals’’

(which might imply a security perspective that considered their ideals as

unwanted and dangerous), we asked the potential participants about their

‘‘strong ideals.’’

We had two reasons for using this approach: a theoretical one and a

practical one. The theoretical reason was that a lot of research on radicali-

zation is conducted from a security perspective (Schmid and Price 2011), in

which scholars try to find ways of counteracting radicalization. From this

perspective, young people who develop strong or extreme ideals are often

considered to be radicals and are thought of as potential dangers to society.

However, by simply considering adolescents and their ideals to be danger-

ous, one overlooks the fact that ideals, even radical ones, are part of a

Sikkens et al. 133

democratic discourse, and that some idealistic young people simply want to

be actively involved in their communities (Van San et al. 2013). We there-

fore chose to approach our respondents as young people with strong ideals,

rather than as radicals.

A practical reason for this approach was that we learned from previous

research (Van San et al. 2013) that words like ‘‘radicalization’’ can stand in

the way of finding respondents. However, our respondents were eager to

talk to us when we told them about our parenting perspective and asked

them about their strong ideals. The online use of words such as radicaliza-

tion could also put the respondent at risk, as some may be monitored by

security services.

Delicacy of wording in the recruitment message was important for

obtaining trust. We stressed to our respondents that we are working for a

university, as universities are usually perceived as neutral institutions.

Moreover, we communicated that we are writing a book instead of doing

research. We did not use the Dutch word for research, onderzoek, because it

could also mean ‘‘investigation,’’ which has strong connotations with police

and security services. In the text, we tried to avoid any normative judgments

toward their ideologies and chose an open-minded approach. Moreover, we

promised the respondents confidentiality and anonymity (see Figure 1).

The people we interviewed were quite suspicious about the government,

institutions, and researchers. It was therefore impractical to ask them to

complete a written informed consent form. However, all our respondents

gave verbal consent to participate in our research. We also received parental

consent for participants who were between 16 and 18 years old. We anon-

ymized all interviews to reduce any possible harm to the respondents by

changing the interviewees’ names and leaving out details that could identify

them. Furthermore, all participants were informed that they could contact us

at any time for further questions and could terminate their participation in

the research whenever they pleased. Two respondents did this.

Figure 1. Recruitment text used in our research.

134 Field Methods 29(2)

Results

Between February 2012 and July 2013, we recruited over 80 respondents.

Fifty-one of the respondents we interviewed were adolescents and young

adults with extreme ideals, 33 of whom were recruited through Facebook.

The other 18 young respondents were recruited in traditional ways at

demonstrations or gatherings. We also interviewed 30 parents, foster par-

ents, and siblings whom, in general, we approached through the young

respondents.

Through Facebook, we were able to recruit 19 young people with

extreme Islamic beliefs, seven people who sympathized with extreme

right-wing ideas, and seven people who were involved in animal activism

or who supported anarchism. The age of the respondents we recruited

through Facebook ranged from 16 to 31 years, with a mean age of 20.5

years. Twenty-one of these respondents were male and 12 were female.

Making Contact

When approaching a potential respondent, we usually sent one invitation

message and after receiving a positive reply, one or two more messages

were sent to arrange a time and place to meet for an interview. We found

that it took far fewer messages to convince people with extreme Islamic

ideals to participate in the research (usually just one), and a lot more

messages if we approached young people with extreme left-wing ideals.

In general, approximately four out of 10 messages were answered.

Although Facebook was very useful in helping us approach respondents

with strong ideals, it did not enable us to approach all groups. Young people

with extreme right-wing sympathies were, for example, difficult to find on

Facebook. They seemed to prefer their own closed and anonymous com-

munity forums, such as Stormfront, rather than Facebook. When we tried to

approach extreme right-wing respondents on Stormfront, all members were

warned within the hour that researchers had tried to contact members. As

noted in the forum, ‘‘Or maybe it was the Secret Service?’’

We found that Facebook is a more open medium than group forums

such as Stormfront in that all layers of society and different generations

use Facebook. Adolescents and young adults with left-wing ideals and

(converted) young Muslims were easy to find on Facebook. One might

imagine that people with extreme ideals would keep their profiles private

so as not to be discovered by the police or secret services, leaving the

researcher with the less extreme public Facebook profiles. Contrary to this

Sikkens et al. 135

assumption, we found that a considerable number of the people with extreme

left-wing and Islamic ideals had made their Facebook profiles public.

Convincing Respondents to Participate

One extreme left-wing female respondent we were trying to convince to

participate in our research refused because she had seen our LinkedIn pages

and found that one of our team members used to work for the local police as

an administrator. So it is important to consider your online persona when

approaching respondents who do not trust you.

Furthermore, we found that Islamic young people were especially enthu-

siastic about participating in our research when we approached them

through Facebook. Their enthusiasm was possibly driven by their desire

to perform Dawah (to spread the word of Allah) but possibly also by the

sincere interest that the researchers showed in their ideals. Left-wing-

oriented idealists, however, were easy to find but hard to convince to

participate in our research. Afraid that it would be government-led research,

they often refused involvement. However, in a few cases, the person was

eventually persuaded to meet us after extensive messaging and chatting on

Facebook. Despite the use of social media, fieldwork within radicalization

research remains a long-term effort; a researcher has to be persistent.

Discussion

In this article, we have shared our experiences of using Facebook as a tool

for finding and approaching respondents who do not trust researchers. In

keeping with Barratt et al. (2015) and Masson et al. (2013), we found that

the use of social network sites can help in the recruitment of a hard-to-reach

research population.

A first major benefit was that Facebook profiles gave us a clear idea

about people’s ideals, so we had a better notion of who to invite for inter-

view: Facebook made a hidden population visible.

We did not use advertisement banners that are common in online field-

work, but rather chose a personal approach. We used private messages to

recruit people with extreme ideals because there exist strong privacy con-

cerns among this population, as having extreme ideals usually involves

membership of stigmatized or illegal groups. We therefore assumed that

the chances of these respondents voluntarily replying to an advertisement

were small. Instead, to engage with respondents from the very beginning of

the process, we contacted them personally.

136 Field Methods 29(2)

A second benefit we found was that an approach via a private Facebook

message gave respondents the power to open, ignore, delete, or contemplate

the request in their own time. Potential participants could then quietly

consider whether they were willing to participate in an interview and they

were able to leave ‘‘the field’’ at any time, making the approach less

intrusive.

A third benefit was that the potential respondent did not have to worry

about group members who might be negative about their participation in

research. When the researcher approaches potential respondents during a

demonstration or event, others might notice them talking to a researcher. In

contrast, when he or she is approached by private Facebook message, par-

ticipation is more likely to be anonymous, which is important for respon-

dents who are distrustful of people outside their own network.

However, a concern that scholars need to take into account when using

Facebook for respondent recruitment is their own online persona, as every

researcher is traceable on the Internet. Palys and Atchinson (2012:357) also

warned that ‘‘the door to the Internet opens both ways,’’ so when recruiting

people who are very distrustful, researchers should consider their online

persona before writing to respondents.

A final possible limitation is that in cases where you are not friends on

Facebook, messages are sent to the ‘‘other folder.’’ Potential respondents

are then not signaled that they have e-mail. However, by paying $1, you can

send your message directly to someone’s inbox.

