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Qualitative Data Analysis for Applied Policy

Research

In: The Qualitative Researcher's Companion

By: Jane Ritchie & Liz Spencer

Edited by: A. Michael Huberman & Matthew B. Miles

Pub. Date: 2011

Access Date: April 17, 2019

Publishing Company: SAGE Publications, Inc.

City: Thousand Oaks

Print ISBN: 9780761911906

Online ISBN: 9781412986274

DOI: https://dx.doi.org/10.4135/9781412986274

Print pages: 305-329

This PDF has been generated from SAGE Research Methods. Please note that the pagination of the

online version will vary from the pagination of the print book.

https://dx.doi.org/10.4135/9781412986274

Qualitative Data Analysis for Applied Policy Research

The last two decades have seen a notable growth in the use of qualitative methods for applied social policy

research. Qualitative research is now used to explore and understand a diversity of social and public policy

issues, either as an independent research strategy or in combination with some form of statistical inquiry. The

wider use of qualitative methods has come about for a number of reasons but is underpinned by the persistent

requirement in social policy fields to understand complex behaviours, needs, systems and cultures.

‘Framework’, the analytic approach described in this chapter, was developed in the context of conducting

applied qualitative research. It was initiated in a specialist qualitative research unit based within an

independent social research institute (Social and Community Planning Research [SCPR]). The work of the

institute spans all areas of social and public policy and is undertaken on behalf of central or local government,

voluntary organizations, universities, or other public bodies, or it is grant funded by research councils and

foundations. All the institute's work can be broadly classified as applied policy research, some of which is

initiated by institute members but most of which is generated by the sponsoring bodies.

Reprinted from Jane Richie and Liz Spencer, “Qualitative Data Analysis for Applied Policy Research,” in

Taylor & Francis Books Ltd. Reprinted by permission.

‘Framework’ has been refined and developed over the years but the general principles of the approach have

proved to be versatile across a wide range of studies. Our aim here is to describe the method in detail; we use

examples to show how the approach can be used to move through the various stages of the analytic process.

Because the method has been developed for applied policy research, we begin with a brief overview of the

kinds of objectives and requirements this sets.

The Nature of Applied Policy Research

Applied research can be broadly distinguished from ‘basic’ or ‘theoretical’ research through its requirements

to meet specific information needs and its potential for actionable outcomes. The social policy field makes

use of both applied and basic research, but a great deal is of the former kind. However, a very high proportion

of applied policy research is quantitative in form, a heritage from the early years of empirical social inquiry

and the result of the dominant requirement of policy-makers for facts (Bulmer, 1982: 40–49). Fortunately,

this is changing, as is the role played by qualitative methods. At one time, the use of qualitative methods

was seen as acceptable if it was confined to a developmental role for statistical investigation. Now it has

become recognized that the contributions of qualitative research are much more wide-ranging and that it has

an important place in its own right. Most significantly it has a key role to play in providing insights, explanations

and theories of social behaviour.

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What qualitative research can offer the policy maker is a theory of social action grounded on the

experiences—the world view—of those likely to be affected by a policy decision or thought to be part

of the problem. (Walker, 1985: 19)

In applied policy research, qualitative methods are used to meet a variety of different objectives. The

questions that need to be addressed will vary from study to study but broadly they can be divided into four

categories: contextual, diagnostic, evaluative and strategic:

Contextual: identifying the form and nature of what exists

e.g. What are the dimensions of attitudes or perceptions that are held?

What is the nature of people's experiences?

What needs does the population of the study have?

What elements operate within a system?

Diagnostic: examining the reasons for, or causes of, what exists

e.g. What factors underlie particular attitudes or perceptions?

Why are decisions or actions taken, or not taken?

Why do particular needs arise?

Why are services or programmes not being used?

Evaluative: appraising the effectiveness of what exists

e.g. How are objectives achieved?

What affects the successful delivery of programmes or services?

How do experiences affect subsequent behaviours?

What barriers exist to systems operating?

Strategic: identifying new theories, policies, plans or actions

e.g. What types of services are required to meet needs?

What actions are needed to make programmes or services more effective?

How can systems be improved?

What strategies are required to overcome newly defined problems?

