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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
 "... the two approaches overlap, with quantitative analyses
           ending up with qualitative considerations,
    and qualitative analyses often requiring quantification."
                      (Mergenthaler 1996:4).




                   Brit Helle Aarskog
          textUrgy AS & University of Bergen
                    October 2010
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
                                                  1




                                                  2



   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
Construct code structures in ATLASti
                                                                       Code structures based on
                                                                       collocation data




                                                                         Text segments identified for
                                                                         in-depth analysis




Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
Texts seen as a system of layered structures
                                Critical Discussion
                                                                                            Opinions in context
                                                                                   Pragmadialectical argumentation theory
          Confrontation     Opening      Argumentation       Conclusion            Genre theories, e.g. Superstructures, ...

                                       refute
                  Standpoints                        Arguments
                                       defend
                                                                                  Speech Act theory, Propositional content,
                                                                                    Cohesion and Coherence, Context,
                                 Speech Acts
                                                                                        Macrostructures, Rhetorics




                                   Sentence                                              Grammatical rules, Syntax,
                                                                                    Microstructures, Metaphores, Styles,
                                                                                   Tense, Adverbial phrases, Pronoun use,
                                      Phrase


                                      Word


                                  Morpheme


Brit Helle Aarskog, textUrgy AS & University of Bergen, Octoberapplied
            Theory presented and techniques 2010                         depend on textual unit and structural level
Generate concordance over selected word types




                                                                       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.
Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
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




                                                                       Settings for sort order
                                                                       with swap



      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.




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


                                                                       Participants learn text statistics
                                                                       by observing results after
                                                                       changing settings




      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
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"


      Text structure: Introducing problem, evaluating                  Problem: I am referring to the issue of fraud,
      existing solutions, a negative evaluation is followed            which is close to being institutionalised in key
      by a solution proposal.                                          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
          Proposal: EU Whistleblower Rights: In the light of           has spoken out, the institutions have an even
          the present lack of options open to employees of             greater need to cover over their failings 
The
          the Communities who seek redress against                     fraud goes on regardless....It doesn't end there.
          institutional failings, the Convention may care to           Beyond the competent authorities refusing to
          consider including a Communities whistleblower               investigate even claims which are easily
          clause setting out the principle of the right of free        checkable..., there have been several reports of
          speech where normal avenues have been                        attempts to intimidate witnesses

          blocked.                                                     Such a climate engenders fraud higher up the
                                                                       chain.
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

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Utilising wordsmith and atlas to explore, analyse and report qualitative data

  • 1. Computer-Aided Qualitative Research Europe 7 & 8 Oct 2010, Lisbon For more information about our events, please visit: http://www.merlien.org
  • 2. Utilising Wordsmith and ATLAS.ti to explore, analyse and report qualitative data "... the two approaches overlap, with quantitative analyses ending up with qualitative considerations, and qualitative analyses often requiring quantification." (Mergenthaler 1996:4). Brit Helle Aarskog textUrgy AS & University of Bergen October 2010
  • 3. 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
  • 4. 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
  • 5. Z-score and discovery of semantic relations 1 2 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
  • 6. Construct code structures in ATLASti Code structures based on collocation data Text segments identified for in-depth analysis Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 7. Texts seen as a system of layered structures Critical Discussion Opinions in context Pragmadialectical argumentation theory Confrontation Opening Argumentation Conclusion Genre theories, e.g. Superstructures, ... refute Standpoints Arguments defend Speech Act theory, Propositional content, Cohesion and Coherence, Context, Speech Acts Macrostructures, Rhetorics Sentence Grammatical rules, Syntax, Microstructures, Metaphores, Styles, Tense, Adverbial phrases, Pronoun use, Phrase Word Morpheme Brit Helle Aarskog, textUrgy AS & University of Bergen, Octoberapplied Theory presented and techniques 2010 depend on textual unit and structural level
  • 8. Generate concordance over selected word types 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. Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 9. Patterns reveal aspects of the texts’ thematic profile Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. 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
  • 14. Frequency data in table for mutual information Settings for sort order with swap 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. Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 15. 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
  • 16. Keyword list, Test file 1 Participants learn text statistics by observing results after changing settings 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 Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 17. 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
  • 18. 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
  • 19. WordSmith data converted into ATLASti formats Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 20. Submit texts and receive lists of word types by grammar class Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 21. 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
  • 22. 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
  • 23. Interaction and Speech Act Analysis Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 24. 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
  • 25. PMEST Identify word types for Actor which signal problems Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 26. 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
  • 27. PMEST Word types/ phrases which confirm problems Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 28. 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" Text structure: Introducing problem, evaluating Problem: I am referring to the issue of fraud, existing solutions, a negative evaluation is followed which is close to being institutionalised in key by a solution proposal. 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 Proposal: EU Whistleblower Rights: In the light of has spoken out, the institutions have an even the present lack of options open to employees of greater need to cover over their failings 
The the Communities who seek redress against fraud goes on regardless....It doesn't end there. institutional failings, the Convention may care to Beyond the competent authorities refusing to consider including a Communities whistleblower investigate even claims which are easily clause setting out the principle of the right of free checkable..., there have been several reports of speech where normal avenues have been attempts to intimidate witnesses
 blocked. Such a climate engenders fraud higher up the chain. Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 29. Thank you for your attention Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  • 30. Computer-Aided Qualitative Research Europe 7 & 8 Oct 2010, Lisbon For more information about our events, please visit: http://www.merlien.org