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Merriam ch 7 5.17.10

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Merriam ch 7 5.17.10

  1. 1. Merriam – Chapter 7 Mining Data from Documents
  2. 2. Mining Data from Documents <ul><li>In contrast to interview and observation data, documents are usually produced for reasons other than the research at hand. </li></ul><ul><li>Documents / Artifacts include: </li></ul><ul><ul><li>Symbolic materials (writing and signs) & non-symbolic (tools and furnishings) </li></ul></ul><ul><ul><li>Artifacts include objects in the environment </li></ul></ul>May 17, 2010 EDFN 506
  3. 3. Mining Data from Documents <ul><li>Public Records </li></ul><ul><ul><li>“ if an event happened, some record of it exits” </li></ul></ul><ul><ul><ul><li>Birth & Death records </li></ul></ul></ul><ul><ul><ul><li>Marriage licenses </li></ul></ul></ul><ul><ul><ul><li>U.S. Census documents </li></ul></ul></ul><ul><ul><ul><li>Police Records </li></ul></ul></ul><ul><ul><ul><li>Court Transcripts </li></ul></ul></ul>May 17, 2010 EDFN 506
  4. 4. Mining Data from Documents <ul><li>Educational Documents </li></ul><ul><ul><li>Parent involvement records </li></ul></ul><ul><ul><li>Notes home to parents </li></ul></ul><ul><ul><li>Memos to teachers </li></ul></ul><ul><ul><li>Policy statements </li></ul></ul><ul><ul><li>School bulletin boards </li></ul></ul>May 17, 2010 EDFN 506
  5. 5. Mining Data from Documents <ul><li>Personal Documents </li></ul><ul><ul><li>Refers to any first-person narrative that describes and individual’s actions, experiences, and beliefs </li></ul></ul><ul><ul><li>Highly subjective </li></ul></ul>May 17, 2010 EDFN 2010
  6. 6. Mining Data from Documents <ul><li>Popular Culture Documents </li></ul><ul><ul><li>Materials designed to entertain, inform, or persuade </li></ul></ul><ul><ul><li>Television, film, radio, newspapers, photography, political cartoons </li></ul></ul><ul><ul><li>Amount of data in popular documents is nearly infinite </li></ul></ul><ul><ul><li>“ Think small” when reviewing this category or documents [UbD] </li></ul></ul>May 17, 2010 EDFN 506
  7. 7. Mining Data from Documents <ul><li>Visual documents </li></ul><ul><ul><li>Film, video, and photography </li></ul></ul><ul><ul><li>Transition from etic to emic position as camera becomes the eyes of an insider’s perspective </li></ul></ul><ul><ul><li>Objectivity is not the goal only honest transparency (in contrast to p. 146) </li></ul></ul>May 17, 2010 EDFN 506
  8. 8. Mining Data from Documents <ul><li>Physical Materials / Artifacts </li></ul><ul><ul><li>Tools, implements, utensils, and instruments of everyday living </li></ul></ul><ul><ul><ul><li>Military “dog tags” </li></ul></ul></ul><ul><ul><li>“ mute evidence” </li></ul></ul><ul><ul><li>Migrates to quantitative as descriptive statistics and created from the frequency of some artifact occurrences </li></ul></ul><ul><ul><li>Longitudinal monitoring devices (time-lapse) </li></ul></ul><ul><ul><ul><li> </li></ul></ul></ul>May 17, 2010 EDFN 506
  9. 9. Mining Data from Documents <ul><li>Researcher-generated documents </li></ul><ul><ul><li>Documents produced for the researcher once the study has begun </li></ul></ul><ul><li>Using Documents in Qualitative Research </li></ul><ul><ul><li>Focus on the research question to narrow your search efforts </li></ul></ul>May 17, 2010 EDFN 506
  10. 10. Mining Data from Documents <ul><li>Emphasis on systematic data collection process </li></ul><ul><li>i.e. search for African American educational leadership data (historical) </li></ul><ul><li>First thing once document is obtained is to assess AUTHENTICITY </li></ul>May 17, 2010 EDFN 506