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Most research attempts to address more than one of these groups of questions. But in applied policy

research, the objectives are usually clearly set and shaped by specific information requirements. Hence any

output from the research needs to be appropriately targeted towards providing ‘answers’, in the form of

greater illumination or understanding of the issues being addressed. This in turn has important implications

for the form and functions of the analysis undertaken.

In addition to the research objectives, there are other features of applied policy research which may shape the

way analysis is undertaken. First, time-scales tend to be shorter rather than longer—usually months rather

than years. If government departments and other public agencies are to maximize their use of research,

then they need ‘answers’ in time to influence their policy or planning decisions. Although all public bodies do

commission longer-term research, a high proportion has a specified deadline, related to some key activity in

the policy process.

Partly as a consequence of limited time-scales, applied research is often carried out by teams of researchers.

These may comprise researchers from different disciplines, or be organized to allow individuals to take

responsibility for different parts of the research process. Either way, this requires an explicit research

methodology which can be viewed, discussed and operated by individuals within the team.

Another common feature is the need for generated data. Although desk research or document analysis

usually forms part of a social policy research project (and occasionally is confined to these approaches

alone), it is more usual to find that new data are collected. This may be in the form of individual interviews,

group discussions or observational work. Certainly, within SCPR, most of the studies have newly generated

interview data (either individual or group) and sometimes an observational component.

Qualitative research meets quite different objectives from quantitative research, and provides a distinctive

kind of information. For applied policy purposes it may therefore be carried out with some kind of linkage to

statistical inquiry (i.e. to help develop, illuminate, explain or qualify statistical research), or it may be entirely

independent. Either way, it is important that the particular contributions that qualitative research can make are

fully exploited.

Finally, there is an important issue to address in relationship to the visibility of qualitative methods. One of

the factors that has almost certainly inhibited the greater use of qualitative methods in social policy fields

is the lack of access that commissioners and funders have to the research process. This is particularly so

in the conduct of qualitative data analysis. If decisions or actions are to be based on qualitative research,

then policy-makers and practitioners need to know how the findings of the research have been obtained. The

research community needs to respond to this by making its methods more explicit. This will bring not only

greater confidence in the methodology, but also a deeper understanding of what qualitative research can do,

and the way in which it can do it.

Aims of Qualitative Data Analysis

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Material collected through qualitative methods is invariably unstructured and unwieldy. A high proportion of

it is text based, consisting of verbatim transcriptions of interviews or discussions, field notes or other written

documents. Moreover, the internal content of the material is usually in detailed and micro form (e.g. accounts

of experiences, descriptions of interchanges, observations of interactions, etc.). The qualitative researcher

has to provide some coherence and structure to this cumbersome data set while retaining a hold of the

original accounts and observations from which it is derived. All of this has implications for the methods of

analysis which are developed.

Qualitative data analysis is essentially about detection, and the tasks of defining, categorizing, theorizing,

explaining, exploring and mapping are fundamental to the analyst's role. The methods used for qualitative

analysis therefore need to facilitate such detection, and to be of a form which allows certain functions to be

performed. These functions will vary depending on the research questions being addressed, but, certainly in

applied policy research, the following are frequently included:

Defining concepts: understanding internal structures;

Mapping the range, nature and dynamics of phenomena;

Creating typologies: categorizing different types of attitudes, behaviours, motivations, etc.;

Finding associations: between experiences and attitudes, between attitudes and behaviours, between

circumstances and motivations, etc.;

Seeking explanations: explicit or implicit;

Developing new ideas, theories or strategies.

‘Framework’ has been developed to help these aims and outputs to be achieved. It is also designed to

facilitate systematic analysis within the demands and constraints of applied policy research previously cited.

To both these ends, the method has certain key features, which were central to its development. These are

summarized in Figure 12.1.

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Figure 12.1 Key Features of ‘Framework’

‘FRAMEWORK’ AS A METHOD OF QUALITATIVE DATA ANALYSIS

‘Framework’ is an analytical process which involves a number of distinct though highly interconnected stages.

