stats

stats

one anova
1. compares within and between variance
  1. f-ratio
2. to report
  1. f(b,w)=...MSE=.... P<.05
    1. use smallest p value possible
3. compares 2 or more means
4. to compute
  1. factor=IV
    1. stats >Descriptive and homogeneity
      1. explore histograms
Post-Hoc
1. BONFERRONI
  1. 0.05/n
    1. Family wise error rate 1-(1-0.05)xn
2. Tukeys
  1. it tests all conditions
    1. click post hoc
3. priori
  1. contrast
    1. for one way anova
      1. eg: 2 -1 -1 to opposite signs to compare
        or 0. 1 -1
        not interested in 0
REPEATED MEASURES ANOVA
1. add, define
  1. if significant then paired T tests using Bonferroni adjustments
    1. if it is one way
      1. 1 Dv ,within participants
2. if it is two way
  1. Analyse >GLM>
    1. This is to get interaction plus main effect
      1. Repeated measures again choose one factor > define > choose that you are interested on for the other level
        FOR SIMPLE MAIN EFFECT
      2. Check sphericity if Maunch is significant use Hyung feldt
Two way ANOVA
1. interaction
  1. The effect one iv varies according to the other iv
    1. compares all cell meas
2. simple main
  1. one iv has an effect on the dv in one particular conditin of the dv
    1. compares all cell means in one condition
      1. to compute
        analyze
        GLM
        univairate
        option descriptives = Marginal means
        paste syntax
        for simple main effect
        add compare to syntax
        /EE: eg compare (sex)
        look at univariate test
3. main effect
  1. an IV has some effect of the Dv
    1. compares marginal means for each condition
t test
1. uses do compare two means
  1. independent
    1. compares two unrelated samples
      1. between participants
  2. paired sample
    1. related
      1. within participants
  3. one sample
    1. compare one sample to pop mean
2. assumptions
  1. normally distribution
    1. Homogeneity
      1. if Levene's is significant = not homogenous..
3. t (DF)=2.56 , p , . 05
  1. if not significant use actual figure
4. To compute
  1. Analyze
    1. Descriptives
      1. explore
  2. compare means
    1. paired sample meand
      1. within
  3. compare means
    1. independent t test
      1. Between
        test variable = DV
  4. if you want one level only eg specidic role
    1. split file
      1. organize by group > role
    2. select cases
      1. if role =1
3 WAY INTERACTION
1. GLM> univariate add 3 factors
  1. if significant look for interaction effects
2. L-matrix
  1. remember the order
  2. test results
  3. look at custom Hypothesis test
MIXED MODEL
1. Analyze-GLM>repeated measures
  1. Within factors add numbers of levels
    1. Define
      1. add Between
        see test of within and test of between ? look at each separate error
2. Follow up
  1. test interaction between 2 at a level of third factor
  2. simple main effect
    1. when you want to compare two levels from diff iv eg male patients select cases if gender = 1 if patients = 1
      1. if I want to compare eg male patients and female patients
        split file compare groups and select if role = 1

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