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Computer-Aided Qualitative Research Europe
7 & 8 Oct 2010, Lisbon
For more information about our events, please visit:
http://www.merlien.org
Utilising Wordsmith and ATLAS.ti to explore, analyse and report qualitative data
Brit Helle Aarskog
textUrgy AS & University of Bergen
October 2010
"... the two approaches overlap, with quantitative analyses ending up with qualitative considerations,
and qualitative analyses often requiring quantification." (Mergenthaler 1996:4).
In this presentation:
� Overview of course sessions in which participants learn how to blend quantitative and qualitative approaches; Participants are guided through an extensive set of practical exercises;
� Integrated tool set in WordSmith 5.0 – wide range of frequency and distribution data for various parameters;
� Tools in ATLASti – flexible facilities for annotations of primary files (audio, video, text, etc.) and tools for linking data (segments, codes and notes);
� I will not talk that much about theory, but rather show a kind of work-flow from:
� Concordances, collocations, Z-score, dispersion plot;
� More advanced options as keyness values and textual patterns revealed via concgrams;
� Export results from WordSmith and import files to ATLASti;
� In-depth analysis of texts focusing on Problem-Solution patterns;
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Important to stress: meaning and context
� An understanding of how language is used in the text is a prerequisite for identifying, extracting and representing the meaning. This understanding can only be achieved by a close study of the textual context - the situations and activities where words and phrases are used.
� Blair refers to Wittgenstein and declares that: � "These situations and activities are our Forms of Life, which is why
we must understand them before we can understand how language is used." (1990:154), and further:
� " ... we don’t start from certain words, but from certain occasions or activities... An expression has meaning only in the stream of life.” (1990:145).
� Conformity regarding the appearance of words in the text is not a sufficient signal for determining conformity in the expressed opinions (meaning). Lists of words, clusters or collocations can thus not signify opinions. � "...the words are simply words that are used in a particular way in
certain kinds of situations." (1990:157).
� A simple example just to give you a general idea
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Z-score and discovery of semantic relations
Collocation generated over ‘Islam*’ over a set of news texts collected from a RSS feed; ‘Muslim’ and ‘Terrorist’ among those with value > 20; New collocations over these two; Terrorist in L position and Terrorist in R position
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
1
2
Construct code structures in ATLASti
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Code structures based on
collocation data
Text segments identified for
in-depth analysis
Texts seen as a system of layered structures
refute
defend
Opinions in context Pragmadialectical argumentation theory Genre theories, e.g. Superstructures, ...
Speech Act theory, Propositional content, Cohesion and Coherence, Context,
Macrostructures, Rhetorics
Critical Discussion
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Confrontation Opening
Argumentation
Conclusion
Standpoints
Arguments
Speech Acts
Sentence
Phrase
Word
Morpheme
Grammatical rules, Syntax, Microstructures, Metaphores, Styles,
Tense, Adverbial phrases, Pronoun use,
Theory presented and techniques applied depend on textual unit and structural level
Generate concordance over selected word types
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
The selected word types in the word list produce a concordance with 594 entries (81 + 513), and where the set contains these two word types, here marked in navy blue to the right.
Patterns reveal aspects of the texts’ thematic profile
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Setting sort order for concordance
Menu for setting sort order for concordances.
Concordance sorted by R1, R2 and then R3 in ascending order and with case sensitivity activated.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Access to full textual context
Extract from text file where sort settings given for entry 326 is marked in the text.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Plots and clusters complement concordances
Plots visualise the
position of word
occurrences
corresponding to the
word types in a
concordance request.
The plots cover for the
word type ‘parliament*’,
here sorted by ‘hits per
1000 words in the text’.
The clusters
provide further
data about the
occurrences of
‘parliament’ in the
set of texts.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Clusters based on whole texts
The tables show 2 word-clusters for two text sets consisting of part I-IV of two
versions of the Constitution for Europe.
The cluster settings are equal, and each entry in the extracted subsets start
with 'european'.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Frequency data in table for mutual information
The part of the table with data about
frequencies of word type 1 and word type 2
in a pair which is according to settings for
jointedness.
Settings for sort order
with swap
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Z-Scores reveal closeness patterns
High z-scores in sample set A
reveal persons' names in sample
set A with about 300 000 words.
