Aim
|
Calculate
|
The aim of the analysis: N for required N,
Power for estimated power and Effect Size for
minimal detectable effect size
|
Sensitivity Analysis
Sensitivity analysis, exploring different plots of possible
combinations of parameters, can be carried out like for any other PAMLj sub-modules. Please visit
Sensitivity
analysis page for more details.
Effect Size Conversion
With this panel is possible to estimate the expected effect size
based on observed t-test, taken from empirical studies or other
empirical sources.
Convert from t-test
|
Observed t-test
|
The observed t-test
|
|
N
|
The observed t-test sample size (number of pairs)
|
|
Cohen’s dz
|
The resulting d_z
|
Convert d to dz
|
Cohen’s d (between)
|
Cohen’s d uncorrected for pairs correlation
|
|
Correlation
|
Correlation between repeated measures
|
|
Cohen’s dz
|
Resulting Cohen’s d_z
|
It should be noted that PAMLj uses
Cohen’s \(d_z\) as the
effect size for power analysis. This is defined as the mean of
the difference scores divided by their standard deviation (Cohen 1988).
Some authors instead suggest starting from Cohen’s
\(d\) for independent samples, defined
as the mean difference divided by the square root of the sum of
the variances of the two measures. If you want to convert this
independent-samples \(d\) into a
paired-samples \(d_z\), accounting for
the expected correlation \(r\) between
measures, you can use the Convert to dz
, which applies the following formula:
\[
d_z = \frac{d}{\sqrt{2(1 - r)}}
\]
Cohen, J. 1988. Statistical Power Analysis for
the Behavioral Sciences. Lawrence Erlbaum Associates.
Comments?
Got comments, issues or spotted a bug? Please open an issue on PAMLj at github or send me an email