An Introduction to Qualitative

Data Analysis for NVivo

Nyree Mason,

Quantitative & Qualitative Research Consultant,

Statistician & Trainer

Purpose of this Presentation

NVivo is a complex program which can be

confusing for people with little or no

experience in conducting qualitative

research.

This presentation aims to provide you with

an introduction to the process of qualitative

data analysis with specific reference to

working in NVivo.

You will learn about:

Creating Coding Schemes

Applying Coding Schemes

Ensuring Reliability and Validity

NVivo-Specific Terminology

The Basic Steps Involved in Setting

Up an NVivo Project

The Purpose of Qualitative Data

Analysis

To use qualitative data to answer

research questions or collect

evidence by:

• Categorising,

• Synthesising, and

• Summarising data

Qualitative Research Methods

To do this we use a standardised method of

analysis called a CODING SCHEME.

The Codes are research themes and act like the

Dependent Variables in your project.

Coding is a process of identifying sections of

data where research themes of interest occur

and applying descriptive labels to them.

We can use Codes to quantify qualitative data

to some extent.

Defining a Coding Scheme

Coding schemes can be constructed

(at least initially) based on:

research questions/hypotheses

previous research findings

info from a random sample or all

of your data (Grounded Theory)

an NVivo Word Frequency Query

A Posteriori

(Exploratory)

A Priori

(Confirmatory)

Defining a Coding Scheme

It is usually an iterative process using both top-down & bottom-up methods. Some general considerations are:

Is it relevant vs interesting?

The KISS principle (Keep It Simple Stupid)

Positive vs Negative contexts and attitudes

“Outliers”

Free vs Tree coding

Tree vs Free Coding

Codes can be:

Hierarchical (“Tree”):

– Parent Nodes (e.g., Assessment)

– Child Nodes (e.g., MCQ, SAQ, Essay)

[allows for “aggregation” of children]

Stand-Alone (“Free”)

NB: Avoid “grandchildren”. NO duplicates. Have

succinct labels.

The problem of subjectivity

You’ll need to devise a coding scheme which is

as objective as possible and satisfies the

requirements of:

Reliability – other researchers would find the

same results & you would find the same

results if you re-analysed the data;

Validity – you are measuring/assessing what

you believe/say you are.

Defining Codes

Ideally, specify exactly & unambiguously

what the code means and under what

conditions it will be applied to your data.

If someone else was coding your data, they

should be able to understand exactly how to

correctly apply the codes and they would apply

them the same way you would.

Ensuring Reliability & Validity

Assess the Reliability and Validity of your

coding scheme early at the beginning of

the coding process using:

Coding Comparison measures

Discussions with colleagues/subject experts

Ensuring Objectivity

Try to eliminate bias as much as possible:

Read with an open mind

Read to both confirm AND falsify any preconceptions/theories/hypotheses you have (and all humans have them so it’s best to make them explicit if you can).

How to Code

Applying Codes

Reliability and Validity Checks

Coding Context

Applying a Coding Scheme

In simple terms, the process of coding is

like highlighting sections of text in an

article and then applying one or more

descriptive labels to it.

Consider Coding Context

How much information you include in your coding

reference (e.g., partial/whole sentences,

paragraphs) is very important for revision and

analysis.

Include enough information to justify how

appropriate your coding was during revision.

Exclude superfluous information if you want to

avoid spurious correlations in analysis.

Example Code: “TV is not educational”

“… television....”

vs.

“I find television very educating.”

vs.

“I find television very educating. Every

time somebody turns on the set, I go into

the other room and read a book.”

Example Coding Query

Relevant

TextIrrelev

ant

Relevant

Text

CONDUCTING QUALITATIVE

DATA ANALYSIS

The process in NVivo

Terminology in NVivo: Nodes

Coding is stored in NVivo as project

items called Nodes (folders containing

all the data you code as belonging to a

theme). You “code your data at Nodes”.

E.g., The “Habitat” Node stores all the

references you have coded as being

related to the concept of “Habitat”. Open

the Node, and you see all the references.

Handy Hint: Contextual Nodes

It’s also a good idea to have a Node for each

question in a survey or interview. Then you can

analyse responses to individual questions more

easily.

Attitudes and non-verbal cues are other

examples of Nodes you can use for analysing

themes in context (positive/negative, sad/angry

etc.)

Terminology in NVivo: Cases

Cases are very important NVivo project

items and store all data references relating

to individual “Cases” (units of measurement

such as people, articles, policies etc.).

Case Nodes allow you to count how many

people/articles mentioned themes X, Y and Z

E.g., The Case item called “Bob” is where all of

Bob’s survey responses are stored.

[]

Terminology in NVivo: Classifications

Case Classifications: describe the Cases in

your analysis (e.g., demographics like age and

gender). You MUST include all the information

you need for analysis (independent/breakdown

variables).

