Power Analyses for Common Designs (Power to the People)
Compute Power for Comparing Two Dependent Correlations, No Variables i...
Compute Power for Comparing Two Dependent Correlations, One Variable i...
Power for Comparing Independent Coefficients in Multiple Regression wi...
Compute Power for Comparing Two Independent Correlations Takes correla...
Power for Comparing Dependent Coefficients in Multiple Regression with...
Power for Comparing Independent R2 in Multiple Regression with Two or ...
Compute power for an Independent Samples t-test Takes means, sds, and ...
Compute Power for Regression Interaction (Correlation/Coefficient Appr...
Compute Power for Regression Interaction (R2 Change Approach)
Compute power for a t test using d statistic Takes d, sample size rang...
Compute power for Pearson's Correlation Takes correlation and range of...
Compute power for an Chi Square 2x3 Takes proportions for each group. ...
Compute power for Chi Square Based on Effect Size Takes phi, degrees o...
Compute Precision Analyses for Standardized Mean Differences
Compute power for a One Factor ANOVA with three levels. Takes means, s...
Compute power for a One Factor ANOVA with three levels and contrasts. ...
Compute power for a One Factor Between Subjects ANOVA with four levels...
Compute power for an Chi Square Goodness of Fit Takes proportions for ...
Compute power for a One Factor ANOVA with four levels. Takes means, sd...
Compute power for Simple Effects in a Two by Two Between Subjects ANOV...
Compute power for a Two by Two Between Subjects ANOVA. Takes means, sd...
Compute power for Multiple Regression with Violated assumptions using ...
Compute power for Multiple Regression with Violated assumptions (Beta)
Compute power for an Chi Square 2x2 Takes proportions for each group. ...
Compute power for a One Factor Within Subjects Linear Mixed Model with...
Compute power for a One Factor Within Subjects LMM Trends with up to f...
Compute power for a One Factor Within Subjects and One Factor Between ...
Compute power for a Two Factor Within Subjects Using Linear Mixed Mode...
Compute power for a Two Factor Within Subjects Using Linear Mixed Mode...
Compute Power for Logistic Regression with a Single Categorical Predic...
Compute Power for Logistic Regression with Continuous Predictors
Compute power for a One Factor MANOVA with up to two levels and up to ...
Compute Precision Analyses for Mean Differences
Compute Power for Mediated (Indirect) Effects Requires correlations be...
Compute Power for Mediated (Indirect) Effects Using Joint Significance...
Compute Power for Mediated (Indirect) Effects Using Joint Significance...
Compute Power for Serial Mediation Effects Requires correlations betwe...
Compute Power for Serial Mediation Effects Requires correlations betwe...
Compute Power for Conditional Process Model 14 Joint Significance Requ...
Compute Power for Model 7 Conditional Processes Using Joint Significan...
Compute power for Multiple Regression with Up to Five Predictors Requi...
Compute Multiple Regression shortcuts with three predictors for Ind Co...
Compute Multiple Regression shortcuts with three predictors (will expa...
Compute power for R2 change in Multiple Regression (up to three predic...
Compute power for Multiple Regression with up to Five Predictors Examp...
Compute power for a Paired t-test Takes means, sd, and sample sizes. A...
Compute power for a single sample proportion test Takes phi, degrees o...
Compute power for Tests of Two Independent Proportions Takes phi, degr...
Compute Precision Analyses for Correlations This approach simply loops...
Compute Precision Analyses for R-Squared This approach simply loops a ...
Compute power for a One Factor Within Subjects and One Factor Between ...
Compute power for a One Factor Within Subjects ANOVA with up to four l...
Compute power for a One Factor Within Subjects Trends with up to four ...
Compute power for a Two Factor Within Subjects ANOVA with up to two by...
Compute power for Simple Effects in Two Factor Within Subjects ANOVA w...
Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) <doi:10.4324/9781315171500>.