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Lecture

Lecture 3

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Department
Psychology
Course
PSY100H1
Professor
Waggoner Denton
Semester
Fall

Description
Lecture 3 (September 18 , 2012) th Morad Moazami The  test  on  October  11  is  multiple  choice.  It  will  be  60  multiple  choice  questions,  and  there  will   be  ne  hour  and  thirty  five  minutes  to  complete  the  exam.     Descriptive  vs.  Correlational  Studies:     Descriptive  Studies:     These  studies  involve  observing  and  classifying  behavior.  For  example,  Shriley  Brice  Heath  in   the  1970s,  went  and  stayed  with  300  families  over  a  period  of  time  and  found  that  middle  class   tended  to  include  their  children  in  their  daily  relationships  more  than  the  lower-­‐class  families.   And  this  was  reflected  in  the  middle-­‐classes  grades  in  school  later  on.     Festinger  and  a  number  of  different  psychologists  in  1956  wanted  to  study  cognitive   dissonance.  That  means  that  if  your  attitude  doesn't  mash  your  behavior,  you  will  think  two   things  that  are  hard  to  believe.  So  they  went  and  stayed  with  this  cult,  called  “The  Seekers”  and   their  leader  was  Marian  Keech.  They  believed  that  on  December  aliens  would  come  down,  and   Keech  would  get  messages  from  this  alien  race,  and  they  believed  that  the  world  would  end.  So   the  psychologists  embedded  themselves  within  this  group,  because  they  were  really  interested   in  what  would  happen  when  this  spaceship  wouldn't  come.  The  world  didn't  end.  Flying  saucers   didn't  come,  and  they  didn't  stop  believing,  instead,  Mrs.  Keech  said  that  she  had  received  a   message  from  the  aliens,  and  that  because  of  their  faith,  they  had  saved  the  universe.  So  they   believed  even  more  strongly  in  their  religion.     In  psychology,  these  kinds  of  studies  are  usually  done  in  the  first  steps  of  research  or  they  are   part  of  a  larger  research  project,  so  these  dissonance  project  people  had  this  chance  to  go  out   in  the  real  world  and  find  cognitive  dissonance  in  real  life.  So  these  steps  add  to  more  rigorous   testing.     Correlational  Studies:     In  correlational  studies,  we  are  specifically  examining  how  variables  are  related  to  one  another.   This  is  a  kind  of  thing  we  do  in  an  experiment.  The  only  difference  is  that  in  correlational   studies,  the  researcher  isn't  manipulating  any  of  the  variables.  For  example,  there  is  research   that  has  gotten  a  lot  of  media  attention  that  links  depression  and  cellphones  to  adolescence,   saying  that  cellphones  and  young  adults  are  related  to  teens  being  more  depressed.  These  are   correlational  studies,  because  they  obviously  aren't  manipulating  these  kinds  of  things.  What  is   unclear  here  is  if  the  kids  who  have  problems  sleeping  to  begin  with  go  to  their  phones  to  try  to   connect  to  other  people,  etc.  It  is  unclear  what  variable  is  cuasing  changes  to  other  variables.   We  cant  say  that  cellphone  use  causes  depression  in  teens.  We  can  just  say  that  teenagers  that   are  more  depressed  use  their  phones  more  often.     Another  example  is  that  self-­‐esteem  is  linked  to  academic  success  and  vice-­‐versa.     Correlational  studies  allow  researchers  to  make  claims  about  associates  between  variables,  but   not  causal  claims.     You  cannot  make  claims  that  one  variable  is  causing  changes  in  another  variable.     The  Third  Variable  Problem  and  Confounds:      The  third-­‐variable  problem:  Is  specific  to  correlational  research,  because  it  arises  when   researchers  cannot  manipulate  the  variable  they  believe  is  causing  changes  in  another  variable.   For  example,  they  find  out  that  children  who  attend  pre-­‐school  have  better  reading  skills,  but   there  could  also  be  a  third  variable  that  is  explaining  changes  in  what  we’re  looking  at.     Confounds  are  thought  in  the  context  of  an  experiment.  You  can  think  of  it  as  a  third  variable   you  are  not  particularly  studying.  It  is  something  else  aside  from  your  independent  variable  that   is  linked  to  your  experiment  –  they  provide  alternative  explanations  to  the  changes  in  your   work.  It  is  a  bad  thing,  because  it  is  between  your  experimental  changes.  For  example,  you’re   interested  in  alcohol  in  regards  to  driving.  You  have  a  group  of  drunk  people  and  non-­‐drunk   people,  and  because  you’re  worried  about  drunk  people  driving,  you  give  them  smart  cars,  and   you  give  the  non-­‐drunks  SUVS.  This  is  a  weak  study,  because  you  have  confounds  (another  type   of  variable:  the  type  of  car  they  are  driving)  that  changes  the  experiment  entirely.     Good  Research  Requires  Data  That  Is…     Good  research  requires  data  that  is:  Accurate,  valid,  and  reliable.     Accuracy:     Whatever  it  is  you  are  measuring  must  be  accurate.     You  have  two  kinds  of  error:  Random  error  and  systematic  error.     Random  error,  for  example,  is  when  your  timer  is  working,  but  you  make  minor  errors  in   regards  to  timing  an  experiment,  for  example,  but  in  the  end,  it  all  cancels  out  ,  because  some   will  be  too  long,  some  too  short,  and  therefore,  it  all  equals  out  in  the  end,  and  won’t  really   affect  the  experiment.  Random  error  is  typically  not  so  bad.  You  want  to  be  as  accurate  as   possible,  but  random  error  is  not  as  bad  as  systematic  error,  because  that  way,  there  is  a   problem  with  one  of  your  variables.  So  for  every  single  trial,  of  for  example  timing  an   experiment,  if  the  clock  doesn't  work,  it’s  not  going  to  correct  itself  out  with  multiple  trials  like   random  error  does.     Valid:     validity  refers  to  the  extent  to  which  the  collected  data  address  the  research  hypothesis  in  the   way  intended.  It  is  the  question  of  whether  you’re  measuring  what  you  mean  to  measure.       Our  question  is  whether  our  processing  speed  increases  over  age.  So  our  hypothesis  is  that   university  students  have  faster  reaction  times  in  the  Stroop  test  compared  to  elementary   students.  So  we  take  data  from  university  students  and  we  take  date  from  elementary  school   students.  But  if  our  question  was  whether  university  students  enjoy  completing  the  Stroop  test   more  as  they  age,  then  our  question  would  be  whether  anyone  would  enjoy  doing  the  Stroop   test.       The  point  is  that  
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