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other ANOVAS (three way anova (analysis (Main effect of DOSE…

- - - - Dose
        
        1
        
        within subjects
        
        2 of each
      - Helper
        
        fairy or elf
        
        between subjects
      - Type of treat
        
        Mince pies
        
        within subjects
        
        Sherry
    - - time taken to deliver presents to the house in seconds
  - - - needs to be NOT signifigant - here :
        
        time taken to deliever presents after 2 sherry
        
        F(1,16),2.953, p=.105
        
        time taken after 1 mince pie
        
        F(1,16)=.204, p=.658
        
        Time taken to deiliever after 1 Sherry
        
        F(1,16)= 3.797,p=.069
        
        time taken after 2 mince pies
        
        F(1,16)=1.003,p=.331
    - - look at between- subjects effects
      - measure_1 table
      - yes there is a significant effect of the type of helper - main effect- F(1,16)=14.4, p=.002
        
        if we ignore the type of treat consumed and how many of these were consumed , elves and fairies differed in their delievery speeds-
        
        look at estimated marginal means - to interpret
        
        look at table 1. ----- measure3 one means and std error
        
        elf (M=22.16) fairy (M=26.02) , fairies took sig longer to deliever presents than the elves
    - - for the main effects and interactions of the other two within subjects factors
        
        type of treat- F(1,16)= 56.32, p<.001 there was a significant main effect of type of treat-ignore how many treats consumed and type of helper , deleivery time was slower after consuming certain types of treat
        
        no post hoc needed as only have two levels :
        
        mean delievery times higher after sherry (M=28.44) than mince pies (M=19.75) that is independent of the type of helper it took longer to deliever presents after having sherry than after mince pies (EMMS)
        
        interaction of type of treat and helper
        
        there was a significant interaction between the type of treat consumed and whether the helper was an elf or a fairy,
        F(1,16)=11.74, p=.003
        
        the effect of type of treat on delivery times was different for elves than for fairies
        
        look at emms- difference between treats different for fairies and elve
        
        delivery times= after eating mince pies similar for elves (M=19.8) than for fairies (M=19.69) thn after drinking sherry, when fairies (M=32.35) took longer than elves (M=24.52) to deliver presents
    - - there was a signifigant main effect of dose - F(1,16)=232.08, p<.001
      - when we ignore the type of treat and whether the helper was an elf or a fairy -delievery time was slower after consuming certain amounts of treats
      - means : men delievery times were greater after two treats (M=32.96) thn after one treat (M=15.22)
        
        loger to deliever presents after helpers had two treats than onl having one
      - INTERACTION> dose and helper
        
        There was a signifigant interaction between amount of treats consumed and whetehr tne helper was an elf or fairy F(1,16)=9.60, p=.007
        
        effect of amount of treats on delivery times different for elves than for fairies
        
        interaction emms - difference in delievery times was greater after having two treats in faireies than elves -
        
        Delievery times = after one treat similar times for elves (M=15.09) and fairies (M=15.35than after two treats , when f took (m=36.69) took longer than elves (M=29.22) to deliver presents
        
        interaction between type of treat and dose
        
        there was a sig intercatioi between type and amount of treats consumed on delivery times , F(1,16)=29.95, p<.001
        
        the effect of type of treats on delivery times different depending on amount of treats consumed
        
        difference indelievry times after having two treats compared with one treat greater for sherry than fro mince pies - delievru times more similar after one sherry (M=16.20) and one mince pie (M=14.24) than after two treats when delivery took longer after having sherry (M=40.67) than after having mince pies (M=25.25)
- - - - iv 1 - gender = independant groups 1
      - iv 2 = attractiveness - repeated measure
    - - of the repeated IV- needs to be non signifigant- w(2), .960, p=.708
        
        within subjects effect
    - - for the independent IV- for each level- attractive - F(1,18),077, p=.785, not signifigant so there is equal error of variences
      - Between subjects effect
    - - tests of within subjects effects
        
        look at the mian effect of looks and the interaction
        
        First line if ssphericity assumed or greenhouse-geisser if not - F(2,36)= 423.73, p<.001
        
        use the error df and the experimental df
        
        interaction F(2,36)=80.43, p<.001
        
        main effect is signifigant
        
        look at the main effect of gender
        
        BETWN SUBJECTS EFFECTS _ F(1,18)=.005, p=.946
      - also look at the means and descriptive stats- to see the differences- when we ignore all other variables ratings were differenct for attractive average and ugly dates (F(2,36)=423.73,p<.001)
      - look at estimates table for the differences - participants rated attractive actors (M-=82.1) more highly than average (M=67.7,p<.001) or ugly (M=55.82, p<.001) actors
      - pairwise comparisons table = the comparison of the different IVs rated average looking actors highly more attractive than the ugly actors (p<.001)
      - analysis of the interaction
        
        using the means create plots to see the interaction effect
        
        graph suggests that the male and female participants give similar ratings for average looking people- but men rate attractive people higher than women do, whereas women rate ugly actors higher than men do
        
        ratings of different levels of attractiveness were different in men and women (F(2,36)=80.43, p<.001)-look at graph to interpret
        
        both male and female interest decreases as attractiveness decreases , but this decrease is more pronounced for men
        
        breaking down the interaction
        
        could split file by gender
        
        run repeated measures ANOVA on looks for the male and female participants
        
        does each gender show a main effect of looks?
        