Source Classifications: describe your

reference material and data sources (e.g.,

interviews, surveys, bibliographic information).

These mainly help with limiting searches and

keeping track of important information.

Terminology in NVivo:

Attributes & Values

Attributes: define all the variables that

each Classification needs to have.

Values: the possible values each Attribute

has.

[Setting values allows you to create a drop-down

menu making data entry easier and reducing

errors]

Classifications, Attributes & Values

Person

Age

18-21

22-25

26-29

Gender

Male

Female

Other

CaseClassification

Attributes

Values

Classification Sheets

ID Gender Age State/Territory

1 Female 26-29 NSW

2 Male 26-29 ACT

3 Male 22-25 Vic

4 Other 18-21 SA

“Person” Classification Sheet

Attributes

Values

Handy Hint

Think of your Classification Attributes as the

Independent Variables in your research design.

E.g., if you think Political Affiliation might affect

how people respond to questions “Affiliation”

is an IV and needs to be an Attribute of a

Person Classification.

Handy Hint

Attributes MUST be mutually exclusive.

E.g., if one Case can be engaged in both

recreational and commercial fishing, you cannot

have one Fishing Attribute, because you can’t tick

more than one box. You need one Attribute for

Recreational AND one for Commercial, with

yes/no/not applicable options.

Handy Hint

For interviews conducted by multiple people,

consider creating an Attribute for Individual

Interviewers within your “Interview” Source

Classification”. Sometimes responses vary

greatly depending on who asks the questions,

and it’s worth assessing.

Analysing in NVivo: STEP 1

Determine your units of measurement, and

these will become your Cases. For example, if you want to know the number of:

people saying X, Y & Z

need a Case item for each person

journal articles including concept Y

need a Case item for each article

[NB Case project items are more easily created during or

after importing data]

Analysing in NVivo: STEP 2

Create all your Case & Data File

Classifications, associated Attributes and

Values.

It is much quicker and easier if you set these

up BEFORE importing data, as you can

Classify your Data Files and Cases during

data import.

Analysing in NVivo: STEP 3

Import and organise your qualitative data.

Put all the data you need to code & analyse in the

Files Folder. Other peripheral documents can be

included as linked files in the Externals Folder.

This will save computing power and make NVivo

run better.

Analysing in NVivo: STEP 4

Create your Cases (if you haven’t done so

during import), and give them relevant

unique ID numbers/Names etc.

Classify your Cases so they appear in the

Classification Sheet for data entry.

Analysing in NVivo: STEP 5

Open the Classification Sheet and apply

the relevant Attribute Values to your

Cases.

Analysing in NVivo: STEP 6

RECOMMENDED: Define your coding

scheme and create a Node for each code

you have in the Nodes Folder of the

Code area.

This can be done/altered at any time, but it

does help to keep the coding within the scope

of your research project. Plus it makes any

implicit assumptions explicit; thus allowing for

active reading to confirm & falsify (à la Popper).

Analysing in NVivo: STEP 7

CODE! ☺Start with a small (random) sample of data first

and check reliability & validity.

Checking Reliability

Ideally 3 people code the same small

random sample of your data. You can

check the inter-coder reliability in NVivo

using a Coding Comparison Query.

Handy Hints: You could also just Recode the

same data weeks later under a different user

ID. Focus only on coding difficult/ambiguous

concepts to save time. Delete duplicate coding

once reliability established.

Analysing in NVivo: STEP 8

ANALYSE! ☺

When you have completed coding, you can

conduct Queries in NVivo to help analyse and

summarise your data.

NOW YOU’RE READY TO

USE NVIVO!

EXTRA EXAMPLES

Developing a Coding Scheme

Example: Climate Change

Example Hypotheses

RESEARCH QUESTION: What factors do

respondents believe are responsible for the

decline in the fishing economy?

• How many believe Climate Change is a factor?

• Of these people, do they see Climate Change

as being caused by human influences, natural

influences or both?

• How many do not believe Climate Change is a

factor because it does not exist?

Coding Scheme Examples

PARENT CODE - Climate change

RULES - Any mention of the following:

terms such as “climate change” or “global warming”,

substantial change in average weather conditions,

increasing/decreasing average temperatures,

change in the time variation of weather,

increase/decrease in the length of seasons or in

number of distinct seasons,

change in amount of extreme weather events.

Coding Scheme Examples cont.

CHILD CODES & RULES:

Caused by Human Influences: Mentions

directly/indirectly caused by pollution, agriculture,

increased population,

Caused by Natural Influences: Mentions caused by

factors beyond human influence such as ocean

variation, orbital variations, solar output, volcanic

activity, seismic activity, etc.

Does not exist: Explicitly rejects the notion that

Climate Change is responsible because it isn’t real.