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Zooming in and zooming out: Mixing qualitative and quantitative approaches to the analysis of textual data Graham Hughes QUIC Conference 2011 University of Surrey 4 th May 2011 n.hughes@surrey.ac.uk Focus of this session Analysis

SLIDE 1

“Zooming in and zooming out”:

Mixing qualitative and quantitative approaches to the analysis of textual data

Graham Hughes

QUIC Conference 2011 – University of Surrey 4th May 2011

n.hughes@surrey.ac.uk

SLIDE 2

Focus of this session

Analysis of responses to open ended

questions asked in survey situations.

An important example of mixing qualitative

and quantitative methods

Relevant, because these types of

questions are being used more often with internet surveys

SLIDE 3

Analyse by case or by question?

Case by Case analysis is routine in

qualitative work.

Question by Question analysis is routine in

quantitative work.

Case Perspective

Header: Respondent Name/ID - (e.g. Case 1234) Question 27 Response by Case 1234 to Q27 Question 31 Response by Case 1234 to Q31 Question 32 Response by Case 1234 to Q32 Question 44 Response by Case 1234 to Q44 etc.

Variable Perspective

Header: Question Name/ID - (e.g. Question 31) Case 1000 Response by Case 1000 to Q31 Case 1005 Response by Case 1005 to Q31 Case 1234 Response by Case 1234 to Q31 Case 1235 Response by Case 1235 to Q31 etc.

SLIDE 4

Data collection methods and OEQ data quality

Life History Interview Survey Interview

Interview Type

Self- completion Face to Face

Flexibility to Respondent

Recorded & transcribed Interviewer mediation

Data Collection

OEQs grouped at start OEQs spread around

Questionnaire Design

Strong theme or varied topics Online or paper

SLIDE 5

Data used in these examples

Professional survey of flooding

experiences

Households in England
Events in 2000
1,250 respondents
Face to Face using CAPI
Mainly closed questions, but 8 OEQs
Interviewers typed responses in real time

SLIDE 6

Who might be asked to analyse it?

Quantitative survey analyst:

– Because you are analysing the rest of the data

Qualitative research analyst:

– Because it’s qualitative and that’s what you do

Online research analyst:

– Because it is easier to phrase open ended questions

SLIDE 7

Why use CAQDAS?

ATLAS.ti, NVivo, MAXqda, QDA Miner
Speed
Accuracy
Transparency
Replicable
Tools designed for the task

SLIDE 8

Four main phases of work

Data preparation & import into CAQDAS
Development & application of themes

(coding)

Exploration of relationships between

codes, respondent attributes, and the texts

Export of selected coding data to SPSS for

further analysis (“Quantitising”)

SLIDE 9

NVivo 8 Main Screen

SLIDE 10

MAXqda Main Screen

SLIDE 11

Developing Themes

A range of possibilities from fully manual

to fully automatic

Our own prejudices and preconceptions

may hold us back

Range of data quantities and qualities

should be considered

Inductive approach based on the data

SLIDE 12

ATLAS.ti v6 – Word Frequency Table

SLIDE 13

NVivo – Word Frequency Tag Cloud

SLIDE 14

QDA Miner/Wordstat – Phrase Finder

SLIDE 15

Manual coding in MAXqda

SLIDE 16

Autocoding in MAXqda

SLIDE 17

Fuzzy Search – Query by Example in QDA Miner

SLIDE 18

Survey Import in NVivo9

SLIDE 19

Survey Import in ATLAS.ti v6

SLIDE 20

Reporting and Checking Codes

“X% of respondents mentioned Y”

– Reference coding - (“signposts”) – Substitution coding – (“exact”)

So, check for errors

– Code definition – Incorrect use – Omissions

And be prepared to merge, split or change

code applications

SLIDE 21

Checking omissions in MAXqda

SLIDE 22

But also, look for difference.
Make rarity a virtue!
“My personal experience was being told my family had been

evacuated when they had not and not being allowed access to my own road when my family were trapped inside”

(R:12284 – male, aged 35/44, working full time, house was flooded) – response to a question about better ways to warn people.

SLIDE 23

MAXqda – Attributes Table

SLIDE 24

Crosstabs in MAXqda

SLIDE 25

NVivo – Matrix Coding Query

SLIDE 26

QDA Miner – Coding by Variable Heatmap

SLIDE 27

Data collection methods and CAQDAS programs