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

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

  1. 1. Computer-Aided Qualitative Research Europe 7 & 8 Oct 2010, Lisbon For more information about our events, please visit: http://www.merlien.org
  2. 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. 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. 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. 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. 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. 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. 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. 9. Patterns reveal aspects of the texts’ thematic profile Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  10. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. WordSmith data converted into ATLASti formats Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  20. 20. Submit texts and receive lists of word types by grammar class Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  21. 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. 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. 23. Interaction and Speech Act Analysis Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  24. 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. 25. PMEST Identify word types for Actor which signal problems Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  26. 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. 27. PMEST Word types/ phrases which confirm problems Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  28. 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. 29. Thank you for your attention Brit Helle Aarskog, textUrgy AS & University of Bergen, October 2010
  30. 30. 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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