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One-Way BG ANOVA. Andrew Ainsworth Psy 420 Obtained from www.csun.edu/~ata20315/psy420/One-Way\%20BG\%20 ANOVA .ppt. Effect Size. A significant effect depends: Size of the mean differences (effect) Size of the error variance Degrees of freedom Practical Significance

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one way bg anova

One-Way BG ANOVA

Andrew Ainsworth

Psy 420

Obtained from

www.csun.edu/~ata20315/psy420/One-Way%20BG%20ANOVA.ppt

effect size
Effect Size
  • A significant effect depends:
    • Size of the mean differences (effect)
    • Size of the error variance
    • Degrees of freedom
  • Practical Significance
    • Is the effect useful? Meaningful?
    • Does the effect have any real utility?
effect size1
Effect Size
  • Raw Effect size –
    • Just looking at the raw difference between the groups
    • Can be illustrated as the largest group difference or smallest (depending)
    • Can’t be compared across samples or experiments
effect size2
Effect Size
  • Standardized Effect Size
    • Expresses raw mean differences in standard deviation units
    • Usually referred to as Cohen’s d
effect size3
Effect Size
  • Standardized Effect Size
    • Cohen established effect size categories
      • .2 = small effect
      • .5 = moderate effect
      • .8 = large effect
effect size4
Effect Size
  • Percent of Overlap
    • There are many effect size measures that indicate the amount of total variance that is accounted for by the effect
effect size5
Effect Size
  • Percent of Overlap
    • Eta Squared
      • simply a descriptive statistic
      • Often overestimates the degree of overlap in the population
effect size6
Effect Size
  • Omega Squared
    • This is a better estimate of the percent of overlap in the population
    • Corrects for the size of error and the number of groups
our example eprs8540
Our Example EPRS8540
  • Eta Squared
  • Small .01
  • Medium .06
  • Large .14

Cohen (1977)

  • Omega Squared
  • Small < .06
  • Medium .06 - .15
  • Large > .15

Cohen (1977)

our example eprs85401
Our Example EPRS8540
  • There was no significant price difference among the three store types (F2, 9 = 3.12, P > .05, ).
references
References
  • Cohen, J. (1977). Statistical power analysis for the behavioral sciences. NY: Academic Press. Cited with regard to intepretation of omega-square.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences . Second ed., Hillsdale, NJ: Erlbaum.
  • Olejnik, S., & Algina, J. (2003). Generalized eta and omega statistics: Measured for effect size for some common research designs, Psychological Methods, 8, 434 – 447.
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