Qualitative Data Gathering and Analysis – Planning the Process

Introduction

This presentation summarises the planning of a qualitative research approach to gathering data from an interview process and subsequent analysis of the data.

Planning for this qualitative process is explored from the perspective of using field notes, software analytical tools and an appraisal framework in order to analyse, interpret and thereafter present findings from textual discourse data.

The planning process further considers the benefits and caveats of approaches to data collection and analysis and the effects on attempting to conduct an effective research process.

A conclusion brings together the various approaches taken in effective planning for data gathering and analysis and demonstrates that careful planning can lead to reliable research results and outcomes.

The Data Type for Analysis

Mixed Method Data Types

Textual/Discourse Data

Heuristics Based Data

Questionnaire Based Data

Effective data analysis requires the researcher to understand the data so that the correct data type is selected and thus meet the needs of the research goal (Denscombe 2010).

Textual discourse provides the necessary data source in this instance. The ultimate research goal is to try to understand how one interlocutor may use certain expressions, in order to forge an affiliation with another person, so as to encourage the other person to cooperate in an evaluation project (Lipovsky 2008).

A competent and perceptive researcher is able the gain the best from the rich textual discourse data, such as that gained through interviews, and in order to more ably reveal deeper meanings bound to the words exchanged between both interviewer and interviewee(Parahoo 2006).

Examples of mixed data-types found in the raw data

Plan to Reduce and Collate Data

Carr (1994) states that some data-types (particularly numerical data) risk misrepresenting or not upholding the true nature of the phenomenon under examination.

Whilst having the research purpose in mind; separating out the data gained from the mixed method approach to the qualitative research means removing mixed data types.

Word processor cutting extraneous data from the raw data

Copies of the data should be backed up before work commenced (Taylor-Powell and Renner 2003).

Text editors can be used as one of many means to strip out extraneous data types from the raw data thus leaving the potentially useful latent data for analysis. Thereafter it can be moved to and organised in another data area for collation, for example, in spreadsheet workbooks (Graneheim and Lundman 2003).

Planning the Recording Process Using Tools

Resources for Data Storage and ManagementSoftware

Databases

Excel Workbooks

Data Analysis Systems e.g.NVivo

Paper Documents

e.g. Field Notes

Data management is vital to keep a project manageable, reliable and ultimately valid (Miles and Huberman 1994).

Software applications (opposite) and physical resources (e.g. field notes and diaries) help categorise, cross reference and keep track of data in chronological order and also in place too (secure storage and access for authorised individuals and research teams) (Taylor-Powell and Renner 2003).

Research data and research participants details stored securely uphold confidentiality and data protection (Denscombe 2010).

The necessity for continued consent from all participants, in order to verify the true record of the data (Parahoo 2006).

Examples of processing tools which can be used in the data recording process

Analysis and Interpretation of Findings

Refocus on the research question before beginning analytical moves.

Revisit the data as a whole, either in field notes or in software and decide on the analytical approach to be taken, i.e. discourse analysis.

Document all analytical moves, so that others can follow the process too (Robson 2002).

Choosing an analytical framework, based on research that is grounded in theory, may help form the initial coding and categorization from which further analysis of the textual data occurs (Parahoo 2006; Miles and Huberman 1994).

Software tools, such as an Excel workbook of spreadsheets or NVivo data analysis, help to filter codes or categories into meaningful patterns.

Relationships between emerging categories lead to findings from the textual data. Software may help with identifying relationships but the researcher’s subjective stance may also be needed to interpret the findings more fully (Taylor-Powell and Renner 2003).

Frameworks and Software can combine in data analysis to produce findings

Testing reliability of the analysis procedure

Researchers Role: Reviewing the role of the researcher: Effects of bias – i.e. researcher value-judgements may increase subjectivity (Pope and Nicolas (2000).

Different Approach: Can the approach toward analysis be generalized by trying different discourse data analysis strategies in order to gain the same outcomes?

Triangulation: When used judicially, triangulation uses different types of textual data sources, or similar studies, to the compare the efficacy of the current study ( Miles and Huberman 1994).

