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Week 5: GEOG 315

by: Tiffany Hu

Week 5: GEOG 315 Geography 315

Tiffany Hu

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Explanation and Understanding in Geography
Class Notes
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This 3 page Class Notes was uploaded by Tiffany Hu on Monday November 2, 2015. The Class Notes belongs to Geography 315 at University of Washington taught by BROWN,MICHAEL P. in Fall 2015. Since its upload, it has received 35 views. For similar materials see Explanation and Understanding in Geography in Geography at University of Washington.


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Date Created: 11/02/15
Lecture  12  (10/26/15)   Validity     1. Face  Validity   a. It  makes  basic  common  sense   2. Criterion  Validity   a. Something  external  that  says  this  is  a  valid  measure   b. Is  a  standard  measure   c. “What  the  experts  think”   3. Construct  Validity   a. Does  measurement  fit  within  original  framework?   b. Being  true  to  your  principles  (including  all  you  believe  exist)   i. Gender:  M      F      (circle  one)  vs.  Gender:  M      F      Other      (Circle   one)   4. Content  Validity   a. Contains  all  content  needed  to  make  appropriate  results   i. Gender:  M      F   1. Missing  content  validity  because  there  is  no  “other”   option   Reliability   1. Between  researcher  and  researched   a. People  being  researched  feel  comfortable  with  researcher  and  feel   they  are  being  open  with  them   2. Between  researchers   a. Researchers  do  the  same  role  but  is  being  done  slightly  different  by   each  researcher   3. Between  researcher  and  self   a. Are  you  reliable  over  time?   Measurement  Quality   1. Accuracy   a. How  valid  measurement  is   2. Precision   a. How  finely  you  measure  something   i. Stopped  Time  @  12:01:53  (more  precise)   ii. 5  min  fast  @3:01  (more  accurate)     Lecture  13  (10/28/15)   Sampling   1. Logic  of  Probability  Sampling:  there  is  equal  likelihood  of  being  selected;   everyone  is  just  as  likely  to  be  sampled  as  anyone  else   a. Sample  types   i. Probability:  population  (or  universe)  is  all  the  possible   members  of  a  group   1. Enrolled  student  =  sample.  However  present  students   could  also  =  sample  but  exclude  some  people  (this   thinking  could  be  applied  in  different  ways,  depending   on  how  you  look  at  it)  trying  to  avoid  bias   b. Frame:  is  a  list  of  the  population  you  are  sampling  from  and  includes   everyone  or  is  a  really  close  approximation  (could  be  a  lot  of  bias  and   is  impossible  not  to  have  bias)   c. Statistics  refers  o  the  sample  error   d. Parameters  refers  to  the  population  error   e. Sample  =  n          Parameter  =  N   i. Reduce  bias  of  sampling  error  by  increasing  the  sample  size.   Also  the  more  heterogeneous  the  sample  the  less  bias  there  is.   1. If  population  =  homogenous  then  sampling  one  element   is  ok  and  saves  a  lot  of  time.  If  heterogeneous  then  not   okay  to  sample  just  one  element.   2. Probability  Sampling   a. SRS  (simple  random  sampling)   i. The  gold  standard  of  sampling   ii. Take  a  frame  and  make  sure  each  element  is  identified     Lecture  14  (10/30/15)   How  big  should  my  sample  be?   1. As  big  as  you  can  afford   2. However,  the  greater  the  homogeneity,  the  smaller  the  sample  size  can  be   Types  of  Sampling  Design   1. Probability   a. SRS   i. Simple  random  sample   ii. Each  element  must  have  the  same  likelihood  of  being  selected   iii. Randomly  generated  number  (or  sample)   b. Systematic  sample   i. 35/10  =  ~4   1. Parameter  =  35   2. Sampling  interval  (k)  =  4   3. Sample  =  10   ii. Random  (started  sampling  at  a  random  point)   th 1. Counted  class  by  4  and  every  4  person  would  stand  up   and  was  then  sampled  (started  counting  at  a  random   spot  in  the  room)   iii. Took  less  time  than  the  SRS  but  is  still  random   iv. Very  good  for  special  data   v. Periodicity:  your  sample  interval  picks  up  on  another  systemic   bias,  it  could  affect  your  sample   c. Stratified  Random  Sample   i. Decreases  possible  sampling  error  and  increases   representativeness   ii. Break  up  samples  into  subgroups  (want  to  make  sure  you  get   the  variation)   1. Gender  for  example  can  be  highly  unequal  when  it   comes  to  number  in  the  major.  Saw  there  are  200  total   and  150  (standard  deviation  0.2)  of  them  are  male  and   50  (standard  deviation  0.09)  are  female.  Since  there  is   such  a  big  disparity  among  the  two  groups,  it  would  be   best  to  take  10%  of  each  group  to  deter  from  under-­‐ representing  the  minority  group   iii. Probability  proportionate  to  size  (PPS)   1. Making  sure  you  don’t  under  or  over  sample  something   iv. Ensure  you  an  have  an  answer  to  what  you’re  asking   v. Have  to  know  of  all  variables  before  sampling   vi. You  stratify  when  you  have  unequal  proportions  and  you  want   to  make  sure  you  get  the  minority  represented  in  there  


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