In Table 1, we have summarized some guidelines for approaching

respondents who do not trust people outside of their own networks.

Conclusion

This study provides additional evidence that Facebook can be a facilitator in

finding and approaching potential respondents who are hard to find in the

Table 1. Guidelines for Approaching Respondents Who Do Not Trust You.

� Create a researchers’ Facebook page in order to be transparent
� To build trust use a positive approach toward the research topic
� To build trust use a personal approach instead of an advertisement
� Show sincere interest
� Be persistent
� Researchers should be aware that their online persona is traceable on the

Internet

Sikkens et al. 137

off-line world because they do not trust anybody outside of their own net-

works. Finding potential interviewees through relevant group pages that

they ‘‘liked’’ on Facebook and subsequently sending them a private recruit-

ment message through Facebook turned out to be effective. The identified

guidelines may benefit the future recruitment of respondents who do not

trust researchers.

Acknowledgment

We would like to thank FORUM, the former Dutch Institute for Multicultural

Affairs, for its material and other support for this research project.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,

authorship, and/or

publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or

publication of this article.

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Mulbjerg Pedersen, H. Nguyen, A. Malm, S. Lenton, D. Korf, A. Klein, J. Heyde,

P. Hakkarainen, V. Asmussen Frank, T. Decorte, M. Bouchard, and T. Blok.

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SAGE Research Methods

Using Software in Qualitative Research: A Step-by-Step

Guide

Author: Christina Silver, Ann Lewins

Pub. Date: 2017

Product: SAGE Research Methods

DOI:

https://dx.doi.org/10.4135/9781473906907

Methods: Coding, Case study research, Memos

Keywords: software

Disciplines: Anthropology, Business and Management, Criminology and Criminal Justice, Communication

and Media Studies, Counseling and Psychotherapy, Economics, Education, Geography, Health, History,

Marketing, Nursing, Political Science and International Relations, Psychology, Social Policy and Public Policy,

Social Work, Sociology

Access Date: January 24, 2023

Publishing Company: SAGE Publications Ltd

City: 55 City Road

Online ISBN: 9781473906907

https://dx.doi.org/10.4135/9781473906907

Qualitative Data Analysis and CAQDAS

This chapter introduces the eclectic field of Computer Assisted Qualitative Data AnalysiS (CAQDAS) in the

context of qualitative research methodology and the techniques of analysis generally. We discuss the prac-

ticalities of research in the software context, outline some basic principles and distinctions which resonate

throughout the book; discuss software developments, debates and functionality; and discuss selected quali-

tative approaches. The remaining chapters build from here, describing some core tasks you might undertake

using CAQDAS packages, illustrated via three case-study examples (Chapter 2). Our overall emphasis is on

the inherent fluidity between the processes involved in analysis and how customised CAQDAS packages re-

flect and reinforce them.

We discuss analysis in the context of technological possibilities. Table 1.1 lists common analytic tasks enabled

by CAQDAS, but software itself does not dictate their sequencing, or whether certain tasks are undertaken or

tools are used. These decisions rest entirely with you, informed by the interplay between methodology, ana-

lytic strategy, technology and practicality.

Table 1.1 Common tasks of analysis supported by CAQDAS packages

Task Analytic rationale

Planning and man-

aging your project

Keep together the different aspects of your work. Aid continuity, and build an audit trail. Later, illustrate your process

and your rigour through transparent writing.

Writing analytic

memos
Manage your developing interpretations by keeping track of ideas as they occur, and building on them as you progress.

Reading, marking

and commenting on

data

Discover and mark interesting aspects in the data as you see them. Note insights as they strike you, linked to the data

that prompted them – enabling retrieval of thoughts together with data.

Searching (for

strings, words,

phrases etc.)

Explore data according to their content. Discover how content differs across data and considering how familiarising with

content helps you understand what is ‘going on’.

Developing a cod- Manage your ideas about your data by creating and applying codes (that represent themes, concepts, categories etc.).

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Page 2 of 32

Using Software in Qualitative Research: A Step-by-Step Guide

https://methods.sagepub.com/book/using-software-in-qualitative-research-2e/i1146.xml

Qualitative research and data analysis

Qualitative research is a broad field that crosses disciplinary, methodological and sector-based boundaries,

and it is important to acknowledge the variety contained within it. Different philosophical, theoretical and

methodological traditions underpin the way researchers think about and do analysis. Much work has been

done elsewhere to make sense of these – often competing and sometimes complementary – scientific princi-

ples. If you are new to the area we point you in the direction of the following in particular: Bryman and Burgess

(1994), Creswell (1998), Mason (2002), Bernard and Ryan (2010), Silverman (2010, 2011), Bazeley (2013)

ing schema The structure and function of a coding scheme depends on methodology, analytic strategy and style of working.

Coding

Capture what is going on in your data. Bring together similar data according to themes, concepts etc. Generate codes

from the data level (inductively) or according to existing ideas (deductively) as necessary; define the meaning and ap-

plication of codes.

Retrieval of coded

segments

Revisit coded data to assess similarity and difference, to consider how coding is helping your analysis, and prioritising

‘where to go next’.

Recoding
Recode into broader or narrower themes or categories if appropriate and necessary.Perhaps bring data back together

and think about them differently.

Organisation of data
Organise data according to known facts and descriptive features to allow consideration of how these aspects play a

role in your understanding.

Hyperlinking Link data to other data segments and/or to other files to track process, contradiction, association etc.

Searching the data-

base and the coding

schema

Test ideas, interrogate subsets for similarity and difference, identify anomalies, or generate another level of coding.

Mapping Manage analytic processes by visualising connections, relationships, patterns, processes, ideas.

Generating output

Report on different aspects of your progress and the project at any stage. Save as files to capture status at an analytic

stage, or to work in other applications. Print off to get away from the computer and think and work in more ‘traditional’

ways.

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Page 3 of 32 Using Software in Qualitative Research: A Step-by-Step Guide

and Saldaña (2013). Neither the scientific and philosophical principles nor the disciplinary and methodologi-

cal subtleties within approaches to qualitative research and analysis are the focus of discussion in this book.

However, reflecting on your ontological and epistemological standpoints (i.e. how you understand the world

to work and how you believe it can be investigated) is important in locating and justifying your research. In

reading the literature you will come across many different terms used to define the context and manner of

inquiry, including perspective, framework, approach, strategy, methodology, and method. There are no clear

boundaries between or hierarchical structure to these terms; they overlap and are used differently in particu-

lar contexts. Categorisation of qualitative research in terms of data collection techniques has a long history,

but detailed discussion concerning the processes and procedures involved in analysis (i.e. what we actually

do) has only occurred more recently (Bryman and Burgess, 1994). This book discusses processes and pro-

cedures of analysis specifically in the context of customised software use.

