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How does gender affect participation in “Techy” or “Fuzzy” at Stanford

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How does gender affect participation in “Techy” or “Fuzzy” at Stanford Powered By Docstoc
					    How gender affects
 participation in “Techy” or
“Fuzzy” Classes at Stanford
          Kaitlin Asrow
           Aron Hegyi
            Katrina Li
          Kate Kourlis
         Project Goal
We are trying to see if there is a
difference in the amount men and women
participate in class according to whether
the class is perceived as “techy,” “fuzzy,”
or “neutral” by male and female Stanford
students.
        Group’s Predictions
 Men would answer more in classes rated
  as “techy” by both men and women.
 Women would answer more in classes
  rated as “fuzzy” by both men and women.
 There would be equal gender participation
  in classes rated as “neutral” by men and
  women.
      Method Used to Determine
     Distinctions between Classes
 Surveyed Language and Gender class students,
  asking them to rank the observed classes on a
  scale from 1-7. 1 being fuzziest and 7 techiest.
 Then, taking the averages separately for men’s
  and women’s responses, we ranked the classes
  as perceived “fuzzy,” “techy,” or “neutral.”
 We determined the results that fell between 3.5
  and 4.5 to be “neutral” classes, which are
  neither distinctly “fuzzy” or “techy.”
     Fuzzy, Techy or Neutral
 Average of all numbers given
 Fuzzy 0 - 3.5
 Neutral 3.5 - 4.5
 Techy 4.5 - 7
                           FUZZY
   History of Ancient Empires        Historical linguistics
   Huck Finn & American Culture      Film
   International Politics-Iran       Feminist studies
   Humanities-Hamlet                 CASA183D: Border Crossings
   PoliSci-Post War                  Theories of Film Practice
    Reconstruction                    Intro. To Queer Theory
   Sociology-Race and Ethnic         Intro to Social Stratification
    Relations                         Modernist Russian poetry
   War in the Modern World           International relations
   Japanese History                   Urban Studies
   IHUM: Myth and Modernity          Political Science
                            TECHY
   Physics for Non Majors              Chemistry
   MS&E - Stochastic Modeling          Engineering-Advanced
   Engineering-Intermediate            Probability and Statistics
   discrete mathematics                computer science-Intermediate
   Biology-Intro                       Computer Science-Advanced
   Mechanical Engineering              MS&E (Finance)
   mathematical logic                  ME (Product Design)
   Biochemistry                        Math-Intermediate
   Engineering Statistics              E50 Intro to MatSci
   Material Science & Engineering      CS106a Section
   Intro to Statistics
               Neutral
 Econ102b - Econometrics
 Linguistics (Lexical Semantics)
 CS 247: Human Computer Interaction
  Design
          Differences in opinion
   Women fuzzy; males           Females neutral;
    neutral                       males fuzzy
    – Lingustics-                 – Intro to Wilderness
      Intermediate                  Skills
    – Linguistics Seminar        Females neutral;
    – Linguistics-Beginning       males techy
                                  – Econ102b -
                                    Econometrics
      STUDENTS                 COMMENTS

      Techy      Fuzzy         Techy   Fuzzy

Men   1238       198     Men   207     225

Women 735        228     Women 79      159
Null Hypotheses

In Techy classes, the proportion of comments from males should equal
the proportion of male students.

In Fuzzy classes, the proportion of comments from males should equal
the proportion of male students.

In Techy classes, the proportion of comments from females should equal
the proportion of female students.

In Fuzzy classes, the proportion of comments from females should equal
the proportion of female students.
Null hypothesis can be rejected (P < .01) in all four
cases.

There is a significant difference between the proportion of male or female
students in each category of class (Techy and Fuzzy) and the proportion of comment
that each gender makes.
                          Techy Classes

80%
                    72%
70%
      63%
60%

50%

40%                                                37%

30%                                                               28%

20%

10%

0%
            Male                                         Female

                   Student Proportion   Comment Proportion
                          Fuzzy Classes

70%

                    59%
60%
                                                   54%
50%   46%
                                                                  41%
40%

30%

20%

10%

0%
            Male                                         Female

                   Student Proportion   Comment Proportion
                          Linguistics (Fuzzy/Neutral)

70%
                                 60%
60%                56%

50%
                                                                44%
                                                                               40%
40%

30%

20%

10%

0%
                         Male                                         Female

                                Student Proportion   Comment Proportion




      In the Fuzzy/Neutral classes, the data are not statistically significant.
          Interesting Findings
 Expectations/disbelief
 Impossibility of classifying linguistics
    – Presuppositions
  Possible Flaws of the Study:
Why do we find males participating
more than females in both “techy”
      and “fuzzy” classes?
   Internal validity
    – Data collection methods
        Some people only recorded every other comment;
         this could have affected the numbers of
         female/male speakers.
    – Subconscious selection
        Maybe males spoke more slowly or their comments
         were shorter, making their tokens easier to collect
         and record.
                       So what?
   Stanford relevance
    – Why do we have the terms “fuzzy” and
      “techy” here at Stanford?
        Fascination with classification
          – Tendency to label/identify ourselves
        Binary view
          – Seeing things as black or white
        Hierarchy
          – Funding issues (how much University affects these
            stereotypes)
THE END

				
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