1. + CATPAC & WordStat Anne D. Sito & Erin Sonenstein COM 633: FA 09

2. + CATPAC

3. + Overview of CATPAC Designed to recognize frequently used words in text Identifies and groups patterns of similar words Provides output of clustering algorithms, perceptual maps, and interactive clustering

4. + Data Preparation: Text

5. + 1. Convert document into.txt file

6. + 2. Inputting Data

7. + 3. Select Text File You Want to Analyze

8. + 4. Select “Make Dendrogram”

9. + 5. Initial Output Screen

10. + 6. Output Data Screen

11. + 7. Output: Dendrogram

12. + 8. Data Presented in ThoughtView 2D

13. + 9. Data Presented in ThoughtView 3D

14. + 10. Thought View 3D (Rotated)

15. + Discussion and Limitations +’s Found words like “you”, “you’ll”, & “and” to be the most used in this text. Examines relationships between words based on proximity in the text. -’s Words are measured based on frequency, not importance. Focuses less on what words “mean” or how they fit together based on dictionaries.

16. + WordStat http://www.provalisresearch.com/wordstat/wordstat.html

17. + Overview of WordStat  Content Analysis Module for SIMSTAT  Specifically designed to process textual information geared for open-ended data which includes: journal articles, speeches, electronic communication, interviews, etc.  Has existing dictionary library and can also run analyses from new dictionaries built by the user  Can perform statistical analyses (i.e., factor analysis, word frequencies, multiple regression, etc.)  KWIC: Key Word In Context tables are available for any included or not included word or word pattern

18. + Data: Comparing Reviews of the Book on Amazon.com Between Men and Women

19. + 1. Create a Text File

20. + 2. Input Text File to WordStat

21. + 3. Define Your Variables

22. + 4. Running the Analysis

23. + 5. Existing Dictionary Was Not Relevant for Our Data

24. + 6. New Dictionary Available Online!

25. + 7. (Free) New Dictionary Download

26. + 8. Import New Dictionary; Maintain Exclusion List

27. + 9. Level 1 Analysis

28. + 10. Level 2 Analysis

29. + 11. Overall Frequencies

30. + 12. Gender Differences

31. + 13. Dendrogram

32. + 14. Clustering

33. + 15. 3-D Figure of Output

34. + 16. Concurrence Matrix

35. + 17. KWIC by Gender

36. + 18. Words by each Text Case

37. + 19. Word Count Category Frequency

38. + 20. Aggression Example

39. + 21. Limitations: Terrific=Anxiety?

40. + Discussion & Limitations Allows multiple independent variables Dictionaries may not always be complete Words in.txt file must be be spelled correctly Could not distinguish between quotes from the book and original thoughts May not account for different usage of certain words, (e.g., combating, terrific)

41. + Any Questions? Thank You!