Although the process is presented as following a particular order—indeed some stages do logically precede

others—there is no implication that ‘Framework’ is a purely mechanical process, a foolproof recipe with

a guaranteed outcome. On the contrary, although systematic and disciplined, it relies on the creative and

conceptual ability of the analyst to determine meaning, salience and connections. Real leaps in analytical

thinking often involve both jumping ahead and returning to rework earlier ideas. The strength of an approach

like ‘Framework’ is that by following a well-defined procedure, it is possible to reconsider and rework ideas

precisely because the analytical process has been documented and is therefore accessible.

The approach involves a systematic process of sifting, charting and sorting material according to key issues

and themes. In order to illustrate the method, and to reflect the context and diversity of its applications in

applied social policy research, five studies are referenced, one or two for each stage of the analytical process.

Table 12.1 outlines the aims, sample, type of data and time-scale for each study.

The five key stages to qualitative data analysis involved in ‘Framework’ are:

familiarization,

identifying a thematic framework,

indexing,

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charting,

mapping and interpretation (this being the stage at which the key objectives of qualitative analysis are

addressed).

Each of these analytical stages is described and illustrated below.

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Familiarization

Before beginning the process of sifting and sorting data, the researcher must become familiar with their

range and diversity, must gain an overview of the body of material gathered. Although she or he will have

been involved in some, if not all, of the data collection, and will have formed hunches about key issues and

emergent themes, it is important at this stage to set these firmly in context by taking stock and gaining a feel

for the material as a whole. Where more than one person has been involved in data collection, the analyst

can have only a partial or ‘second-hand’ grasp of colleagues’ material. Even where the analyst has been the

sole interviewer, it is likely that recollections will be selective and partial.

Essentially, familiarization involves immersion in the data: listening to tapes, reading transcripts, studying

observational notes. In some cases it is possible to review all the material at the familiarization stage, for

example where only a few interviews have been carried out, or where there is a generous timetable for the

research. However, more often than not in applied policy research, the timetable is too pressing or the volume

of material too extensive, and a selection must be made for this initial stage.

How the material is selected will depend on a number of features of the data collection process, such as:

the range of methods used,

the number of researchers involved,

the diversity of people and circumstances studied,

the time period over which the material was collected,

the extent to which the research agenda evolved or was modified during that time.

When making a selection, it is important to ensure that a range of different cases, sources, and time periods

are reviewed. For example, in the study of barriers to dental care, material was collected by three researchers,

through individual and group interviews amongst regular, intermittent and nonattenders. The analyst chose to

review both individual and group data for different types of attenders and to include data collected by different

researchers. For the study of sexual attitudes and behaviours, five researchers were involved in interviewing,

partly to ease the interviewing burden, but also to evaluate the impact of interviewer characteristics, such

as age and gender. Consequently, interviews were selected for review to include different interviewers at

different stages of the fieldwork period as well as a mix of gender and age of respondents.

During the familiarization stage, the analyst listens to and reads through the material, listing key ideas and

recurrent themes. Where a study aims to explore aspects of the research process as well as substantive

issues, for example in the study of sexual attitudes and behaviour, notes are also made on the general

atmosphere of the interview and the ease or difficulty of exploring particular subjects.

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Identifying a Thematic Framework

During the familiarization stage, the analyst is not only gaining an overview of the richness, depth and

diversity of the data, but also beginning the process of abstraction and conceptualization. While reviewing

the material, the analyst will be making notes, recording the range of responses to questions posed by

the researchers themselves, jotting down recurrent themes and issues which emerge as important to

respondents themselves.

Once the selected material has been reviewed, the analyst returns to these research notes and attempts to

identify the key issues, concepts and themes according to which the data can be examined and referenced.

That is, she or he sets up a thematic framework within which the material can be sifted and sorted. When

identifying and constructing this framework or index, the researcher will be drawing upon a priori issues (those

informed by the original research aims and introduced into the interviews via the; topic guide), emergent

issues raised by the respondents themselves, and analytical themes arising from the recurrence or patterning

of particular views or experiences.

The first version of an index is often largely descriptive and heavily rooted in a priori issues. It is then applied

to a few transcripts when categories will be refined and become more responsive to emergent and analytical

themes. For these refinements, the researcher looks for conceptualizations which encapsulate and represent

diversity of experience, attitude, circumstance, etc.

