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Comm106 Week 6 notes

by: Erica Evans

Comm106 Week 6 notes Comm106

Erica Evans
GPA 3.9

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Notes from 2/10/2016 and 2/12/2016
Communication Research Methods
Jennifer Pan
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This 3 page Bundle was uploaded by Erica Evans on Monday February 15, 2016. The Bundle belongs to Comm106 at Stanford University taught by Jennifer Pan in Fall 2016. Since its upload, it has received 36 views. For similar materials see Communication Research Methods in Communication Studies at Stanford University.

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Date Created: 02/15/16
Comm106   2/10/2016     Causal  explanations:       Why  are  women  paid  less  than  men?     • There  are  many  different  causal  explanations,  they  could  have  many  different   steps     • Break  them  down  into  intervening  variables  that  you  can  test  one  by  one     • You  can’t  just  prove  “sexism”     • You  can  test:  does  time  off  lead  to  lack  of  promotions?     • You  can  test:  do  people  have  notions  of  appropriate  gender  behavior     • You  can  test:  do  women  negotiate  with  less  aggression  than  men     • Let’s  say  childcare  responsibilities  are  the  reason  women  earn  less  than  men:     • Separate  the  units  on  the  dependent  variable,  and  look  at  effects  on   independent  variable     • A  hypothesis  is  a  testable  statement  about  the  empirical  relationship   between  an  independent  variable  and  a  dependent  variable.     • State  a  relationship  between  two  variables:  dependent  and  independent.   “Comparing”     • Be  “Specific”     • Make  an  assertion  about  what  will  change  if  we  adjust  one  value,  the   “direction”     • 1)  Comparing,  2)  Specific,  3)  Direction   • In  a  comparison  of  [units  of  analysis]  those  having  [one  value  on  the   independent  variable]  will  be  more  likely  to  have  [one  value  on  the   dependent  variable]  than  will  those  having  [a  different  value  on  the   independent  variable]     • In  comparison  of  women  those  have  more  childcare  responsibilities  will  be   more  likely  to  have  higher  salaries  than  will  those  having  less  childcare   responsibilities.       • Nominal,  dependent  variable:  cross-­‐tabulation     • Ordinal,  dependent  variable  cross-­‐tabulation     • Interval,  dependent  variable:  comparison  of  means       Men  shave  their  face  more  than  women     • Indpendent  variable  =  gender  (nominal)     • Dependent  variable  =  frequency  of  shaving  (ordinal)  –  never,  rarely,   sometimes,  often,  always     Cross  Tabulation:     • Make  a  table  to  show  the  value  for  each  combination  of  variables     • Females  that  shave:  never,  rarely,  sometimes,  often,  always   • Males  that  shave:  never,  rarely,  sometimes,  often,  always   • We  can  then  compare  horizontally     • The  cross  tab  shows  the  distribution  across  the  values  of  the  dependent   variable,  the  difference  between  the  independent  variables       Comparison  of  Means:     • Per  capita  milk  consumption  in  Kg/capita     • Countries  are  nominal,  and  the  average  per  capita  milk  consumption  is   continuous     • There  would  be  too  many  values  if  we  did  cross  tabulation,  so  we  just   compare  the  means       Graphing:     • Put  the  independent  variable  on  the  x  axis  and  independent  variable  on  the  y   axis     • Positive  relationship  =  both  variables  increase  together     • As  value  of  one  variable  increases,  the  other  decreases  =  negative   relationship   • Relationship  is  linear  if  the  points  make  roughly  a  straight  line     Comm106   2/12/2016       How  can  we  confirm  a  hypothesis?     • The  Null  hypothesis  is  the  skeptical  assumption   • We  write  the  null  hypothesis  as  H(sub)0   • The  alternative  hypothesis  –  H(sub)A     • Says  there  is  a  relationship  between  what  we  are  observing     • Either  we  reject  the  null  hypothesis  or  we  fail  to  reject  H0     • Be  careful  about  your  language!  If  you  can  reject  the  null,  you  cannot  say  you   know  the  alternative  hypothesis  is  true!     • What  if  we  make  mistakes?     • When  the  null  is  correct  and  we  reject  it,  it  is  type  one   •  When  the  null  should  be  rejected  and  we  do  not  reject  it,  this  is  type  2     • Type  1  error:     • Traditional  threshold  is  5%    -­‐-­‐  Given  that  the  null  hypothesis  is  true,  we  will   reject  it  with  a  probability  of  5%.     • The  P-­‐value  tells  us  what  is  statistically  significant       Statistical  Significance:     • When  people  say  something  is  statistically  significant,  that  might  not  be  large     • Women  are  .0002%  more  likely  than  men  to  watch  Fox  News     • Or  it  might  not  be  substantively  important:  like  women  have  a  better  sense  of   smell  than  men     • A  difference  is  not  statistically  significant    


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