The extent of diversity in the field is well illustrated by comparing the work of three authors, all of whom

wrote during the 1990s yet conceptualised qualitative research rather differently. Tesch (1990) distinguished

27 forms of qualitative research (see Figure 1.1; p. 23). Woolcott (1994) differentiated qualitative research

strategies according to six styles of collecting data (archival strategies, interview strategies, non-participant

observation strategies, participant observation strategies, field study, ethnography). Miles and Huberman

(1994: 7) argue that while a ‘core’ of recurring features exist across qualitative research, they are ‘configured

and used differently in any particular research tradition’. They distinguish between three traditions: interpre-

tivism (including phenomenology, social interactionism, semiotics, deconstructionism, ethnomethodology and

hermeneutics); social anthropology (including ethnography, life history, grounded theory, ecological psycholo-

gy, narrative studies and case-study analysis; and collaborative social research (action research).

The range of ways used to describe qualitative research and analysis illustrates the difficulty of adequately

reflecting the diversity in how general principles intersect to result in specific strategies. Most authors concede

there to be much overlap between the distinctions they draw; there is often even blurring between under-

standings amongst different authors using the same terms. Researchers combine data collection methods in

qualitative research design and borrow elements from various approaches in developing specific strategies

for investigating new social problems or for using different forms of data.

Problems in categorisation systems are illustrated particularly clearly in contemporary writings about mixed

methods. As more authors enter the debate, a tendency to generate increasingly specific categorical systems

to reflect diversity ensues. Increasingly subtle differentiations complexify to such a degree that the area can

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Page 4 of 32 Using Software in Qualitative Research: A Step-by-Step Guide

become more difficult to access for novice researchers. Nevertheless, broad overviews and summaries are

important in gaining entry to any field of scientific inquiry.

The use of customised software is not required in order to conduct robust analysis. But its use enables us to

be more transparent in how we go about analysis because the tasks we engage in, their sequence, role and

documentation can be more easily illustrated than when working manually.

The practicalities of research in the software context

The availability of customised qualitative software occurred within a diverse methodological field, which has

only become more varied with digital technology, big data and the rise of applied, commercial and citizen-re-

search. In addition there is increasing discussion of mixed methods approaches to research and analysis and

the use of visual methods. Reflection about of the rise of qualitative software and the implications of its use

must be done in the context of the practicalities of research, in which analysis is understood as a core activity

throughout an iterative process.

Whatever the characteristics of a particular study, there are certain core elements involved in doing research.

Planning is paramount (Box 1.1). Authors usually discuss several aspects in planning and conducting re-

search. Mason (2002), for example, discusses ‘questions of strategy’, ‘generating qualitative data’, and

‘analysing qualitative data’; Boolsen (2006) distinguishes between ‘problem formulation’, ‘research design’,

‘data collection’ and ‘analysis’. In our experience researchers often plan data collection carefully, but neglect

to put the same degree of effort into planning the analysis.

In the context of the use of software, much less has been written about research design than in relation to

qualitative (and, increasingly, mixed methods) approaches more generally. Di Gregorio and Davidson (2008)

wrote the first comprehensive discussion of research design in the specific context of software use that tran-

scends individual products. In further opening up discussion about the role of software in designing and con-

ducting research, we identify six key tasks in setting up a software project to reflect initial research design

(Silver and Lewins, 2014). These tasks reflect the sense in which CAQDAS packages are essentially project

management tools which can be used from the earliest moments of conceiving a research idea, through all

the phases of planning and implementation of analysis to the tasks of writing up an account for publication,

preparing for a conference presentation or organising a thesis (Chapter 2).

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These tasks are: (i) managing and referencing literature; (ii) defining research topic and questions; (iii) repre-

senting theoretical frameworks; (iv) incorporating research materials; (v) defining factual features; (vi) devel-

oping analytical areas of interest. They are inherently interrelated, occurring in tandem rather than as discrete

stages. One of the main benefits of using qualitative software is that flexibility can be built into analytic designs

to reflect changes as projects evolve. This is a common thread through this book. This way of thinking about

setting up a software project emphasises the importance of making explicit what you plan to do and how you

plan to do it. Woolf (2014a) describes these essential elements as the strategies and tactics of analysis.

Managing and referencing literature

Reviewing existing literature concerning your broad topic is a fundamental early task. Technological devel-

opments mean that this process is changing rapidly and significantly. Many journals have electronic versions

providing free or easy access to full-text articles. Bibliographic software has developed to the point that it

is quick and easy to transfer reference lists and online material directly into libraries, along with associated

metadata. CAQDAS packages have also developed significantly in this area, with several now enabling the di-

rect importation of PDF files and references from bibliographic software. Conducting a literature review within

qualitative software is not only feasible, but also incredibly useful. Chapter 5 distinguishes between direct and

indirect handling of literature, via annotating and coding full-text articles and/or developing critical appraisals

about and linking within and between them. However you chose to proceed, integrating literature with the rest

of your work through a CAQDAS package enables you to later systematically compare existing literature with

your analysis.

Formulating the research problem and defining the research questions

Formulating the research problem is more than just deciding on the topic. It is informed by your ontological

and epistemological standpoint and your familiarisation with and critiquing of the literature, both of which help

you rationalise why the area you are interested in requires further research. If you use software to facilitate

the literature review, it makes sense also to write up your formulation of the research problem and define the

initial research questions within the software project (Chapter 5). You can thus be explicit about your interest,

assumptions, expectations and prejudices and link your writing to the literature that contributes to your prob-

lem formulation.

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Representing theoretical frameworks

Whether your intention is to work within a clear theoretical framework, perhaps through applying existing the-

ory or testing hypotheses on new bodies of data or areas of conceptual interest, or to develop theory from

empirical data, you will never be working within a theoretical vacuum. Contrasting ways of working can broad-

ly be distinguished according to the direction you are working in; whether top-down (deductive) or bottom-up

(inductive). These approaches, and their combination (abduction), are discussed in the context of coding in

Chapter 7. Whatever its role, it is important to relate your conceptualisation of the research problem to ex-

isting theory, to represent that within the software project at the outset and reflect how it evolves during the

project. That might happen via memo-writing (Chapter 10) and/or the visualisation of theoretical contexts in

visual maps (Chapter 11). You will be able to refer back to these ideas at later stages and compare initial

assumptions and expectations with the analysis as you proceed.

Incorporating research materials

Data collection is all about constructing the best possible dataset in order to investigate the research problem.

Under ideal circumstances, what data are required to answer the research questions? What data are avail-

able? Could you construct a suitable dataset from existing sources and conduct secondary analysis, or do

you need to collect new data? What instruments will you use to generate new data if required? How will you

ensure data are of sufficient quality? In the context of setting up a software project, you can create locations

for storing data and other research materials early on (Chapter 5). You do not yet need to have data ready

to incorporate. You may even change your mind and work with different materials later on, but thinking about

data, how they are related to one another and how they will be handled as soon as possible is an important

part of research design and software project set-up.

Defining factual features

Factual features are known characteristics about data and respondents (Chapter 12). Depending on your de-

sign you may sample on this basis; for example, if conducting a comparative case study in which you are

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focusing on two or more organisations, settings or other entities. Alternatively, you may be interested in com-

paring how individuals with certain socio-demographic characteristics think about, experience or talk about

an issue. One-case designs also include comparative elements, although these typically relate to features

within cases rather than across multiple ones. In addition are analytic facts identified as salient through the

processes of interpretation. Factual features often pertain to information which stays constant within a project.

However, longitudinal designs include some such features which change over time, and these can constitute

core comparative aspects. Either way, these aspects can be well handled in software.

Developing analytical areas of interest

There are many different approaches to qualitative data analysis, some of which we discuss in more detail

below, and in Chapter 7. The main focus of this book is on how software packages specifically designed for

the purpose may support your approach to analysis.1 As such, this book will not tell you what your approach

should be, or what the specific means are by which you will achieve them. Although this chapter discusses

some common analytic strategies and the rest of the book discusses how analytic tasks can be supported by

CAQDAS packages, this is done in broad terms. You should therefore read this book in tandem with the wider

literature on qualitative research and data analysis, if you are not already familiar with it.

‘Analysis’ is often written about or conceived as a discrete stage in a research project. This is the result of hav-

ing to separate phases of work or analytic processes in order to describe and discuss them without causing

confusion. In many respects we are doing the same here, in this chapter, and throughout the book. However,

conducting research is not a linear, one-directional task (see Figure 2.1; p. 45). The elements that comprise

any project are interrelated and fluid. Analysis is not a stage of work with clear boundaries. You analyse from

the first moments of conceiving the idea of a project, locating it within your ‘world-view’ and formulating the

problem through design, data collection and into writing up. Doing a literature review is a form of analysis

(Chapter 5). Deciding whether, how and what to transcribe is an analytic act (Chapter 7). Developing a cod-

ing scheme (Chapter 9) and linking data and concepts (Chapter 11) are analytic. Writing up an account is a

form of analysis (Chapter 10). Designing a research project forces you to be explicit about what you want to

analyse and how you intend to do so (see Silver and Lewins, 2014). You will get more out of your project and

your use of software tools if you come to software clear in your mind what your analytic strategy is and what

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processes you need to go through in order to apply it and answer your research questions.

CAQDAS packages are project management tools which have potential benefits for all qualitative and mixed

methods projects – far and beyond the ways they are most usually described, critiqued and reported. We dis-

cuss this further in Chapter 2, in describing our conceptualisation of their role in supporting the many phases

of work.

Box 1.1 analyticnotes

Research design and software project set-up

Designing a research project is all about planning how you intend to carry out the research. What methods

of data collection or generation will you employ? Why? And what will be the implications of doing so? What

restrictions are there on the way you will proceed, arising from the circumstances in which you will work?

What are the likely consequences of your design choices? What is your analytic focus likely to be? How will

you handle changes in focus? These sorts of questions should guide the way you set up a software project.

Although it is common to plan research, often this is done primarily in relation to data collection. It is just as

important, however, to plan the analysis. Using software from the outset will help with integrating all types of

planning into your work.

Some basic principles and distinctions

Throughout this book we return to some central ideas which frame the way we perceive analysis and discuss

and teach software. In preparing yourself for reading this book, and your general preparation for thinking

about and engaging with software the most important of these are the following:

Analysis constitutes a series of processes which, although having distinct characteristics in their own

right, are fluid and overlapping.

Analytic work can be distinguished according to levels of abstraction from data and directions of

work, and these are reflected in the way you can work with software.

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Approaches to analysis are usefully distinguished according to whether they are essentially code-

based or non-code-based.

Analysis requires the ability to cut through data in different dimensions.

Making reference to the case-study examples (Chapter 2), we illustrate that the use of customised software

significantly facilitates these aspects, allowing either the strict adherence to an established and documented

analytic strategy, or the creation of one in relation to the specific needs of an individual project.

Analytic processes

Qualitative analysis, rather than being a linear set of procedures which follow on from one another in a logical,

one-directional way, is better understood as a process. This is not a new idea. Bryman and Burgess noted

in the mid-1990s, for example, that the previous decade had seen a number of ‘shifts in emphasis’ in the

way research methodology was being discussed, including that ‘stages of social investigation have been re-

placed with the idea of research as a social process which requires careful scrutiny’ (1994: 1). Since then,

many different qualitative approaches have been discussed in similar ways. Software explicitly designed for

the purposes of qualitative data analysis reflects and reinforces its non-linear and fluid nature. We prioritise

such fluidity in the way we discuss software tools and encourage you to think about and use them. The linear

format of this book limits us somewhat in this regard. However, having the idea of fluidity in your mind from the

outset is important. Refer to our conceptualisation of the ‘core analytic activities’ (Figure 2.1; p. 45) and the

sense in which analytic tasks are carried out through the use of software tools iteratively and incrementally..

This view of software will set the foundation to enable you to make the most of your chosen package.

Something that has characterised qualitative data analysis generally, and the use of CAQDAS more partic-

ularly, is an absence of specific and detailed accounts of how analysis proceeds. There is much discussion

about qualitative data analysis as a ‘craft’ which has resulted in a sense of mystery around what analysts ac-

tually do. We illustrate the role of software in ‘opening up’ qualitative data analysis, in making apparent both

the processes involved, and the impact of technology on methodology. Whatever type of researcher you are,

you have a responsibility to document and reflect on what you do, and the impact of your processes on the

results. Using CAQDAS will not do this for you, or, in or of itself, make you a ‘better’ researcher. But it will pro-

vide you with tools that more directly and immediately allow you to illustrate and justify your analytic strategy.

A theme of this book, then, is that CAQDAS provides the tools, but you, the researcher must use them wisely.

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Levels and directions of work

Analysis happens at different ‘levels’. In discussing the role of software in analysis we distinguish between

four:

Sometimes you work at the data level, carefully viewing and reading relevant material and consider-

ing its importance in relation to the formulation of the research problem, the existing literature, and

the adequacy of the data you have before you. You might make notes (usually called ‘annotations’)

about what you see, which help you to identify and reflect upon interesting aspects of the data and

decide what to do next, in order to move the analysis forward. You link data segments that appear to

be associated.

Much (but not all) qualitative data analysis employs the mechanism of ‘coding’ to organise ideas

about what is important in data, in relation to research questions. This might be thought of as the in-

dexing level (depending on your analytic approach and methodological context). However described,

it refers to the organisation of data according to what you, as the researcher, deem to be interesting

in the materials you have before you. In some approaches, this level of work builds on work previous-

ly done at the ‘data’ level. Elements of work done at this level of work might also be conceptualised

as ‘thematic’ if you consider codes to constitute themes.

If we think of coding (and other means of organising ideas such as linking) data segments as a

process of indexing meaning or content; mapping out what is ‘going on’, it follows that at some point

we need to ‘move on’ and work at a more conceptual level. This often proves challenging when us-

ing CAQDAS for the first time; regardless of how the terms ‘codes’, ‘themes’, ‘concepts’ and ‘cate-

gories’ are understood and conceived as operating within analysis. Difficulties can arise in doing so

for several reasons, not least your confidence in analytic process and confidence in experimenting

with software tools such that you can manipulate them to suit your needs. Issues in using the tech-

nology can be particularly challenging amongst researchers who are learning about software at the

same time as learning about methodology, or amongst experienced analysts who are new to the use

of technology for analytic purposes (Silver and Rivers, 2014). This is understandable and to be ex-

pected. Yet the tools that allow you to interrogate patterns, relationships and connections within and

between data (Chapter 13) are where the significant potential power of using software, in compari-

son to working on paper, lie. We hope that this book will give you the confidence to experiment with

software tools beyond those that appear most straightforward in the context of your methodological

framework, analytic strategy, practical project demands and personal style of working.

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The importance of stepping back from data and your developing analysis of them is a recurrent

theme in this book. From a process point of view this entails working at a more abstract level.

Whether your analysis is directly informed by theory (deductive), oriented around theory-building (in-

ductive), or employing elements of both (abductive), creating connections between data, codes and

your comments about them is an integral part of analysis. CAQDAS packages provide ‘mapping’,

‘modelling’ or ‘networking’ tools which enable you to interrogate connections according to earlier

work, or to create connections according to you current thinking about (sub)sets. However you work,

diagrammatic visualisations facilitate thinking in non-linear ways. We see the use of writing tools in

tandem with mapping tools as integral to getting the most out of work at the abstract level specifically,

and your use of software more generally.

Chapter 7 discusses different approaches to coding, distinguishing between inductive, deductive and abduc-

tive. This relates in part to the level at which you start and the direction of analysis. In broad terms, inductive

approaches tend to be ‘theory-building’, starting at the data level and working up towards the abstract

levels.

Deductive approaches are often characterised as ‘theory-testing’ in which the project is driven or informed by

an existing theoretical framework which is applied to, or tested on, a new body of data. Abductive approaches

combine approaches. This is simplistic of course; rather than seeing different ways of working as mutually

exclusive or distinct from one another, analysis often involves working in different ways, at different levels, in

different directions, at different times. The use of software facilitates such flexible, iterative and incremental

processes.

Code-based and non-code-based approaches

CAQDAS packages largely grew out of social science disciplines and are distinguished from other software

tools by the sense in which they offer qualitative approaches to qualitative data (see below). Many now offer

much more, but given their historical roots and the debates around their use, it is relevant to make some

broad comments about their versatility in terms of analytic techniques generally. We do this in part by distin-

guishing between ‘code-based’ and ‘non-code-based’ approaches.

The packages we discuss in this book all have powerful and sophisticated coding tools. These can be used to

facilitate a range of approaches to analysis (Chapter 7). Many methodologies employ coding as a means of

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organising ideas about data; therefore CAQDAS packages have wide appeal and are extensively used across

sectors, disciplines, methodologies and analytic approaches. Amongst early criticisms were contentions that

software served to homogenise qualitative data analysis because it prioritised coding, thereby encouraging

code-based approaches at the expense of alternative ways of navigating data (Coffey et al., 1996; Lee and

Fielding 1996). Although such criticisms have since been superseded by software developments, it is relevant

to highlight the role of coding in analysis (whether supported by software or not).

The first point to make is that coding is not in and of itself analysis. Whether using software or not, coding is

a device to organise ideas about data (Chapters 2 and 7). Coding is a common aspect of analysis and facil-

itates interpretive thinking. But it is essentially an indexing process, in which you catalogue that a particular

segment of data is about, or a general instance of, something in which you are interested. Coding in software

has a number of advantages over paper-based coding (Chapters 7–9), but technically a code is simply a po-

sition in a database system to which you link data segments. Codes need not, therefore, be conceived of, or

used as, interpretive devices in the sense in which they are primarily discussed in the literature. They can be

used in that way, of course. And many, perhaps most, who use software, do so. But you can use them for a

whole range of purposes that transcend customary, methodological ways of working. Thinking about codes

and the process of coding in narrow ways might be limiting and homogenising, but that is to do with the user,

not the technique or the software itself.

However, some types of associations exist within data for which coding tools are inadequate. A classic exam-

ple is when a respondent makes a comment which s/he later contradicts. The two statements might be about

the same general topic, but they communicate something quite different. Associating them with the same

code does not record that level of nuance. Or when reviewing literature, you read something which reminds

you of a passage you read in a different article, and you want to note that association. Or in analysing the

way a text builds an argument – perhaps in a political speech or other form of public discourse – you want

to track how rhetorical or other linguistic devices are used to make a point. These are just some examples

of instances in which coding fails to offer sufficient flexibility. Many CAQDAS packages include the addition-

al ability to hyperlink between passages of data in addition to coding them (Chapter 6). These devices offer

more flexibility in handling non-linear and non-thematic linkages such as those listed above (Silver and Field-

ing, 2008; Silver and Patashnick, 2011).

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Cuts through data

The need to make comparisons across, and interrogate patterns and relationships within data is inherent to

analysis. Distinguishing between the different cuts through the dataset you wish to make is a useful device

in thinking about how tools might be employed for your analytic needs. Cutting through data ‘horizontally’ is

typically about focusing on one area of interest across all data (or subsets). For example, you might focus on

an individual code or theme and retrieve all data segments linked to it, regardless of the data files in which

they occur. You are thus able to start thinking about how a particular topic or theme occurs across the whole

dataset, to think more analytically and start interrogating more deeply. Cutting through data horizontally in this

way allows you to ask questions like: Are the segments coded here equivalent, or do they need recoding in

order to handle peculiarities, contradictions, nuances? Are there some segments which are not adequate in-

stances of the code? Do they need uncoding, reconceptualising, disregarding? How does this sort of retrieval

change the way you are thinking about the code? What further questions does cutting through the data in this

way raise?

In addition, you may be interested in focusing attention on one particular data file, an interview transcript or

set of field notes, for example, and visualising how multiple codes occur in relation to one another, ‘verti-

cally’, throughout. This might be related to sequence, proximity or embeddedness. You might consider the

relative occurrence of all codes, or selectively choose those that you envisage might be related, or will help

you identify interesting patterns in the way you have coded. This can lead to further questioning of data, to

ascertain, for example, whether a pattern in coding identified in one data file also occurs in other, related da-

ta. Language-oriented approaches can benefit in particular from this type of visualisation, or those in which

the sequence of code application is of particular interest. For example, in analysing political discourse there

may be a focus on how an argument is constructed. Considering the position of codes that capture particular

linguistic devices according to the way sentences are structured, for example, would enable a comparison of

how different politicians craft an argument. This type of coding can be combined with more content-based,

descriptive or thematic types, in order to subsequently investigate whether arguments relating to particular

topics are constructed differently. Consideration of the relative position of code occurrence in this way enables

you to remain at the data level, although working in this way also allows you to combine the indexing and data

levels.

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Cutting through data horizontally and vertically need not be distinct activities. Some software packages allow

you to visualise both dimensions at the same time. Such concurrent working offers means of ‘playing’ with

data and the connections within in many different ways. You can also combine working with data and codes

like this with interrogation on the basis of the factual characteristics pertaining to respondents and data. For

example, having sampled for and interviewed respondents with different socio-demographic characteristics

(e.g. gender, age, role), you can investigate the differences in how men and women think about, discuss and

experience particular topics. This can be done both horizontally and vertically.

Throughout the book we illustrate how different ways of viewing data can facilitate your analysis. These are

not restricted to vertical and horizontal cuts, but distinguishing on this basis offers a good starting point for

considering how you intend to work with and compare data and explore the patterns and relationships be-

tween them.

The rise of qualitative software

Software programs supporting qualitative analysis have a relatively long history. The earliest handling of tex-

tual data developed on mainframe computers during the 1960s. These concordance-type tools provided quick

listings of word usage, frequency and standardised measurements appropriate for certain types of quanti-

tative content analysis and other language-oriented approaches. Basic data management systems geared

around storing and indexing large volumes of textual information have also been available since the early

days of personal computing. There are now many such software programs providing a variety of sophisticat-

ed management and linking tools that support the range of digitised information researchers collect to inform

their work. The internet ‘revolution’ has broadened the range of possibilities almost infinitely. However, the

tools we discuss in this book, categorised as CAQDAS, developed from the mid-1980s and share particular

characteristics arising from their developmental origins.

What types of software do we categorise as CAQDAS?

Tesch (1990) began the process of relating analysis types and software tools. Weitzman and Miles (1995)

built on her work, creating a taxonomy of qualitatively oriented software packages. The CAQDAS acronym is

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well understood across disciplines as broadly referring to software designed to assist the analysis of quali-

tative data. It was coined by Raymond Lee and Nigel Fielding following the 1989 Surrey Research Methods

Conference which first brought together pioneers in the field. The subsequent CAQDAS Networking Project

(established in 1994) had the effect of ‘fixing’ the acronym.2

However, there has been rather a fuzzy conception about which packages CAQDAS includes. We make a

broad definition here. Software which falls under the CAQDAS ‘umbrella’ includes a wide range of packages,

but their general principles are concerned with taking a qualitative approach to qualitative data. Qualitative da-

ta include text, visual and multimedia forms of non-numerical, or unstructured material (Chapter 4). A qualita-

tive approach often includes a need to interpret data through the identification and possibly coding of themes,

concepts, processes, contexts, etc., in order to build explanations or theories or to test or enlarge a theory

(see below and Chapter 7). Qualitative data collection techniques include in-depth interviews, focus groups

and participant observation. Approaches to qualitative research and analysis include action research, ethnog-

raphy, ethnomethodology, hermeneutics and phenomenology. Qualitative analysis strategies include ground-

ed theory, thematic analysis, Framework analysis, conversation and narrative analysis. Different approaches

may employ a range of data types and analytic strategies, and the techniques employed and processes fol-

lowed to undertake analysis cut across approaches and strategies.

The qualitative strategies we refer to are distinct from ‘quantitative content analysis’ or ‘text mining’ tech-

niques, in which the statistics of word or phrase frequencies and their occurrence relative to other words or

phrases are the basis of analytic work (see Holsti, 1969). We refer to tools that support such approaches in

varying degrees. Where we include them it is because they have a focus on the qualitative as well. Chapter 3

provides an overview of the packages we are mainly concerned with in this book.

Which is the ‘best’ CAQDAS package?

This is perhaps the most frequently asked question, yet it is impossible to answer! All the packages we use

and teach have tools in common, plus their own distinctive features (Chapter 3). The purpose of software is

not to provide a methodological or analytic framework. The tools available support certain tasks differently,

and there is some debate about whether individual packages may ‘steer’ the way analysis is performed. Users

should take into account practicalities such as the way software programs are taught, and how much empha-

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sis is placed on an ‘ideal’ way of using software. To promote only one way of using any package undervalues

both the software and the methodological independence of the researcher. Creeping homogeneity helps no

one in the long run except the person who is trying to sell you a method or product. As the researcher, you

are the expert. You remain in control of the interpretive process and you decide which tools within a software

package best facilitate your approach to analysis. You also have the responsibility for being transparent about

your processes and ensuring the quality of your interpretation (Chapter 10).

Whichever package you choose, you will be able to utilise a selection of tools which will facilitate data man-

agement and analysis. Software developments and blurring boundaries mean that tools within a given pack-

age may not be appropriate for all qualitative approaches. Equally, just because a function is available does

not mean you have to use it. We caution against choosing a package simply because it is the one you have

the ‘easiest’ (e.g. immediate or free) access to, or that seems the most sophisticated. However, if you do not

have a choice, you will usually be able to make a package work for you.

Analytic strategies in the context of software use

Methods of data collection cut across methodologies. Approaches may employ broadly interpretive tech-

niques for analysis, but the respective sensitivities and beliefs about the nature of data and knowledge mean

that the starting points of interpretation can be quite different. The significance (or not) of language and cultur-

al contexts and also the purposes of research can be so divergent that a sentence of speech or an observed

action will have different significance to an ethnographer compared with, for example, a conversation analyst,

an ethnomethodologist or a grounded theorist.

Tesch (1990), in rationalising common and differentiating elements in the context of software, distinguished

types of qualitative research according to where the research interest lies: (i) the characteristics of language;

(ii) the discovery of regularities; (iii) the comprehension of the meaning of text/action; and (iv) reflection (see

Figure 1.1). These distinctions reflect overarching philosophical stances and priorities. Although defining qual-

itative data as ‘any information…that is not expressed in numbers’ (1990: 55) Tesch explicitly focused on tex-

tual forms in her discussions.

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Figure 1.1 Graphic overview of qualitative research types (Tesch, 1990: 72–73)

Tesch herself acknowledged difficulties with these classifications, recognising that whilst some are represen-

tative of an epistemological stance, others are more about method. Thus at a conceptual level they are not

equivalent. Much has changed since she was writing, but Tesch’s work continues to have broad relevance

to the intersection between qualitative methodology and technology. It is not within the scope of this book to

discuss in detail the range of qualitative research types or systematically illustrate how software may support

them. Indeed, our focus is on the qualitative strategies more specifically, and the way software tools can be

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employed to undertake them.

Therefore, as a lead-in to the task-based chapters that follow, we outline five strategies that can be well sup-

ported by CAQDAS packages. These are discourse analysis, narrative inquiry, Framework analysis, ground-

ed theory and thematic analysis. In addition we discuss mixed methods research and visual analysis as

broader approaches. Some are more commonly discussed in the context of software than others, and not all

are included in Tesch’s scheme. Our choices for discussing each in this chapter reflect our observations in

one of three respects:

they are commonly used by researchers employing CAQDAS;

they are well supported by CAQDAS but are infrequently reported upon in the context of software

use; and

there is significant potential growth areas for qualitative methodology and software in the future.

In discussing these strategies and approaches our aim is to provide a broad introduction. We do not claim

to be presenting an exhaustive overview of qualitative research, approaches to analysis or the way software

tools are used. However, we add comments regarding the history and context of software use; where appro-

priate, this includes packages which typically fall outside of the CAQDAS collection. We refer you to specific

texts where you can gain more detailed information.

Analysis of discourse

Discourse analysis refers to a broad range of language-based approaches to the analysis of texts that con-

sider the way knowledge is produced and used. This might entail a focus on particular types of discourse

(e.g. medical, political, legal); the use of implicit theories to make sense of social action (e.g. economics,

power, gender relations); or devices used to structure discourses and their intentions (e.g. rhetoric, linguistic

devices, interaction) (Spencer et al., 2003; Silverman, 2001). Researchers across a wide range of disciplines

employ variants of discourse analysis to study a multitude of aspects of social life, and in so doing have de-

veloped specific strategies. Conversation analysis, Foucauldian discourse analysis (Willig, 2001) and critical

discourse analysis are frequently discussed derivatives, but there are many others (for an overview see Gee

and Handford, 2012).

