NSE 222 W3 QUALITATIVE DATA ANALYSIS AND DATA MANAGEMENT

data collection and analysis pt 2

• most commonly data collection and analysis are done concurrently in qualittive research although obviously at least some data needs to be collected prior to analysis

• many researchers beleive that the stages of data collection adn data analysis should be integrated, whereas others beleieve that these strategies should be seperate

qualitative data

• qualitative researchers gather data from variety of sources, including interviews, observations , narratives adn focus groups

• the open nature of qualitative inquiry typically results in the collection of more data than required and is referred to as “fat data”

• most researchers wish to use the original words from the participants so that the researchers own interpretations and eprsonal biases are not juxtaposed with the aprticipants thoughts

• the presences of the original words allow the reader to check the authenticity of the data

data analysis and data collection

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methods of organizing data

Manual methods (kinetic) - labour intensive

useful when amount of data is small

conceptual files

labor intensive

Computer assisted qualitative data analysis software (CAQDAS) - how usually used

faster, can handle large volumes of data

not as hands on as manual sorting

risk of turning cognitive process into a mechanical and technical activity

All data must be backed up and stored in multiple places,

whats data reduction

• Ongoing process as data are collected - data saturation when no new emerging/redundancy of themes

• Process of selecting, focusing, simplifying, abstracting, and transforming the data

• Organized into meaningful clusters (themes or structured meaning units)

• Thematic analysis: Process of recognizing and recovering the emergent themes - most freq occuring

• Memos are kept to help organize data, write personal notes to self.

• Data are coded—given a tag or label according to theme/category.

• Codebook is used to organize code into lists.

• Researcher immerses self in the data during this stage, often for weeks or months!

data display

• An organized, compressed assembly of information that permits conclusion drawing and action

• Graphs, flow charts, matrixes, model

• Profiles or Vignettes

• Narratives or direct quotations - DQ used as much as possible

• Must  ensure that the presentation supports the findings and relays what needs to be known

whats the computer program for coding

• nvivo

coding

• considerable variety in the process of coding

• steps in coding data:

  • as sampling , identifying themes, building code books and amrking texts

• three types of coding - soem or all types

  • descriptive - of pt may elicited

  • , topic - topic/issue

  • , analytic

• researchers use some or all of these types of coding

qualitative data management and organization - coding

• Main task to develop system to classify/index material

• Requires thorough reading of transcripts to identify underlying ideas, concepts (codes)

• Codes can vary in detail and level of abstraction

• Data are converted to smaller manageable units and closely examined for patterns and meanings

• important concepts are then given a name or label (code)

process of coding

2 Steps:

First Step: Cycle Coding (summarizes chunks of Data)

Second Step: Themes/ pattern codes (occurs after data collection)

The first cycle codes are organized into smaller

 categories: themes or constructs during the second step

The coding process itself is analysis (Miles et al., 2014).

questions to ask in developing a coding scheme

• what is this

• what is going on

• what does it stand for

• what else is this like

• what is this distinct from

coding example image

coding scheme image

manual coding example

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text coding exmaple

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colour coding = cooding example

colour ex

qualitative data management and organization - coding qualitative data

• Once codes (categories) are determined, all data are reviewed for content and are “coded” or  assigned to appropriate codes  categories

• May take several readings of material

• As coding proceeds researcher often discovers category system was incomplete or inadequate

• Necessary to review large portion of data before completing categorization scheme and reread/recode data as required

• Narrative materials are usually non linear – paragraphs from interviews may contain elements of several codes/categories

qualitative analysis

Begins with collecting bits of information and piecing them together,

building a mosaic or a picture of the human experience being studied.

When one steps away from the work, the whole picture emerges

Let’s explore how we collect these “bits” of data and make sense of them

qualitative data analysis

• The overall goal is to make meaning out of massive amounts of text (data).

• The purpose of Data analysis is to answer the research question

• Many methods available but in the end each study is unique and is reliant on the creativity, intellect, style and experience of the researcher.

overview of data analysis

When does data collection end and data analysis begin?

qualitative analysis is not a linear process; rather, it is cyclical, transformative, reciprocal, and iterative. 

Common features to qualitative data analysis:

Affixing codes or themes to a set of field notes, interview transcripts, or documents

Sorting and shifting though these coded materials to identify similar phrases, relationships between variables, patterns, themes, distinct differences between subgroups, and common sequences

 Isolating these patterns and processes, and commonalities and differences, and taking them out to the field in the next wave of data collection

Noting reflections of other remarks in the margins

Gradually elaborating a small set of assertions, propositions, and generalizations that cover the consistencies discerned in the database

Confronting those generalizations with a formalized body of knowledge in the form of constructs or theories

qualitative data analysis - challenges

Qualitative researchers analyze text, not numbers (interview data are usually tape   recorded and transcribed [typed up]   verbatim prior to analysis)

No universal rules 

Labour intensive, complex, creative,

Requires strong inductive skills,

Organizing and making meaning of enormous volumes of narrative data,

Reducing data for reporting

qualitative analysis principles

No standard rules for qualitative analysis

Data management and organization (reductionist)

Transcribing & reviewing data

Developing category scheme

Coding data using the category scheme

Data analysis (constructionist)

Search for patterns/themes in the data

Validation of thematic analysis

Weaving themes together to integrated picture of phenomenon

phases of data analysis

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qualitative anlaysis for different research traditions

Each qualitative research approach has its own data analysis process:

Phenomenology

Ethnography

Grounded theory

Participatory action research (Action research

phenomenological analysis

• Immersion in the data—read and reread

• Extract significant statements.

• Determine relationship among themes.

• Describe phenomena and themes.

• Synthesize themes into a consistent description of phenomenon.

ethnographic analysis

• Immerse in the data.

• Identify patterns and themes.

• Take cultural inventory.

• Interpret findings.

• Compare findings to the literature.

grounded theory analysis

• Examine each line of data line by line.

• Divide data into discrete parts.

• Compare data for similarities/differences.

• Compare data with other data collected, continuously—constant comparative method.

• Cluster into categories.

• Develop categories.

• Determine relationships among categories.

case study analysis

• Identify unit of analysis.

• Code continuously as data are collected.

• Find commonalities, themes.

• Analyze field notes.

• Review and identify patterns and connections.

analytic procedures

• Data management = reductionist

• Converting large volumes of data into more manageable segments

• Analysis strategies = constructionist

• Putting segments together into meaningful patterns

• Inductive process

• Analysis usually begins with search for themes

conclusion drawing verification

• The challenge for the researcher is to stay open to new ideas, themes, and concepts as they appear.

• Conclusion drawing is the description of the relationship between the themes.

• Verification occurs as the data are collected.

• Described as “doing abstraction”

generating meaning

1. Note patterns, themes  11. Intervening variables

2. See plausibility    12. Chain of evidence

3.  Clustering    13. Conceptual coherence

4.  Make metaphors    

5.  Counting    

6.  Contrast/compare  

7. Partition variables

8. Subsume particulars

9. Factoring

10. Note relationships

critiquing criteria

• Is the method of analysis clear?

• Is it appropriate for the study?

• Can you follow the analysis step by step?

• Is there evidence that the interpretation accurately reflected what was said?

• Are credibility, auditability, fittingness, and trustworthiness accounted for?