JASP ARMS
Inhoud
Multiple linear regression (MLR) Frequentist.....................................................................................1
Doing the analysis:..........................................................................................................................1
Checking the assumptions:.............................................................................................................1
Interpreting MLR.............................................................................................................................4
Hierarchical multiple regression.....................................................................................................4
Multiple Linear Regression (MLR) Bayesian........................................................................................5
Interpreting MLR.............................................................................................................................6
Hierarchical multiple regression.....................................................................................................6
Create a dummy variable....................................................................................................................7
Do this in JASP................................................................................................................................7
Dummy variables for Bayesian analyses.........................................................................................7
Factorial ANOVA.................................................................................................................................7
Doing the analysis (Frequentist).....................................................................................................7
Checking assumptions....................................................................................................................8
Follow-up tests...............................................................................................................................9
Bayesian factorial ANOVA...............................................................................................................9
Informative hypotheses testing....................................................................................................10
ANCOVA............................................................................................................................................10
Checking assumptions..................................................................................................................10
Perform the ANCOVA (frequentist)...............................................................................................10
Check the assumptions (Bayesian)................................................................................................10
Perform the ANCOVA (Bayesian)...................................................................................................11
Repeated measures ANOVA..............................................................................................................11
Perform frequentist repeated measures analysis.........................................................................11
Perform Bayesian repeated measures analyses............................................................................12
Mediation.....................................................................................................................................12
Multiple linear regression (MLR) Frequentist
Doing the analysis:
Regression -> Classical -> Linear regression. Put the dependent and independent variables in the right
boxes.
Checking the assumptions:
There are linear relationships between the dependent variable and each of the continuous
independent variables.
, Check this using a scatterplot. A scatterplot has the (continuous) predictor on the x-axis and the
outcome on the y-axis and uses dots to represent the combination of x-y-scores for each case in the
data. A linear relationship means that the scores in the scatterplot form a cloud with an oval shape
that can be describe reasonably well by a straight line.
How to make a scatterplot: Descriptives -> Add the variables in the Variables box -> Scroll down to
Plots and tick Correlation plots in Basic plots
There are no outliers.
An outlier is a case that deviates strongly from other cases in the data set. You can check them by
looking at scatterplots, boxplots, Standard residuals and Cook’s distance.
How to check: Scroll down to Statistics, tick Casewise diagnostics and then select either Standard
residual or Cook’s distance.
A rule of thumb for the standardized residuals is that the values should be between -3.3 and +3.3.
Values bigger or smaller than that indicate potential outliers.
Cook’s distance indicates the overall influence of a respondent on the model. As a rule of thumb, we
maintain that values for Cook’s distance must be lower than 1. Values higher than 1 indicate
influential cases.
What to do with outliers?
1. Do nothing
2. Exclude the data point from the analysis
3. Change the data point either to the ‘correct’ value (only if the outlier is known to be an error
and when the correct value is known), or to a less extreme value. This way this case still has a
large score, but not so extreme that it will completely dominate the results of the analysis.
Absence of multicollinearity
Multicollinearity indicates whether the relationship between two or more independent variables is
too strong. Scroll down to Statistics and tick Collinearity diagnostics.
Inhoud
Multiple linear regression (MLR) Frequentist.....................................................................................1
Doing the analysis:..........................................................................................................................1
Checking the assumptions:.............................................................................................................1
Interpreting MLR.............................................................................................................................4
Hierarchical multiple regression.....................................................................................................4
Multiple Linear Regression (MLR) Bayesian........................................................................................5
Interpreting MLR.............................................................................................................................6
Hierarchical multiple regression.....................................................................................................6
Create a dummy variable....................................................................................................................7
Do this in JASP................................................................................................................................7
Dummy variables for Bayesian analyses.........................................................................................7
Factorial ANOVA.................................................................................................................................7
Doing the analysis (Frequentist).....................................................................................................7
Checking assumptions....................................................................................................................8
Follow-up tests...............................................................................................................................9
Bayesian factorial ANOVA...............................................................................................................9
Informative hypotheses testing....................................................................................................10
ANCOVA............................................................................................................................................10
Checking assumptions..................................................................................................................10
Perform the ANCOVA (frequentist)...............................................................................................10
Check the assumptions (Bayesian)................................................................................................10
Perform the ANCOVA (Bayesian)...................................................................................................11
Repeated measures ANOVA..............................................................................................................11
Perform frequentist repeated measures analysis.........................................................................11
Perform Bayesian repeated measures analyses............................................................................12
Mediation.....................................................................................................................................12
Multiple linear regression (MLR) Frequentist
Doing the analysis:
Regression -> Classical -> Linear regression. Put the dependent and independent variables in the right
boxes.
Checking the assumptions:
There are linear relationships between the dependent variable and each of the continuous
independent variables.
, Check this using a scatterplot. A scatterplot has the (continuous) predictor on the x-axis and the
outcome on the y-axis and uses dots to represent the combination of x-y-scores for each case in the
data. A linear relationship means that the scores in the scatterplot form a cloud with an oval shape
that can be describe reasonably well by a straight line.
How to make a scatterplot: Descriptives -> Add the variables in the Variables box -> Scroll down to
Plots and tick Correlation plots in Basic plots
There are no outliers.
An outlier is a case that deviates strongly from other cases in the data set. You can check them by
looking at scatterplots, boxplots, Standard residuals and Cook’s distance.
How to check: Scroll down to Statistics, tick Casewise diagnostics and then select either Standard
residual or Cook’s distance.
A rule of thumb for the standardized residuals is that the values should be between -3.3 and +3.3.
Values bigger or smaller than that indicate potential outliers.
Cook’s distance indicates the overall influence of a respondent on the model. As a rule of thumb, we
maintain that values for Cook’s distance must be lower than 1. Values higher than 1 indicate
influential cases.
What to do with outliers?
1. Do nothing
2. Exclude the data point from the analysis
3. Change the data point either to the ‘correct’ value (only if the outlier is known to be an error
and when the correct value is known), or to a less extreme value. This way this case still has a
large score, but not so extreme that it will completely dominate the results of the analysis.
Absence of multicollinearity
Multicollinearity indicates whether the relationship between two or more independent variables is
too strong. Scroll down to Statistics and tick Collinearity diagnostics.
