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Intro
Page 1
Effect Size Determination ProgramLast Updated 07/25/2001byDavid B. Wilson, Ph.D.University of Maryland, College Park
Introduction
The Effect Size Determination Program is for use with the book Practical Meta-Analysis written by Mark W. Lipsey and David B. Wilson and published by Sage. The creation of this program was supported by HSRI.
This program computes standardized mean difference effect sizes (d) and the correlation coefficients (r) from summary statistics, such as means and standard deviations, t-tests, frequencies, etc. It is useful during the coding phase of a meta-analysis for converting reported results into an effect size index.
To use, "click" on the "Main Menu" button with the mouse. From the Main Menu, select the desired transformation, again by "clicking" on the approrpiate button. Enter the requested data in all cells that are shaded in yellow. The effect size is computed automatically when all necessary data cells are filled in.
Intro
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Intro
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Intro
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Menu
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Main Menu
Menu
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Menu
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Means & SDs
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Means and Standard Deviation, Post-Treatment Scores
Treatment Group Comparison GroupMean = Mean =
SD = SD =n = n =
d = r =
Means & SDs
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Means & SDs
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Means & t-test
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Means and t-test, Post-Treatment Scores
Treatment Group Comparison GroupMean = Mean =
n = n =
t-value = d = r =
Means & t-test
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Means & t-test
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Mean Gain Scores
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Mean Gain Scores and Gain Score Standard Deviation
Treatment Group Comparison GroupMean = Mean =
SD = SD =n = n =
Pre & Post-Test Scores Correlation =
d = r =
Mean Gain Scores
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Mean Gain Scores
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t-test (Independent)
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Independent t-test, No Means Reported
t-value = d = Treatment n = r =
Comparsion n =
t-test (Independent)
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t-test (Independent)
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t-test (p-value only)
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Independent t-test, p-value only
p-value = d = df = r =
Note: This will always return a positive value
t-test (p-value only)
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t-test (p-value only)
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t-test (Dependent)
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Dependent t-test, No Means Reported
t-value = d = n (pairs) = r =
r for paired values =
t-test (Dependent)
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t-test (Dependent)
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Proportions1
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Proportion or Frequency (Dichotomous)
Logit Method Probit MethodFrequency with Positive Outcome d = d =
Treatment Group Successes = r = r = Treatment Group n =
Control Group Successes = Control Group n =
Logit Method Probit MethodProportion with Positive Outcome d = d =
Treatment Group = r = r = Control Group =
Logit = logged odds-ratio
Proportions1
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Proportions1
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Proportions2
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Proportions (Ordinal Polychotomous)
Treatment Group Comparison Group Tx Group n =Values Percent Values Percent
0 0 Comp Grp n =1 12 2 Tx Mean =3 3 Tx SD =4 45 5 Comp Mean =6 6 Comp SD =7 78 8 d = 9 9 r =
Proportions2
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Proportions2
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Frequencies
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Frequencies (Ordinal Polychotomous)
Treatment Group Comparison Group Tx Mean =Values Frequency Values Frequency Tx SD =
0 01 1 Comp Mean =2 2 Comp SD =3 34 4 d = 5 5 r = 6 67 78 89 9
Frequencies
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Frequencies
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F-test (k=2)
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Oneway ANOVA with Two Groups (k=2)
F-value = d = Treatment n = r =
Comparison n =Note: This will always return a positive value
F-test (k=2)
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F-test (k=2)
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F-test (k>2)
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Oneway ANOVA with Three or More Groups (k>2)
Indicate MSw =Means n Group Type*
Method 1:d = r =
F-value =
Method 2:d = r =
Group Type: 1 = treatment group, -1 = comparison group, 0 = other
F-test (k>2)
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F-test (k>2)
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Oneway ANCOVA
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Oneway Analysis of Covariance
Treatment Group Comparison GroupMean = Mean =
MS error = d = df error = r =
r* =
r* = correlation between the covariate and the dependent measure
Oneway ANCOVA
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Oneway ANCOVA
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Chi-Square
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Chi-Square (df=1)
Chi-Square = d = r =
Note: This will always return a positive value
Total N =
Chi-Square
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Chi-Square
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Chi-Square (p-value only)
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Chi-Square, p-value only (df=1)
p-value = d = r =
Note: This will always return a positive value
Total N =
Chi-Square (p-value only)
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Chi-Square (p-value only)
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Correlation
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Correlation and Mean Difference Effect Size Conversion
r = 0.23 d = 0.4727
d = 0.47 r = 0.2288
Correlation
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Correlation
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Weighted Mean
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Weighted Mean
Means n Weighted Mean =
Weighted Mean
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Weighted Mean
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Pooled SD
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Pooled SD
SDs n Pooled SD =
Pooled SD
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Pooled SD
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