An Aid to Effect Size Manipulation

Applications

Regression Analysis : A simple regression analysis has R2 = 0.30.For a simple regression, the effect size is f2= R2/ (1- R2). Thus, the effect size is f2=0.43

t-test: Independent samples t-test showed that the scores of phonics class with N=25 ;Mean=85.04;SD=5.49 and the whole language class with N=25;M=81.16;SD=6.96 differed significantly (t=2.19,p<.05) with the phonics class having better scores. The effect size was 0.63.

Steps for Manipulation

The following are the procedures of some statistical test given as examples :-

1. Mean and Standard Deviation (pooled or standard ):

What is the mean of the experimental or follow-up group?

What is the mean of the comparison or baseline group?

Do these two groups have the same variance?

If yes, used the pooled standard deviation . If no, use the comparison or baseline group standard deviation.If you are not sure and have both, use the comparison or baseline version.

Given the above step, what is the standard deviation.

Calculate "observed" effect size.

ES(effect size)={(Mean exp/post) - (Mean comparison/pre) } / (Std. Dev. Pooled) Or, ES={(Mean exp/post) - (Mean comparison/pre) } / (Std. Dev. Comparison/pre)

Chi-Square

What is the Chi-square

What is the number of observations (n)

Compute "observed" effect size

ES = SQRT((4*(chi-square))/(n-(chi-square)))

Our Help

You will be assisted by our statisticians to perform the task of calculating the effect size needed in your dissertation. We will help you to understand what statistical test will be appropriate for your research study.

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