Additional Coder: Using an additional coder to test the current coding scheme or to generate a new coding schema.

Peer review: Asking other researchers for another perspective (Miles and Huberman 1994; Polit and Beck 2006). 

Testing reliability of the analysis

Current Research Analysis

Comparative research study Z

Comparative research study Y

Comparative research study X

Example of using triangulation to test reliability of data analysis

Presenting the findingsFindings from the data can be displayed in a number of forms – graphically and textually. Examples include:-

Excel Workbooks - Contextual references to occurrences in the data recorded across all spreadsheets within a workbook. Data, categories and coding sets can be compared when filtered or sorted by Excel tools (Meyer and Avery 2009).

Sophisticated Computer Representation – NVivo: representation of analysis across a broad range of media, i.e. text, image, audio and web.

Tabular representation - Structured listings of codes and representation of instances of code samples, together with samples of corresponding discourse text data (Taylor-Powell and Renner 2003).

Textual Information – Used to compliment table data. Data which is not part of the current study can be used for comparative purposes. Examples: text may come from or contribute to other academic sources for use as research evidence.

Graphs and other diagrams - Representation of data trends; frequency, distribution and comparison of data during analysis. Various formats: bar, line, scatter plots or radar charts. Other illustrations, such as representations of theoretical frameworks used in guiding the research process.

Context - All representations must contextualise the findings in terms of linking findings back to the raw data. Constructing an audit trail of the research process enables readers to identify key research decisions (Robson 2002). Examples of representations of

research findings

Strengths and Weakness

Using Mixed methods: Data collected with mixed methods potentially complicate the data structure and may cause the data processing and analysis process to become protracted and in terms of funding, costly (Denscombe 2010).

Researcher competency: Researchers with poor interview skills could come away with data which is not wholly representative of the participants interaction, thus risks invalidating the data.

Bias: Interpretation can depend on the degree of subjectivity of the researcher (Robson 2002)

Poor theoretical frameworks: Poor choices of frameworks risk introducing flaws into the research process and invalidating it (Lipovsky 2008; Miles and Huberman 1994).

Decontextualizing: Poor discourse analysis can shift the focus from what is being analysed to the analysis process itself. This risks losing the meaning of data, when interpreted out of context (Miles and Huberman 1994; Denscombe 2010).

Lack of generalizability: Small scale studies, based on minimal data, may not be representative and can not be generalised.

Representations of Reality: Qualitative research is grounded in realistic situations (Denscombe 2010).

Tolerance: Social existence is uncertain by nature. “The essence of life is statistical improbability on a colossal scale” but qualitative analysis allows for ambiguities which are a reflection of the social reality (Dawkins R (1999) cited in Parahoo 2006; Denscombe 2010)

Interviews: Allow the pursuit of enquiry for both interview participants. This potentially provides a rich source of data for subsequent analysis and interpretation (Campbell 1999).

Alternative Outcomes: Differing researcher subjective interpretations allow for more than one research outcome, offering alternative views of a social situation.

Deconstruction of Data: Competent discourse analysis deconstructs data so that the words people use reveal clear meaning in the use of words and sometimes hidden meanings too (Miles and Huberman 1994).

Benefits Caveats

Conclusion

A competent and well practiced researcher is more able to gain the most from a well planned research project.

Effective research planning involves the researcher knowing how to compare and deploy the many available documentation and software processing systems that are available for data collection, processing and subsequent presentation.

Failure to plan qualitative research methods can lead the researcher into a protracted and costly research process. This may result in the need for a whole reappraisal of the research itself or even failure to reach the research goal.

Researchers conducting qualitative research may use their contemporaries in order to conduct peer reviews on approaches to data collection, analysis and interpretation. Being prepared to collaborate in this way adds to the reliability of a research process.

A well planned and coordinated research process results in research projects which not only produce more reliable research outcomes but they are more efficient in terms time spent on the research but also more cost effective in terms of using funding too.

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Mark G. Hopewell - ID 20048791 - Qualitative data gathering and analysis assignment - Research Portfolio of Research Skills MATC 2012