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Qualitative Data Analysis: An introduction


Qualitative Data Analysis: An introduction Carol Grbich Chapter 22. Incorporating data from multiple sources: mixed methods Mixed Methods Key points The advantages of ... – PowerPoint PPT presentation

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Title: Qualitative Data Analysis: An introduction

Qualitative Data Analysis An introduction
  • Carol Grbich
  • Chapter 22. Incorporating data from multiple
    sources mixed methods

Mixed Methods
  • Key points
  • The advantages of combining quantitative and
    qualitative data are that you can maximize the
    impact of both
  • For mixed methods to be successful, issues of
    sampling, design, data analysis and data
    presentation need careful attention
  • Two ways of mixing methods regarding design are
    concurrent and sequential, but other new mixes
    are emerging
  • Does qualitative data miss out in such a mix? and
    what is the next move?

A brief history of Qualitative and Quantitative
  • The three stages of debate relating to
    quantitative and qualitative approaches
  • 1. never the twain shall meet.
  • 2. rapprochement
  • 3. co-operation and mixing

What are the major differences between
qualitative and quantitative?
  • Qualitative is an inductive approach
  • Questions tend to be exploratory and open ended
    and data is often in narrative form.
  • Reality is seen as a shifting feast, subjectivity
    is usually viewed as important
  • P power is shared with the participants who are
    the experts on the matter under investigation.
  • Analysis predominantly deals with meanings,
    descriptions, values and characteristics of
    people and things.
  • The outcome sought is the development of
    explanatory concepts and models
  • Widespread generalisation (apart from logical
    generalisation that is from similar instance to
    similar instance) is avoided.

What are the major differences between
qualitative and quantitative
  • Quantitative is deductive
  • Reality is seen as static and measurable
  • Objectivity (distance, neutrality) and linearity
    (cause effect) may be sought
  • Outcomes are pre specified , hypotheses l dictate
    questions and approach.
  • Researcher control of the total process is
    paramount, precision and predictability are
  • Statistical approaches identify numbers and
    clarify relationships between variables.
  • Theory testing is the key and generalisation and
    predictability the desired outcomes.
  • Survey and experimental research are the main
    design options.
  • Conclusions drawn follow logically from certain
    premises - usually rule based - which are
    themselves often viewed as proven, valid or

Advantages of combining quantitative and
qualitative results
  • clarifying and answering more questions from
    different perspectives
  • enhancing the validity of your findings
  • increasing the capacity to cross check one data
    set against another
  • Providing the detail of individual experiences
    behind the statistics.
  • Helping in the development of particular
  • Tracking stages over time.

Philosophical integration of qualitative and
quantitative approaches
  • Two current options
  • Pragmatism
  • seeks ways through the polarised
    quantitative qualitative debate to find
    practical solutions to the problem of differing
    ideologies and methodologies recognising culture,
    context, individual experience, the constructed
    nature of reality, uncertainty, eclecticism,
    pluralism and the need for creative innovation of
  • The Transformative paradigm
  • multiple realities are shaped by knowledge is
    historically and socially situated
  • issues of power between researcher and researched
    need to be explicitly addressed
  • the incorporation of qualitative and quantitative
    methods are appropriate.
  • the transformative ethical orientation
    comprises a strong human rights agenda
    within notions of beneficence and social justice

Conducting mixed methods research prior questions
  • Is your research question one for which mixed
    methods would be the best approach?
  • If so, which design would be the best?
  • A mutual research design? involving acceptance
    that the two approaches come from completely
    different paradigms , celebrating their
    differences and keeping them separate within the
    design process the separate but together
  • Mixed methods?
  • at which points will mixing occur? Design?
    Analysis? Interpretation?
  • What sampling approaches will you utilise from
    the probability and non-probabily suite?
  • How are you going to manage data analysis?   
  • quantitizing - converting qualitative data into
    quantitative data or qualitizing - converting
    quantitative data into qualitative data
  • To what degree will you qualitatively analyse
    quantitative data and vice versa?
  • How are you going to display your results? -
    Separately? Integrated? consolidated?

Mixed method design
  • Various forms of labeling and terminology have
    been used for mixed method design synergy,
    integration, triangulation, concurrent, parallel,
    merging, concurrent, sequential, exploratory and
    explanatory. Concurrent or sequential are the 2
    main options
  • 1. Concurrent or parallel methods
  • Here you would consider using multiple reference
    points where separate data sets are collected at
    the same time with the ultimate aim of merging
    the two data sets either,
  • in a visual display such as a matrix
  • by transforming the data (see quantitizing and
    qualititzing data in crossover/mixed analyses
    below) or
  • in the final discussion.
  • Design might involve using dual sites with the
    same sampling approach but with different data
    (quantitative and qualitative) then using the
    synthesised results to build up a complex

Design 2. Sequential explanatory/exploratory
  • You could undertake a qualitative study to
    explore a particular issue or phenomenon and
    using an iterative approach you could create
    hypotheses from these results which you could
    test using a survey or experimental design.
  • Or, you could develop a short questionnaire
    survey to elicit key issues which can then be
    explained in depth using qualitative approaches
    of interviewing and observation. Synthesis of the
    two sets of results is needed to clarify the dual
    outcomes and to utilise the increased validity
    these two approaches provide.

A typical sequencing design
  • Stage 1 Representative survey of the population
  • Stage 2 Exploratory qualitative interviews or
    focus groups to tease out the findings of the
  • Stage 3 Hypotheses generated from stages 1 and 2
    are tested in various interventions which are
    then evaluated
  • Stage 4 Participatory action research where the
    participants take control of the development,
    implementation and evaluation of the most
    successful of these interventions.

Issues to consider in attempting to combine data
  • You need to be familiar with both quantitative
    and qualitative approaches
  • Mixing of paradigms, data collection, analysis
    and interpretation, takes time and skills to do
  • Combined designs are more expensive than single
  • Are there benefits to converting qualitative to
    quantitative data?

Crossover/mixed analysis
  • Suggestions
  • reduce dimensionality of either data set
    (quantifying to basics)
  • integrate data display (visual presentation of
    both sets as one)
  • transform data (Qual to quantb(numerical codes)
    and quant to qual (themes) for analysis)
  • correlate data (correlate results from
    quantitizing and qualitizing)
  • consolidate data (merging multiple data sets to
    create new codes, variables etc)
  • compare data (compare findings)
  • integrate data (into one or two sets of data)
  • use warrented assertion analysis (seeking
    meta-inferences from both sets)
  • import data (using follow-up findings from
    qualitative to inform quantitative analysis and
    vice versa)

Presentation of dual results
  • Separate data sets
  • Requires a very large results section and
    requires regular summaries of data findings which
    will need to culminate in a final drawing
    together of the findings so that the reader can
    make sense of the diversity presented.
  • Combined data sets
  • Amalgamate the findings in such a way that a neat
    display of graphical information occurs, followed
    by a few carefully chosen qualitative quotes to
    display the homogeneity (or diversity) of the
    data gathered. Matrixes can bring together
    variables, themes and cases as can lists,
    network diagrams and graphical displays.
  • Multiple data sets
  • Currently the majority of data collected is still
    within the survey/interview/observation/document
    analysis framework with the documents
    traditionally being written communications
    displayed in a variety of creative ways.
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