Data Characteristics:

Select variables in the order that reflects the time points that were measured. Example Data Set 1 has 14% missing values to illustrate special cases of identifying sudden gains. Example Data Set 2 has complete data.
Note: To explore the impact of missing data we recommend using example dataset 2, which has complete data (Dataset 1 has 14% missing data).

Select Criteria:

Note: The cut-off value for Crit 1 needs to be positive to identify sudden gains and negative to identify sudden losses.
Note: Percentage change threshold to be used for the second sudden gains criterion.
Note: For discussions about the third criterion see for example Tang et al. (2005 , 2015) , Vittengl et al. (2005 , 2015) , and Lutz et al. (2013) .

Results:

Sudden Gains Criteria Applied:

                                
Descriptives of Sudden Gains:

                                

Distribution of Pregain Session Numbers:
Average Sudden Gain Magnitude:
Note: To change the selected gain for the byperson data set, go to the 'Output byperson Data Set' panel at the top.
Formatting Options:

Trajectories of BDI scores for a selection of participants:
The figure below shows the percentage of missing data in at each time point in panel A. The percentage of analysed versus not analysed session to session intervals is shown in panel B.

Enter Values:

Pregain Values:
Postgain Values:
Note: To enter missing values leave the box blank. Missing values will be visualised as red points sligtly below all available data points.

Select Criteria:

Note: The cut-off value for Crit 1 needs to be positive to identify sudden gains and negative to identify sudden losses.
Note: Percentage change threshold to be used for the second sudden gains criterion.
Note: For discussions about the third criterion see for example Tang et al. (2005 , 2015) , Vittengl et al. (2005 , 2015) , and Lutz et al. (2013) .

Summary:


            

Visualiation of Values: