One way bg anova
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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

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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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