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by: Melanie Maino


Melanie Maino

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Chapter 10 & 11 notes.
Introduction to Research Methods in Psychology
Brianna Stinebaugh
Class Notes
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This 3 page Class Notes was uploaded by Melanie Maino on Thursday March 31, 2016. The Class Notes belongs to PSYC314 at Towson University taught by Brianna Stinebaugh in Spring 2016. Since its upload, it has received 12 views. For similar materials see Introduction to Research Methods in Psychology in Psychlogy at Towson University.


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Date Created: 03/31/16
Chapter  10:  Between  Subject  Design:  Factorial  Designs   • Review  of  Between  Subjects:  Each  participants  is  only  receiving  one  level  of   the  independent  level;  different  participants  for  each  treatment  condition   • Factorial  Designs:  2  or  more  independent  variables  and  each  independent   variable  has  at  least  two  levels   o Independent  variables  and  factors  are  used  interchangeably   o Simplest  factorial  design  is  called  2  Factor  Experiment  because  you   are  only  dealing  with  two  factors  (or  2  IV)   ▯ 2  kinds  of  information   • 1-­‐  Main  Effect:  information  about  the  effects  of  each   independent  variable/  factor   • 2-­‐  Interaction:  seeing  if  one  independent  variable  has  an   influence  on  another  independent  variable  /factor   • Main  Effects:  the  effects  or  actions  of  one  independent  variable   o As  we  see  a  change  in  behavior  we  will  see  that  there  will  be  a  change   associated  in  value  of  independent  variable     o Number  of  independent  variables=  the  number  of  possible  significant   main  effects   o ANOVA=  analysis  of  variance  (more  than  two  variables)   • Interactions:  specifically  looking  at  relationships  that  may  exist  between   variables/factors   o if  effects  of  one  IV  depend  on  the  effects  of  another  IV=  interaction   o main  effect  of  1  factor  (IV)  altered  by  another  factor=  interaction   o Number  of  possible  main  interactions  depends  of  the  number  of  IV   ▯ Ex.  2  IV  =1  possible  interaction   o More  variables  =  the  number  of  possible  interactions  increases   o If  you  have  more  than  two  independent  variables  then  you  can  get  a   higher  order  interaction  (the  number  of  possible  interactions  will   increase)   o 3  factor  experiment   ▯ interaction  between  all  three  variables   ▯ also  possible  to  have  an  interaction  between  two  variables  and   not  the  third   ▯ ex.  success  in  the  class  room  (DV)  what  influences  it?-­‐-­‐>  pick   three  variables  (personality  of  prof.,  work  load,  &  teaching   style)   o ANOVA  will  be  run  for  significant  interactions   • Short  hand  notation  helps  us  describe  the  actual  design  that  we  are  using.   o 1-­‐  number  of  IV   o 2-­‐  number  of  levels  per  IV   • 2  factor  Experiment   o 2x2  (2  by  2  factorial  design)   o numbers  represent  the  factors  present  within  the  study   o numerical  value  represent  the  actual  number  of  levels  for  each  IV   • 2  factors   o 1st  factor=  two  levels   o 2nd  factor=  two  levels   o 4  treatment  conditions  present   • 2x3   o 2  factors  (2,3)   o treat.  condition=  6   • 5x4x2x3   o 4  factors  (5,4,2,3,)   o treat.  condition=  120   • 2x3x2   o 3  factors  (2,3,2)   o treat.  condition=  12   April  6   Chapter  10…   • Factorial  designs  should  be  as  simple  as  possible:   o 1-­‐  a  lot  of  participants;  the  more  variables/  levels  the  more  treatment   conditions  the  more  participants  you  will  need   o 2-­‐  more  treatment  conditions  the  more  the  #  of  possible  main  effects   and  interactions  increase     o 3-­‐  hard  to  identify  all  the  specific  interaction  ▯  so  your  statistical   analysis  becomes  harder   Chapter  11  Within  Subjects  Designs   • Within  Subjects:  when  each  participants  receives  all  treatment  conditions   • NEED  to  measure  each  dependent  variable  after  each  treatment  is  given   o Ex  noise  effects  on  concentration…DV=  concentration  IV=  noise  (quiet   like  the  library,  noisy  like  a  café)   • More  than  1  independent  variable  (factor)▯  factorial  design   • Within  subject  design  needs  less  participants  than  between  subjects   • Example…how  quickly  it  takes  to  identify  facial  expressions  from  a  picture?   o IV=  expressions  in  pictures  (happy,  sad,  angry,  embarrassed)   o IV=  sex  (females,  males)   o 4X2=  8  treatment  conditions   ▯ female,  happy   ▯ female,  sad   ▯ female,  angry   ▯ female,  embarrassed   ▯ male,  happy,     ▯ male,  sad   ▯ male,  angry   ▯ male,  embarrassed   • Mixed  Factorial  Design:  factorial  design  that  combines  one  factor  that  is   manipulated  as  a  within  subjects  design  and  a  second  factor  that  is   manipulated  as  a  within  subjects  design  and  a  second  factor  that  is   manipulated  as  a  between  subjects  design  (pre  determined  factor  like  age  or   gender)   o Example:  caffeine  effecting  men  &  women;  2  groups  one  women  one   men;  6  groups  differentiated  on  two  separate  dimensions  (caffeine   and  gender);  3X2  factorial  design  (6  treatment  cond.)      


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