Devising and refining a thematic framework is not an automatic or mechanical process, but involves both

logical and intuitive thinking. It involves making judgements about meaning, about the relevance and

importance of issues, and about implicit connections between ideas. In applied social policy research, it also

involves making sure that the original research questions are being fully addressed.

The development of a thematic framework can be illustrated from the study of the living standards in

unemployment, where one set of issues to be explored concerned patterns of expenditure in unemployment

compared with those when last in work. This area of questioning (as outlined in the extract from the topic

guide) and the emergent issues noted at the familiarization stage led to index categories as in Figure 12.2.

It will be seen that some of the index categories were virtually identical to specified areas of questioning (e.g.

1.3 Items and activities reduced); others were newly defined from the emergent themes (e.g. 1.5 Changing

patterns over time). It should also be noted that the full index contained a total of 59 categories (i.e. 1.1, 2.1,

2.2, etc.), within 8 major subject headings.

Indexes provide a mechanism for labelling data in manageable ‘bites’ for subsequent retrieval and

exploration. They should therefore not be overelaborate in detail at this stage as the analyst needs to retain an

overview of all the categories. The more interpretative stages of analysis, which take place later, will produce

the refinement of what is contained in each category.

If there is more than one population being studied (as, for example, in the case of the YTS [Youth Training

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Scheme] study), then it may be necessary to develop separate indexes for each group. Alternatively, it

may be possible to keep a common index but deal with additional elements in the material through extra

subcategories. Generally it is preferable to keep a common index for the different groups being studied as

this helps immediately to identify both common and divergent themes.

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Figure 12.2 Development of a Thematic Framework

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Indexing

‘Indexing’ refers to the process whereby the thematic framework or index is systematically applied to the data

in their textual form. Although any textual material can be indexed in this way, the method has mainly been

applied to transcriptions of individual and group interviews. All the data, not just those selected for review, are

read and annotated according to the thematic framework. Indexing references are recorded on the margins of

each transcript by a numerical system which links back to the index, or by a descriptive textual system based

directly on the index headings.

Again, applying an index is not a routine exercise as it involves making numerous judgements as to the

meaning and significance of the data. For each passage, the analyst must infer and decide on its meaning,

both as it stands and in the context of the interview as a whole, and must record the appropriate indexing

reference. Single passages often contain a number of different themes each of which needs to be referenced;

multiple indexing of this kind can often begin to highlight patterns of association within the data. Of course,

this process of making judgements is subjective, and open to differing interpretations. By adopting a system

of annotating the textual data, however, the process is made visible and accessible to others; others can see

for themselves how the data are being sifted and organized, research colleagues can ‘try out’ the framework

and pool their experiences; the analyst can ‘check out’ the basis of his or her assumptions.

Figure 12.3 shows a page of an indexed transcript from the living standards study. The first column on the

right-hand side replicates the index numbers assigned and any research notes that were made. The far

column shows the content of the index categories to which these relate. These would not normally appear on

the transcript but have been shown here for the purpose of clarification.

In Figure 12.3, it is possible to see that several different index prefixes appear on one page, even within one

speech passage (e.g. 4.1, 1.3, 1.6, 1.4). It is quite common to find that different major topics are connected

and interwoven in this way and this is one of the values of indexing. Once these are labelled, the analyst

is able to access each reference and, more crucially, to see patterns and the contexts in which they arise.

As already suggested, these juxtapositions are often one of the early clues to associations for subsequent

stages of analysis.

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Figure 12.3 Example of an Indexed Transcript

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Charting

Having applied the thematic framework to individual transcripts, the analyst needs to build up a picture of

the data as a whole, by considering the range of attitudes and experience for each issue or theme. Data

are ‘lifted’ from their original context and rearranged according to the appropriate thematic reference. This

process, referred to as charting, is described below.

Charts are devised with headings and subheadings which may be drawn from the thematic framework, from

a priori research questions, or according to considerations about how best to present and write up the study.

