# Effect Size Calculators

Refer to this page for formulae and citations.

Two groups ANOVA, OLS & HLM
One-sample Partial eta-squared (Fixed effects)
Independent-samples R-squared (OLS)
Paired-samples Intraclass Correlation Coefficient
Odds/risk/absolute ratios & NNT HLM / multilevel Pseudo R-squared's

Developed by James Uanhoro, a graduate student within the Quantitative Research, Evaluation & Measurement program @ OSU

I have run out of resources to sustain fitting the multilevel models, so for now, the ICC and multilevel R-squared sections are down.

All other components of this page have very low computational cost, so they will continue to function for the forseeable future.

## One-sample t-test

### Inputs

 Sample mean: Population mean: Sample standard deviation: Sample size: Confidence Interval: %

### Results (CI using noncentral t distribution)

 Cohen's d: Lower limit on d: Upper limit on d:

## Independent-samples t-test

### Inputs

Sample 1Sample 2
Mean: Mean:
Standard deviation: Standard deviation:
Sample size: Sample size:
Confidence Interval: %

### Results (CI using noncentral t distribution)

 Hedges' g (Unbiased): Lower limit on d: Conversion from g to r: Upper limit on d:

## Paired-samples t-test

### Inputs

Sample 1Sample 2
Mean: Mean:
Standard deviation: Standard deviation:
Number of pairs: r:
Confidence Interval: %
AverageRepeated measures

### Results (CI using noncentral t distribution)

 Hedges's g - average (recommended): Lower limit on d: Hedges's g - repeated measures: Upper limit on d:

## Odds/risk/absolute ratios & Number needed to treat

### Inputs

 Outcome Frequency Yes No Treatment Control Method (Odds-ratio): Median-unbiased estimation (mid-p)Conditional maximum likelihood estimation (Fisher)Unconditional maximum likelihood estimation (Wald)Small sample adjustment (small) Method (Relative-risk): Unconditional maximum likelihood estimation (Wald)Small sample adjustment (small)Bootstrap estimation (boot) Compute relative risk reduction in place of relative risk?: YesNo Confidence Interval: %

### Results

 Odds ratio: Risk ratio/Relative risk: Lower limit on odds ratio: Lower limit on risk ratio: Upper limit on odds ratio: Upper limit on risk ratio: Number needed to treat: Absolute risk:

## Partial eta-squared (Fixed effects)

### Inputs

 F-value: Confidence Interval: % Numerator degrees of freedom: Denominator degrees of freedom:

It is recommended that you use the 90% CI if you have an alpha level of 5%.

### Results (CI using noncentral F distribution)

 Partial eta-squared: Lower limit on partial eta-squared: Partial omega-squared: Upper limit on partial eta-squared: Cohen's f: Lower limit on Cohen's f: Upper limit on Cohen's f:

Partial eta-squared and omega-squared calculated here should only be interpreted if all your factors are manipulated not observed (such as gender), and you have no covariates.
Additionally, the confidence intervals produced here will differ from the confidence intervals produced in the OLS section. The calculation for the intervals returned here assumes the predictors are planned/fixed as in an experiment.

## R-squared (OLS)

### Inputs

 R-squared: Confidence Interval: % Numerator degrees of freedom: Denominator degrees of freedom:

It is recommended that you use the 90% CI if you have an alpha level of 5%.

### Results

 Lower limit on R-squared: Upper limit on R-squared:

I have run out of resources to sustain fitting the multilevel models, so for now, the ICC and multilevel R-squared sections are down.

## Intraclass Correlation Coefficient

### Inputs

Export all your variables into a csv file. The first row has to be the variable names - without spaces within variable names. To minimize problems, files should be ASCII and should not contain missing values.

Method:

### Results - CI are always calculated from One-Way ANOVA (95% CI)

 Estimate of ICC: Variance between: Variance within: Lower limit on ICC: Upper limit on ICC: Clusters analyzed: Average per cluster (k): Design effect (DEFF): Root DEFF (DEFT):

Note: Average per cluster is less than mean for unbalanced designs.

I have run out of resources to sustain fitting the multilevel models, so for now, the ICC and multilevel R-squared sections are down.

## HLM / multilevel Pseudo R-squared

### Inputs

Export all your variables into a csv file. The first row has to be the variable names - without spaces within variable names. To minimize problems, files should be ASCII and should not contain missing values.

Optimization Method:

### Results

 Marginal R-squared: Conditional R-squared: Variance between: Variance within: Average random effect: Residual ICC: Clusters analyzed: Model converged?:

Check the results for convergence. If they do not converge, try another optimization method from the drop down menu above.