1 / 171

Cultural Research

Cultural Research. Západočeská Univerzita (ZČU) James Boster. Sources of Anthropological Data. Západočeská Univerzita (ZČU) James Boster. Artifacts. Documents. Behavioral Observation. Participant Observa tion. Interviews. Systematic Data Collection. Západočeská Univerzita (ZČU)

lucine
Download Presentation

Cultural Research

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Cultural Research Západočeská Univerzita (ZČU) James Boster

  2. Sources of Anthropological Data Západočeská Univerzita (ZČU) James Boster

  3. Artifacts

  4. Documents

  5. Behavioral Observation

  6. Participant Observation

  7. Interviews

  8. Systematic Data Collection Západočeská Univerzita (ZČU) James Boster

  9. Goal • Data for the quantitative comparison & aggregation of individual responses. • Information to build a data matrix.

  10. Basic Concepts and Definitions Západočeská Univerzita (ZČU) James Boster

  11. Guiding Metaphor • Data sets are spaces with a Cartesian coordinate system. • Variables are the space’s dimensions. • Cases are points in the space or the endpoints of vectors from the origin. • Data place all points on all dimensions.

  12. Array • An ordered arrangement of data elements. • a vector is a one-dimensional array • a matrix is a two-dimensional array

  13. Vector • A magnitude and a direction in multidimensional space. • A one-dimensional array.

  14. Matrix • A set of vectors which define a multidimensional space. • A two-dimensional array with more than one row and column.

  15. Matrix • columns = dimensions = variables • rows = vectors = points = cases

  16. Indices/Subscripts • Numbers which specify the row and column of a cell in a matrix.

  17. Conventions • Matrices have m rows and n columns. • The variable i indexes the rows, and the variable j indexes the columns. • If the matrix is stored in M, each cell is indexed as M[i,j]; with row subscript first and column subscript second.

  18. Conventions • Matrices are typically accessed the way we read a page in a book. • first, left to right across each line. • next, top to bottom down the page.

  19. Types of Matrices • Square vs. rectangular • equal or unequal number of rows and columns. • Symmetrical vs. asymmetrical • either equal or unequal to its transpose. • Similarity vs. distance • high values are either close or far.

  20. Square Matrix

  21. Rectangular Matrix

  22. Symmetrical Matrices

  23. Asymmetrical Matrices

  24. Similarity Matrix

  25. Distance Matrix

  26. Structured elicitation • Similarity judgment • Freelists • Identification • Frame substitution tasks • Ranking and rating

  27. Domains • Facial expression of emotion • Color classification • Acts of destruction • Emotionally evocative situations • Locomotion

  28. Similarity Judgments

  29. Why elicit similarity judgments? • The judgment of similarity and difference is a fundamental cognitive process that other cognitive acts of cultural interest depend on: • same/different judgments • categorization and classification • propositional knowledge • decision making • theory (or cultural model) construction

  30. Why elicit similarity judgments? • Can give quick overview of a domain • A’ara personality descriptors (White) • as map

  31. Why elicit similarity judgments? • Can give quick overview of a domain • A’ara personality descriptors (White) • as map • as cluster diagram

  32. Why elicit similarity judgments? • Can give quick overview of a domain • A’ara personality descriptors (White) • as map • as cluster diagram • students' gender role terms (Holland and Skinner) • males

  33. Why elicit similarity judgments? • Can give quick overview of a domain • A’ara personality descriptors (White) • as map • as cluster diagram • students' gender role terms (Holland and Skinner) • male map • female map

  34. Why elicit similarity judgments? • Can reveal differences between groups • Atlantic fish (Boster & Johnson) • novice student map

  35. Why elicit similarity judgments? • Can reveal differences between groups • Atlantic fish (Boster & Johnson) • novice student map • expert recreational fisherman map

  36. Why elicit similarity judgments? • Can reveal similarities between groups • personality descriptors • A’ara • Oriya • US.

More Related