Analysing and
Interpreting Data
Chapter 11
‘All meanings, we know, depend on the key
of interpretation.’
-George Eliot
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
2
Effective Data Analysis

Effective data analysis involves
 keeping
your eye on the main game
 managing your data
 engaging in the actual process of quantitative
and / or qualitative analysis
 presenting your data
 drawing meaningful and logical conclusions
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
3
The Big Picture

Analysis should be approached as a
critical, reflective, and iterative process
that cycles between data and an
overarching research framework that
keeps the big picture in mind
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
4
Managing Data

Regardless of data type, managing your data
involves
 familiarizing
yourself with appropriate software
 developing a data management system
 systematically organizing and screening your data
 entering the data into a program
 and finally ‘cleaning’ your data
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
5
Statistics
Being able to do statistics no longer
means being able to work with formula
 It’s much more important for researchers
to be familiar with the language and logic
of statistics, and be competent in the use
of statistical software

O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
6
Data Types

Different data types demand discrete
treatment, so it’s important to be able to
distinguish variables by
 cause
and effect (dependent or independent)
 measurement scales (nominal, ordinal,
interval, and ratio)
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
7
Descriptive Statistics

Descriptive statistics are used to
summarize the basic feature of a data set
through
 measures
of central tendency (mean, mode,
and median)
 dispersion (range, quartiles, variance, and
standard deviation)
 distribution (skewness and kurtosis)
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
8
Inferential Statistics

Inferential statistics allow researchers to assess
their ability to draw conclusions that extent
beyond the immediate data, e.g.
 if
a sample represents the population
 if there are differences between two or more groups
 if there are changes over time
 if there is a relationship between two or more
variables
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
9
Selecting Statistical Tests

Selecting the right statistical test relies on
 knowing
the nature of your variables
 their scale of measurement
 their distribution shape
 types of question you want to ask
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
10
Presenting Quantitative Data


Presenting quantitative data often
involves the production of graphs and
tables
These need to be
1.
2.
selectively generated so that they make
relevant arguments
informative yet simple, so that they aid
reader’s understanding
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
11
Qualitative Data Analysis (QDA)
In qualitative data analysis there is a
common reliance on words and images to
draw out rich meaning
 But there is an amazing array of
perspectives and techniques for
conducting an investigation

O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
12
The QDA Process


Qualitative data analysis creates new
understandings by exploring and interpreting
complex data from sources without the aid of
quantification
Data source include
 interviews
 group discussions
 observation
 journals
 archival documents,
etc
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
13
Uncovering and Discovering
Themes

The methods and logic of qualitative data
analysis involve uncovering and
discovering themes that run through raw
data, and interpreting the implication of
those themes for research questions
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
14
More on the QDA Process

Qualitative data analysis generally involves
 moving
through cycles of inductive and deductive
reasoning
 thematic exploration (based on words, concepts,
literary devises, and nonverbal cues)
 exploration of the interconnections among themes

Qualitative data analysis software can help with
these tasks
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
15
Specialist QDA Strategies

There are a number of paradigm and discipline
based strategies for qualitative data analysis
including
 content analysis
 discourse analysis
 narrative analysis
 conversation analysis
 semiotics
 hermeneutics
 grounded theory
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
16
Presenting Qualitative Data
Effective presentation of qualitative data
can be a real challenge
 You’ll need to have a clear storyline, and
selectively use your words and/or images
to give weight to your story

O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
17
Drawing Conclusions
Your findings and conclusions need to flow
from analysis and show clear relevance to
your overall project
 Findings should be considered in light of

 significance
 current
research literature
 limitations of the study
 your questions, aims, objectives, and theory
O'Leary, Z. (2005) RESEARCHING
REAL-WORLD PROBLEMS: A Guide to
Methods of Inquiry. London: Sage.
Chapter 11.
18