Hammersley (2002) in summarising the field, distinguishes between ‘types’ of discourse analysis in terms of

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their focus; the sorts of knowledge they claim to make; and in the kinds of technique they employ. Others

distinguish more specifically between approaches. Glynos et al. (2009), for example, identify six, highlight-

ing differences and similarities according to the dimensions of ontology, focus and purpose. Dick (2004) in

contrast, discusses discourse analysis as a range of approaches, from descriptive variants that aim at under-

standing conventions such as ‘turn-taking’ to analytic variants that focus more on generating understandings

of the use of language in specific social contexts. Wooffitt (2005) does a good job of demystifying the area,

discussing conversation analysis as a key methodological approach to the analysis of verbal interaction, but

also outlining distinctive features of various approaches to discourse analysis more generally, such as discur-

sive psychology, rhetorical psychology, speech act theory, critical discourse analysis and Foucauldian forms

of discourse analysis.

A discourse analysis can be conducted on data generated through various methods, including primary forms

such as interviews, discussions, life histories and secondary forms such as policy documents, newspaper

articles and speeches. Often, relatively small amounts of data and/or numbers of texts are utilised when con-

ducting a discourse analysis, as they are analysed at a very fine level of detail, although with the support of

software this need not be the case.

Box 1.2 functionality notes

Software tools for language-oriented approaches

Language-oriented approaches rely on close consideration of the presence or physical relation of the occur-

rence of words, phrases and structures. The text-mining type functionality provided by some CAQDAS pack-

ages (Chapters 3 and 6) enables such patterns in texts to be reliably found, coded (Chapter 7) and retrieved

(Chapter 8), and these are particularly useful for derivatives of discourse analysis. The sophistication of these

tools varies quite considerably, so consider options carefully if this type of functionality forms the basis of your

work. You might simply need to locate occurrences, mark, write about and output them. You may not code

them, but doing so will improve your repeated access to them. Getting the most out of the software might

mean coding for certain linguistic devices and coding for the context in which they occur. You can then com-

pare how certain devices are used differently in contextual discourses.

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https://methods.sagepub.com/book/using-software-in-qualitative-research-2e/i1305.xml

https://methods.sagepub.com/book/using-software-in-qualitative-research-2e/i1762.xml

https://methods.sagepub.com/book/using-software-in-qualitative-research-2e/i1918.xml

https://methods.sagepub.com/book/using-software-in-qualitative-research-2e/i2076.xml

Narrative inquiry, in contrast, is characterised by a focus on the sequencing of textual characteristics rather

than data fragmentation or reduction which is inherent in thematic (code-)based approaches. As such, pre-

serving the natural features of texts, including both structural and sequential elements, is paramount and

code-based tools may be seen as inappropriate for this task. Some CAQDAS packages provide two-dimen-

sional spaces for ‘mapping’ connections. These could be useful for approaches needing to create graphic

representations of relationships and structures revealed, for example, by conversations in a work or social

setting. In narrative analysis, specific methods and formalised traditions vary in terms of how software might

help in anything other than improved general management of and access to them. However, hyperlinking de-

vices might provide ways of linking between structures within an account or across several (Chapters 5 and

11). For less structured approaches, for example to life history accounts or the observation of work, links be-

tween points in the data may be useful for tracking a chronology or a set of procedures (Chapter 6).

Narrative inquiry

Narrative inquiry is concerned with the structure of accounts, or stories, focusing on how they are constructed,

including processes, sequences, intentions and meanings. It is used to investigate experiences, how they

are known about, made sense of, and communicated. Narrative inquiry has increased in popularity in recent

decades, frequently attributed to what has been termed the ‘linguistic’ or ‘narrative’ turn which is seen to have

occurred during the 1990s (Atkinson, 1997; Lieblich et al., 1998; Fenton and Langley, 2011).

Authors differ in the ways they conceptualise the traditions, foci and processes of narrative inquiry and the

practicalities of analysis. Bernard and Ryan (2010), for example, identify four major traditions: sociolinguis-

tics, hermeneutics, phenomenology and grounded theory. Daiute and Lightfoot (2004), in contrast, in their

edited volume organise the writings of authors from different disciplines according to ‘literary readings’, ‘so-

cial-relational readings’ and ‘readings through the forces of history’. They see these as the three main ways of

conceptualising narrative analysis, although they concede that they are neither discrete nor used in the same

way by researchers. Lieblich et al. (1998) divide approaches to narrative analysis according to different ways

of reading: ‘the holistic-content reading’; ‘the holistic-form reading’; the ‘categorical-content reading’; and the

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https://methods.sagepub.com/book/using-software-in-qualitative-research-2e/i1612.xml

https://methods.sagepub.com/book/using-software-in-qualitative-research-2e/i2505.xml

https://methods.sagepub.com/book/using-software-in-qualitative-research-2e/i1762.xml

‘categorical-form reading’. Riessman (2005) adds to these by further distinguishing between the performative

or dialogical aspect of narrative and visual narratives. These examples of the range of ways of understanding

narrative inquiry encapsulate the complexity of the field.

Rather than being a single or uniform approach or method, therefore, narrative analysis is characterised by

diversity; not only is it utilised across disciplines and informed by a range of theoretical traditions, but also

it constitutes a mix of methodological approaches. The ‘texts’ that are analysed may be ‘naturally occurring’

(such as documents generated for other purposes, for example, diaries) or collected through speaking with or

interviewing research participants (often, but not exclusively, in the form of ‘oral history’ type interviewing). Its

forms also utilise a variety of analytic strategies, including both quantitative and qualitative practices.

Framework analysis

Framework analysis is a specific method for analysing qualitative datasets. A matrix-based method for order-

ing and synthesising data, Framework analysis was originally developed during the 1980s at the UK-based

National Centre for Social Research and is now a widely used method that supports case-based and themat-

ic approaches to qualitative data analysis. At its core is the idea of a ‘thematic framework’ which is used to

‘classify and organise data according to key themes, concepts and emergent categories’ (Ritchie et al., 2003:

220). In contrast to other methods, Framework focuses on the synthesis of data, involving the creation of

summaries of verbatim data rather than on data reduction activities through the use of coding. Fruber (2010)

illustrates a five-phase process involved in undertaking an analysis of pregnant women suffering from mild

to moderate psychological distress using the Framework method. This involved phases of (i) data immer-

sion/familiarisation; (ii) developing a theoretical framework; (iii) indexing (coding); (iv) charting (using matrix

charts); and (v) synthesising (summarising). Others break the process down slightly more. Whatever the ex-

act processes involved, and how they progress, the development of summaries is achieved in such a way

as to maintain context, language and meaning. Framework analysis has commonalities with other forms of

qualitative analysis, including grounded theory and thematic analysis. It is distinct, however, in its focus on

summarising and synthesising data and their display and analysis through the use of matrices, rather than on

the use of coding as the main basis of analytic work.

