sue bell training day - 2012

A Presenta*on from The Fes*val of NewMR – Training Day

3 December 2012

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Introduction to qualitative analysis Sue Bell, Susan Bell Research

©2012 Susan Bell Research Phone 02 9451 1234 Fax 02 9451 1122 Web www.sbresearch.com.au

Susan Bell, Susan Bell Research, Australia Festival of NewMR 2012 – Training Day - Session 1

Festival of NewMR Training December 2012

Introduction to qualitative analysis

©2012 Susan Bell Research 3 Susan Bell, Susan Bell Research, Australia Festival of NewMR 2012 – Training Day - Session 1

Agenda

Why? What? How? Some examples

1

2

3

4


1 Why?

©2012 Susan Bell Research 5 Susan Bell, Susan Bell Research, Australia Festival of NewMR 2012 – Training Day - Session 1 5

Why: to make sense out of this


Analysing is more than summarising. You need to be flexible in the way you look at this data. Ultimately, the best qual analysis helps you interpret the data to solve your client’s problem.


2 What?


What do you analyse? Everything!


How much you analyse depends on the project

Light •  Easy •  Transitory •  Low client

involvement

Dense •  Intense •  Keep/strategic


The project and the information your client needs determine the best way to analyse.


3 How?


How: an iterative – THOROUGH - sequence

Seven stages

1.  Fieldwork stage – while collecting your data

2. Organise / manage the output of the fieldwork

3. Segment the data

4. Categorise the findings

5. Observe patterns

6. Interpret what the findings mean

7. Report the analysis and interpretation


Five ways to organise / categorise qual data

Annotate

Manual code

Big piece of paper

Excel

Movable


Case study 1: annotate

The project

•  10 face to face depth interviews audio-taped and transcribed

•  No need to segment the sample.

Solution

•  Annotate the transcripts identifying key themes, eg:

•  Customer service defined by ‘smiling customer’, customer service defined by ‘achieved their outcome’ and so on.

Helped deconstruct perceptions of customer service


Case study 2: manually code

The project:

•  About 1000 people contributed to a conversation online, which was unmmoderated. This created a large unwieldy data set.

The solution:

•  Create a very large code frame to manually code all of the data.

•  Then categorise themes together.

CODE Theme

1 more time to think

2 more longer term thinking

3 more big picture thinking

4 more aware of worlds issues / more informed

5 to contribute to a better society

6 to understand policy

7 industries where we have a competitive adv

8 economists to understand monetary policy

9 a direct / better democracy

10 etc.

Helped reduce large data set to a one-page executive summary.


Case study 3: Paper / whiteboard

The project

•  Three face to face groups – different spec for each group (e.g. heavy users, light users …).

•  Notes taken in groups (not transcribed).

•  Wanted to compare across user groups.

The solution

•  Summarise key themes on one sheet of paper /whiteboard.

Helped to quickly compare across user groups in a small study


Case study 4: excel

The project

•  50 depth interviews.

•  Sample segmented into 4 types.

•  Detailed understanding needed.

The solution

•  Summarise in Excel.

•  Allows searching, sorting, as well as ‘eye-balling’.

•  Can track back to individual transcripts (though not linked).

Respondent Type

Gender Age

Occupa*on

S*ll have policy Background

1

denied/complaint

M 30s truck driver

No. Has cut up credit card and is paying it off slowly.

Divorced during claim process; 3 young boys

2

part paid

F Mortgage broker

No. Cancelled it. Not

necessary now as have no debt; pay off credit cards every month

Currently on maternity leave. Not sure when

going back as was injured at work.

3

paid in full

F nurse and has been teacher

yes husband

4

paid in full

F nurse husband; 2 children, 9 and 4 yrs

Helped understand a complex process in detail


Case study 5 - movable (post its) example

The project

•  Online BBFG

•  Data from forum discussion and Smart Boards,

•  Similar themes emerged across all the threads – needed to synthesise.

The solution

•  Post it notes that could be grouped and regrouped.

•  Could also have used cards.

Helped to untangle closely-related themes


The way you analyse depends on the kind of data you are working with.


Qual Analysis Software

Different types available

1. Coding, such as NVivo and ATLAS

2. Synthesising – Word Cloud type such as Leximancer (or very simplistically Wordle)

Key benefits can be

•  Manage very large data sets

•  Especially if multiple researchers

•  Storage place for all your data, including visuals, video etc (depending on the software).

•  Effective search – links to verbatims well


Susan Bell Research

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