**INSTRUCTION**
This folder contains: (1) the database adopted to perform the longitudinal cluster analysis; (2) R code adopted to perform the analysis; (3) outputs of the analysis; (4) results of the analysis.
**1- Database**
A file with the .SAV extension is a SPSS data file. File with the .CSV extension contains the database that should be used to run the analysis in R.
**2- Syntax**
The file “cluster_NtraiettorieByFacet.R” contains the R syntax file. Analysis were performed in R i386 3.3.2.
**3- Output**
File with .PDF extension contains the cluster solution we selected for each facet of calling. A file with .xlsx extension is an Excel file, which contains the annual rate of change for each facet of calling and the annual rate of change in calling from data available in the literature.
**ANALYTICAL PROCEDURE**
We clustered individuals based on their trajectories of change in six facets of calling over time using the R package “TSclust” (Montero & Vilar, 2014). This package implements a clustering procedure proposed by Chouakria and Nagabhushan (2007), which is based on the temporal correlation coefficient CORT (Chouakria-Douzal, 2003). The clustering procedure allows the researcher to modulate the contribution of the proximity of trajectories with respect to the behavior (as expressed by coefficient CORT) and their proximity with respect to values (e.g., the distance between scores at any time point). We maximized the contribution of proximity with respect to behavior (i.e., the shape of trajectories). We manually assigned to the stability cluster the individuals who showed differences from Time 1 to Time 2 and from Time 2 to Time 3 that were smaller in size than .1 standard deviations. The clustering procedure was implemented to distinguish the remaining individuals.