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Box 1.3 functionality notes

Software tools for Framework analysis

Having developed it initially for in-house use, NatCen released the FrameWork software in 2009 for sale to

the wider academic and applied research community. In 2011, NatCen formed a partnership with QSR Inter-

national, its functionality was subsumed within NVivo, and FrameWork software taken off the market. There

is not a wide literature concerning the framework method, or the degree to which it is facilitated by software

applications, and that which does exist tends to originate from NatCen.

If using Framework analysis – or other matrix-based approaches – or indeed, simply needing to summarise

or write about what is seen in a general sense, the bespoke summary-writing spaces provided by some pack-

ages will work well (Chapter 10). But you can always approximate the functionality through the use of stan-

dard memo-writing spaces where the specific tools are not available. You may do this before, after, or to the

exclusion of coding. Comparison is likely inherent to these approaches, and factual data organisation (Chap-

ter 12) will enable you to later interrogate writing and coding accordingly (Chapter 13).

Grounded theory

Originated by Glaser and Strauss (1967), grounded theory is a well-known and frequently discussed form of

qualitative research. It comprises a methodological approach rather than simply being an analytic or coding

strategy. Since the first descriptions there have been many adaptations. In The Discovery of Grounded Theo-

ry (1967), Glaser and Strauss created an organised and interactive approach to the collection and analysis of

data, using what they called the ‘constant comparative method’. The history of their co-operation is interesting

and relevant to subsequent modifications. Strauss was instrumental in the development of the Doctorate of

Nursing Science (DNS) at the University of California at San Francisco, and both he and Glaser had a shared

research interest in chronic illness and dying. The development of grounded theory was in part pragmatic,

arising from a need to create a text for DNS students that systematised a way of dealing with qualitative data.

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https://methods.sagepub.com/book/using-software-in-qualitative-research-2e/i2617.xml

https://methods.sagepub.com/book/using-software-in-qualitative-research-2e/i2734.xml

It was also a response to perceptions that qualitative data analysis had somehow lost its empirical connec-

tion to data in an over-preoccupation with theory. The text included in it new routines but also documented

techniques already in use by the Chicago School. There was little writing of methodological texts up to that

point (Morse et al., 2009). Personal descriptions in Morse et al. (2009) reveal how these creative moments in

qualitative methodological history occurred and how a range of the ‘second generation’ of grounded theorists

applied their own modifications to substantive research projects. Glaser continues to support and reinforce

the original principles of the 1967 text and to stress inductive ‘emergence’ of codes and categories and theo-

ry-free starting points.

Amongst the second generation who influenced later strands of grounded theory were several DNS students

and associated postdoctoral researchers. We focus on two of these, since they possibly comprise the most in-

fluential developments. Firstly, Strauss with Juliet Corbin diverged from the original work and wrote Basics of

Qualitative Research in 1990, developing grounded theory to such an extent that Glaser questioned whether

it had any relationship to the original and suggested it was effectively another qualitative method. Corbin col-

laborated for 16 years with Anselm Strauss until his death, and continues to apply and adjust grounded the-

ory to current substantive contexts and at a practical level to computer usage, having included reference to

MAXQDA in the 3rd edition of Basics of Qualitative Research, published in 2008. Secondly, Kathy Charmaz,

in many publications between 1975 and 2002 and with Anthony Bryant (Bryant and Charmaz, 2007) consol-

idated a constructivist version of grounded theory that accounts for beliefs about the relative, context-laden

nature of interpretation. Although maintaining many of the features espoused by Glaser, Strauss and Corbin,

constructivist grounded theory attends also to the active role of the researcher in research generally, and par-

ticularly in analytic processes; the interplay between researcher and data that results in the use of codes,

development or categories and the theoretical account. Thus the effect of prior knowledge and existing liter-

ature, as well as the issue of reflexivity, are highlighted. Charmaz makes the point that grounded theory is

primarily a way of thinking about data and, as such, cannot be standardised. Constructivist grounded theory

attends to the ways in which theoretical development is tied to the engagement with epistemological issues,

and as such illustrates the role of both deduction and induction in the analytic process (Charmaz, 2006).

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Box 1.4 analytic notes

Features of grounded theory (1967)

At the heart of the original grounded theory method was a basic principle, the ‘constant comparative method’.

Its main features are as follows:

• A coding process (later to become known as open coding) consists of annotations in the margin ex-

pressed as codes based on social constructs or on the respondent’s own language (later labelled in

vivo codes).

• Data segments are compared, thus refining ideas about this and subsequent categ

ories.

• Memos are an important aspect, and should be kept updated about the development of each cate-

gory.

• Collecting, coding and analysing data should occur concurrently; thus ongoing ‘theoretical sampling’

of data is performed to enable further comparisons to be made of different groups and settings.

• Categories are further refined and relationships among them identified.

• Categories are reduced to smaller set of more abstract higher-level concepts – allowing the possibil-

ity of generality or the production of formal theory.

• The collection of more data retains the principle of being grounded in the data and permits further

incidents to be analysed in the light of these concepts – allowing the modification of these concepts.

• When concepts are not being modified any further, categories are said to be theoretically saturated.

Theoretical saturation means that the analysis of more incidents is not adding further to ideas, it

merely ‘adds bulk to coded data and nothing to theory’.

Box 1.5 functionality notes

Software tools for theory-building approaches

Theory-building approaches are characterised by the need to move beyond description through writing (Chap-

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ters 6 and 10) and indexing of data through basic coding (Chapter 7) to generate themes, concepts or cate-

gories (Chapters 8 and 9). Grounded theory and thematic analysis are examples of theory-building approach-

es. CAQDAS packages support processes involved in generating theory very well, and you will find most of

the tools discussed in this book useful at various moments.

Working deductively, you will likely have a theoretical framework at the outset which can be represented in

the software as a map, model or network (Chapter 11). Working inductively, you will be working towards gen-

erating such a visual representation of your interpretation or theory. Software facilitates either approach or a

combination of both (Chapter 7). You will need to record the factual features of data and respondents (Chap-

ter 12) in order to make comparisons within and between cases (Chapter 13). You will need to write about

your processes and analytic insights in identifying patterns and relationships and developing and testing the-

ories.

Thematic analysis

Thematic analysis is a commonly used approach to the analysis of qualitative data, yet is relatively infrequent-

ly described or discussed in specific terms. Although those who write about it often understand it to constitute

a method of analysis in and of itself (Braun and Clarke, 2013; Fereday and Muir Cochrane, 2006; Attride-Stir-

ling, 2001), its techniques are used in many other approaches, and therefore its status is debated. Outside of

these debates, it can be seen as constituting a set of analytic processes applicable in a variety of theoretical

contexts, disciplines and topics of investigation (Boyatzis, 1998). This applicability is seen to relate to its in-

herent flexibility as well as its independence from theory and epistemology, as contrasted to other approaches

such as conversation analysis, interpretive phenomenological analysis and grounded theory which have clear

roots in particular traditions (Braun and Clarke, 2006). Indeed, elements inherent to thematic analysis are also

evident in these approaches, and as such thematic analysis is applicable to theoretically driven research and

more applied approaches. It might be argued that thematic analysis is the definitive ‘code-based’ approach

in the sense that it entails a process of encoding qualitative information (Fereday and Muir Cochrane, 2006).

In attending to an identified absence in the literature concerning specific procedures for conducting thematic

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