How the charts are laid out will depend on whether analysis is to be thematic (for each theme across all

respondents) or by case (for each respondent across all themes). Where a thematic approach is adopted,

charts are drawn up for each key subject area, and entries made for several respondents on each chart. The

ordering and grouping of the individual cases may be linked to characteristics or dimensions that are known or

believed to have a significant effect on patterns of experience or behaviour etc. The essential point, however,

is that cases are always kept in the same order for each subject chart, so that the whole data set for each

case can easily be reviewed. Where a case approach is used, one or two charts may be drawn up for each

case, with subjects recorded in the same order.

In the case of the living standards study, a thematic approach was followed, and six major subject charts were

constructed. These covered:

patterns of management,patterns of expenditure,personal and social effects,effects on family

life,standards of living (definition and changes),employment: activity, attitudes to and job search.

Figure 12.4 shows some of the headings for the chart ‘Patterns of expenditure’. Several families were

entered on each chart, grouped according to the ratio of their income in unemployment to that when last

employed, known as the replacement ratio. By keeping a consistent order for the families on each chart,

comparisons could be made between or within cases. It can also be seen that some of the chart headings

were identical to index categories (e.g. Items reduced, Two weekly patterns), others reflected newly emergent

themes identified while indexing the data (e.g. Periods of new control). For example, this latter heading was

introduced to chart data about a period often described in the interviews when families had to introduce new

levels of expenditure control.

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Figure 12.4 Example of Subject Chart Headings

Whereas some methods of qualitative analysis rely on a ‘cut and paste’ approach, whereby ‘chunks’ of

verbatim texts are regrouped according to their index reference, charting involves abstraction and synthesis.

Each passage of text, which has been annotated with a particular reference, is studied and a distilled

summary of the respondent's views or experiences is entered on the chart. The level of detail recorded varies

between projects and between researchers, from lengthy descriptions to cryptic abbreviations for each entry.

However, the original text is referenced so that the source can be traced and the process of abstraction can

be examined and replicated. Illustrative passages for possible quotation are also referenced by transcript

page numbers at this stage.

In the study of recruitment of young people with disabilities to YTS, one of the key subjects to be charted

was the way in which different parties defined and interpreted the term ‘disability’. Under the overall heading

‘definitions of disability’, further subheadings were elaborated to include: the ‘official definition’ (as endorsed

by the government agency), the respondents' ‘own definition’, and ‘grey areas’ or ambiguities of interpretation

or application. Charts were then constructed separately for each group of respondents: careers officers,

agency staff and scheme providers. Transcripts were studied according to the appropriate index references,

and a summary of each respondent's views entered on the chart. Figure 12.5 shows an example of the chart

constructed for careers officers, and illustrates the kind of entries recorded together with the page referencing

system.

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Figure 12.5 Example of Subject Chart Entries

Mapping and Interpretation

When all the data have been sifted and charted according to core themes, the analyst begins to pull together

key characteristics of the data, and to map and interpret the data set as a whole. Although emergent

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categories, associations and patterns will have been noted and recorded during the indexing and charting

phases, the serious and systematic process of detection now begins. It is here that the analyst returns to the

key objectives and features of qualitative analysis outlined at the beginning of this chapter, namely:

defining concepts,mapping range and nature of phenomena,creating typologies,finding

associations,providing explanations,developing strategies, etc.

Which of these the analyst chooses to attempt will be guided by the original research questions to be

addressed, and by the themes and associations which have emerged from the data themselves.

Whichever route is followed, the basic processes are the same: the analyst reviews the charts and research

notes; compares and contrasts the perceptions, accounts, or experiences; searches for patterns and

connections and seeks explanations for these internally within the data. Piecing together the overall picture

is not simply a question of aggregating patterns, but of weighing up the salience and dynamics of issues, and

searching for a structure rather than a multiplicity of evidence.

This part of the analytical process is the most difficult to describe. Any representation appears to suggest

that the analyst works in a mechanical way, making obvious conceptualizations and connections, whereas

in reality each step requires leaps of intuition and imagination. The whole process of immersion in the data

triggers associations, the origins of which the analyst can scarcely recognize. Because this crucial part of the

process is so difficult to encapsulate, a number of different examples are given below, and an attempt made

to crystallize and convey the logical and creative pathways followed.

Defining Concepts

In the course of charting references to a particular phenomenon, the analyst may well have begun to identify

a number of associated features or descriptions. At this stage, however, she or he systematically examines

the charted material, searching for key dimensions and themes.

