Compute Multiple Regression shortcuts with three predictors (will expand to handle two to five)
Requires correlations between all variables as sample size. Means and sds are option. Also computes Power(All)
Compute Precision Analyses for R-Squared
This approach simply loops a function from MBESS
Compute power for R2 change in Multiple Regression (up to three predictors)
Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Example code below for three predictors. Expand as needed for four or five
Compute power for a One Factor Between Subjects ANOVA with four levels
Takes means, sds, and sample sizes for each group
Compute power for a One Factor ANOVA with four levels.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for a One Factor ANOVA with three levels.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute Multiple Regression shortcuts with three predictors for Ind Coefficients
Requires correlations between all variables as sample size. Means and sds are option. Also computes Power(All)
Compute Power for One or Two Factor ANCOVA with a single covariate
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Factor A can have up to four levels, Factor B, if used, can only be two
Compute power for Multiple Regression with Up to Five Predictors
Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Compute power for a One Factor ANOVA with three levels and contrasts.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute Power for Comparing Two Dependent Correlations, No Variables in Common
Takes correlations and range of values. First variable in each pair is termed predictor, second is DV
Compute power for a Two by Two Between Subjects ANOVA.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for Simple Effects in a Two by Two Between Subjects ANOVA with two levels for each factor.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Power for Comparing Dependent Coefficients in Multiple Regression with Two or Three Predictors
Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Compute Power for Comparing Two Dependent Correlations, One Variable in Common
Takes correlations and range of values
Power for Comparing Independent R2 in Multiple Regression with Two or Three Predictors
Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Compute power for Pearson's Correlation
Takes correlation and range of values
Compute Precision Analyses for Standardized Mean Differences
Compute power for a Two Factor Within Subjects Using Linear Mixed Models with up to two by four levels.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for a Two Factor Within Subjects Using Linear Mixed Models with up to two by four levels.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for a One Factor Within Subjects Linear Mixed Model with up to four levels.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute Power for Mediated (Indirect) Effects
Requires correlations between all variables as sample size.
This approach calculates power for the Sobel test.
The medjs function calculates power based on joint significance (recommended)
Compute power for an Independent Samples t-test
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute Precision Analyses for Mean Differences
Compute Power for Mediated (Indirect) Effects Using Joint Significance
Requires correlations between all variables as sample size.
This is the recommended approach for determining power
Compute power for a One Factor Within Subjects and One Factor Between LMM with up to two by four levels (within).
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for a One Factor Within Subjects LMM Trends with up to four levels.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Power for Comparing Independent Coefficients in Multiple Regression with Two or Three Predictors
Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)
Compute Power for Comparing Two Independent Correlations
Takes correlations and range of values
Compute Power for Mediated (Indirect) Effects Using Joint Significance
Requires paths for all effects (and if 2 mediators, correlation)
Standard deviations/variances set to 1.0 so paths are technically standardized
Compute Precision Analyses for Correlations
This approach simply loops a function from MBESS
Compute power for Tests of Two Independent Proportions
Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by user
This test uses what is sometimes called the chi-square test for comparing proportions
Compute power for a Paired t-test
Takes means, sd, and sample sizes. Alpha is .05 by default, alternative values may be entered by user.
correlation (r) defaults to .50.
Compute power for a single sample proportion test
Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by user
Compute Power for Regression Interaction (Correlation/Coefficient Approach)
Compute Power for Regression Interaction (R2 Change Approach)
Compute Power for Conditional Process Model 14 Joint Significance
Requires correlations between all variables as sample size.
This is the recommended approach for determining power
Compute Power for Model 7 Conditional Processes Using Joint Significance
Requires correlations between all variables as sample size
Several values default to zero if no value provided
This is the recommended approach for determining power
Compute Power for Serial Mediation Effects
Requires correlations between all variables as sample size.
This approach calculates power for the serial mediation using
joint significance (recommended) and path coefficients
Compute power for a t test using d statistic
Takes d, sample size range, type of test, and tails.
Compute power for a One Factor Within Subjects and One Factor Between ANOVA with up to two by four levels (within).
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for a Two Factor Within Subjects ANOVA with up to two by four levels.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for Simple Effects in Two Factor Within Subjects ANOVA with up to two by four levels.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for a One Factor Within Subjects Trends with up to four levels.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for a One Factor Within Subjects ANOVA with up to four levels.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute Power for Serial Mediation Effects
Requires correlations between all variables as sample size.
This approach calculates power for the serial mediation using
joint significance (recommended)
Compute power for an Chi Square 2x3
Takes proportions for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for Chi Square Based on Effect Size
Takes phi, degrees of freedom, and a range of sample sizes. Alpha is .05 by default, alternative values may be entered by user
Compute power for an Chi Square Goodness of Fit
Takes proportions for up to six group. Alpha is .05 by default, alternative values may be entered by user
Compute power for Multiple Regression with Violated assumptions (Beta)
Compute power for a One Factor MANOVA with up to two levels and up to four measures.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alternative values may be entered by user
Compute Power for Logistic Regression with a Single Categorical Predictor
Compute power for Multiple Regression with Violated assumptions using Resamples
Compute Power for Logistic Regression with Continuous Predictors
Compute power for an Chi Square 2x2
Takes proportions for each group. Alpha is .05 by default, alternative values may be entered by user
Compute power for Multiple Regression with up to Five Predictors
Example code below for three predictors. Expand as needed for four or five