        could do Bonferroni corrected post hocs
        
        or run pairwise t tests for each level of looks and correct with boneferroni but this increases family wise error
        
        could also - split file by looks comparing male vs female for 3 levels - independent t tests- m and female in different groups sig = 0,05/ number of comparisons
      - write up
        
        is the second iv sig? yes interpret and if 2 -need means if 3 need contrasts p or ph
        
        is the interaction sig? yes interpret differences - take one IV do comparsions across the other IV differ within each level of the IV(a)
        
        is the first main effect sig? if yes interpret where differenves occur- if 2 conditions only need means- if 3 need further contrasts - planned or post hoc
        
        graph signifigcant finsings
        
        were assumptions met ? report stats even if NS homogeneity of varience or sphericity
        
        what type of ANOVA used? to address the question? predictions? ivs? how many and levels? DV?
- - - - must be categorical
      - DV= single continuous variable
    - - (+) ability to analyse interactions. ie drug A and drug B have no individual effects but could interact
        
        separate one-way anovas (one with drug A and B) would not look at interactions
      - (+)more powerful:
        
        adding factors can help reduce the error term by accounting for variance that would otherwise be unexplained
- - - - 48 students
      - IVs:
        
        Alcohol consumed (none/2/4)
        
        three levels #
        
        Gender: male and female ( 2levels)
      - DV
        
        Attractiveness rating
  - - - look at Levene's test
        
        non significant is good!
        
        F(5,42), 1.53, p=.202
      - Tests of between subjects effects
        
        look at the 2 iv lines
        
        Gender =F(1,42)=2.03, p=.161
        
        Alcohol: F(2,42), 20.07, p<.001
        
        'overall the amount of alcohol influenced attractiveness of the mate selected by participants, F(2,42)=20.07, p<.001). there was no main effect of the participants gender on the attractiveness of the selected mate (F(1,42) =2.03, p=.16).
      - Interaction: Tests of between subjects effects
        
        gender*alcohol : F(2,42), 11.91, p<.001
      - breaking down the main effect : with plots
        
        look at descriptives : the means !
        
        not every plot will be the same : not every group will be significantly different to the others - got to test this!
        
        breaking down the main effectwith planned and post hoc
        
        one tailed = planned - directional
        
        two tailed - non directional- post hoc
        
        levenes significant:
        
        Games Howell
        
        not sig? #
        
        different number of participants in each condition?
        
        Slightly
        
        1 more item...
        
        very
        
        1 more item...
        
        NO
        
        1 more item...
        
        IF 3+ CONDITIONS in the IV #
      - breaking down the interaction effect
        
        hints as to when its significant:
        
        Non-parallel lines- interaction graph - depends on the degree which they are not parrallel
        
        Crossing lines on an interaction graph
        
        doesn't ALWAYS mean significant #
        
        PLOTS
        
        line plots
        
        males attractiveness ratings overall after alocohol levels is lower than women
        
        Have to run separate independant measures T-Tests
        
        split by alcohol consumption- to compare men and women
        
        there was a gender difference when 4 pints were consumed (t(14)=-4.78, p<.001
        there was no gender difference when non alcohol was consumed (t(14)= 1.543, p=.145.
        there was no gender difference when 2 pints were consumed (t(14)= .876 , p=.396
        
        interaction conclusion:
        
        there was a signifigant interaction between the amount of alcohol consumed and the gender of the person selecting a mate, on the attractiveness of the mate selected (F(2,42)=11.91, p<.001).
        This indicates that male and female genders were affected differently by alcohol. specifically, there was a significant gender difference after 4 pints of alcohol consumption (t(14)=-4.78, p <.001).
        
        1 more item...
        
        look at the INDEPENDANT SAMPLES T TESTS
        
        need to apply boneferroni correction
        
        (0.5/3)
        
        significant gender difference- when drinking 4 pints - males have lower attractiveness ratings after 4 pints (m= 35.63, SD= 10.84) than women (m=57.5,SD=7.07)
  - - - if 2 conditions -only MEANS
      - If 3+ need further contrasts (planned or post hoc)
- - - - Personality , (lots of charisma, average charisma, no charisma)
      - LOOKS (attractive, average, ugly) 3 levels
      - 2x3 repeated anova
    - - mauchleys test: need to meet assumption for Looks? W=.90, X2 (2) =0.81, p=.667 - assumption met
      - Personality?W=.85, X2(2)=1.29, p=.524
      - looks *personality? W=.05, X2 (9) =22.76, p=.008
        
        Significant? so use corrected F test!
        
        if e=<.75 use the GG
        
        W=.05, x2(9)=22.76, p=.579
        
        If > .75 use Huynh-Feldt
    - - was there a significant main effect of looks?
      - Yes F(2,18)=66.44, p<.001
        
        when we ignore all other variables ratings different for attractiveness , average and unattractive dates
        
        LOOK AT EMMS and post hocs to interpret
        
        particpants gabe