So, for example, in the living standards study, an analysis was undertaken of how the terms ‘living standards’

and ‘standards of living’ were defined by study participants. The case study families, between them, identified

nine factors that had a bearing on their judgements of living standards. These were:

the amount of disposable income they had,the items of expenditure they could or could not afford,the

level of choice or constraint that surrounded their pattern of expenditure,the level of financial security

that was felt,the degree of struggle involved in making ends meet,the material possessions they had,

or could attain,the degree to which expectations were fulfilled,the extent to which self-esteem could

be upheld,the feelings of contentment that surrounded life.

These elements emerged during the course of people describing their present standards of living, or what it

had been like before, or how they defined a good or poor standard of living or simply their understanding of

the term.

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A further example of clarifying definitions is taken from the study of the recruitment to YTS of young people

with disabilities, where identifying the range and diversity of concepts of disability was important in order to

understand how guide-lines were implemented, and to gain an understanding of the recruitment process.

By first listing the characteristics associated with disability, and then ordering them on the dimension of

inclusivity-exclusivity, it was possible to devise a scale of definitions, and to identify how different labels were

applied. Figure 12.6 illustrates this three-stage process.

Mapping the Range and Nature of Phenomena

A core function of qualitative research is to identify the form and nature of a phenomenon, and where

appropriate, to map the polarities. In the study of volunteering, a central objective of the research was to

identify key reasons why people might become volunteers. By reviewing the charts for references to attitudes,

experiences, images and deterrents, it was possible to draw out key dimensions of motivations to volunteer

(see Figure 12.7).

Figure 12.6 Process of Defining Dimensions of a Concept

Creating Typologies

Having identified key dimensions or characteristics of particular social phenomena, the researcher may

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decide to move on to multidimensional analysis, as in the creation of typologies, where two or more

dimensions are linked at different points, giving a range of types of cases. In the study of sexual attitudes

and behaviours, it was important to establish the nature of people's sexual histories and life-styles in order to

understand the context of their views and actions. Key dimensions of sexual life-style were identified as the

number of sexual partners over time, and the basis of the relationship(s) or encounter(s). By plotting people's

histories and current sexual activity along these two dimensions, a typology of sexual life-styles could be

constructed (see Figure 12.8).

Finding Associations

In the course of indexing and charting interview material the analyst may become aware of a patterning

of responses; for example it may appear that people with certain characteristics or experiences also hold

particular views or behave in particular ways. At this stage the analyst will systematically check for

associations among attitudes, behaviours, motivations, etc, either those made explicit by respondents

themselves or those derived from implicit connections.

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Figure 12.7 Mapping Motivations

In the study of the recruitment of young people with disabilities to YTS, a systematic search among references

to disability revealed that particular groups of respondents interpreted ‘disability’ and applied labels in

quite different ways. This association was identified through the process of constructing a central ‘labels’

chart across all respondent groups. The five different labels associated with disability were chosen to form

subheadings, and respondents were plotted according to their use of particular terms, as shown in Table

12.2. For the sample as a whole, the central chart revealed a clustering of types of respondent under each of

the different terms. Whereas careers officers tended to hold the most inclusive definitions and to use labels

with the least stigma attached, managing agents of basic schemes held the narrowest definitions, frequently

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referring to ‘the handicapped’ and ‘the disabled’. This pattern of association is represented diagrammatically

in Figure 12.9.

Figure 12.8 Typologies of Sexual Life-Styles

Providing Explanations

A common objective in applied qualitative research is to explain, as well as to illuminate, people's attitudes,

experiences and behaviour. Explanations may be sought in order to address the questions that triggered the

research in the first place, or to account for issues and patterns of behaviour which arise from the research

itself.

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In the study of sexual attitudes and behaviour, it emerged from the analysis that young heterosexual men

and women, whose sexual lifestyles suggested they would be wise to practise safe sex, were taking no

preventative measures at all. They did not consider themselves to be in a high risk group such as people who

‘do drugs’, or are ‘promiscuous’. By unpacking how people defined the term promiscuous, it was possible

to explain why people did not apply the label to themselves. Key characteristics associated with promiscuity

were identified as:

the basis of the relationship (casual),feelings about sexual partners (absence of),number of partners

over time (compared with own experience),number of partners co-terminously (more than one).