  11. 11. Mining Data from Documents <ul><li>Authenticity: </li></ul><ul><ul><li>History of document </li></ul></ul><ul><ul><li>Acquisition of document </li></ul></ul><ul><ul><li>Is document complete as original </li></ul></ul><ul><ul><li>Why was it produced? </li></ul></ul><ul><ul><li>Author? </li></ul></ul><ul><ul><li>How did the author create the document? </li></ul></ul><ul><ul><li>Creator’s bias? </li></ul></ul><ul><ul><li>Other documents available to “shed additional light” on current document </li></ul></ul>May 17, 2010 EDFN 506
  12. 12. Mining Data from Documents <ul><li>Primary vs. Secondary Sources </li></ul><ul><ul><li>Primary sources are those in which the originator of the document is recounting firsthand experience with the phenomenon of interest </li></ul></ul><ul><ul><li>Secondary sources are reports of a phenomenon by those who have not directly experience the phenomenon of interest </li></ul></ul>May 17, 2010 EDFN 506
  13. 13. Mining Data from Documents <ul><li>Coding / Content Analysis </li></ul><ul><li>Data Collection and Coding are often carried out by novices using protocols and trained to count units of analysis </li></ul><ul><li>“ The aim is to be systematic and analytic, but not rigid…” </li></ul>May 17, 2010 EDFN 506
  14. 14. Mining Data from Documents <ul><li>Limitations and Strengths of Documents </li></ul><ul><ul><li>Reflecting on the value of the document in the context of the research question </li></ul></ul><ul><ul><li>Personal accounts or official documents may only provide one side of the story </li></ul></ul><ul><ul><li>May not be in a form that is useful if created for other purposes </li></ul></ul><ul><ul><li>Authenticity and accuracy difficult to determine </li></ul></ul>May 17, 2010 EDFN 506
  15. 15. Mining Data from Documents <ul><li>May prove preferable to interviews and observations </li></ul><ul><ul><li>Unobtrusive </li></ul></ul><ul><ul><li>Non-reactive (physical traces) </li></ul></ul>May 17, 2010 EDFN 506
  16. 16. Mining Data from Documents <ul><li>Online Data Sources </li></ul><ul><ul><li>Difficulty is found in realizing who is not included because of issues of access </li></ul></ul><ul><ul><li>For the most part, with caution, online sources can provide an extension of the three methods of qualitative data collection </li></ul></ul><ul><ul><li>Real vs. online personalities (Goffman’s idyllic presentation) </li></ul></ul>May 17, 2010 EDFN 506
  17. 17. Mining Data from Documents <ul><li>Transiency of Web documents </li></ul><ul><ul><li>Links disappear over time, although there is always an electronic footprint </li></ul></ul><ul><ul><li>Include these concerns in any analysis, description, or discussion of using online data sources </li></ul></ul>May 17, 2010 EDFN 506
  18. 18. Mining Data from Documents <ul><li>Effects of the Medium on Data Gathering </li></ul><ul><ul><li>Important benefit is that people may be more willing to communicate electronically if the subject matter requires a veil of privacy </li></ul></ul><ul><ul><li>Greater likelihood that remote informants can be reached </li></ul></ul><ul><ul><li>THE RESEACHER’S RESPONSIBILITY MUST BE TO DESCRIBE TOOLS AND METHODS, AS WELL AS THEIR POTENTIAL EFFECTS ON THE WORK. </li></ul></ul>
  19. 19. Mining Data from Documents <ul><li>Ethical Issues </li></ul><ul><ul><li>Stanley Milgram </li></ul></ul><ul><ul><ul><li>1) </li></ul></ul></ul><ul><ul><ul><li>2) </li></ul></ul></ul><ul><ul><ul><li>3) </li></ul></ul></ul><ul><ul><li>Protection of participants identity and original data </li></ul></ul><ul><ul><li>Who “owns” the data? </li></ul></ul>May 17, 2010 EDFN 506
  20. 20. Mining Data from Documents <ul><li>Ethical Concerns: </li></ul><ul><ul><li>Obtain informed consent </li></ul></ul><ul><ul><li>Ensure confidentiality and security of information </li></ul></ul><ul><ul><li>Predetermine what is public and what is private </li></ul></ul><ul><ul><li>Debriefing procedures for participants (Milgram) </li></ul></ul><ul><ul><li>Participants vs. subjects </li></ul></ul>May 17, 2010 EDFN 506

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    May. 1, 2012


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