High z-scores in sample set
C also reveal persons'
names – a collection with
about 5 million words.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Keyword list, Test file 1
With a p-value 0.05, the list for the
source file Reuter-Test-1-Sport-09-02-
09 includes 46 keywords here sorted
by keyness value
With a p-value 0.0000001, the list for
the source file Reuter-Test-1-Sport-09-
02-09 includes 16 keywords here
sorted by keyness value
Participants learn text statistics by observing results after changing settings
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Keyword plot, Test file 1
Plot diagram that reveals the dispersion of
keywords in order of how they occur in 8
text segments.
When opening the source file (entry under
‘filenames’), the 4 first keywords in this
sorting order show to be part of the news
report’s title.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Keyword links, Test file 3
Relations between keywords which indicate thematic relations within a text.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
WordSmith data converted into ATLASti formats
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Submit texts and receive lists of word types by grammar class
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Make data sets manually in for instanceTextPad
Clusters from WordSmith are edited into a form that can be applied as codes in ATLASti.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Main sections in the screen-play
Situation
Aspect of Situation requiring a Response
Response to Aspect of Situation requiring a Response
Result of Response to Aspect of Situation requiring a Response
Evaluation of Result of Response to Aspect of Situation requiring a Response
Michael Hoey, 1994
Abbreviations: Situation, Problem, Solution, and Evaluation - the components in the textual SPSE-pattern.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Interaction and Speech Act Analysis
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Actors and other World Building Elements
� The reader and writer are not characters in the text world depicted - rather they are participants in the language situation in which the text has been formed. (Werth 1999)
� Thus, the producers of text and its consumers are outside the text.
� Characters are the (juridical) persons mentioned in the text.
� Characters are referred to via noun phrases, e.g: Mother, minister, husband, teacher,....
� Characters are referred to via personal pronouns, e.g. You, he, her, they, them…
� Participants can announce their presence by pronouns, e.g. I, me, mine, we, our
� Noun phrases: focus on nouns and their modifiers (adjectives), in particular noun phrases referring to problems and solutions, and generate thematic profiles for words occurring left and right of these (n-grams).
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
PMEST
Identify word types
for Actor which
signal problems
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Function-advancing Propositions
� Text involves motion � The motion is entirely notional
� The focus of attention is moving
� Superstructures may be considered as metaphorical paths - they do not denote movement, but some kind of non-physical activity expressed in motion terms. � Move from assertions about situation, the negative evaluation
of a situation to problem statements, evaluating problems and selecting the most important problem, proposing solutions and comparing solutions before selecting a solution, evaluating solutions possibly giving rise to new problems....the new situation is related to the new problem....
� ...while connectors are relational elements, and therefore correspond to the ground, and are thus verb-like entities...(Werth, 1999:338)
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
PMEST Word types/
phrases which
confirm problems
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
THE EUROPEAN CONVENTION - THE SECRETARIAT Brussels, 7 July 2003, CONV 844/03, CONTRIB 380, COVER NOTE From: Secretariat, To: The Convention Subject: Contribution of Mr David Heathcoat-Amory, member of the Convention: "Systems of Mismanagement"
Proposal: EU Whistleblower Rights: In the light of the present lack of options open to employees of the Communities who seek redress against institutional failings, the Convention may care to consider including a Communities whistleblower clause setting out the principle of the right of free speech where normal avenues have been blocked.
Problem: I am referring to the issue of fraud, which is close to being institutionalised in key sectors... Instead of more political institutions, we need a real reform of the system. To establish how this must be achieved, we have first to analyse something of the fraud and other failings which have come to light, which has only happened because of the determination and selflessness of whistleblowers. The personal experiences of several confirm a general trend. Initial complaints are filed away in the system. …Then, the administrative machine kicks in. The employee is hauled in before his or her senior grades, who try to determine precisely how much he knows before instructing him to keep silent… Health frequently suffers. The Sword of Damocles finally falls...a promising career is finished…And all for nothing. Because someone has spoken out, the institutions have an even greater need to cover over their failings …The fraud goes on regardless....It doesn't end there. Beyond the competent authorities refusing to investigate even claims which are easily checkable..., there have been several reports of attempts to intimidate witnesses… Such a climate engenders fraud higher up the chain.
Text structure: Introducing problem, evaluating existing solutions, a negative evaluation is followed by a solution proposal.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Thank you for your attention
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Computer-Aided Qualitative Research Europe
7 & 8 Oct 2010, Lisbon
For more information about our events, please visit:
http://www.merlien.org