It became clear that whereas others were described as promiscuous on any one of these counts, people did

not label their own behaviour in this way unless all the conditions applied. So, for example, people who had

casual sexual encounters or more than one partner at a time did not consider themselves promiscuous if they

liked their partners and judged them to be ‘nice people’; people who changed partners every few months felt

they were exempt because the relationship was ‘serious’ while it lasted, and so on.

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Figure 12.9 Mapping Patterns

Developing Strategies

Much of the research carried out in the policy field has a strategic component; policy-makers commission

research into attitudes, behaviours and experiences because they wish to inform their policy decisions. As

a result of the process of analyzing qualitative data, and identifying underlying motivations, patterns and

explanations, it may be possible to develop strategies for change which arise directly from the qualitative

material itself. For example, the volunteering study identified a number of positive and negative conditions

which accounted for whether or not people became volunteers. By drawing out the implications of these

conditions, it was possible to develop a strategy for attracting and retaining volunteers which directly

addressed those issues (see Figure 12.10).

A second example is provided by the dental health study, where a number of barriers to dental care were

identified. These in turn led to the formulation of a number of specific strategies that would help people

become more regular attenders. Some of these arose out of explicit suggestions made by the respondents

themselves, others were derived indirectly from the nature of the problems. So, for example, one key area

concerned the cost of dental treatment; specific suggestions made in relation to this were:

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Figure 12.10 Developing Strategies

reduce or abolish dental charges,introduce a clear charging system,inform patients of the cost of

treatment before commitment to carrying it out,ensure privacy when determining eligibility for free

treatment,(for long-term attenders) provide an incentive of first course of treatment free.

Conclusions

Because of the nature of SCPR's work, we (and other researchers with us) have had the opportunity to apply

‘Framework’ on numerous studies. The method, of course, needs to be adapted to suit the aims and coverage

of a specific piece of research, but it has proved flexible for a range of different types of studies. It has been

applied to in-depth and group interviewing, longitudinal studies, case studies, and projects involving different

groups or subpopulations of participants. ‘Framework’ has also been successfully used jointly by two or more

researchers working on a single project, in some cases cross-institutionally.

It will perhaps be apparent that some parts of the process have been relatively easy to display, while others

have been much more difficult to capture. Nevertheless, we believe these more elusive stages, particularly

those which involve inductive and interpretative thinking, can be made explicit in some form. As already

emphasized, we believe this accessibility is important for those who commission and use qualitative research

for public policy purposes.

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We wish to stress that the analysis method described above is just one approach for synthesizing and

interpreting qualitative data. Although it does accommodate all the features we believe to be important, there

are certainly other approaches that would equally satisfy these requirements. But, until recently, this has been

difficult to know since qualitative researchers have not made their analytic tools accessible. It is important that

individual researchers provide documentation of their methods and techniques of analysis, not just in volumes

of this kind, but also in research publications and proposals. Only by so doing will the research community

widen its pool of analytic knowledge and extend its methodological base.

RitchieJane and SpencerLiz

References

Bulmer, M.(1982) The Uses of Social ResearchLondon: Allen & Unwin.

Finch, H.(1988) Barriers to the Receipt of Dental CareLondon: Social and Community Planning Research.

Ritchie, J.(1990) Thirty FamiliesLondon: HMSO.

Spencer, L. and Whelan, E.(1938) Going on YTS: The Recruitment of Young People With

DisabilitiesSheffield: Training Agency.

Spencer, L.Faulkner, A., and Keegan, J.(1988) Talking About SexLondon: Social and Community Planning

Research.

Thomas, A. and Finch, H.(1990) On VolunteeringBerkhampsted: The Volunteer Centre, UK.

Walker, R.(1985) Applied Qualitative ResearchAldershot: Gower.

http://dx.doi.org/10.4135/9781412986274.n12

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http://dx.doi.org/10.4135/9781412986274.n12

Qualitative Data Analysis for Applied Policy Research

In: The Qualitative Researcher's Companion